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Analysis of restricted mean survival time for length-biased data.

ABSTRACT: In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this article, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of length-biased sampling. To assess the covariate effects on the RMST, a semiparametric regression model that directly relates the covariates and the RMST is assumed. Based on the model, we develop unbiased estimating equations to obtain consistent estimators of covariate effects by properly adjusting for informative censoring and length bias. Stochastic process theories are used to establish the asymptotic properties of the proposed estimators. We investigate the finite sample performance through simulations and illustrate the methods by analyzing a prevalent cohort study of dementia in Canada.

SUBMITTER: Lee CH

PROVIDER: S-EPMC5843504 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Analysis of restricted mean survival time for length-biased data.

Lee Chi Hyun CH Ning Jing J Shen Yu Y

Biometrics 20170908 2

In clinical studies with time-to-event outcomes, the restricted mean survival time (RMST) has attracted substantial attention as a summary measurement for its straightforward clinical interpretation. When the data are subject to length-biased sampling, which is frequently encountered in observational cohort studies, existing methods to estimate the RMST are not applicable. In this article, we consider nonparametric and semiparametric regression methods to estimate the RMST under the setting of l ...[more]

PMID: 28886217

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Project description:OBJECTIVES:To use restricted mean survival time, which summarizes treatment effects in terms of event-free time over a fixed time period, to evaluate the benefit of pravastatin therapy for primary prevention of cardiovascular disease in older adults. DESIGN:Secondary analysis of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial-Lipid-Lowering Trial (ALLHAT-LLT). SETTING:Ambulatory setting. PARTICIPANTS:Individuals aged 65 and older (mean aged 71, 49% female) free of cardiovascular disease (N=2,867). INTERVENTION:Pravastatin 40 mg/d (n=1,467) versus usual care (n=1,400). MEASUREMENTS:We estimated the difference in RMST for total and coronary heart disease (CHD)-free survival between the pravastatin and usual care groups over the 6-year trial period and used parametric survival models to estimate RMST differences projected over 10 years. RESULTS:Over 6 years, individuals treated with pravastatin lived (RMST 2,008.1 days), on average, 33.7 fewer days than those receiving usual care (RMST 2,041.8 days) (difference -33.7 days, 95% confidence interval (CI)=-67.0 to -0.5 days, p=.047). Pravastatin-treated individuals lived RMST 2,088.1 days), on average, 18.7 more days free of CHD over 6 years than those receiving usual care (RMST 2,069.4 days), but this difference was not statistically significant (difference 18.7 days, 95% CI=-10.4-47.8 days, p=.21). The 10-year projection showed that pravastatin-treated individuals would live 108.1 fewer days (95% CI=-204.5 to -14.1, p=.03) than those receiving usual care, although treated individuals would gain 77.9 days (95% CI=3.8-159.6, p=.046) of CHD-free survival. CONCLUSION:RMST provides an intuitive and explicit way to express the effect of pravastatin therapy on CHD-free and overall survival in older adults free of cardiovascular disease. This measure allows a more personalized interpretation than hazard ratios of the benefits and risks of a medical intervention for decision-making.

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Analysis of restricted mean survival time for length-biased data.

Publications

Analysis of restricted mean survival time for length-biased data.

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