Affordable Access

deepdyve-link
Publisher Website

Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient.

Authors
  • Collins, Shane D1
  • Peek, Niels2
  • Riley, Richard D3
  • Martin, Glen P4
  • 1 Research Department of Oncology, Cancer Institute, Faculty of Medical Sciences, School of Life & Medical Sciences, University College London, London, UK; Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
  • 2 Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.
  • 3 Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Staffordshire, UK.
  • 4 Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK. Electronic address: [email protected]
Type
Published Article
Journal
Journal of clinical epidemiology
Publication Date
May 01, 2021
Volume
133
Pages
53–60
Identifiers
DOI: 10.1016/j.jclinepi.2020.12.011
PMID: 33383128
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Developing clinical prediction models (CPMs) on data of sufficient sample size is critical to help minimize overfitting. Using prostate cancer as a clinical exemplar, we aimed to investigate to what extent existing CPMs adhere to recent formal sample size criteria, or historic rules of thumb of events per predictor parameter (EPP)≥10. A systematic review to identify CPMs related to prostate cancer, which provided enough information to calculate minimum sample size. We compared the reported sample size of each CPM against the traditional 10 EPP rule of thumb and formal sample size criteria. About 211 CPMs were included. Three of the studies justified the sample size used, mostly using EPP rules of thumb. Overall, 69% of the CPMs were derived on sample sizes that surpassed the traditional EPP≥10 rule of thumb, but only 48% surpassed recent formal sample size criteria. For most CPMs, the required sample size based on formal criteria was higher than the sample sizes to surpass 10 EPP. Few of the CPMs included in this study justified their sample size, with most justifications being based on EPP. This study shows that, in real-world data sets, adhering to the classic EPP rules of thumb is insufficient to adhere to recent formal sample size criteria. Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Report this publication

Statistics

Seen <100 times