Power and sample size calculation for paired right-censored data based on survival copula models

Research output: Contribution to journalArticle

Abstract

Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan–Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user.

Original languageEnglish
Pages (from-to)1565-1582
Number of pages18
JournalCommunications in Statistics: Simulation and Computation
Volume47
Issue number6
DOIs
Publication statusPublished - 2018 Jul 3

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Paired Data
Copula Models
Sample Size Calculation
Survival Model
Right-censored Data
Sample Size
Size determination
Kaplan-Meier
Log-rank Test
Sample Size Determination
Testing
Survival Time
Parametric Model
Clinical Trials
Statistic
Planning
Statistics
Calculate
Alternatives

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

Cite this

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title = "Power and sample size calculation for paired right-censored data based on survival copula models",
abstract = "Sample size determination is essential during the planning phases of clinical trials. To calculate the required sample size for paired right-censored data, the structure of the within-paired correlations needs to be pre-specified. In this article, we consider using popular parametric copula models, including the Clayton, Gumbel, or Frank families, to model the distribution of joint survival times. Under each copula model, we derive a sample size formula based on the testing framework for rank-based tests and non-rank-based tests (i.e., logrank test and Kaplan–Meier statistic, respectively). We also investigate how the power or the sample size was affected by the choice of testing methods and copula model under different alternative hypotheses. In addition to this, we examine the impacts of paired-correlations, accrual times, follow-up times, and the loss to follow-up rates on sample size estimation. Finally, two real-world studies are used to illustrate our method and R code is available to the user.",
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