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1.  Change-point diagnostics in competing risks models: Two posterior predictive p-value approaches 
Test (Madrid, Spain)  2007;16(1):145-171.
This paper presents a Bayesian diagnostic procedure for examining change-point assumption in the competing risks model framework. It considers the family of distributions arising from the cause-specific model as reported by Chiang (Introduction to stochastic processes in biostatistics. Wiley, New York, 1968) upon which change-points are added to accommodate possible distributional heterogeneity. Model departure, due to misspecification of change-points associated with either the overall survival distribution or cause-specific probabilities, is quantified in terms of a sequence of cumulative-sum statistics between each pair of adjacent change-points assumed. When assessing the asymptotic behavior of each sequence of cumulative-sum statistics using its posterior predictive p-values, see Rubin (Ann Stat 12:1151–1172, 1984) and partial posterior predictive p-values as reported by Bayarri and Berger (J Am Stat Assoc 95:1127–1142, 2000), we show that both types of p-values attain their greatest departure from 0.5 at the change-point that is missed in the assumed model, from which a diagnostic procedure is formalized. Statistical power of these two types of p-values is discussed.
PMCID: PMC4226243  PMID: 25392679
Change-point; Competing risks; Posterior predictive p-values
3.  Comments on: dynamic relations for sparsely sampled Gaussian processes 
Test (Madrid, Spain)  2010;19(1):50-53.
PMCID: PMC2999881  PMID: 21151714
4.  Missing data methods in longitudinal studies: a review 
Test (Madrid, Spain)  2009;18(1):1-43.
Incomplete data are quite common in biomedical and other types of research, especially in longitudinal studies. During the last three decades, a vast amount of work has been done in the area. This has led, on the one hand, to a rich taxonomy of missing-data concepts, issues, and methods and, on the other hand, to a variety of data-analytic tools. Elements of taxonomy include: missing data patterns, mechanisms, and modeling frameworks; inferential paradigms; and sensitivity analysis frameworks. These are described in detail. A variety of concrete modeling devices is presented. To make matters concrete, two case studies are considered. The first one concerns quality of life among breast cancer patients, while the second one examines data from the Muscatine children’s obesity study.
PMCID: PMC3016756  PMID: 21218187
Expectation-maximization algorithm; Incomplete data; Missing completely at random; Missing at random; Missing not at random; Pattern-mixture model; Selection model; Sensitivity analyses; Shared-parameter model
5.  Comments on: A review on empirical likelihood methods for regression 
Test (Madrid, Spain)  2009;18(3):463-467.
PMCID: PMC2996722  PMID: 21152356

Results 1-5 (5)