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1.  Efficient p-value evaluation for resampling-based tests 
Biostatistics (Oxford, England)  2011;12(3):582-593.
The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very computationally intensive. Here, we propose an efficient p-value evaluation procedure by adapting the stochastic approximation Markov chain Monte Carlo algorithm. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be 100–500 000 times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10 − 6). With its computational burden reduced by this proposed procedure, the versatile resampling-based test would become computationally feasible for a much wider range of applications. We demonstrate the application of the new method by applying it to a large-scale genetic association study of prostate cancer.
PMCID: PMC3114653  PMID: 21209154
Bootstrap procedures; Genetic association studies; p-value; Resampling-based tests; Stochastic approximation Markov chain Monte Carlo
2.  Statistical inference on the penetrances of rare genetic mutations based on a case–family design 
Biostatistics (Oxford, England)  2010;11(3):519-532.
We propose a formal statistical inference framework for the evaluation of the penetrance of a rare genetic mutation using family data generated under a kin–cohort type of design, where phenotype and genotype information from first-degree relatives (sibs and/or offspring) of case probands carrying the targeted mutation are collected. Our approach is built upon a likelihood model with some minor assumptions, and it can be used for age-dependent penetrance estimation that permits adjustment for covariates. Furthermore, the derived likelihood allows unobserved risk factors that are correlated within family members. The validity of the approach is confirmed by simulation studies. We apply the proposed approach to estimating the age-dependent cancer risk among carriers of the MSH2 or MLH1 mutation.
PMCID: PMC2883298  PMID: 20179148
Case–family design; Penetrance; Proportional hazards model; Rare mutation; Unobserved risk factors

Results 1-2 (2)