Statistical methods for heaped data / Hao Wang.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Wang, Hao.

Contributor:

Heitjan, Daniel F., advisor.

University of Pennsylvania.

Language:

English

Subjects (All):

Penn dissertations--Epidemiology and Biostatistics.

Epidemiology and Biostatisticsy--Penn dissertations.

Epidemiology and Biostatistics.

Academic Dissertations as Topic.

Medical Subjects:

Epidemiology and Biostatistics.

Academic Dissertations as Topic.

Local Subjects:

Penn dissertations--Epidemiology and Biostatistics.

Epidemiology and Biostatisticsy--Penn dissertations.

Physical Description:

x, 94 pages : illustrations ; 29 cm

Production:

2009.

Summary:

Heaping is a common type of measurement error emerging when data are collected with various degrees of coarseness. We say that a dataset is "heaped" when it contains a mixture of exact and rounded-off values. Parameter estimates derived from heaped data can be misleading if the errors imparted by heaping are ignored. This dissertation describes methods to account for the measurement errors inherent in heaped data. Based on the concept that each observed outcome represents a true value potentially distorted by measurement error, which may include heaping, we formulate two latent variables; one representing the underlying true value, the second characterizing the misreporting behavior responsible for measurement errors. We propose probability models for the two latent processes and describe Bayesian methods to estimate the model parameters. The sensitivity of the inferences to the specification of the underlying distributions is assessed using Bayes factors. We apply graphical posterior predictive checks to evaluate the adequacy of the fitted model using multiple imputations of the latent true variable.

The development and evaluation of our modeling approach is divided into three projects. In the first project, we propose a strategy for modeling univariate heaped data and describe its application to data acquired from a smoking cessation trial to assess the efficacy of the antidepressant drug bupropion. In the second project, we extend the method to model longitudinal heaped data by incorporating separate random effects into the models for the underlying latent processes. We summarize the results obtained when the two latent processes were used to characterize an interview technique, known as time-line follow back, that is widely used in smoking cessation trials to assess daily cigarette consumption. In the third project, we propose a method to correct for digit preference in blood pressure (BP) measurements. Through an application of the method to systolic BP measurements from REGARDS, a large national population-based cohort study to evaluate risk factors for stroke, we assess the effect of digit preference on the misclassification of hypertension.

Notes:

Adviser: Daniel F. Heitjan.

Thesis (Ph.D. in Epidemiology and Biostatistics) -- University of Pennsylvania, 2009.

Includes bibliographical references.