Bayesian predictive model averaging approach to joint longitudinal‐survival modeling: Application to an immuno‐oncology clinical trial

Author:

Yao Zixuan1ORCID,Morita Satoshi1ORCID,Nishida Sumiyuki2,Sugiyama Haruo3

Affiliation:

1. Department of Biomedical Statistics and Bioinformatics Kyoto University Graduate School of Medicine Kyoto Japan

2. Department of Respiratory Medicine and Clinical Immunology Osaka University Graduates School of Medicine Osaka Japan

3. Department of Cancer Immunology Osaka University Graduates School of Medicine Osaka Japan

Abstract

SummaryIn immuno‐oncology clinical trials, multiple immunological biomarkers are usually examined over time to comprehensively and appropriately evaluate the efficacy of treatments. Because predicting patients' future survival statuses on the basis of such recorded longitudinal information might be of great interest, joint modeling of longitudinal and time‐to‐event data has been intensively discussed as a toolkit to implement such a prediction. To achieve a desirable predictive performance, averaging over multiple candidate predictive models to account for the model uncertainty might be a more suitable statistical approach than selecting the single best model. Although Bayesian model averaging can be one of the approaches, several problems related to model weights with marginal likelihoods have been discussed. To address these problems, we here propose a Bayesian predictive model averaging (BPMA) method that uses Bayesian leave‐one‐out cross‐validation predictive densities to account for the subject‐specific and time‐dependent nature of the prediction. We examine the operating characteristics of the proposed BPMA method in terms of the predictive accuracy (ie, the calibration and discrimination abilities) in extensive simulation studies. In addition, we discuss the strengths and limitations of the proposed method by applying it to an immuno‐oncology clinical trial in patients with advanced ovarian cancer.

Funder

Japan Agency for Medical Research and Development

Japan Science and Technology Agency

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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