Dataset Information

A rank test for bivariate time-to-event outcomes when one event is a surrogate.

ABSTRACT: In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial's primary endpoint. A time-to-first endpoint (e.g., death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally as one correlated with the true endpoint of interest. Settings of interest include those where the surrogate indicates a beneficial outcome so that the usual time-to-first endpoint of death or surrogate event is nonsensical. We present a new two-sample test for bivariate, interval-censored time-to-event data, where one endpoint is a surrogate for the second, less frequently observed endpoint of true interest. This test examines whether patient groups have equal clinical severity. If the true endpoint rarely occurs, the proposed test acts like a weighted logrank test on the surrogate; if it occurs for most individuals, then our test acts like a weighted logrank test on the true endpoint. If the surrogate is a useful statistical surrogate, our test can have better power than tests based on the surrogate that naively handles the true endpoint. In settings where the surrogate is not valid (treatment affects the surrogate but not the true endpoint), our test incorporates the information regarding the lack of treatment effect from the observed true endpoints and hence is expected to have a dampened treatment effect compared with tests based on the surrogate alone. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

SUBMITTER: Shaw PA

PROVIDER: S-EPMC4938716 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A rank test for bivariate time-to-event outcomes when one event is a surrogate.

Shaw Pamela A PA Fay Michael P MP

Statistics in medicine 20160405 19

In many clinical settings, improving patient survival is of interest but a practical surrogate, such as time to disease progression, is instead used as a clinical trial's primary endpoint. A time-to-first endpoint (e.g., death or disease progression) is commonly analyzed but may not be adequate to summarize patient outcomes if a subsequent event contains important additional information. We consider a surrogate outcome very generally as one correlated with the true endpoint of interest. Settings ...[more]

PMID: 27059817

Similar Datasets

Project description:Background/aimsCardiovascular outcome trials, among others, aim to assess the beneficial effects of a treatment on multiple event-time outcomes, such as the time to a myocardial infarction and the time to a stroke. The traditional approach is to conduct a simple analysis of a composite outcome defined as the time to the first component event using a logrank test or the Cox Proportional Hazards regression model. This ignores information from other component events after the first. The composite outcome analysis also treats all initial outcome events as equally important, for example, non-fatal myocardial infarction is as important as cardiovascular death.MethodsHerein, we describe the application of the Wei-Lachin multivariate one-sided (or one-directional) test to the analysis of multiple event-time outcomes. The test is based on the unweighted mean of the treatment group coefficients from individual Cox proportional hazards models fit to the outcomes, where the covariance of the set of coefficients is obtained from a partitioning of the information sandwich estimate. A weighted test is also described, weighing the outcomes by a scoring of their clinical importance. These and other methods are compared with application to the Prevention of Events with Angiotensin-Converting Enzyme Inhibition cardiovascular outcome study.ResultsThe Wei-Lachin test provides an inference with strong control of the type 1 error probability on the difference between groups for the set of outcomes considered. However, it does not provide an inference on the individual components specifically with control of the overall type 1 error probability. By direct computation of relative efficiency and by simulation, we show that the power of the Wei-Lachin one-directional test can be greater than that of the traditional composite outcome analysis based on the time to the first observed component event.ConclusionThe Wei-Lachin multivariate one-directional test may be more powerful than the traditional analysis of a composite outcome defined as the time to the first component outcomes experienced by each subject.

Dataset Information

A rank test for bivariate time-to-event outcomes when one event is a surrogate.

Publications

A rank test for bivariate time-to-event outcomes when one event is a surrogate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets