The package provides functions to apply pooling, backward and forward selection of logistic and Cox regression prediction models in multiply imputed data sets using Rubin’s Rules (RR), the D1, D2, D3 and the median p-values method. The model can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors.

Validation of the prediction models can be performed with cross-validation or bootstrapping in multiply imputed data sets and pooled model performance measures as AUC value, R-square, scaled Brier score and calibration plots are generated. Also a function to externally validate logistic prediction models in multiple imputed data sets is available and a function to compare models in multiply imputed data.

## Installation

You can install the released version of psfmi with:

install.packages("psfmi")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mwheymans/psfmi")

## Example

This example shows you how to apply forward selection with a model that includes a restricted cubic spline function and an interaction term.

library(psfmi)
#> Registered S3 methods overwritten by 'car':
#>   method                          from
#>   influence.merMod                lme4
#>   cooks.distance.influence.merMod lme4
#>   dfbeta.influence.merMod         lme4
#>   dfbetas.influence.merMod        lme4

pool_lr <- psfmi_lr(data=lbpmilr, formula = Chronic ~ rcs(Pain, 3) + JobDemands + rcs(Tampascale, 3) +
factor(Satisfaction) + Smoking + factor(Satisfaction)*rcs(Pain, 3) ,
p.crit = 0.05, direction="FW", nimp=5, impvar="Impnr",
method="D1")
#> Entered at Step 1 is - rcs(Pain,3)
#> Entered at Step 2 is - factor(Satisfaction)
#>
#> Selection correctly terminated,
#> No new variables entered the model
pool_lr$RR_model_final #>$Final model
#>                    term   estimate std.error  statistic        df     p.value
#> 1           (Intercept) -3.6027668 1.5427414 -2.3353018  60.25659 0.022875170
#> 2 factor(Satisfaction)2 -0.4725289 0.5164342 -0.9149838 145.03888 0.361718841
#> 3 factor(Satisfaction)3 -2.3328994 0.7317131 -3.1882707 122.95905 0.001815476
#> 4      rcs(Pain, 3)Pain  0.6514983 0.4028728  1.6171315  51.09308 0.112008088
#> 5     rcs(Pain, 3)Pain'  0.4703811 0.4596490  1.0233483  75.29317 0.309419924
#>           OR   lower.EXP upper.EXP
#> 1 0.02724823 0.001245225 0.5962503
#> 2 0.62342367 0.224644070 1.7301016
#> 3 0.09701406 0.022793375 0.4129150
#> 4 1.91841309 0.854476033 4.3070942
#> 5 1.60060402 0.640677978 3.9987846