Non-parametric covariance methods for incidence density analyses of time-to-event data from a randomized clinical trial and their complementary roles to proportional hazards regression
✍ Scribed by Catherine M. Tangen; Gary G. Koch
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 164 KB
- Volume
- 19
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
✦ Synopsis
The principal response criteria for many clinical trials involve time-to-event variables. Usual methods of analysis for this type of response criterion include product-limit estimators of cumulative survival for the treatment groups, (stratified) logrank tests to compare treatments, and proportional hazards regression models with treatment and relevant covariates. When adjustment for covariates is of some importance, the relative roles of these methods may be of some concern, particularly for confirmatory clinical trials which must provide convincing findings to regulatory agencies. Unadjusted methods may have lower power, but there are issues regarding adjustment for covariates that may be controversial. These issues include applicability of proportional hazards assumptions, whether the correct model has been specified, and whether there is parallelism between treatments for relationships with covariates. One way to address these issues is to use non-parametric analysis of covariance strategies with extensions to log incidence density estimation. The principal basis for this method is no association between covariates and treatment groups as provided by randomized assignment of patients to groups. The background theory and strategies for computation are described for this method. Aspects of its application are illustrated for a clinical trial with two treatment groups and 722 patients. The objective of analysis for this clinical trial is evaluation of treatment effects with and without adjustment for 22 a priori covariates and a stratification for three geographical regions.