Package 'psfmi'

Description

Data from a placebo-controlled RCT with leukemia patients

Usage

data(anderson)

Format

A data frame with 348 observations on the following 5 variables.

remission

continuous:remission in weeks

status

dichotomous

treatment

dichotomous: 0=placebo, 1=verum

sex

dichotomous: 0=female, 1=male

log_wbc

continuous: Log (number of white blood cells)

Examples

data(anderson)
## maybe str(anderson)

Description

Original dataset of patients with a aortadissection

Usage

data(aortadis)

Format

A data frame with 226 observations on the following 10 variables.

Gender

dichotomous, 1=yes, 0=no

Age

continuous

Age_C

categorical: 0 = < 50 years, 1 = 50-59 years, 2 = 60-69 years, 3 = 70-79 years, 4 = 80 years and older

Aortadis

dichotomous, 1=yes, 0=no

Acute

dichotomous, 1=yes, 0=no

Acute3

categorical: 0 = No, 1 = Little, 2 = Much

Stomach_Ache

dichotomous, 1=yes, 0=no

Hyper

dichotomous, Hypertensio, 1=yes, 0=no

Smoking

dichotomous, 1=yes, 0=no

Radiation

dichotomous, 1=yes, 0=no

Examples

data(aortadis)
## maybe str(aortadis)

Description

Data of a non-experimental study in more than 300 elderly women

Usage

data(bmd)

Format

A data frame with 348 observations on the following 5 variables.

bmd

continuous

age

continuous: years

menopaus

continuous: age of menopause

weight

continuous: weight in kg

walkscor

dichotomous: score on a walking test, 0=normal, 1=impaired

Examples

data(bmd)
## maybe str(bmd)

Description

bw_single Backward selection of Linear and Logistic regression models using as selection method the likelihood-ratio Chi-square value.

Usage

bw_single(
  data,
  formula = NULL,
  Outcome = NULL,
  predictors = NULL,
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  model_type = "binomial"
)

Arguments

Details

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+".

Value

An object of class smods (single models) from which the following objects can be extracted: original dataset as data, final selected model as RR_model_final, model at each selection step RR_model_setp, p-values at final step according to selection method as multiparm_final, and at each step as multiparm_step, formula object at final step as formula_final, and at each step as formula_step and for start model as formula_initial, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and Outcome, anova_test, p.crit, call, model_type, predictors_final for names of predictors in final selection step and predictors_initial for names of predictors in start model.

Author(s)

Martijn Heymans, 2020

References

http://missingdatasolutions.rbind.io/

Description

Data about concentration of ß2-microglobuline in urine as indicator for possible damage to the kidney

Usage

data(chlrform)

Format

A data frame with 348 observations on the following 5 variables.

pt_id

continuous

sport

categorical: 0 = football player, 1 = outdoorswimmer and 2 = indoor swimmer)

gammagt

continuous: liver damage

b2

continuous: beta2 microglobuline in mg per mol

age

continuous: age in years

Examples

data(chlrform)
## maybe str(chlrform)

Description

Long dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS)

Usage

data(chol_long)

Format

A data frame with 588 observations on the following 7 variables.

ID

continuous

fitness

continuous

Smoking

dichotomous, 1=yes, 0=no

Sex

dichotomous

Time

categorical

Cholesterol

continuous

SumSkinfolds

continuous

Examples

data(chol_long)
## maybe str(chol_long)

Description

Wide dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS)

Usage

data(chol_wide)

Format

A data frame with 147 observations on the following 7 variables.

ID

continuous

Cholesterol1

continuous

SumSkinfolds1

continuous

Cholesterol2

continuous

SumSkinfolds2

continuous

Cholesterol3

continuous

SumSkinfolds3

continuous

Cholesterol4

continuous

SumSkinfolds4

continuous

fitness

continuous

Smoking

dichotomous

Sex

dichotomous

Examples

data(chol_wide)
## maybe str(chol_wide)

Description

coxph_bw Backward selection of Cox regression models in single complete dataset using as selection method the partial likelihood-ratio statistic.

Usage

coxph_bw(
  data,
  formula = NULL,
  status = NULL,
  time = NULL,
  predictors = NULL,
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL
)

Arguments

Details

A typical formula object has the form Surv(time, status) ~ terms. Categorical variables has to be defined as Surv(time, status) ~ factor(variable), restricted cubic spline variables as Surv(time, status) ~ rcs(variable, 3). Interaction terms can be defined as Surv(time, status) ~ variable1*variable2 or Surv(time, status) ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+".

Value

An object of class smods (single models) from which the following objects can be extracted: original dataset as data, final selected model as RR_model_final, model at each selection step RR_model, p-values at final step multiparm_final, and at each step as multiparm, formula object at final step as formula_final, and at each step as formula_step and for start model as formula_initial, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and time, status, p.crit, call, model_type, predictors_final for names of predictors in final selection step and predictors_initial for names of predictors in start model and keep.predictors for variables that are forced in the model during selection.

Author(s)

Martijn Heymans, 2021

References

http://missingdatasolutions.rbind.io/

Examples

lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract first imputed dataset
res_single <- coxph_fw(data=lbpmicox1, p.crit = 0.05, formula=Surv(Time, Status) ~
                           Previous +  Radiation + Onset + Age + Tampascale + 
                           Pain + JobControl + factor(Satisfaction), 
                           spline.predictors = "Function",
                           nknots = 3)
         
res_single$RR_model_final
res_single$multiparm_final

Description

coxph_bw Forward selection of Cox regression models in single complete dataset using as selection method the partial likelihood-ratio statistic.

Usage

coxph_fw(
  data,
  formula = NULL,
  status = NULL,
  time = NULL,
  predictors = NULL,
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL
)

Arguments

Details

A typical formula object has the form Surv(time, status) ~ terms. Categorical variables has to be defined as Surv(time, status) ~ factor(variable), restricted cubic spline variables as Surv(time, status) ~ rcs(variable, 3). Interaction terms can be defined as Surv(time, status) ~ variable1*variable2 or Surv(time, status) ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+".

Value

An object of class smods (single models) from which the following objects can be extracted: original dataset as data, final selected model as RR_model_final, model at each selection step RR_model, p-values at final step multiparm_final, and at each step as multiparm, formula object at final step as formula_final, and at each step as formula_step and for start model as formula_initial, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and time, status, p.crit, call, model_type, predictors_final for names of predictors in final selection step and predictors_initial for names of predictors in start model and keep.predictors for variables that are forced in the model during selection.

Author(s)

Martijn Heymans, 2021

References

http://missingdatasolutions.rbind.io/

Examples

lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract first imputed dataset
res_single <- coxph_bw(data=lbpmicox1, p.crit = 0.05, formula=Surv(Time, Status) ~
                           Previous +  Radiation + Onset + Age + Tampascale + 
                           Pain + JobControl + factor(Satisfaction), 
                           spline.predictors = "Function",
                           nknots = 3)
         
res_single$RR_model_final
res_single$multiparm_final

Description

Dataset of low back pain patients with missing values in 2 variables

Usage

data(day2_dataset4_mi)

Format

A data frame with 100 observations on the following 8 variables.

ID

continuous: unique patient numbers

Pain

continuous: Pain intensity

Tampa

continuous: Fear of Movement scale

Function

continuous: Functional Status

JobSocial

continuous

FAB

continuous: Fear Avoidance Beliefs

Gender

dichotomous: 1 = male, 0 = female

Radiation

dichotomous: 1 = yes, 0 = no

Examples

data(day2_dataset4_mi)
## maybe str(day2_dataset4_mi)

Description

glm_bw Backward selection of Linear and Logistic regression models in single dataset using as selection method the likelihood-ratio test.

Usage

glm_bw(
  data,
  formula = NULL,
  Outcome = NULL,
  predictors = NULL,
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  model_type = "binomial"
)

Arguments

Details

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+".

Value

An object of class smods (single models) from which the following objects can be extracted: original dataset as data, model at each selection step RR_model, final selected model as RR_model_final, p-values at final step multiparm_final, and at each step as multiparm, formula object at final step as formula_final, and at each step as formula_step and for start model as formula_initial, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and Outcome, p.crit, call, model_type, predictors_final for names of predictors in final selection step and predictors_initial for names of predictors in start model and keep.predictors for variables that are forced in the model during selection.

Author(s)

Martijn Heymans, 2021

References

http://missingdatasolutions.rbind.io/

See Also

psfmi_perform

Examples

data1 <- subset(psfmi::lbpmilr, Impnr==1) # extract first imputed dataset
res_single <- glm_bw(data=data1, p.crit = 0.05, formula=Chronic ~
       Tampascale + Smoking + factor(Satisfaction), model_type="binomial")
         
res_single$RR_model_final

res_single <- glm_bw(data=data1, p.crit = 0.05, formula=Pain ~
         Tampascale  + Smoking + factor(Satisfaction), model_type="linear")
         
res_single$RR_model_final

Description

glm_fw Forward selection of Linear and Logistic regression models in single dataset using as selection method the likelihood-ratio test statistic.

Usage

glm_fw(
  data,
  formula = NULL,
  Outcome = NULL,
  predictors = NULL,
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  model_type = "binomial"
)

Arguments

Details

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+".

Value

An object of class smods (single models) from which the following objects can be extracted: original dataset as data, model at each selection step RR_model, final selected model as RR_model_final, p-values at final step multiparm_final, and at each step as multiparm, formula object at final step as formula_final, and at each step as formula_step and for start model as formula_initial, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and Outcome, p.crit, call, model_type, predictors_final for names of predictors in final selection step and predictors_initial for names of predictors in start model and keep.predictors for variables that are forced in the model during selection.

Author(s)

Martijn Heymans, 2021

References

http://missingdatasolutions.rbind.io/

See Also

psfmi_perform

Examples

data1 <- subset(psfmi::lbpmilr, Impnr==1) # extract first imputed dataset
res_single <- glm_fw(data=data1, p.crit = 0.05, formula=Chronic ~
       Tampascale + Smoking + factor(Satisfaction), model_type="binomial")
         
res_single$RR_model_final

res_single <- glm_fw(data=data1, p.crit = 0.05, formula=Pain ~
         Tampascale  + Smoking + factor(Satisfaction), model_type="linear")
         
res_single$RR_model_final

Description

Original dataset of elderly patients with a hip fracture

Usage

data(hipstudy)

Format

A data frame with 426 observations on the following 18 variables.

pat_id

continuous: unique patient numbers

Gender

dichotomous: 1 = male, 0 = female

Age

continuous: Years

Mobility

categorical: 1 = No tools, 2 = Stick / walker, 3 = Wheelchair / bed

Dementia

dichotomous: 2=yes, 1=no

Home

categorical: 1 = Independent, 2 = Elderly house, 3 = Nursering

Comorbidity

continuous: Number of Co_morbidities (0-4)

ASA

continuous: ASA score (1-4)

Hemoglobine

continuous: Hemoglobine pre-operative

Leucocytes

continuous: Leucocytes preoperative

Thrombocytes

continuous: Thrombocytes preoperative

CRP

continuous: C-reactive protein (CRP) preoperative

Creatinine

continuous: Creatinine preoperative

Urea

continuous: Urea preoperative

Albumine

continuous: Albumin preoperative

Fracture

dichotomous: 1 = per or subtrochanter fracture, 0 = collum fracture

Delay

continuous: time till operation in days

Mortality

dichotomous: 1 = yes, 0 = no

Examples

data(hipstudy)
## maybe str(hipstudy)

Description

External dataset of elderly patients with a hip fracture

Usage

data(hipstudy_external)

Format

A data frame with 381 observations on the following 17 variables.

Gender

dichotomous: 1 = male, 0 = female

Age

continuous: Years

Mobility

categorical: 1 = No tools, 2 = Stick / walker, 3 = Wheelchair / bed

Dementia

dichotomous: 2=yes, 1=no

Home

categorical: 1 = Independent, 2 = Elderly house, 3 = Nursering

Comorbidity

continuous: Number of Co-morbidities

ASA

continuous: ASA score

Hemoglobine

continuous: Hemoglobine preoperative

Leucocytes

continuous: Leucocytes preoperative

Thrombocytes

continuous: Thrombocytes preoperative

CRP

continuous: Creactive protein (CRP) preoperative

Creatinine

continuous: Creatinine preoperative

Urea

continuous: Urea preoperative

Albumine

continuous: Albumin preoperative

Fracture

dichotomous: 1 = per or subtrochanter fracture, 0 = collum fracture

Delay

continuous: time till operation in days

Mortality

dichotomous: 1 = yes, 0 = no

Examples

data(hipstudy_external)
## maybe str(hipstudy_external)

Description

Dataset of the Hoorn Study

Usage

data(hoorn_basic)

Format

A data frame with 250 observations on the following 12 variables.

patnr

continuous

sbldsys1

continuous: Systolic Blood Pressure 1

sbldsys2

continuous: Systolic Blood Pressure 2

sbldds1

continuous: Diastolic Blood Pressure 1

sbldds2

continuous: Diastolic Blood Pressure 2

sex

dichotomous: 1=male, 2=female

sfructo

continuous: fructosamine level in the blood

sglucn

continuous

dmknown

dichotomous: 0=no, 1=yes

dmdiet

dichotomous: 0=no, 1=yes

infarct

dichotomous: 0=no, 1=yes

hypten

dichotomous: 0=no, 1=yes

Examples

data(hoorn_basic)
## maybe str(hoorn_basic)

Description

hoslem_test the Hosmer and Lemeshow goodness of fit test.

Usage

hoslem_test(y, yhat, g = 10)

Arguments

Value

The Chi-squared test statistic, the p-value, the observed and expected frequencies.

Author(s)

Martijn Heymans, 2021

References

Kleinman K and Horton NJ. (2014). SAS and R: Data Management, Statistical Analysis, and Graphics. 2nd Edition. Chapman & Hall/CRC.

See Also

pool_performance

Examples

fit <- glm(Mortality ~ Dementia + factor(Mobility) + ASA + 
   Gender + Age, data=hipstudy, family=binomial) 
   pred <- predict(fit, type = "response")
  
  hoslem_test(fit$y, pred)

Description

Data of a patient-control study regarding the relationship between MI and smoking

Usage

data(infarct)

Format

A data frame with 420 observations on the following 10 variables.

ppnr

continuous

infarct

dichotomous: 1=yes, 0=no

smoking

dichotomous: 1=yes, 0=no

alcohol

categorical

active

dichotomous: 1=active, 0=inactive

sex

dichotomous: 1=male, 0=female

profession

categorical: 1=epidemiologist, 2=statistician, 3=other

bmi

continuous: body mass index

sys

continuous: systolic blood pressure

dias

continuous: diastolic blood pressure

Examples

data(infarct)
## maybe str(infarct)

Description

5 imputed datasets of the first 10 centres of the IPDNa dataset in the micemd package.

Usage

data(ipdna_md)

Format

A data frame with 13390 observations on the following 13 variables.

.imp

a numeric vector

.id

a numeric vector

centre

cluster variable

gender

dichotomous

bmi

continuous

age

continuous

sbp

continuous

dbp

continuous

hr

continuous

lvef

dichotomous

bnp

categorical

afib

continuous

bmi_cat

categorical

Examples

data(ipdna_md)
## maybe str(ipdna_md)

#summary per study
by(ipdna_md, ipdna_md$centre, summary)

Description

km_estimates Kaplan-Meier risk estimates for Net Reclassification Index analysis for Cox Regression Models

Usage

km_estimates(data, p0, p1, time, status, t_risk, cutoff)

Arguments

Details

Follow-up for which cases and controls are determined. For censored cases before this follow-up the expected risk of being a case is calculated by using the Kaplan-Meier value to calculate the expected number of cases. These expected numbers are used to calculate the NRI proportions. (These are not shown by function nricens).

Value

An object from which the following objects can be extracted:

• data dataset.

• prob_orig outcome risk probabilities at t_risk for reference model.

• prob_new outcome risk probabilities at t_risk for new model.

• time name of time variable.

• status name of status variable.

• cutoff cutoff value for survival probability.

• t_risk follow-up time used to calculate outcome (risk) probabilities.

• reclass_totals table with total reclassification numbers.

• reclass_cases table with reclassification numbers for cases.

• reclass_controls table with reclassification numbers for controls.

• totals totals of controls, cases, censored cases.

• km_est totals of cases calculated using Kaplan-Meiers risk estimates.

• nri_est reclassification measures.

Author(s)

Martijn Heymans, 2023

References

Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795-802.

Steyerberg EW, Pencina MJ. Reclassification calculations for persons with incomplete follow-up. Ann Intern Med. 2010;152(3):195-6 (author reply 196-7).

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

Examples

library(survival)
  lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract dataset
  
  fit_cox0 <- 
      coxph(Surv(Time, Status) ~ Duration + Pain, data=lbpmicox1, x=TRUE)
  fit_cox1 <- 
      coxph(Surv(Time, Status) ~ Duration + Pain + Function + Radiation, 
      data=lbpmicox1, x=TRUE)

  p0 <- risk_coxph(fit_cox0, t_risk=80)
  p1 <- risk_coxph(fit_cox1, t_risk=80)
  
  res_km <- km_estimates(data=lbpmicox1,
                      p0=p0,
                      p1=p1,
                      time = "Time",
                      status = "Status",
                      cutoff=0.45,
                      t_risk=80)

Description

Kaplan-Meier (KM) estimate at specific time point

Usage

km_fit(time, status, t_risk)

Arguments

Value

KM estimate at specific time point

Author(s)

Martijn Heymans, 2023

References

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

See Also

km_fit

Description

Original dataset with missing values

Usage

data(lbp_orig)

Format

A data frame with 159 observations on the following 15 variables.

Chronic

dichotomous

Gender

dichotomous

Carrying

categorical

Pain

continuous

Tampascale

continuous

Function

continuous

Radiation

dichotomous

Age

continuous

Smoking

dichotomous

Satisfaction

categorical

JobControl

continuous

JobDemands

continuous

SocialSupport

continuous

Duration

continuous

BMI

continuous

Examples

data(lbp_orig)
## maybe str(lbp_orig)

Description

Five multiply imputed datasets

Usage

lbpmi_extval

Format

A data frame with 400 rows and 17 variables.

Impnr

a numeric vector

ID

a numeric vector

Chronic

dichotomous

Gender

dichotomous

Carrying

categorical

Pain

continuous

Tampascale

continuous

Function

continuous

Radiation

dichotomous

Age

continuous

Smoking

dichotomous

Satisfaction

categorical

JobControl

continuous

JobDemands

continuous

SocialSupport

continuous

Duration

continuous

BMI

continuous

Examples

data(lbpmi_extval)
 ## maybe str(lbpmi_extval)\

Description

10 imputed datasets

Usage

data(lbpmicox)

Format

A data frame with 2650 observations on the following 18 variables.

Impnr

a numeric vector

patnr

a numeric vector

Status

dichotomous event

Time

continuous follow up time variable

Duration

continuous

Previous

dichotomous

Radiation

dichotomous

Onset

dichotomous

Age

continuous

Tampascale

continuous

Pain

continuous

Function

continuous

Satisfaction

categorical

JobControl

continuous

JobDemand

continuous

Social

continuous

Expectation

a numeric vector

Expect_cat

categorical

Examples

data(lbpmicox)
## maybe str(lbpmicox)

Description

10 imputed datasets

Usage

data(lbpmilr)

Format

A data frame with 1590 observations on the following 17 variables.

Impnr

a numeric vector

ID

a numeric vector

Chronic

dichotomous

Gender

dichotomous

Carrying

categorical

Pain

continuous

Tampascale

continuous

Function

continuous

Radiation

dichotomous

Age

continuous

Smoking

dichotomous

Satisfaction

categorical

JobControl

continuous

JobDemands

continuous

SocialSupport

continuous

Duration

continuous

BMI

continuous

Examples

data(lbpmilr)
## maybe str(lbpmilr)

Description

1 development dataset

Usage

data(lbpmilr_dev)

Format

A data frame with 108 observations on the following 16 variables.

ID

a numeric vector

Chronic

dichotomous

Gender

dichotomous

Carrying

categorical

Pain

continuous

Tampascale

continuous

Function

continuous

Radiation

dichotomous

Age

continuous

Smoking

dichotomous

Satisfaction

categorical

JobControl

continuous

JobDemands

continuous

SocialSupport

continuous

Duration

continuous

BMI

continuous

Examples

data(lbpmilr_dev)
## maybe str(lbpmilr_dev)

Description

Data regarding the development of lung and heartvolume of unborn babies in the 18 till 34 week of pregnancy

Usage

data(lungvolume)

Format

A data frame with 152 observations on the following 6 variables.

pat_id

continuous

week

continuous: week pregnancy

weight

continuous: weight in grams

lungvol

continuous: lung volume

heartvol

continuous: heart volume

Nweek

categorical: Percentile Group of week

Examples

data(lungvolume)
## maybe str(lungvolume)

Description

Data of a study among women with breast cancer

Usage

data(mammaca)

Format

A data frame with 1207 observations on the following 10 variables.

id

continuous

time

continuous, Time (months)

status

dichotomous: 1=yes, 0=no

er

Estrogen Receptor Status, 1=positive, 0=negative

age

continuous

histgrad

categorical

ln_yesno

lymph nodes, 0=no, 1=yes

pathsd

dichotomous: Pathological Tumor Size

pr

dichotomous: Progesterone Receptor Status, 0=negative, 1=positive

Examples

data(mammaca)
## maybe str(mammaca)

Description

Data of 613 patients with meningitis

Usage

data(men)

Format

A data frame with 420 observations on the following 10 variables.

pt_id

continuous

sex

dichotomous: 0=male, 1=female

predisp

dichotomous: 0=no, 1=yes

mensepsi

categorical: disease characteristics at admission, 1=menigitis, 2=sepsis, 3=other

coma

dichotomous: coma at admission, 0=no, 1=coma

diastol

continuous: diastolic blood pressure at admission

course

dichotomous: disease course, 0=alive, 1=deceased

Examples

data(men)
## maybe str(men)

Description

mivalext_lr External validation of logistic prediction models

Usage

mivalext_lr(
  data.val = NULL,
  data.orig = NULL,
  nimp = 5,
  impvar = NULL,
  formula = NULL,
  lp.orig = NULL,
  cal.plot = FALSE,
  plot.indiv,
  val.check = FALSE,
  g = 10,
  groups_cal = 10,
  plot.method = "mean"
)

Arguments

Details

The following information of the externally validated model is provided: calibrate with information of pooled_int and pooled_slope that is the pooled linear predictor (LP), after the LP is freely estimated in each external imputed dataset Outcome ~ a + LP (provides information about miscalibration in intercept and slope), pooled_offset_int as Outcome ~ a + offset(LP) and pooled_offset_slope as Outcome ~ a + LP + offset(LP) with information about miscalibration in intercept and slope separately by using an offset procedure (see Steyerberg, p. 300), coef_pooled with the pooled coefficients when the model is freely estimated in imputed datasets, ROC pooled ROC curve (back transformed after pooling log transformed ROC curves), R2 pooled Nagelkerke R-Square value (back transformed after pooling Fisher transformed values), HLtest pooled Hosmer and Lemeshow Test (using function pool_D2). In addition information is provided about nimp, impvar, formula, val_ckeck, g and coef_check. When the external validation is very poor, the R2 can become negative due to the poor fit of the model in the external dataset (in that case you may report a R2 of zero).

Value

A mivalext_lr object from which the following objects can be extracted: calibrate with information about mis-calibration in intercept and slope with and without offset procedure, coef_pooled, coefficients pooled, ROC results as ROC, R squared results as R2, Hosmer and Lemeshow test as HL_test, nimp, formula, impvar, val.check, g, coef.check and groups_cal.

References

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd Edition. Springer, New York, NY, 2015.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

http://missingdatasolutions.rbind.io/

Examples

mivalext_lr(data.val=lbpmilr, nimp=5, impvar="Impnr", 
  formula = Chronic ~ Gender + factor(Carrying)  + Function + 
  Tampascale + Age, lp.orig=c(-10, -0.35, 1.00, 1.00, -0.04, 0.26, -0.01),
  cal.plot=TRUE, val.check = FALSE)

Description

nri_cox Net Reclassification Index for Cox Regression Models

Usage

nri_cox(data, formula0, formula1, t_risk, cutoff, B = FALSE, nboot = 10)

Arguments

Details

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+". If a formula object is used set predictors, cat.predictors, spline.predictors or int.predictors at the default value of NULL.

Follow-up for which cases nd controls are determined. For censored cases before this follow-up the expected risk of being a case is calculated by using the Kaplan-Meier value to calculate the expected number of cases.These expected numbers are used to calculate the NRI proportions but are not shown by function nricens.

Value

An object from which the following objects can be extracted:

• data dataset.

• prob_orig outcome risk probabilities at t_risk for reference model.

• prob_new outcome risk probabilities at t_risk for new model.

• time name of time variable.

• status name of status variable.

• cutoff cutoff value for survival probability.

• t_risk follow-up time used to calculate outcome (risk) probabilities.

• reclass_totals table with total reclassification numbers.

• reclass_cases table with reclassification numbers for cases.

• reclass_controls table with reclassification numbers for controls.

• totals totals of controls, cases, censored cases.

• km_est totals of cases calculated using Kaplan-Meiers risk estimates.

• nri_est reclassification measures.

Author(s)

Martijn Heymans, 2023

References

Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795-802.

Steyerberg EW, Pencina MJ. Reclassification calculations for persons with incomplete follow-up. Ann Intern Med. 2010;152(3):195-6; author reply 196-7.

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

Examples

library(survival)
  lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract one dataset
  risk_est <- nri_cox(data=lbpmicox1, formula0 = Surv(Time, Status) ~ Duration + Pain,
                   formula1 = Surv(Time, Status) ~ Duration + Pain + Function + Radiation,
                   t_risk = 80, cutoff=c(0.45), B=TRUE, nboot=10)

Description

nri_est Calculation of proportion of Reclassified persons and NRI for Cox Regression Models

Usage

nri_est(data, p0, p1, time, status, t_risk, cutoff)

Arguments

Details

Follow-up for which cases nd controls are determined. For censored cases before this follow-up the expected risk of being a case is calculated by using the Kaplan-Meier value to calculate the expected number of cases. These expected numbers are used to calculate the NRI proportions but are not shown by function nricens.

Value

An object from which the following objects can be extracted:

• prop_up_case proportion of cases reclassified upwards.

• prop_down_case proportion of cases reclassified downwards.

• prop_up_ctr proportion of controls reclassified upwards.

• prop_down_ctr proportion of controls reclassified downwards.

• nri_plus proportion reclassified for events.

• nri_min proportion reclassified for nonevents.

• nri net reclassification improvement.

Author(s)

Martijn Heymans, 2023

References

Cook NR, Ridker PM. Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures. Ann Intern Med. 2009;150(11):795-802.

Steyerberg EW, Pencina MJ. Reclassification calculations for persons with incomplete follow-up. Ann Intern Med. 2010;152(3):195-6; author reply 196-7.

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

Examples

library(survival)
  lbpmicox1 <- subset(psfmi::lbpmicox, Impnr==1) # extract dataset
  
  fit_cox0 <- 
    coxph(Surv(Time, Status) ~ Duration + Pain, data=lbpmicox1, x=TRUE)
  fit_cox1 <- 
    coxph(Surv(Time, Status) ~ Duration + Pain + Function + Radiation, 
    data=lbpmicox1, x=TRUE)

  p0 <- risk_coxph(fit_cox0, t_risk=80)
  p1 <- risk_coxph(fit_cox1, t_risk=80)
  
  nri <- nri_est(data=lbpmicox1,
                      p0=p0,
                      p1=p1,
                      time = "Time",
                      status = "Status",
                      cutoff=0.45,
                      t_risk=80)

Description

pool_auc Calculates the pooled C-statistic and 95 by using Rubin's Rules. The C-statistic values are log transformed before pooling.

Usage

pool_auc(est_auc, est_se, nimp = 5, log_auc = TRUE)

Arguments

Value

The pooled C-statistic value and the 95

Author(s)

Martijn Heymans, 2021

See Also

psfmi_perform, pool_performance

Description

pool_compare_model Compares the fit and performance of prediction models in multiply imputed data sets by using clinical important performance measures

Usage

pool_compare_models(
  pobj,
  compare.predictors = NULL,
  compare.group = NULL,
  cutoff = 0.5,
  boot_auc = FALSE,
  nboot = 1000
)

Arguments

Details

The fit of the models are compared by using the D3 method for pooling Likelihood ratio statistics (method of Meng and Rubin). The pooled AIC difference is calculated according to the formula AIC = D - 2*p, where D is the pooled likelihood ratio tests of constrained models (numerator in D3 statistic) and p is the difference in number of parameters between the full and restricted models that are compared. The pooled AUC difference is calculated, after the standard error is obtained in each imputed data set by method DeLong or bootstrapping. The NRI categorical and continuous and IDI are calculated in each imputed data set and pooled.

Value

An object from which the following objects can be extracted:

• DR_stats p-value of the D3 statistic, the D3 statistic, LRT fixed is the likelihood Ratio test value of the constrained models.

• stats_compare Mean of LogLik0, LogLik1, AIC0, AIC1, AIC_diff values of the restricted (containing a 0) and full models (containing a 1).

• NRI pooled values for the categorical and continuous Net Reclassification improvement values and the Integrated Discrimination improvement.

• AUC_stats Pooled Area Under the Curve of restricted and full models.

• AUC_diff Pooled difference in AUC.

• formula_test regression formula of full model.

• cutoff Cutoff value used for reclassification values.

• formula_null regression formula of null model

• compare_predictors Predictors used in full model.

• compare_group group of predictors used in full model.

References

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Consentino F, Claeskens G. Order Selection tests with multiply imputed data Computational Statistics and Data Analysis.2010;54:2284-2295.

Examples

pool_lr <- psfmi_lr(data=lbpmilr, p.crit = 1, direction="FW", nimp=10, impvar="Impnr", 
 Outcome="Chronic", predictors=c("Radiation"), cat.predictors = ("Satisfaction"),
 int.predictors = NULL, spline.predictors="Tampascale", nknots=3, method="D1")

 res_compare <- pool_compare_models(pool_lr, compare.predictors = c("Pain", "Duration", 
 "Function"), cutoff = 0.4)
 res_compare

Description

pool_D2 The D2 statistic to combine the Chi square values across Multiply Imputed datasets.

Usage

pool_D2(dw, v)

Arguments

Value

The pooled chi square values as the D2 statistic, the p-value, the numerator, df1 and denominator, df2 degrees of freedom for the F-test.

Author(s)

Martijn Heymans, 2021

References

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

Examples

pool_D2(c(2.25, 3.95, 6.24, 5.27, 2.81), 4)

Description

pool_D4 The D4 statistic to combine the likelihood ratio tests (LRT) across Multiply Imputed datasets according method D4.

Usage

pool_D4(data, nimp, impvar, fm0, fm1, robust = TRUE, model_type = "binomial")

Arguments

Value

The D4 statistic, the numerator, df1 and denominator, df2 degrees of freedom for the F-test.

Author(s)

Martijn Heymans, 2021

References

Chan, K. W., & Meng, X.-L. (2019). Multiple improvements of multiple imputation likelihood ratio tests. ArXiv:1711.08822 [Math, Stat]. https://arxiv.org/abs/1711.08822

Grund, Simon, Oliver Lüdtke, and Alexander Robitzsch. 2021. “Pooling Methods for Likelihood Ratio Tests in Multiply Imputed Data Sets.” PsyArXiv. January 29. doi:10.31234/osf.io/d459g.

Examples

fm0 <- Chronic ~ BMI + factor(Carrying) + 
  Satisfaction + SocialSupport + Smoking
fm1 <- Chronic ~ BMI + factor(Carrying) + 
  Satisfaction +  SocialSupport + Smoking +
  Radiation

psfmi::pool_D4(data=lbpmilr, nimp=10, impvar="Impnr",
               fm0=fm0, fm1=fm1, robust = TRUE)

Description

pool_intadj Provides pooled adjusted intercept after shrinkage of the pooled coefficients in multiply imputed datasets for models selected with the psfmi_lr function and internally validated with the psfmi_perform function.

Usage

pool_intadj(pobj, shrinkage_factor)

Arguments

Details

The function provides the pooled adjusted intercept after shrinkage of pooled regression coefficients in multiply imputed datasets. The function is only available for logistic regression models without random effects.

Value

A pool_intadj object from which the following objects can be extracted: int_adj, the adjusted intercept value, coef_shrink_pooled, the pooled regression coefficients after shrinkage, coef_orig_pooled, the (original) pooled regression coefficients before shrinkage and nimp, the number of imputed datasets.

References

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd edition). Springer, New York, NY, 2015.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

res_psfmi <- psfmi_lr(data=lbpmilr, nimp=5, impvar="Impnr", Outcome="Chronic",
           predictors=c("Gender", "Pain","Tampascale","Smoking","Function", 
           "Radiation", "Age"), p.crit = 1, method="D1", direction="BW")
 res_psfmi$RR_Model

## Not run: 
 set.seed(100)
 res_val <- psfmi_perform(res_psfmi, method = "MI_boot", nboot=10, 
   int_val = TRUE, p.crit=1, cal.plot=FALSE, plot.indiv=FALSE)
 res_val$intval

 res <- pool_intadj(res_psfmi, shrinkage_factor = 0.9774058)
 res$int_adj
 res$coef_shrink_pooled
 
## End(Not run)

Description

pool_performance Pooling performance measures for logistic and Cox regression models.

Usage

pool_performance(
  data,
  formula,
  nimp,
  impvar,
  plot.indiv,
  model_type = "binomial",
  cal.plot = TRUE,
  plot.method = "mean",
  groups_cal = 10
)

Arguments

Details

A typical formula object for logistic regression models has the form formula = Outcome ~ terms. For Cox regression models the formula object must be defined as Surv(time, status) ~ terms. For Cox models calibration curves can not be generated.

Examples

perf <- pool_performance(data=lbpmilr, nimp=5, impvar="Impnr", 
 formula = Chronic ~ Gender + Pain + Tampascale + 
 Smoking + Function + Radiation + Age + factor(Carrying), 
 cal.plot=TRUE, plot.method="mean", 
 groups_cal=10, model_type="binomial")
 
 perf$ROC_pooled
 perf$R2_pooled

Description

pool_reclassification Function to pool categorical and continuous NRI and IDI over Multiply Imputed datasets

Usage

pool_reclassification(datasets, cutoff = cutoff)

Arguments

Details

This function is called by the function pool_compare_model

Author(s)

Martijn Heymans, 2020

Description

pool_RR Rubin's Rules

Usage

pool_RR(est, se, conf.level = 0.95, n, k)

Arguments

Author(s)

Martijn Heymans, 2021

Description

psfmi_coxr Pooling and backward or forward selection of Cox regression prediction models in multiply imputed data using selection methods D1, D2 and MPR.

Usage

psfmi_coxr(
  data,
  formula = NULL,
  nimp = 5,
  impvar = NULL,
  time,
  status,
  predictors = NULL,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  strata.variable = NULL,
  nknots = NULL,
  p.crit = 1,
  method = "RR",
  direction = NULL
)

Arguments

Details

The basic pooling procedure to derive pooled coefficients, standard errors, 95 confidence intervals and p-values is Rubin's Rules (RR). However, RR is only possible when the model included continuous or dichotomous variables. Specific procedures are available when the model also included categorical (> 2 categories) or restricted cubic spline variables. These pooling methods are: “D1” is pooling of the total covariance matrix, ”D2” is pooling of Chi-square values and “MPR” is pooling of median p-values (MPR rule). Spline regression coefficients are defined by using the rcs function for restricted cubic splines of the rms package. A minimum number of 3 knots as defined under knots is required.

A typical formula object has the form Surv(time, status) ~ terms. Categorical variables has to be defined as Surv(time, status) ~ factor(variable), restricted cubic spline variables as Surv(time, status) ~ rcs(variable, 3). Interaction terms can be defined as Surv(time, status) ~ variable1*variable2 or Surv(time, status) ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+". If a formula object is used set predictors, cat.predictors, spline.predictors or int.predictors at the default value of NULL. For Cox models also a strata variable is allowed to include in the formula as Surv(time, status) ~ strata(variable) + terms.

Value

An object of class pmods (multiply imputed models) from which the following objects can be extracted:

• data imputed datasets

• RR_model pooled model at each selection step

• RR_model_final final selected pooled model

• multiparm pooled p-values at each step according to pooling method

• multiparm_final pooled p-values at final step according to pooling method

• multiparm_out (only when direction = "FW") pooled p-values of removed predictors

• formula_step formula object at each step

• formula_final formula object at final step

• formula_initial formula object at final step

• predictors_in predictors included at each selection step

• predictors_out predictors excluded at each step

• impvar name of variable used to distinguish imputed datasets

• nimp number of imputed datasets

• status name of the status variable

• time name of the time variable

• method selection method

• p.crit p-value selection criterium

• call function call

• model_type type of regression model used

• direction direction of predictor selection

• predictors_final names of predictors in final selection step

• predictors_initial names of predictors in start model

• keep.predictors names of predictors that were forced in the model

• strata.variable names of the strata variable in the model

Vignettes

https://mwheymans.github.io/psfmi/articles/psfmi_CoxModels.html

Author(s)

Martijn Heymans, 2020

References

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Enders CK (2010). Applied missing data analysis. New York: The Guilford Press.

van de Wiel MA, Berkhof J, van Wieringen WN. Testing the prediction error difference between 2 predictors. Biostatistics. 2009;10:550-60.

Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

pool_coxr <- psfmi_coxr(formula = Surv(Time, Status) ~ Pain + Tampascale +
                       Radiation + Radiation*Pain + Age + Duration + Previous,
                     data=lbpmicox, p.crit = 0.05, direction="BW", nimp=5, impvar="Impnr",
                     keep.predictors = "Radiation*Pain", method="D1")
                     
 pool_coxr$RR_model_final
 
 pool_coxr <- psfmi_coxr(formula = Surv(Time, Status) ~ Pain + Tampascale +
                       Previous + strata(Radiation), data=lbpmicox, p.crit = 0.05, 
                       direction="BW", nimp=5, impvar="Impnr", method="D1")
                     
 pool_coxr$RR_model_final

Description

psfmi_lm Pooling and backward or forward selection of Linear regression models in multiply imputed data using selection methods RR, D1, D2 and MPR.

Usage

psfmi_lm(
  data,
  formula = NULL,
  nimp = 5,
  impvar = NULL,
  Outcome = NULL,
  predictors = NULL,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  p.crit = 1,
  method = "RR",
  direction = NULL
)

Arguments

Details

The basic pooling procedure to derive pooled coefficients, standard errors, 95 confidence intervals and p-values is Rubin's Rules (RR). However, RR is only possible when the model included continuous or dichotomous variables. Specific procedures are available when the model also included categorical (> 2 categories) or restricted cubic spline variables. These pooling methods are: “D1” is pooling of the total covariance matrix, ”D2” is pooling of Chi-square values and “MPR” is pooling of median p-values (MPR rule). Spline regression coefficients are defined by using the rcs function for restricted cubic splines of the rms package. A minimum number of 3 knots as defined under knots is required.

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+". If a formula object is used set predictors, cat.predictors, spline.predictors or int.predictors at the default value of NULL.

Value

An object of class pmods (multiply imputed models) from which the following objects can be extracted:

• data imputed datasets

• RR_model pooled model at each selection step

• RR_model_final final selected pooled model

• multiparm pooled p-values at each step according to pooling method

• multiparm_final pooled p-values at final step according to pooling method

• multiparm_out (only when direction = "FW") pooled p-values of removed predictors

• formula_step formula object at each step

• formula_final formula object at final step

• formula_initial formula object at final step

• predictors_in predictors included at each selection step

• predictors_out predictors excluded at each step

• impvar name of variable used to distinguish imputed datasets

• nimp number of imputed datasets

• Outcome name of the outcome variable

• method selection method

• p.crit p-value selection criterium

• call function call

• model_type type of regression model used

• direction direction of predictor selection

• predictors_final names of predictors in final selection step

• predictors_initial names of predictors in start model

• keep.predictors names of predictors that were forced in the model

Author(s)

Martijn Heymans, 2021

References

Enders CK (2010). Applied missing data analysis. New York: The Guilford Press.

van de Wiel MA, Berkhof J, van Wieringen WN. Testing the prediction error difference between 2 predictors. Biostatistics. 2009;10:550-60.

Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

pool_lm <- psfmi_lm(data=lbpmilr, formula = Pain ~ factor(Satisfaction) + 
  rcs(Tampascale,3) + Radiation + 
  Radiation*factor(Satisfaction) + Age + Duration + BMI,
  p.crit = 0.05, direction="FW", nimp=5, impvar="Impnr", 
  keep.predictors = c("Radiation*factor(Satisfaction)", "Age"), method="D1")
  
  pool_lm$RR_model_final

Description

psfmi_lr Pooling and backward or forward selection of Logistic regression models across multiply imputed data using selection methods RR, D1, D2, D3, D4 and MPR.

Usage

psfmi_lr(
  data,
  formula = NULL,
  nimp = 5,
  impvar = NULL,
  Outcome = NULL,
  predictors = NULL,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  p.crit = 1,
  method = "RR",
  direction = NULL
)

Arguments

Details

The basic pooling procedure to derive pooled coefficients, standard errors, 95 confidence intervals and p-values is Rubin's Rules (RR). However, RR is only possible when the model included continuous or dichotomous variables. Specific procedures are available when the model also included categorical (> 2 categories) or restricted cubic spline variables. These pooling methods are: “D1” is pooling of the total covariance matrix, ”D2” is pooling of Chi-square values, “D3” and "D4" is pooling Likelihood ratio statistics (method of Meng and Rubin) and “MPR” is pooling of median p-values (MPR rule). Spline regression coefficients are defined by using the rcs function for restricted cubic splines of the rms package. A minimum number of 3 knots as defined under knots is required.

A typical formula object has the form Outcome ~ terms. Categorical variables has to be defined as Outcome ~ factor(variable), restricted cubic spline variables as Outcome ~ rcs(variable, 3). Interaction terms can be defined as Outcome ~ variable1*variable2 or Outcome ~ variable1 + variable2 + variable1:variable2. All variables in the terms part have to be separated by a "+". If a formula object is used set predictors, cat.predictors, spline.predictors or int.predictors at the default value of NULL.

Value

An object of class pmods (multiply imputed models) from which the following objects can be extracted:

• data imputed datasets

• RR_model pooled model at each selection step

• RR_model_final final selected pooled model

• multiparm pooled p-values at each step according to pooling method

• multiparm_final pooled p-values at final step according to pooling method

• multiparm_out (only when direction = "FW") pooled p-values of removed predictors

• formula_step formula object at each step

• formula_final formula object at final step

• formula_initial formula object at final step

• predictors_in predictors included at each selection step

• predictors_out predictors excluded at each step

• impvar name of variable used to distinguish imputed datasets

• nimp number of imputed datasets

• Outcome name of the outcome variable

• method selection method

• p.crit p-value selection criterium

• call function call

• model_type type of regression model used

• direction direction of predictor selection

• predictors_final names of predictors in final selection step

• predictors_initial names of predictors in start model

• keep.predictors names of predictors that were forced in the model

Vignettes

https://mwheymans.github.io/psfmi/articles/psfmi_LogisticModels.html

Author(s)

Martijn Heymans, 2020

References

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Enders CK (2010). Applied missing data analysis. New York: The Guilford Press.

Meng X-L, Rubin DB. Performing likelihood ratio tests with multiply-imputed data sets. Biometrika.1992;79:103-11.

van de Wiel MA, Berkhof J, van Wieringen WN. Testing the prediction error difference between 2 predictors. Biostatistics. 2009;10:550-60.

Marshall A, Altman DG, Holder RL, Royston P. Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines. BMC Med Res Methodol. 2009;9:57.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

pool_lr <- psfmi_lr(data=lbpmilr, formula = Chronic ~ Pain + 
  factor(Satisfaction) + rcs(Tampascale,3) + Radiation + 
  Radiation*factor(Satisfaction) + Age + Duration + BMI,
  p.crit = 0.05, direction="FW", nimp=5, impvar="Impnr", 
  keep.predictors = c("Radiation*factor(Satisfaction)", "Age"), method="D1")
  
  pool_lr$RR_model_final

Description

psfmi_mm Pooling and backward selection for 2 level (generalized) linear mixed models in multiply imputed datasets using different selection methods.

Usage

psfmi_mm(
  data,
  nimp = 5,
  impvar = NULL,
  clusvar = NULL,
  Outcome,
  predictors = NULL,
  random.eff = NULL,
  family = "linear",
  p.crit = 1,
  cat.predictors = NULL,
  spline.predictors = NULL,
  int.predictors = NULL,
  keep.predictors = NULL,
  nknots = NULL,
  method = "RR",
  print.method = FALSE
)

Arguments

Details

The basic pooling procedure to derive pooled coefficients, standard errors, 95 confidence intervals and p-values is Rubin's Rules (RR). Specific procedures are available to derive pooled p-values for categorical (> 2 categories) and spline variables. print.method allows to choose between the pooling methods: D1, D2 and D3 and MPR for pooling of median p-values (MPR rule). The D1, D2 and D3 methods are called from the package mitml. For Logistic multilevel models (that are estimated using the glmer function), the D3 method is not yet available. Spline regression coefficients are defined by using the rcs function for restricted cubic splines of the rms package. A minimum number of 3 knots as defined under knots is required.

Value

An object of class smodsmi (selected models in multiply imputed datasets) from which the following objects can be extracted: imputed datasets as data, selected pooled model as RR_model, pooled p-values according to pooling method as multiparm, random effects as random.eff, predictors included at each selection step as predictors_in, predictors excluded at each step as predictors_out, and family, impvar, clusvar, nimp, Outcome, method, p.crit, predictors, cat.predictors, keep.predictors, int.predictors, spline.predictors, knots, print.method, model_type, call, predictors_final for names of predictors in final step and fit.formula is the regression formula of start model.

References

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Enders CK (2010). Applied missing data analysis. New York: The Guilford Press.

Meng X-L, Rubin DB. Performing likelihood ratio tests with multiply-imputed data sets. Biometrika.1992;79:103-11.

van de Wiel MA, Berkhof J, van Wieringen WN. Testing the prediction error difference between 2 predictors. Biostatistics. 2009;10:550-60.

mitml package https://cran.r-project.org/web/packages/mitml/index.html

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

http://missingdatasolutions.rbind.io/

Examples

## Not run: 
  pool_mm <- psfmi_mm(data=ipdna_md, nimp=5, impvar=".imp", family="linear",
  predictors=c("gender", "afib", "sbp"), clusvar = "centre",
  random.eff="( 1 | centre)", Outcome="dbp", cat.predictors = "bmi_cat",
  p.crit=0.15, method="D1", print.method = FALSE)
  pool_mm$RR_Model
  pool_mm$multiparm

## End(Not run)

Description

psfmi_mm_multiparm Function to pool according to D1, D2 and D3 methods

Usage

psfmi_mm_multiparm(
  data,
  nimp,
  impvar,
  Outcome,
  P,
  p.crit,
  family,
  random.eff,
  method,
  print.method
)

Arguments

Examples

## Not run: 
 psfmi_mm_multiparm(data=ipdna_md, nimp=5, impvar=".imp", family="linear",
 P=c("gender", "bnp", "dbp", "lvef", "bmi_cat"),
 random.eff="( 1 | centre)", Outcome="sbp",
 p.crit=0.05, method="D1", print.method = FALSE)

## End(Not run)

Description

psfmi_perform Evaluate Performance of logistic regression models selected with the psfmi_lr function of the psfmi package by using cross-validation or bootstrapping.

Usage

psfmi_perform(
  pobj,
  val_method = NULL,
  data_orig = NULL,
  int_val = TRUE,
  nboot = 10,
  folds = 3,
  nimp_cv = 5,
  nimp_mice = 5,
  p.crit = 1,
  BW = FALSE,
  direction = NULL,
  cv_naive_appt = FALSE,
  cal.plot = FALSE,
  plot.method = "mean",
  groups_cal = 5,
  miceImp,
  ...
)

Arguments

Details

For internal validation five methods can be used, cv_MI, cv_MI_RR, MI_cv_naive, MI_boot and boot_MI. Method cv_MI uses imputation within each cross-validation fold definition. By repeating this in several imputation runs, multiply imputed datasets are generated. Method cv_MI_RR uses multiple imputation within the cross-validation definition. MI_cv_naive, applies cross-validation within each imputed dataset. MI_boot draws for each bootstrap step the same cases in all imputed datasets. With boot_MI first bootstrap samples are drawn from the original dataset with missing values and than multiple imputation is applied. For multiple imputation the mice function from the mice package is used. It is recommended to use a minumum of 100 imputation runs for method cv_MI or 100 bootstrap samples for method boot_MI or MI_boot. Methods cv_MI, cv_MI_RR and MI_cv_naive can be combined with backward selection during cross-validation and with methods boot_MI and MI_boot, backward and forward selection can be used. For methods cv_MI and cv_MI_RR the outcome in the original dataset has to be complete.

Value

A psfmi_perform object from which the following objects can be extracted: res_boot, result of pooled performance (in multiply imputed datasets) at each bootstrap step of ROC app (pooled ROC), ROC test (pooled ROC after bootstrap model is applied in original multiply imputed datasets), same for R2 app (Nagelkerke's R2), R2 test, Scaled Brier app and Scaled Brier test. Information is also provided about testing the Calibration slope at each bootstrap step as interc test and Slope test. The performance measures are pooled by a call to the function pool_performance. Another object that can be extracted is intval, with information of the AUC, R2, Scaled Brier score and Calibration slope averaged over the bootstrap samples, in terms of: Orig (original datasets), Apparent (models applied in bootstrap samples), Test (bootstrap models are applied in original datasets), Optimism (difference between apparent and test) and Corrected (original corrected for optimism).

Author(s)

Martijn Heymans, 2020

References

Heymans MW, van Buuren S, Knol DL, van Mechelen W, de Vet HC. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007(13);7:33.

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd edition). Springer, New York, NY, 2015.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

Harel, O. (2009). The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics, 36(10), 1109-1118.

Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB. Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. 2014;14:116.

Wahl S, Boulesteix AL, Zierer A, Thorand B, van de Wiel MA. Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. BMC Med Res Methodol. 2016;16(1):144.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Description

psfmi_stab Stability analysis of predictors and prediction models selected with the psfmi_lr, psfmi_coxr or psfmi_mm functions of the psfmi package.

Usage

psfmi_stab(
  pobj,
  boot_method = NULL,
  nboot = 20,
  p.crit = 0.05,
  start_model = TRUE,
  direction = NULL
)

Arguments

Details

The function evaluates predictor selection frequency in stratified or cluster bootstrap samples. The stratification factor is the variable that separates the imputed datasets. The same bootstrap cases are drawn in each bootstrap sample. It uses as input an object of class pmods as a result of a previous call to the psfmi_lr, psfmi_coxr or psfmi_mm functions. In combination with the psfmi_mm function a cluster bootstrap method is used where bootstrapping is used on the level of the clusters only (and not also within the clusters).

Value

A psfmi_stab object from which the following objects can be extracted: bootstrap inclusion (selection) frequency of each predictor bif, total number each predictor is included in the bootstrap samples as bif_total, percentage a predictor is selected in each bootstrap sample as bif_perc and number of times a prediction model is selected in the bootstrap samples as model_stab.

Vignettes

https://mwheymans.github.io/psfmi/articles/psfmi_StabilityAnalysis.html

References

Heymans MW, van Buuren S. et al. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007;13:7-33.

Eekhout I, van de Wiel MA, Heymans MW. Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis. BMC Med Res Methodol. 2017;17(1):129.

Sauerbrei W, Schumacher M. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med. 1992;11:2093–109.

Royston P, Sauerbrei W (2008) Multivariable model-building – a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. (2008). Chapter 8, Model Stability. Wiley, Chichester

Heinze G, Wallisch C, Dunkler D. Variable selection - A review and recommendations for the practicing statistician. Biom J. 2018;60(3):431-449.

http://missingdatasolutions.rbind.io/

Examples

pool_lr <- psfmi_coxr(formula = Surv(Time, Status) ~ Pain + factor(Satisfaction) + 
   rcs(Tampascale,3) + Radiation + Radiation*factor(Satisfaction) + Age + Duration + 
   Previous + Radiation*rcs(Tampascale, 3), data=lbpmicox, p.crit = 0.157, direction="FW",
   nimp=5, impvar="Impnr", keep.predictors = NULL, method="D1")

 pool_lr$RR_Model
 pool_lr$multiparm

## Not run: 
 stab_res <- psfmi_stab(pool_lr, direction="FW", start_model = TRUE,
     boot_method = "single", nboot=20, p.crit=0.05)
 stab_res$bif
 stab_res$bif_perc
 stab_res$model_stab

## End(Not run)

Description

psfmi_validate Evaluate Performance of logistic regression models selected with the psfmi_lr function of the psfmi package by using cross-validation or bootstrapping.

Usage

psfmi_validate(
  pobj,
  val_method = NULL,
  data_orig = NULL,
  int_val = TRUE,
  nboot = 10,
  folds = 3,
  nimp_cv = 5,
  nimp_mice = 5,
  p.crit = 1,
  BW = FALSE,
  direction = NULL,
  cv_naive_appt = FALSE,
  cal.plot = FALSE,
  plot.method = "mean",
  groups_cal = 5,
  miceImp,
  ...
)

Arguments

Details

For internal validation five methods can be used, cv_MI, cv_MI_RR, MI_cv_naive, MI_boot and boot_MI. Method cv_MI uses imputation within each cross-validation fold definition. By repeating this in several imputation runs, multiply imputed datasets are generated. Method cv_MI_RR uses multiple imputation within the cross-validation definition. MI_cv_naive, applies cross-validation within each imputed dataset. MI_boot draws for each bootstrap step the same cases in all imputed datasets. With boot_MI first bootstrap samples are drawn from the original dataset with missing values and than multiple imputation is applied. For multiple imputation the mice function from the mice package is used. It is recommended to use a minumum of 100 imputation runs for method cv_MI or 100 bootstrap samples for method boot_MI or MI_boot. Methods cv_MI, cv_MI_RR and MI_cv_naive can be combined with backward selection during cross-validation and with methods boot_MI and MI_boot, backward and forward selection can be used. For methods cv_MI and cv_MI_RR the outcome in the original dataset has to be complete.

Value

A psfmi_perform object from which the following objects can be extracted: res_boot, result of pooled performance (in multiply imputed datasets) at each bootstrap step of ROC app (pooled ROC), ROC test (pooled ROC after bootstrap model is applied in original multiply imputed datasets), same for R2 app (Nagelkerke's R2), R2 test, Scaled Brier app and Scaled Brier test. Information is also provided about testing the Calibration slope at each bootstrap step as interc test and Slope test. The performance measures are pooled by a call to the function pool_performance. Another object that can be extracted is intval, with information of the AUC, R2, Scaled Brier score and Calibration slope averaged over the bootstrap samples, in terms of: Orig (original datasets), Apparent (models applied in bootstrap samples), Test (bootstrap models are applied in original datasets), Optimism (difference between apparent and test) and Corrected (original corrected for optimism).

Author(s)

Martijn Heymans, 2020

References

Heymans MW, van Buuren S, Knol DL, van Mechelen W, de Vet HC. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007(13);7:33.

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis (2nd edition). Springer, New York, NY, 2015.

Van Buuren S. (2018). Flexible Imputation of Missing Data. 2nd Edition. Chapman & Hall/CRC Interdisciplinary Statistics. Boca Raton.

Harel, O. (2009). The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics, 36(10), 1109-1118.

Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB. Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. 2014;14:116.

Wahl S, Boulesteix AL, Zierer A, Thorand B, van de Wiel MA. Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. BMC Med Res Methodol. 2016;16(1):144.

EW. Steyerberg (2019). Clinical Prediction MOdels. A Practical Approach to Development, Validation, and Updating (2nd edition). Springer Nature Switzerland AG.

http://missingdatasolutions.rbind.io/

Examples

pool_lr <- psfmi_lr(data=lbpmilr, formula = Chronic ~ Pain + JobDemands + rcs(Tampascale, 3) +
           factor(Satisfaction) + Smoking, p.crit = 1, direction="FW",
           nimp=5, impvar="Impnr", method="D1")
           
pool_lr$RR_model

res_perf <- psfmi_validate(pool_lr, val_method = "cv_MI", data_orig = lbp_orig, folds=3,
            nimp_cv = 2, p.crit=0.05, BW=TRUE, miceImp = miceImp, printFlag = FALSE)
            
res_perf

## Not run: 
 set.seed(200)
  res_val <- psfmi_validate(pobj, val_method = "boot_MI", data_orig = lbp_orig, nboot = 5,
  p.crit=0.05, BW=TRUE, miceImp = miceImp, nimp_mice = 5, printFlag = FALSE, direction = "FW")
  
  res_val$stats_val

## End(Not run)

Description

Risk calculation at specific time point for Cox model

Usage

risk_coxph(mod, t_risk)

Arguments

Value

Cox regression Risk estimates at specific time point.

Author(s)

Martijn Heymans, 2023

References

Pencina MJ, D'Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med. 2011;30(1):11-21

Inoue E (2018). nricens: NRI for Risk Prediction Models with Time to Event and Binary Response Data. R package version 1.6, <https://CRAN.R-project.org/package=nricens>.

See Also

nri_cox

Description

Nagelkerke's R-square calculation for logistic regression / glm models

Usage

rsq_nagel(fitobj)

Arguments

Value

The value for the explained variance.

Author(s)

Martijn Heymans, 2020

See Also

psfmi_perform, pool_performance

Description

R-square calculation for Cox regression models

Usage

rsq_surv(fitobj)

Arguments

Value

The value for the explained variance.

Author(s)

Martijn Heymans, 2021

References

F. Harrell. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd Edition. Springer, New York, NY, 2015.

See Also

pool_performance

Description

Dataset with blood pressure measurements

Usage

data(sbp_age)

Format

A data frame with 30 observations on the following 3 variables.

pat_id

continuous

sbp

continuous: systolic blood pressure

age

continuous: age (years)

Examples

data(sbp_age)
## maybe str(sbp_age)

Description

Dataset with blood pressure measurements

Usage

data(sbp_qas)

Format

A data frame with 32 observations on the following 5 variables.

pat_id

continuous

sbp

continuous: systolic blood pressure

bmi

continuous: body mass index

age

continuous: age (years)

smk

dichotomous: 0 = no, 1 = yes

Examples

data(sbp_qas)
## maybe str(sbp_qas)

Description

Calculates the scaled Brier score

Usage

scaled_brier(obs, pred)

Arguments

Value

The value for the scaled Brier score.

Author(s)

Martijn Heymans, 2020

See Also

psfmi_perform, pool_performance

Description

Survival data about smoking

Usage

data(smoking)

Format

A data frame with 20 observations on the following 3 variables.

smoking

dichotomous: 1=yes, 0=no

time

continuous: Survival time in years

death

dichotomous: Status at end of study

Examples

data(smoking)
## maybe str(smoking)

Description

stab_single Stability analysis of predictors and prediction models selected with the glm_bw.

Usage

stab_single(pobj, nboot = 20, p.crit = 0.05, start_model = TRUE)

Arguments

Details

The function evaluates predictor selection frequency in bootstrap samples. It uses as input an object of class smods as a result of a previous call to the glm_bw.

Value

A psfmi_stab object from which the following objects can be extracted: bootstrap inclusion (selection) frequency of each predictor bif, total number each predictor is included in the bootstrap samples as bif_total, percentage a predictor is selected in each bootstrap sample as bif_perc and number of times a prediction model is selected in the bootstrap samples as model_stab.

References

Heymans MW, van Buuren S. et al. Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Med Res Methodol. 2007;13:7-33.

Sauerbrei W, Schumacher M. A bootstrap resampling procedure for model building: application to the Cox regression model. Stat Med. 1992;11:2093–109.

Royston P, Sauerbrei W (2008) Multivariable model-building – a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. (2008). Chapter 8, Model Stability. Wiley, Chichester.

Heinze G, Wallisch C, Dunkler D. Variable selection - A review and recommendations for the practicing statistician. Biom J. 2018;60(3):431-449.

http://missingdatasolutions.rbind.io/

Examples

model_lr <- glm_bw(formula = Radiation ~ Pain + factor(Satisfaction) + 
   rcs(Tampascale,3) + Age + Duration + JobControl + JobDemands + SocialSupport, 
   data=lbpmilr_dev, p.crit = 0.05)

## Not run: 
 stab_res <- stab_single(model_lr, start_model = TRUE, nboot=20, p.crit=0.05)
 stab_res$bif
 stab_res$bif_perc
 stab_res$model_stab

## End(Not run)

Description

Dataset of persons from the The Amsterdam Growth and Health Longitudinal Study (AGHLS)

Usage

data(weight)

Format

A data frame with 450 observations on the following 7 variables.

ID

continuous

SBP

continuous: Systolic Blood Pressure

LDL

continuous: Cholesterol

Glucose

continuous

HDL

continuous: Cholesterol

Gender

dichotomous: 1=male, 0=female

Weight

continuous: bodyweight

Examples

data(weight)
## maybe str(weight)