A suite of programs for the design, development, and validation of clinical prediction models

These models predict outcome values (for example, pain intensity) or outcome risks (for example, five-year mortality risk) in individuals from a target population (for example, pregnant women; cancer patients). Development and validation of such models is a complex process, with a myriad of statistical methods, validation measures, and reporting options. It is therefore not surprising that there is considerable evidence of poor methodology in such studies.

 

In this presentation, I will introduce a suite of ancillary software packages with the prefix “pm”. The pm-suite of packages aims to facilitate the implementation of methodology for building new models, validating existing models and transparent reporting. All packages are in line with the recommendations of the TRIPOD guidelines, which provide a benchmark for the reporting of prediction models.

 

I will showcase a selection of packages to aid in each stage of the life cycle of a prediction model, from the initial design (for example, sample-size calculation using pmsampsize and pmvalsampsize), to development and internal validation (for example, calculating model performance usingpmstats), external validation (for example, flexible calibration plots of performance in new patients using pmcalplot), and model updating (for example, comparing updating methods using pmupdate).

Through an illustrative example, I will demonstrate how these packages allow researchers to perform common prediction modeling tasks quickly and easily while standardizing methodology.