Browsing by Author "Dr. Marie Davidian, Committee Co-Chair"

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Adjustment for Measurement Error
(2009-11-02) Elliott, Laine E; Dr. Anastasios Tsiatis, Committee Member; Dr. Daowen Zhang, Committee Member; Dr. Marie Davidian, Committee Co-Chair; Dr. Len Stefanski, Committee Co-Chair
A variety of complications arise when imperfect measurements, W, are observed in place of a true variable of interest, X. In the context of linear and non-linear regression models where X is a covariate, regression parameter estimators obtained when W is substituted for X may be substantially biased. Many strategies for correcting for measurement error depend on the specific modeling or regression context and can be intractable in highly non-linear models. In addition, previous methods often assume that the measurement error is normally distributed. In our work, we focus on re-creating the distribution of X from the observed W, either as the primary quantity of interest or as a means to improving parameter estimation. We obtain estimators of X for which the first M sample moments are unbiased for the corresponding moments of X. We investigate the benefit of substituting these estimates in density estimation, logistic regression and survival models. We compare this moment adjusted imputation (MAI) approach to existing alternatives in applications with normally distributed measurement error. We identify an important case of chi-square measurement error and propose a variety of methods to adjust for it, including a version of MAI. We find that MAI is often superior and has the advantage that once the estimates of X are obtained, they can be substituted in any model, including complicated non-linear models.
Assessing Agreement with Intraclass Correlation Coefficient and Concordance Correlation Coefficient
(2009-08-03) Chen, Chia-Cheng; Dr. Anastasios A. Tsiatis, Committee Member; Dr. Marie Davidian, Committee Co-Chair; Dr. Huiman X. Barnhart, Committee Chair; Dr. Huixia Wang, Committee Member
Accurate and precise measurement serves as a basis for studies in bioscience research. Agreement studies are often concerned with assessing whether different observers (e.g. machines, raters, methods, instruments, laboratories, assays, devices, etc.) for measuring responses on the same subject or sample can produce similar results. The intraclass correlation coefficient (ICC) and the concordance correlation coefficient (CCC) are two popular scaled indices (with values between -1 and 1) for assessing agreement (closeness) for continuous measurements, where these two indices may take the systematic shifts into account when assessing reliability between multiple observers. We conducted systematic and in-depth comparisons of these two indices under a general model since ICC depends on specific ANOVA models while CCC does not. Usually, the ICC and CCC are used for data without and with replications based on subject and observer effects only. However, we can not use the methodology if repeated measurements rather than replications are collected. There exist some ICC and CCC type indices for assessing agreement with repeated measurements. However, there is no CCC for random observers and random time points, we consider a new CCC for repeated measures where both observers and time are treated as random effects and also summarize other remaining combinations of random or fixed factors for observers and time. Finally, we compare ICCs and CCCs for data with repeated measurements.
Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data
(2004-07-24) Leon, Selene; Dr. Anastasios A. Tsiatis, Committee Chair; Dr. Marie Davidian, Committee Co-Chair
Inference on treatment effect in a pretest–posttest study is a routine objective in medicine, public health, and other fields, and a number of approaches have been advocated. Typically, subjects are randomized to two treatments, the response is measured at baseline and a prespecified follow–up time, and interest focuses on the effect of treatment on follow—up mean response. Covariate information at baseline and in the intervening period until follow—up may also be collected. Missing posttest response for some subjects is routine, and disregarding these missing cases can lead to biased and inefficient inference. Despite the widespread popularity of this design, a consensus on an appropriate method of analysis when no data are missing, let alone on an accepted practice for taking account of missing follow—up response, does not exist. We take a semiparametric perspective, making no assumptions about the distributions of baseline and posttest responses. Exploiting the work of Robins et al. (1994), we characterize the class of all consistent estimators for treatment effect, identify the efficient member of this class, and propose practical procedures for implementation. The result is a unified framework for handling pretest—posttest inferences when follow—up response may be missing at random that allows the analyst to incorporate baseline and intervening information so as to improve efficiency of inference. Simulation studies and application to data from an HIV clinical trial illustrate the utility of the approach.