The package provides functions to apply pooling, backward and forward selection of logistic and Cox regression prediction models in multiply imputed data sets using Rubinâ€™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.

You can install the released version of psfmi with:

And the development version from GitHub with:

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)
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 0.001324739 0.5604621
#> 2 0.62342367 0.226561226 1.7154616
#> 3 0.09701406 0.023120005 0.4070815
#> 4 1.91841309 0.870983375 4.2254639
#> 5 1.60060402 0.650163744 3.9404431
```