Evaluation of the proportional hazards assumption and covariate adjustment methods in comparative surgical observational studies with time-to-event endpoints

Abstract

Introduction: Comparative studies on surgical treatments with time-to-event endpoints have provided substantial evidence for clinical practice, but the accurate use of survival data analysis and the control of confounding bias remain big challenges.

Methods: This was a survey of surgical studies with survival outcomes published in four general medical journals and five general surgical journals in 2021. The two most concerned statistical issues were evaluated, including confounding control by propensity score analysis (PSA) or multivariable analysis and testing of proportional hazards (PH) assumption in Cox model.

Results: A total of 74 studies were included, comprising 63 observational studies and 11 randomized controlled trials. Among the observational studies, the proportion of studies utilizing PSA in surgical oncology and non-oncology studies was similar (40.9 % versus 36.8 %, P = 0.762). However, the former reported a significantly lower proportion of PH assumption assessments compared to the latter (13.6 % versus 42.1 %, P = 0.020). Twenty-five observational studies (25/63) used PSA methods, but two-thirds of them (17/25) showed unclear balance of baseline data after PSA. And the proportion of PH assumption testing after PSA was slightly lower than that before PSA, but the difference was not statistically significant (24.0 % versus 28.0 %, P = 0.317). Comprehensive suggestions were given on confounding control in survival analysis and alternative resolutions for non-compliance with PH assumption.

Conclusion: This study highlights suboptimal reporting of PH assumption evaluation in observational surgical studies both before and after PSA. Efforts and consensus are needed with respect to the underlying assumptions of statistical methods.

Keywords: Observational studies; Propensity score analysis; Proportional hazards assumption; Surgical oncology; Time-to-event endpoint.