Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Aug 19;23(1):188.
doi: 10.1186/s12874-023-02008-1.

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Affiliations

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

Paula Dhiman et al. BMC Med Res Methodol. .

Abstract

Background: Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome.

Methods: We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size.

Results: A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63-82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66-84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84).

Conclusions: Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model.

Keywords: Methodology; Prediction model; Sample size.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
PRISMA flowchart of included studies
Fig. 2
Fig. 2
Scatterplot of the actual number of events used to develop the prediction model against the minimum required sample size as calculated by the Riley et al. formulae. Blue triangle = studies where the events per predictor parameter was ≥ 10; red circles = studies where the events per predictor parameter was < 10. The 45-degree reference line indicates where the used sample size was equal to the minimum required sample size

References

    1. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162:55. doi: 10.7326/M14-0697. - DOI - PubMed
    1. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: A Tool to assess risk of Bias and Applicability of Prediction Model Studies: explanation and elaboration. Ann Intern Med. 2019;170:W1. doi: 10.7326/M18-1377. - DOI - PubMed
    1. Riley RD, Collins GS. Stability of clinical prediction models developed using statistical or machine learning methods. arXiv 2022; arXiv:2211.01061 [stat.ME]. - PMC - PubMed
    1. Wynants L, Calster BV, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. doi: 10.1136/bmj.m1328. - DOI - PMC - PubMed
    1. Navarro CLA, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ. 2021;375:n2281. doi: 10.1136/bmj.n2281. - DOI - PMC - PubMed

Publication types

LinkOut - more resources