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Robust Covariate‐Adjusted Log‐Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data

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  • Rui Song
  • Michael R. Kosorok
  • Jianwen Cai

Abstract

Summary Recurrent events data are frequently encountered in clinical trials. This article develops robust covariate‐adjusted log‐rank statistics applied to recurrent events data with arbitrary numbers of events under independent censoring and the corresponding sample size formula. The proposed log‐rank tests are robust with respect to different data‐generating processes and are adjusted for predictive covariates. It reduces to the Kong and Slud (1997, Biometrika84, 847–862) setting in the case of a single event. The sample size formula is derived based on the asymptotic normality of the covariate‐adjusted log‐rank statistics under certain local alternatives and a working model for baseline covariates in the recurrent event data context. When the effect size is small and the baseline covariates do not contain significant information about event times, it reduces to the same form as that of Schoenfeld (1983, Biometrics39, 499–503) for cases of a single event or independent event times within a subject. We carry out simulations to study the control of type I error and the comparison of powers between several methods in finite samples. The proposed sample size formula is illustrated using data from an rhDNase study.

Suggested Citation

  • Rui Song & Michael R. Kosorok & Jianwen Cai, 2008. "Robust Covariate‐Adjusted Log‐Rank Statistics and Corresponding Sample Size Formula for Recurrent Events Data," Biometrics, The International Biometric Society, vol. 64(3), pages 741-750, September.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:3:p:741-750
    DOI: 10.1111/j.1541-0420.2007.00948.x
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    References listed on IDEAS

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    1. James M. Robins & Dianne M. Finkelstein, 2000. "Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log-Rank Tests," Biometrics, The International Biometric Society, vol. 56(3), pages 779-788, September.
    2. Ronald E. Gangnon, 2004. "Sample-size formula for clustered survival data using weighted log-rank statistics," Biometrika, Biometrika Trust, vol. 91(2), pages 263-275, June.
    3. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    4. Pei-Yun Chen & Anastasios A. Tsiatis, 2001. "Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups," Biometrics, The International Biometric Society, vol. 57(4), pages 1030-1038, December.
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    1. Hussein R. Al-Khalidi & Yili Hong & Thomas R. Fleming & Terry M. Therneau, 2011. "Insights on the Robust Variance Estimator under Recurrent-Events Model," Biometrics, The International Biometric Society, vol. 67(4), pages 1564-1572, December.
    2. Ming-Hui Chen & Joseph G. Ibrahim & Donglin Zeng & Kuolung Hu & Catherine Jia, 2014. "Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome," Biometrics, The International Biometric Society, vol. 70(4), pages 1003-1013, December.

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