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

An ever-increasing number of research questions focus on the development and validation of clinical prediction models to inform individual diagnosis and prognosis in healthcare. These models predict outcome values (e.g., pain intensity) or outcome risks (e.g., 5-year mortality risk) in individuals from a target population (e.g., 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 talk I will introduce a suite of ancillary software packages with the prefix 'pm'. The pm-suite of packages aim 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 (e.g., sample size calculation using pmsampsize and pmvalsampsize), to development and internal validation (e.g., calculating model performance using pmstats), external validation (e.g., flexible calibration plots of performance in new patients using pmcalplot), and model updating (e.g., comparing updating methods using pmupdate). Through an illustrative example I will demonstrate how these packages allow researchers to perform common prediction modelling tasks quickly and easily while standardising methodology.