Population-averaged models for diagnostic accuracy studies and meta-analysis

Powers, James

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March 22, 2019

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Powers, James
- Affiliation: Gillings School of Global Public Health, Department of Biostatistics

Abstract

Modern medical decision making often involves one or more diagnostic tools (such as laboratory tests and/or radiographic images) that must be evaluated for their discriminatory ability to detect presence (or absence) of current health state. The first paper of this dissertation extends regression model diagnostics to the Receiver Operating Characteristic (ROC) curve generalized linear model (ROC-GLM) in the setting of individual-level data from a single study through application of generalized estimating equations (GEE) within a correlated binary data framework (Alonzo and Pepe, 2002). Motivated by the need for model diagnostics for the ROC-GLM model (Krzanowski and Hand, 2009), GEE cluster-deletion diagnostics (Preisser and Qaqish, 1996) are applied in an example data set to identify cases that have undue influence on the model parameters describing the ROC curve. In addition, deletion diagnostics are applied in an earlier stage in the estimation of the ROC-GLM, when a linear model is chosen to represent the relationship between the test measurement and covariates in the control subjects. The second paper presents a new model for diagnostic test accuracy meta-analysis. The common analysis framework for the meta-analysis of diagnostic studies is the generalized linear mixed model, in particular, the bivariate logistic-normal random effects model. Considering that such cluster-specific models are most appropriately used if the model for a given cluster (i.e. study) is of interest, a population-average (PA) model may be appropriate in diagnostic test meta-analysis settings where mean estimates of sensitivity and specificity are desired. A PA model for correlated binomial outcomes is estimated with GEE in the meta-analysis of two data sets. It is compared to an indirect method of estimation of PA parameters based on transformations of bivariate random effects model parameters. The third paper presents an analysis guide for a new SAS macro, PAMETA (Population-averaged meta-analysis), for fitting population-averaged (PA) diagnostic accuracy models with GEE as described in the second paper. The impact of covariates, influential clusters and observations is investigated in the analysis of two example data sets.

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