POWER CONSIDERATIONS FOR CLINICAL TRIALS USING MULTIVARIATE TIME-TO-EVENT DATA

1997 ◽  
Vol 16 (8) ◽  
pp. 865-882 ◽  
Author(s):  
MICHAEL D. HUGHES
Keyword(s):  
Clinical Trials ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

Group sequential monitoring based on the maximum of weighted log-rank statistics with the Fleming–Harrington class of weights in oncology clinical trials

2020 ◽  
Vol 29 (12) ◽  
pp. 3525-3532
Author(s):  
Thomas J Prior
Keyword(s):  
Clinical Trials ◽  
Treatment Effect ◽  
Proportional Hazards ◽  
Rank Test ◽  
Event Data ◽  
Time To Event ◽  
Survival Curves ◽  
Delayed Treatment ◽  
Time To Event Data ◽  
Log Rank Test

Clinical trials in oncology often involve the statistical analysis of time-to-event data such as progression-free survival or overall survival to determine the benefit of a treatment or therapy. The log-rank test is commonly used to compare time-to-event data from two groups. The log-rank test is especially powerful when the two groups have proportional hazards. However, survival curves encountered in oncology studies that differ from one another do not always differ by having proportional hazards; in such instances, the log-rank test loses power, and the survival curves are said to have “non-proportional hazards”. This non-proportional hazards situation occurs for immunotherapies in oncology; immunotherapies often have a delayed treatment effect when compared to chemotherapy or radiation therapy. To correctly identify and deliver efficacious treatments to patients, it is important in oncology studies to have available a statistical test that can detect the difference in survival curves even in a non-proportional hazards situation such as one caused by delayed treatment effect. An attempt to address this need was the “max-combo” test, which was originally described only for a single analysis timepoint; this article generalizes that test to preserve type I error when there are one or more interim analyses, enabling efficacious treatments to be identified and made available to patients more rapidly.

Some Fundamental Statistical Concepts in Clinical Trials and their Application in Herpes Zoster

1995 ◽  
Vol 6 (6_suppl) ◽  
pp. 28-33 ◽  
Author(s):  
R. Kay
Keyword(s):  
Clinical Trials ◽  
Herpes Zoster ◽  
P Value ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
General Terms ◽  
Correct Application ◽  
Intent To Treat ◽  
Definition Of

The correct application of appropriate statistical methods in clinical trials is essential for deriving valid conclusions. This paper covers a number of the key principles, with particular relevance to studies in herpes zoster. The distinct roles of the P-value and the confidence interval are investigated and their use in the assessment of the equivalence of two treatments evaluated. The principle of intent-to-treat is discussed and is seen to be vitally important for obtaining correct conclusions. The definition of end-points in the assessment of pain in herpes zoster is controversial; it is argued here that the only valid approach is to consider the time from start of treatment to complete cessation of all pain. Finally, methods for the evaluation of time to event data are explained in general terms, and particular drawbacks relevant to herpes zoster are discussed.

Bayesian design of clinical trials using joint models for longitudinal and time-to-event data

Biostatistics ◽  
2020 ◽  
Author(s):  
Jiawei Xu ◽  
Matthew A Psioda ◽  
Joseph G Ibrahim
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Hazard Ratio ◽  
Type I ◽  
Event Data ◽  
Time To Event ◽  
Joint Models ◽  
Modeling Framework ◽  
Time To Event Data ◽  
Longitudinal Outcome

Summary Joint models for longitudinal and time-to-event data are increasingly used for the analysis of clinical trial data. However, few methods have been proposed for designing clinical trials using these models. In this article, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment’s effect on the time-to-event endpoint using a flexible trajectory joint model. By incorporating the longitudinal outcome trajectory into the hazard model for the time-to-event endpoint, the joint modeling framework allows for non-proportional hazards (e.g., an increasing hazard ratio over time). Inference for the time-to-event endpoint is based on an average of a time-varying hazard ratio which can be decomposed according to the treatment’s direct effect on the time-to-event endpoint and its indirect effect, mediated through the longitudinal outcome. We propose an approach for sample size determination for a trial such that the design has high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint and where predictive longitudinal outcome measures are also collected periodically during follow-up.

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