Collins, S, Peek, N, Riley, RD and Martin, G (2021) Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient. Journal of Clinical Epidemiology, 133. pp. 53-60. ISSN 1878-5921

[thumbnail of Prostate Cancer CPM Sample Sizes_manuscript accepted.docx] Text
Prostate Cancer CPM Sample Sizes_manuscript accepted.docx - Accepted Version

Download (143kB)
[thumbnail of Sample sizes of prediction model studies.pdf]
Sample sizes of prediction model studies.pdf - Published Version

Download (811kB) | Preview


OBJECTIVE: 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. STUDY DESIGN AND SETTING: 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. RESULTS: 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. CONCLUSION: 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.

Item Type: Article
Additional Information: � 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( licenses/by-nc-nd/4.0/).
Uncontrolled Keywords: Prediction Models; Prostate Cancer; Sample Size; Development; Validation
Subjects: R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Medicine and Health Sciences > School of Primary, Community and Social Care
Depositing User: Symplectic
Date Deposited: 10 Dec 2020 11:53
Last Modified: 17 Feb 2021 09:28

Actions (login required)

View Item
View Item