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1.  Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring 
Biostatistics (Oxford, England)  2010;11(4):676-692.
While the commonly used log-rank test for survival times between 2 groups enjoys many desirable properties, sometimes the log-rank test and its related linear rank tests perform poorly when sample sizes are small. Similar concerns apply to interval estimates for treatment differences in this setting, though their properties are less well known. Standard permutation tests are one option, but these are not in general valid when the underlying censoring distributions in the comparison groups are unequal. We develop 2 methods for testing and interval estimation, for use with small samples and possibly unequal censoring, based on first imputing survival and censoring times and then applying permutation methods. One provides a heuristic justification for the approach proposed recently by Heinze and others (2003, Exact log-rank tests for unequal follow-up. Biometrics 59, 1151–1157). Simulation studies show that the proposed methods have good Type I error and power properties. For accelerated failure time models, compared to the asymptotic methods of Jin and others (2003, Rank-based inference for the accelerated failure time model. Biometrika 90, 341–353), the proposed methods yield confidence intervals with better coverage probabilities in small-sample settings and similar efficiency when sample sizes are large. The proposed methods are illustrated with data from a cancer study and an AIDS clinical trial.
doi:10.1093/biostatistics/kxq021
PMCID: PMC2950789  PMID: 20439258
Accelerated failure time models; Imputation; Log-rank test; Permutation tests
2.  Statistical monitoring of clinical trials with multivariate response and/or multiple arms: a flexible approach 
Biostatistics (Oxford, England)  2008;10(2):310-323.
Randomized clinical trials with a multivariate response and/or multiple treatment arms are increasingly common, in part because of their efficiency and a greater concern about balancing risks with benefits. In some trials, the specific types and magnitudes of treatment group differences that would warrant early termination cannot easily be specified prior to the onset of the trial and/or could change as the trial progresses. This underscores the need for more flexible monitoring methods than traditional approaches. This paper extends the repeated confidence bands approach for interim monitoring to more general settings where there can be a multivariate response and/or multiple treatment arms and where the metrics for comparing treatment groups can change during the conduct of the trial. We illustrate the approach using the results of a recent AIDS clinical trial and examine its efficiency and robustness via simulation.
doi:10.1093/biostatistics/kxn037
PMCID: PMC2648904  PMID: 19015160
Group sequential analysis; Interim review; Multiple comparisons; Multiple end points; Nonparametric inference; Repeated confidence bands

Results 1-2 (2)