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1.  Efficient Analysis of Q-Level Nested Hierarchical General Linear Models Given Ignorable Missing Data 
The international journal of biostatistics  2013;9(1):10.1515/ijb-2012-0048 /j/ijb.2013.9.issue-1/ijb-2012-0048/ijb-2012-0048.xml.
This paper extends single-level missing data methods to efficient estimation of a Q-level nested hierarchical general linear model given ignorable missing data with a general missing pattern at any of the Q levels. The key idea is to reexpress a desired hierarchical model as the joint distribution of all variables including the outcome that are subject to missingness, conditional on all of the covariates that are completely observed; and to estimate the joint model under normal theory. The unconstrained joint model, however, identifies extraneous parameters that are not of interest in subsequent analysis of the hierarchical model, and that rapidly multiply as the number of levels, the number of variables subject to missingness, and the number of random coefficients grow. Therefore, the joint model may be extremely high dimensional and difficult to estimate well unless constraints are imposed to avoid the proliferation of extraneous covariance components at each level. Furthermore, the over-identified hierarchical model may produce considerably biased inferences. The challenge is to represent the constraints within the framework of the Q-level model in a way that is uniform without regard to Q; in a way that facilitates efficient computation for any number of Q levels; and also in a way that produces unbiased and efficient analysis of the hierarchical model. Our approach yields Q-step recursive estimation and imputation procedures whose qth step computation involves only level-q data given higher-level computation components. We illustrate the approach with a study of the growth in body mass index analyzing a national sample of elementary school children.
doi:10.1515/ijb-2012-0048
PMCID: PMC3898356  PMID: 24077621
Child Health; Hierarchical General Linear Model; Ignorable Missing Data; Maximum Likelihood; Multiple Imputation
2.  Designs combining instrumental variables with case-control: estimating principal strata causal effects 
The international journal of biostatistics  2012;8(1):10.2202/1557-4679.1355 /j/ijb.2012.8.issue-1/1557-4679.1355/1557-4679.1355.xml.
doi:10.2202/1557-4679.1355
PMCID: PMC3608098  PMID: 22499727
3.  Exact Calculations of Average Power for the Benjamini-Hochberg Procedure* 
Exact analytic expressions are developed for the average power of the Benjamini and Hochberg false discovery control procedure. The result is based on explicit computation of the joint probability distribution of the total number of rejections and the number of false rejections, and expressed in terms of the cumulative distribution functions of the p-values of the hypotheses. An example of analytic evaluation of the average power is given. The result is confirmed by numerical experiments and applied to a meta-analysis of three clinical studies in mammography.
doi:10.2202/1557-4679.1103
PMCID: PMC3020656  PMID: 21243075
hypothesis testing; multiple comparisons; false discovery; distribution of rejections; meta-analysis

Results 1-3 (3)