STRATOS Topic Group 2 – Selection of variables and functional forms in multivariable analysis: Results from a review of Covid-19 prognostic models.

STRATOS Topic Group 2 (TG2) deals with "Selection of variables and functional forms in multivariable analysis". In multivariable regression, researchers build models relating an outcome variable to a set of predictor variables with the aim to either accurately predict the outcome or to investigate how the outcome is associated with the predictors. There is currently no known state-of-the-art method for variable and functional form selection in multivariable analysis [1]. Therefore, TG2 works on identifying and evaluating methods currently used in practice and conducting neutral comparison studies with the aim to develop consensus-based guidance.

As part of these efforts, we have re-evaluated prognostic models included in a systematic review [2] of diagnostic and prognostic models that were published during the Covid-19 pandemic. Our primary aim was to assess what model building strategies practitioners relied on when prediction models for Covid-19 were urgently needed. In our talk, we will describe which methods are commonly used, illustrate their weaknesses, identify misunderstandings related to their application and highlight good practices that should receive more attention.

[1] Sauerbrei W, Perperoglou A, Schmid M, et al. State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues. Diagn Progn Res. 2020; 4(1). doi:10.1186/s41512-020-00074-3
[2] Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020; m1328. doi:10.1136/bmj.m1328

Variable selection

Functional form selection

Regression modelling

Multivariable analysis

Covid-19

Predictive modelling