DOI QR코드

DOI QR Code

Sample size calculation for comparing time-averaged responses in K-group repeated binary outcomes

  • 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)
  • Received : 2018.03.29
  • Accepted : 2018.04.16
  • Published : 2018.05.31

Abstract

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.

Keywords

References

  1. Diggle PJ, Heagerty P, Liang KY, and Zeger SL (2013). Analysis of Longitudinal Data (2nd ed.), Oxford University Press, Oxford.
  2. Emrich L and Piedmonte M (1991). A method for generating high-dimensional multivariate binary variates, The American Statistician, 45, 302-304.
  3. Jung SH and Ahn C (2003). Sample size estimation for GEE method for comparing slopes in repeated measurements data, Statistics in Medicine, 22, 1305-1315. https://doi.org/10.1002/sim.1384
  4. Liang KY and Zeger SL (1986). Longitudinal data analysis for discrete and continuous outcomes using Generalized Linear Models, Biometrika, 84, 3-32.
  5. Lou Y, Cao J, Zhang S, and Ahn C (2017a). Sample size calculations for time-averaged difference of longitudinal binary outcomes, Communications in Statistics-Theory and Methods, 46, 344-353. https://doi.org/10.1080/03610926.2014.991040
  6. 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. https://doi.org/10.1080/03610926.2016.1260744
  7. Parmar M, Carpenter J, and Sydes MR (2014). More multiarm randomised trials of superiority are needed, The Lancet, 384, 283-284. https://doi.org/10.1016/S0140-6736(14)61122-3
  8. PASS14 (2015). Power Analysis and Sample Size Software, NCSS LLC.
  9. Overall J and Doyle S (1994). Estimating sample sizes for repeated measurement design, Controlled Clinical Trials, 15, 100-123.
  10. Zhang S and Ahn C (2012). Sample size calculations for the time-averaged differences in the presence of missing data, Contemporary Clinical Trials, 33, 550-556. https://doi.org/10.1016/j.cct.2012.02.004
  11. Zeger SL, Liang KY, and Albert PS (1988). Models for longitudinal data: a generalized estimating equation approach, Biometrics, 44, 1049-1060. https://doi.org/10.2307/2531734
  12. 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. https://doi.org/10.1016/j.csda.2012.08.013