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  "Title": "Data and Statistical Analyses after Multiple Imputation",
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  "Authors@R": "c(\nperson(\"Martijn\", \"Heymans\", email = \"mw.heymans@amsterdamumc.nl\", role=c(\"cre\", \"aut\"),\ncomment = c(ORCID = \"0000-0002-3889-0921\")),\nperson(\"Jaap\", \"Brand\", email = \"jaapbrand2651965@gmail.com\", role=c(\"ctb\")))",
  "Description": "Statistical Analyses and Pooling after Multiple\nImputation. A large variety of repeated statistical analysis\ncan be performed and finally pooled. Statistical analysis that\nare available are, among others, Levene's test, Odds and Risk\nRatios, One sample proportions, difference between proportions\nand linear and logistic regression models. Functions can also\nbe used in combination with the Pipe operator. More and more\nstatistical analyses and pooling functions will be added over\ntime. Heymans (2007) <doi:10.1186/1471-2288-7-33>. Eekhout\n(2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009)\n<doi:10.1093/biostatistics/kxp011>. Marshall (2009)\n<doi:10.1186/1471-2288-9-57>. Sidi (2021)\n<doi:10.1080/00031305.2021.1898468>. Lott (2018)\n<doi:10.1080/00031305.2018.1473796>. Grund (2021)\n<doi:10.31234/osf.io/d459g>.",
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  "License": "GPL (>= 2)",
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  "Repository": "https://mwheymans.r-universe.dev",
  "Date/Publication": "2025-10-26 14:17:17 UTC",
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    "pool_propdiff_ac",
    "pool_propdiff_nw",
    "pool_propdiff_wald",
    "pool_risk_ratio",
    "pool_scalar_RR",
    "pool_t_test",
    "prop_nna",
    "prop_wald",
    "propdiff_ac",
    "propdiff_wald",
    "risk_ratio",
    "t_test"
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        "tbl",
        "data.frame"
      ],
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        "Gender",
        "Carrying",
        "Pain",
        "Tampascale",
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        "Radiation",
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        "Satisfaction",
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        "Duration",
        "BMI"
      ],
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      "tojson": true
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      "class": [
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        "Status",
        "Time",
        "Duration",
        "Previous",
        "Radiation",
        "Onset",
        "Age",
        "Tampascale",
        "Pain",
        "Function",
        "Satisfaction",
        "JobControl",
        "JobDemand",
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        "Expectation",
        "Expect_cat"
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      "table": true,
      "tojson": true
    },
    {
      "name": "lbpmilr",
      "title": "Data of 159 Low Back Pain Patients",
      "object": "lbpmilr",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Impnr",
        "ID",
        "Chronic",
        "Gender",
        "Carrying",
        "Pain",
        "Tampascale",
        "Function",
        "Radiation",
        "Age",
        "Smoking",
        "Satisfaction",
        "JobControl",
        "JobDemands",
        "SocialSupport",
        "Duration",
        "BMI"
      ],
      "rows": 1590,
      "table": true,
      "tojson": true
    }
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  "_help": [
    {
      "page": "bf_test",
      "title": "Calculates the Brown-Forsythe test.",
      "topics": [
        "bf_test"
      ]
    },
    {
      "page": "cindex",
      "title": "Calculates the c-index and standard error",
      "topics": [
        "cindex"
      ]
    },
    {
      "page": "cor_est",
      "title": "Calculates the correlation coefficient",
      "topics": [
        "cor_est"
      ]
    },
    {
      "page": "cor2fz",
      "title": "Fisher z transformation of correlation coefficient",
      "topics": [
        "cor2fz"
      ]
    },
    {
      "page": "df2milist",
      "title": "Turns a data frame with multiply imputed data into an object of class 'milist'",
      "topics": [
        "df2milist"
      ]
    },
    {
      "page": "f2chi",
      "title": "Converts F-values into Chi Square values",
      "topics": [
        "f2chi"
      ]
    },
    {
      "page": "fz2cor",
      "title": "Fisher z back transformation of correlation coefficient",
      "topics": [
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      ]
    },
    {
      "page": "glm_mi",
      "title": "Direct Pooling and model selection of Linear and Logistic regression models across multiply imputed data.",
      "topics": [
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      ]
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      "title": "Takes the inverse of a logit transformed value",
      "topics": [
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      ]
    },
    {
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      "title": "Takes the inverse of logit transformed parameters and calculates the confidence intervals",
      "topics": [
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    },
    {
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      "title": "Dataset of 159 Low Back Pain Patients with missing values",
      "topics": [
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      ]
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      "title": "Survival data of 265 Low Back Pain Patients",
      "topics": [
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      "title": "Data of 159 Low Back Pain Patients",
      "topics": [
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      "title": "Calculates the Levene's test",
      "topics": [
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      ]
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    {
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      "title": "Turns a list object with multiply imputed datasets into an object of class 'milist'.",
      "topics": [
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      ]
    },
    {
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      "title": "Logit transformation of parameter estimates",
      "topics": [
        "logit_trans"
      ]
    },
    {
      "page": "mids2milist",
      "title": "Turns a 'mice::mids' object into an object of class 'milist' to be further used by 'miceafter::with'",
      "topics": [
        "mids2milist"
      ]
    },
    {
      "page": "odds_ratio",
      "title": "Calculates the odds ratio (OR) and standard error.",
      "topics": [
        "odds_ratio"
      ]
    },
    {
      "page": "pool_bftest",
      "title": "Calculates the pooled Brown-Forsythe test.",
      "topics": [
        "pool_bftest"
      ]
    },
    {
      "page": "pool_cindex",
      "title": "Calculates the pooled C-index and Confidence intervals",
      "topics": [
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      ]
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    {
      "page": "pool_cor",
      "title": "Calculates the pooled correlation coefficient and Confidence intervals",
      "topics": [
        "pool_cor"
      ]
    },
    {
      "page": "pool_D2",
      "title": "Combines the Chi Square statistics across Multiply Imputed datasets",
      "topics": [
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      ]
    },
    {
      "page": "pool_D4",
      "title": "Pools the Likelihood Ratio tests across Multiply Imputed datasets ( method D4)",
      "topics": [
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    },
    {
      "page": "pool_glm",
      "title": "Pools and selects Linear and Logistic regression models across multiply imputed data.",
      "topics": [
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      ]
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      "title": "Calculates the pooled Levene test.",
      "topics": [
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      ]
    },
    {
      "page": "pool_odds_ratio",
      "title": "Calculates the pooled odds ratio (OR) and related confidence interval.",
      "topics": [
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      ]
    },
    {
      "page": "pool_prop_nna",
      "title": "Calculates the pooled proportion and confidence intervals using an approximate Beta distribution.",
      "topics": [
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      ]
    },
    {
      "page": "pool_prop_wald",
      "title": "Calculates the pooled proportion and standard error according to Wald across multiply imputed datasets.",
      "topics": [
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      ]
    },
    {
      "page": "pool_prop_wilson",
      "title": "Calculates the pooled single proportion confidence intervals according to Wilson across multiply imputed datasets.",
      "topics": [
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      ]
    },
    {
      "page": "pool_propdiff_ac",
      "title": "Calculates the pooled difference between proportions and standard error according to Agresti-Caffo across multiply imputed datasets.",
      "topics": [
        "pool_propdiff_ac"
      ]
    },
    {
      "page": "pool_propdiff_nw",
      "title": "Calculates the pooled difference between proportions and confidence intervals according to Newcombe-Wilson (NW) across multiply imputed datasets.",
      "topics": [
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      ]
    },
    {
      "page": "pool_propdiff_wald",
      "title": "Calculates the pooled difference between proportions and standard error according to Wald across multiply imputed datasets.",
      "topics": [
        "pool_propdiff_wald"
      ]
    },
    {
      "page": "pool_risk_ratio",
      "title": "Calculates the pooled risk ratio (RR) and related confidence interval.",
      "topics": [
        "pool_risk_ratio"
      ]
    },
    {
      "page": "pool_scalar_RR",
      "title": "Rubin's Rules for scalar estimates",
      "topics": [
        "pool_scalar_RR"
      ]
    },
    {
      "page": "pool_t_test",
      "title": "Calculates the pooled t-test and Confidence intervals",
      "topics": [
        "pool_t_test"
      ]
    },
    {
      "page": "prop_nna",
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      "topics": [
        "prop_nna"
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    },
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    },
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      "page": "with.milist",
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