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Likelihood-based inference for longitudinal binary data can be obtained using a generalized linear mixed model (Breslow, N. and Clayton, D. G., 1993, Journal of the American Statistical Association 88, 9-25; Wolfinger, R. and O'Connell, M., 1993, Journal of Statistical Computation and Simulation 48, 233-243), given the recent improvements in computational(More)
Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any(More)
We detected clonal mosaicism for large chromosomal anomalies (duplications, deletions and uniparental disomy) using SNP microarray data from over 50,000 subjects recruited for genome-wide association studies. This detection method requires a relatively high frequency of cells with the same abnormal karyotype (>5-10%; presumably of clonal origin) in the(More)
OBJECTIVE The effects of paraoxonase (PON1) activity and of genetic variation in the PON1 promoter and coding region on carotid artery disease (CAAD) were investigated. METHODS AND RESULTS We identified functional promoter polymorphisms and examined their effects in a cohort with and without CAAD. We used the full sequences in 23 white subjects to(More)
OBJECTIVES To evaluate the reliability and diagnostic accuracy of high-resolution MRI of the median nerve in a prospectively assembled cohort of subjects with clinically suspected carpal tunnel syndrome (CTS). METHODS The authors prospectively identified 120 subjects with clinically suspected CTS from five Seattle-area clinics. All subjects completed a(More)
We propose and compare two approaches for regression analysis of multilevel binary data when clusters are not necessarily nested: a GEE method that relies on a working independence assumption coupled with a three-step method for obtaining empirical standard errors, and a likelihood-based method implemented using Bayesian computational techniques.(More)
Marginalized models (Heagerty, 1999, Biometrics 55, 688-698) permit likelihood-based inference when interest lies in marginal regression models for longitudinal binary response data. Two such models are the marginalized transition and marginalized latent variable models. The former captures within-subject serial dependence among repeated measurements with(More)
OBJECTIVE This study examined the incremental cost-effectiveness of a collaborative care intervention for depression compared with consult-liaison care. METHODS A total of 354 patients in a Department of Veterans Affairs (VA) primary care clinic who met the criteria for major depression or dysthymia were randomly assigned to one of the two care models.(More)
We examine the use of internal controls for estimating the expected initial copy number of the target in a polymerase chain reaction (PCR). We base our investigation on an extended branching-process model. In terms of that model, we delineate the necessary assumptions for this methodology to yield approximately unbiased answers, and we provide means for(More)