Addressing small sample size bias in multiple‐biomarker trials: Inclusion of biomarker‐negative patients and Firth correction. Issue 2 (1st August 2017)

• Record Type: Journal Article
• Authors: Habermehl, Christina; Benner, Axel; Kopp‐Schneider, Annette
• Is Part Of: Biometrical journal. Volume 60:Issue 2(2018:Mar.)
• Page Start: 275
• Page End: 287
• Publication Date: 2017-08-01
• Subjects: biomarkers -- biomarker‐negative patients -- Cox proportional hazards model -- Firth correction -- small sample size bias -- Survival -- Umbrella trials; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201600226
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: In recent years, numerous approaches for biomarker‐based clinical trials have been developed. One of these developments are multiple‐biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low‐prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker‐negative patients at the screening stage. The small sample sizes may make it unfeasible to analyze the subtrials individually. This imposes the need to develop new approaches for the analysis of such trials. With an expected large group of biomarker‐negative patients, it seems reasonable to explore options to benefit from including them in such trials. We consider advantages and disadvantages of the inclusion of biomarker‐negative patients in a multiple‐biomarker trial with a survival endpoint. We discuss design options that include biomarker‐negative patients in the study and address the issue of small sample size bias in such trials. We carry out a simulation study for a design where biomarker‐negative patients are kept in the study and are treated with standard of care. We compare three different analysis approaches based on the Cox model to examine if the inclusion of biomarker‐negative patients can provide a benefit with respect to bias and variance of the treatment effect estimates. We apply the Firth correction to reduce the small sample size bias. The results of the simulation study suggest that for small sample situations, the Firth correction should be applied to adjust for the small sample size bias. Additional to the Firth penalty, the inclusion of biomarker‐negative patients in the analysis can lead to further but small improvements in bias and standard deviation of the estimates.