Variance Calculations for Direct Adjusted Survival Curves, with Applications to Testing for No Treatment Effect

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Abstract
Several investigators have recently constructed survival curves adjusted for imbalances in prognostic factors by a method which we call direct adjustment. We present methods for calculating variances of these direct adjusted survival curves and their differences. Estimates of the adjusted curves, their variances, and the variances of their differences are compared for non‐parametric (Kaplan‐Meier), semi‐parametric (Cox) and parametric (Weibull) models applied to censored exponential data. Semi‐parametric proportional hazards models were nearly fully efficient for estimating differences in adjusted curves, but parametric estimates of individual adjusted curves may be substantially more precise. Standardized differences between direct adjusted survival curves may be used to test the null hypothesis of no treatment effect. This procedure may prove especially useful when the proportional hazards assumption is questionable.