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Thesis (M.A.S.)--Memorial University of Newfoundland, 2004. / Bibliography: leaves 77-78.

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Keenan A. Pituch. Vita. Includes bibliographical references.

Thesis--University of Michigan. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (v. 2, leaves 476-496).

published_or_final_version / Dentistry / Master / Master of Philosophy

Not available / text

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Laura M. Stapleton. Vita. Includes bibliographical references.

Three longitudinal pulmonary function data sets were analyzed by several statistical methods for the purposes of: 1) determining to what degree the conclusions of an analysis for a given data set are method dependent; 2) assessing the properties of each method across the different data sets; 3) studying the correlates of FEV₁ decline including physical, behavioral, and respiratory factors, as well as city of residence and type of work. 4) assessing the appropriateness of modelling the standard linear relationship of FEV₁ with time and providing alternative approaches; 5) describing longitudinal change in various lung function variables, apart from FEV₁. The three data sets were comprised of (1) yearly data on 141 veterans with mild chronic bronchitis, taken at three Canadian centres, for a maximum of 23 years of follow-up; their mean age at the start of the study was 49 years (s.d.=9) and only 10.6% were nonsmokers during the follow-up; (2) retrospective data on 384 coal workers categorized into four groups according to vital status (dead or alive) and smoking behavior, with irregular follow-up intervals ranging from 2 to 12 measurements per individual over a period of 9 to 30 years; (3) a relatively balanced data set on 269 grain workers and a control group of 58 civic workers, which consisted of 3 to 4 measurements taken over an average follow-up of 9 years. Their mean age at first measurement was 37 years (s.d.=10) and 53.2% of the subjects did not smoke. A review of the pulmonary and statistical literature was carried out to identify methods of analysis which had been applied to calculate annual change in FEV₁. Five methods chosen for the data analyses were variants of ordinary least squares approaches. The other four methods were based on the use of transformations, weighted least squares, or covariance structure models using generalized least squares approaches. For the coal workers, the groups that were alive at the time of ascertainment had significantly smaller average FEV₁ declines than the deceased groups. Post-retirement decline in FEV₁ was shown by one statistical method to significantly increase for coal workers who smoked, while a significant decrease was observed for nonsmokers. Veterans from Winnipeg consistently showed the lowest decline estimates in comparison to Halifax and Toronto; recorded air pollution measurements were found to be the lowest for Winnipeg, while no significant differences in smoking behavior were found between the veterans of each city. The data set of grain workers proved most ameniable to all the different analytical techniques, which were consistent in showing no significant differences in FEV₁ decline between the grain and civic workers groups and the lowest magnitude of FEV₁ decline. It was shown that quadratic and allometric analyses provided additional information to the linear description of FEV₁ decline, particularly for the study of pulmonary decline among older or exposed populations over an extended period of time. Whether the various initial lung function variables were each predictive of later decline was dependent on whether absolute or percentage decline was evaluated. The pattern of change in these lung function measures over time showed group differences suggestive of different physiological responses. Although estimates of FEV₁ decline were similar between the various methods, the magnitude and relative order of the different groups and the statistical significance of the observed inter-group comparisons were method-dependent No single method was optimal for analysis of all three data sets. The reliance on only one model, and one type of lung function measurement to describe the data, as is commonly found in the pulmonary literature, could lead to a false interpretation of the result Thus a comparative approach, using more than one justifiable model for analysis is recommended, especially in the usual circumstances where missing data or irregular follow-up times create imbalance in the longitudinal data set. / Graduate and Postdoctoral Studies / Graduate

Methods of testing for a location shift between two populations in a longitudinal study are investigated when the data of interest are ordered, categorical and non-linear. A non-standard analysis involving modelling of data over time with transition probability matrices is discussed. Next, the relative efficiencies of statistics more frequently used for the analysis of such categorical data at a single time point are examined. The Wilcoxon rank sum, McCullagh, and 2 sample t statistic are compared for the analysis of such cross sectional data using simulation and efficacy calculations. Simulation techniques are then utilized in comparing the stratified Wilcoxon, McCullagh and chi squared-type statistic in their efficiencies at detecting a location shift when the data are examined over two time points. The distribution of a chi squared-type statistic based on the simple contingency table constructed by merely noting whether a subject improved, stayed the same or deteriorated is derived. Applications of these methods and results to a data set of Multiple Sclerosis patients, some of whom were treated with interferon and some of whom received a placebo are provided throughout the thesis and our findings are summarized in the last Chapter. / Science, Faculty of / Statistics, Department of / Graduate

In longitudinal settings, modeling the covariance structure of repeated measure data is essential for proper analysis. The first paper in this three-paper dissertation presents a survey of four journals in the fields of Education and Psychology to identify the most commonly used methods for analyzing longitudinal data. It provides literature reviews and statistical details for each identified method. This paper also offers a summary table giving the benefits and drawbacks of all the surveyed methods in order to help researchers choose the optimal model according to the structure of their data. Finally, this paper highlights that even when scholars do use more advanced methods for analyzing repeated measure data, they very rarely report (or explore in their discussions) the covariance structure implemented in their choice of modeling. This suggests that, at least in some cases, researchers may not be taking advantage of the optimal covariance patterns. This paper identifies a gap in the standard statistical practices of the fields of Education and Psychology, namely that researchers are not modeling the covariance structure as an extension of fixed/random effects modeling. The second paper introduces the General Serial Covariance (GSC) approach, an extension of the Linear Mixed Modeling (LMM) or Hierarchical Linear Model (HLM) techniques that models the covariance structure using spatial correlation functions such as Gaussian, Exponential, and other patterns. These spatial correlations model the covariance structure in a continuous manner and therefore can deal with missingness and imbalanced data in a straightforward way. A simulation study in the second paper reveals that when data are consistent with the GSC model, using basic HLMs is not optimal for the estimation and testing of the fixed effects. The third paper is a tutorial that uses a real-world data set from a drug abuse prevention intervention to demonstrate the use of the GSC and basic HLM models in R programming language. This paper utilizes variograms (a visualization tool borrowed from geostatistics) among other exploratory tools to determine the covariance structure of the repeated measure data. This paper aims to introduce the GSC model and variogram plots to Education and Psychology, where, according to the survey in the first paper, they are not in use. This paper can also help scholars seeking guidance for interpreting the fixed effect-parameters.