Prevalence and consequences of non-proportional hazards in surgical randomized controlled trials

Comparative Outcomes of Surgical and Transcatheter Aortic Valve Replacement: A Meta-Analysis and Parametric Extrapolation of Clinical Trials

INTRODUCTION

We aimed to pool randomized clinical trials (RCTs) comparing surgical aortic valve replacement (SAVR) with transcatheter aortic valve replacement (TAVR) and extrapolate pooled time-to-event data to compare long-term outcomes.

METHODS

An electronic database search was performed for RCTs comparing SAVR with TAVR. The most current longest follow-up data for each RCT were included. Data were pooled using a random-effects model. Survival data were pooled for Kaplan-Meier analysis as well as parametric modeling with extrapolation.

RESULTS

Seven RCTs comprising 7774 patients were included. Mean valve gradient at 5 y was comparable between SAVR [11 mmHg (3.7; 18.3)] and TAVR [8.1 mmHg (1.9; 14.3)] (P = 0.38). TAVR had a higher mean valve area at 30 d, 1 y, and 2 y [1.68 cm2 (1.22; 2.13) versus 1.8 cm2 (1.35; 2.25), P = 0.02]. SAVR had a higher freedom from any paravalvular leak at 30 d and 1 y [86% (81; 90) versus 39% (36; 41), P < 0.01]. All-cause death was lower in the SAVR group at 5 y [39% (29; 50) versus 43% (31; 57), P < 0.01]. Although no differences were seen between SAVR and TAVR in the pooled Kaplan-Meier analysis of all-cause mortality and composite of all-cause mortality or stroke, parametric modeling with extrapolation showed significant divergence for both outcomes.

CONCLUSIONS

Pooled all-cause mortality as well as pooled composite of all-cause mortality or stroke indicated better survival with SAVR at 5 y. Long-term parametric extrapolation also indicated superior survival with SAVR.

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

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.