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American Journal of Epidemiology|July 21, 2020
Multiple-Imputation Variance Estimation in Studies With Missing or Misclassified Inclusion CriteriaMark J Giganti, Bryan E ShepherdThe Annals of Applied Statistics|October 1, 2020
ACCOUNTING FOR DEPENDENT ERRORS IN PREDICTORS AND TIME-TO-EVENT OUTCOMES USING ELECTRONIC HEALTH RECORDS, VALIDATION SAMPLES, AND MULTIPLE IMPUTATIONMark J Giganti, Pamela A Shaw, Guanhua Chen, et al.AIDS Research and Human Retroviruses|February 4, 2015
A Comparison of Seven Cox Regression-Based Models to Account for Heterogeneity Across Multiple HIV Treatment Cohorts in Latin America and the CaribbeanMark J Giganti, Paula M Luz, Yanink Caro-Vega, et al.BMC Public Health|January 1, 2020
The impact of data quality and source data verification on epidemiologic inference: a practical application using HIV observational dataMark J Giganti, Bryan E Shepherd, Yanink Caro-Vega, et al.Biometrics|February 2, 2011
Accounting for data errors discovered from an audit in multiple linear regressionBryan E Shepherd, Chang YuJournal of the American Statistical Association|October 1, 2010
Test of Association Between Two Ordinal Variables While Adjusting for CovariatesChun Li, Bryan E ShepherdJournal of Anxiety Disorders|February 26, 2008
Developing a Symptom Validity Test for posttraumatic stress disorder: application of the binomial distributionKenneth R Morel, Bryan E ShepherdJournal of Acquired Immune Deficiency Syndromes (1999)|November 1, 2016
Brief Report: Assessing and Interpreting the Association Between Continuous Covariates and Outcomes in Observational Studies of HIV Using SplinesBryan E Shepherd, Peter F Rebeiro, Pageof 30