This chapter describes the opportunities and challenges involved in prediction model research using individual participant data (IPD) meta-analysis. It begins by outlining the various types of prediction model research, and then describes the importance and conduct of IPD meta-analysis projects for each type. The chapter emphasizes the importance of evaluating prediction model performance in terms of calibration, discrimination and clinical utility, and the need to examine heterogeneity in performance across studies, settings and subgroups of interest. by meta-analysing standardised estimates of model performance, any remaining heterogeneity in performance only reflects the use of invalid model coefficients, thereby highlighting whether local updating of model coefficients is necessary for that target population. External validation of an existing prediction model may incorporate updating or tailoring of the prediction model equation, which is often needed to improve the performance in the setting or population at hand.
|Title of host publication
|Individual Participant Data Meta‐Analysis
|Subtitle of host publication
|A Handbook for Healthcare Research
|Richard D. Riley, Jayne F. Tierney, Lesley A. Stewart
|John Wiley & Sons Inc.
|Published - 2021