standardized I\* , as described in Oden and Oden et al. The following lines of Dr. Tango's tables are affected: i) I\* normal approximation and I\* chi-square approximation for alpha"0)05 and alpha"0)01 in Tango's tables I and III. ii) I\* critical values for alpha"0)05 and alpha"0)01 in Tango's Tab
TESTING FOR TREATMENT DIFFERENCES WITH DROPOUTS PRESENT IN CLINICAL TRIALS – A COMPOSITE APPROACH
✍ Scribed by WEICHUNG JOSEPH SHIH; HUI QUAN
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 395 KB
- Volume
- 16
- Category
- Article
- ISSN
- 0277-6715
No coin nor oath required. For personal study only.
✦ Synopsis
A major problem in the analysis of clinical trials is missing data from patients who drop out of the study before the predetermined schedule. In this paper we consider the situation where the outcome measure is a continuous variable and the final outcome at the end of the study is the main interest. We argue that the hypothetical complete-data marginal mean averaged over the dropout patterns is not as relevant clinically as the conditional mean of the completers together with the probability of completion or dropping out of the trial. We first take the pattern-mixture modelling approach to factoring the likelihood function, then direct the analysis to the multiple testings of a composite of hypotheses that involves the probability of dropouts and the conditional mean of the completers. We review three types of closed step-down multiple-testing procedures for this application. Data from several clinical trials are used to illustrate the proposed approach.
📜 SIMILAR VOLUMES
## Abstract In a randomized clinical trial (RCT), noncompliance with an assigned treatment can occur due to serious side effects, while missing outcomes on patients may happen due to patients' withdrawal or loss to follow up. To avoid the possible loss of power to detect a given risk difference (RD