Wang, Jijia
(Department of Statistical Science, Southern Methodist University)
,
Zhang, Song
(Department of Clinical Sciences, UT Southwestern Medical Center)
,
Ahn, Chul
(Department of Clinical Sciences, UT Southwestern Medical Center)
In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum eff...
In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum effect is achieved (Overall and Doyle, Controlled Clinical Trials, 15, 100-123, 1994). The sample size formula has been investigated by many researchers for the evaluation of TAD in two treatment groups. For the evaluation of TAD in multi-arm trials, Zhang and Ahn (Computational Statistics & Data Analysis, 58, 283-291, 2013) and Lou et al. (Communications in Statistics-Theory and Methods, 46, 11204-11213, 2017b) developed the sample size formulas for continuous outcomes and count outcomes, respectively. In this paper, we derive a sample size formula to evaluate the TAD of the repeated binary outcomes in multi-arm trials using the generalized estimating equation approach. This proposed sample size formula accounts for various correlation structures and missing patterns (including a mixture of independent missing and monotone missing patterns) that are frequently encountered by practitioners in clinical trials. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of design parameters. The results show that the empirical powers and the empirical Type I errors are close to nominal levels. We illustrate our proposed method using a clinical trial example.
In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum effect is achieved (Overall and Doyle, Controlled Clinical Trials, 15, 100-123, 1994). The sample size formula has been investigated by many researchers for the evaluation of TAD in two treatment groups. For the evaluation of TAD in multi-arm trials, Zhang and Ahn (Computational Statistics & Data Analysis, 58, 283-291, 2013) and Lou et al. (Communications in Statistics-Theory and Methods, 46, 11204-11213, 2017b) developed the sample size formulas for continuous outcomes and count outcomes, respectively. In this paper, we derive a sample size formula to evaluate the TAD of the repeated binary outcomes in multi-arm trials using the generalized estimating equation approach. This proposed sample size formula accounts for various correlation structures and missing patterns (including a mixture of independent missing and monotone missing patterns) that are frequently encountered by practitioners in clinical trials. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of design parameters. The results show that the empirical powers and the empirical Type I errors are close to nominal levels. We illustrate our proposed method using a clinical trial example.
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제안 방법
In this paper, we investigate sample size calculation for the comparison of time-averaged responses among K ≥ 3 groups where a binary outcome is repeatedly measured over the study period.
In this study, we derived a sample size formula to compare the time-averaged responses of repeated binary outcomes among K groups. This proposed sample size formula can accommodate arbitrary correlation structures, missing patterns, marginal observation probabilities, and unbalanced experimental designs.
Patients will be randomly assigned to one of three treatment groups with an equal probability, and followed monthly from September to April (beginning in October, hence J = 7) to determine the patient’s disease status (present or absent).
, 6). We investigate three missing patterns: independent missing (IM), monotone missing (MM), and mixed missing (MIX). Under IM, missing measurements occur independently over time with δjj′ = δjδj ′ for ≠ j, j′.
참고문헌 (12)
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Lou Y, Cao J, Zhang S, and Ahn C (2017a). Sample size calculations for time-averaged di？erence of longitudinal binary outcomes, Communications in Statistics-Theory and Methods, 46, 344-353.
Lou Y, Cao J, and Ahn C (2017b). Sample size estimation for comparing rates of change in K-group repeated count outcomes, Communications in Statistics-Theory and Methods, 46, 11204-11213.
PASS14 (2015). Power Analysis and Sample Size Software, NCSS LLC.
Overall J and Doyle S (1994). Estimating sample sizes for repeated measurement design, Controlled Clinical Trials, 15, 100-123.
Zhang S and Ahn C (2012). Sample size calculations for the time-averaged di？erences in the presence of missing data, Contemporary Clinical Trials, 33, 550-556.
Zhang S and Ahn C (2013). Sample size calculation for comparing time-averaged responses in ${\kappa}$ -group repeated-measurement studies, Computational Statistics & Data Analysis, 58, 283-291.
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