Welcome to Missing Data Solutions

December 28, 2022

Most examples and solutions will be concentrated on Missing Data topics. Posts may also concern Regression modeling, Clinical Prediction models and Spline regression modeling. Most examples originate from FAQs asked during statistical consultations or during courses. Examples are provided for applied researchers and frequenly in RStudio. RStudio is an open source free package with a lot of possibilities, making it easy to share code so that you can use the code example and adjust it to your own needs.

Prediction Modeling with the Cox model - all about the baseline hazard

December 27, 2022

This post is about different functions of the survival package, like basehaz, predict.coxph, survfit, termplot, what kind of results they provide and how they are calculated.

External validation of a Cox prognostic model

February 4, 2021

This blog shows how to externally validate a Cox regression prognostic model according to all steps in the paper of Royston & Altman. R code available.

Multiple Imputation and Cross-validation

February 4, 2021

With the function psfmi_perform of the psfmi package it is possible to combine Cross-validation with Multiple Imputation for internal validation.

Steps to externally validate a prediction model

January 1, 2021

External validation means that the performance of a prediction model is studied in a new (external) patient dataset that is not used to develop the model. It is important to use the correct predicted probabilities to evaluate if the external validation of the model is successful!

Model Stability analysis after Multiple Imputation

January 11, 2020 in Model Stability, Bootstrapping, BIF

With the psfmi_stab function of the psfmi package it is possible to do important stability analyses of the models and predictors selected during backward selection after multiple imputation.

psfmi package update

January 7, 2020

With the psfmi_mm function pooling and selection of (generalized) linear mixed models, i.e linear and logistic mixed models, is possible. With the psfmi_stab function the stability of models after using psfmi_lr, psfmi_coxr and psfmi_mm can be evaluated. The latter function uses (single) bootstrapping for the psfmi_lr and psfmi_coxr functions and cluster bootstrapping for the psfmi_mm function. With the function the bootstrap inclusion frequency of predictors and models can be estimated.

Welcome to Missing Data Solutions

Prediction Modeling with the Cox model - all about the baseline hazard

External validation of a Cox prognostic model

Multiple Imputation and Cross-validation

Steps to externally validate a prediction model

Model Stability analysis after Multiple Imputation

psfmi package update

Martijn W Heymans