Computationally efficient inference for center effects based on restricted mean survival time

Abstract

Restricted mean survival time (RMST) has gained increased attention in biostatistical and clinical studies. Directly modeling RMST (as opposed to modeling then transforming the hazard function) is appealing computationally and in terms of interpreting covariate effects. We propose computationally convenient methods for evaluating center effects based on RMST. A multiplicative model for the RMST is assumed. Estimation proceeds through an algorithm analogous to stratification, which permits the evaluation of thousands of centers. We derive the asymptotic properties of the proposed estimators and evaluate finite sample performance through simulation. We demonstrate that considerable decreases in computational burden are achievable through the proposed methods, in terms of both storage requirements and run time. The methods are applied to evaluate more than 5000 US dialysis facilities using data from a national end-stage renal disease registry.

Keywords: censored data; center effect; facility profiling; failure time; restricted mean survival time.

Figure 1

True and estimated values and standard deviation of $\widehat{\eta }$ for L = 1.8 and L = 5.4

Figure 2

Computational time for our proposed and conventional methods with different J’s and number of patients per center

Figure 3

Histogram of estimated J = 5,301 center-specific RMST μ_j’s

Figure 4

Point estimator and confidence interval of J = 5,301 rescaled η_j’s