title: A computational approach to compare regression modelling strategies in prediction research.
abstract: It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research.
  
  
  
  A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots.
  
  
  
  The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set.
  
  
  
  The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands. R.Pajouheshnia@umcutrecht.nl.
authors.forename: Romin
authors.initials: R
authors.lastname: Pajouheshnia
authors.affiliation: Catholic University of Leuven, Research Unit for Quantitative Psychology and Individual Differences, Leuven, Belgium.
authors.forename: Wiebe R
authors.initials: WR
authors.lastname: Pestman
authors.affiliation: Scientific Institute for Quality of Healthcare, IQ Healthcare, Radboud University Medical Centre, Nijmegen, The Netherlands.|Department for Health Evidence, Section of Biostatistics, Radboud University Medical Centre, Nijmegen, The Netherlands.
authors.forename: Steven
authors.initials: S
authors.lastname: Teerenstra
authors.affiliation: Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands.
authors.forename: Rolf H H
authors.initials: RH
authors.lastname: Groenwold
mesh_terms: D019295:Computational Biology / Q000379:methods*; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008962:Models, Theoretical; D012044:Regression Analysis; D015203:Reproducibility of Results; D012106:Research / Q000592:standards*