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published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

Most everybody is ambitious about their own careers. Most of us aspire to be promoted to positions with greater responsibilities and benefits and have a clear sense of what we mean by a "successful career." Politicians are no different, and there is no apparent reason why they should be. However, unlike what happens in other occupations, politicians are forced periodically---i.e., at the end of each term they serve---to make a decision about what to do with their careers. This decision is made under the uncertainty about their ability to continue their careers according to their plans. The possibility of electoral defeat spares no one in spite of all that politicians do to avoid being voted out of office. Thus, at the end of each term, politicians must ponder what they want to do with their careers or where they want to go next. Politicians inform their decisions with their beliefs about their performance in office---or their performance as challengers---and their assessments of the difficulty of winning office in the following election. This raises the question about why some politicians decide to stay in office. Concretely, why do some politicians decide to get reelected while others seek election in "higher" or even "lower" offices? And also, why are some politicians more successful in having lasting careers? I focus on the career decisions that politicians make routinely and in the duration of their careers by considering individual and district factors that explain why politicians decide to run for particular offices and the length of their tenures.

The essays in this dissertation explore issues related to health and retirement of older Americans, using longitudinal data on older Americans from ten waves of the Health and Retirement Study (1992-2010). The first essay explores the effect of both subjective and relatively more objective physical and mental health conditions on the probability of exit from full-time employment. Eight health indices (factors) are created from a wide range of health measures by principal component analysis. The effect of these health factors on the time until exit from full-time employment is empirically examined in a proportional hazard model. Single and competing risk specifications are estimated that allow for multiple spells of full-time employment and control for unobserved heterogeneity. The main results suggest that increase in functional limitation factor makes an individual more likely to exit via any route in general and the complete retirement route in particular. For mental health problems, increase in the depression factor increases the likelihood of exit from full-time employment via the complete retirement, part-time work and unemployment routes. While increase in cognitive disorders factor has no significant effect on the likelihood of exit via complete retirement, but increases the likelihood of exit via the disability route. These results have implications for public policies targeted towards retaining older workers within the labor market. The second essay examines the effect of retirement on post retirement physical and mental health and the extent to which the effects differ across these different health outcomes. The inherent issue of reverse causality between health and retirement that leads to endogeneity is addressed by using multiple sample stratification and instrumental variable estimation strategies. The stratified samples include individuals who are physically and mentally healthy prior to their retirement so that pure effect of retirement on post retirement health may be found. Five different instruments for complete retirement are also used to deal with endogeneity. The sample stratification results unanimously indicate that complete retirement has adverse effect on post retirement physical and mental health. While the instrumental variables approach results are mixed and are based on the choice of instrument for complete retirement.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 103-113) Also available in print.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 438-442).

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (p. 257-261).

An increasing number of studies have shown an inverse association between a personal history of cancer (PHC) and dementia/Alzheimer’s disease (AD), both in those using dementia/AD as the outcome or cancer as the outcome. This is the first study to examine this potential association in Japanese Americans; and to examine family history of cancer and its association with incident dementia. Also, the association between these two diseases in the parents of participants were analyzed. The Kame Project, conducted from 1992 through 2001 in King County, Washington was a population-based, prospective cohort study of older Japanese Americans. Conversion to incident dementia was observed throughout the follow-up period and diagnosed by standard criteria in a consensus conference. A PHC did not have a significant association with the development of dementia. Differences between this study and those conducted previously showing an inverse association between cancer and dementia or AD included a lower age of the present cohort, race/ethnicity, focus on all-cause dementia vs. AD and adjustment for the competing risk of death. A family history of cancer was inversely associated with the development of dementia. There were statistically significant trends for a dose-response association between the numbers of affected relatives with cancer and risk for dementia. The findings are most likely explained by an inverse genetic association between cancer and dementia. Older Japanese Americans (the parents) with a history of cancer were nearly 2.5 times less likely to have a history of dementia than those without a cancer history.

Indiana University-Purdue University Indianapolis (IUPUI) / In studies with competing risks outcomes, misidentifying the event-type responsible for the observed failure is, by definition, an act of misclassification. Several authors have established that such misclassification can bias competing risks statistical analyses, and have proposed statistical remedies to aid correct modeling. Generally, these rely on adjusting the estimation process using information about outcome misclassification, but invariably assume that outcome misclassification is non-differential among study subjects regardless of their individual characteristics. In addition, current methods tend to adjust for the misclassification within a semi-parametric framework of modeling competing risks data. Building on the existing literature, in this dissertation, we explore the parametric modeling of competing risks data in the presence of outcome misclassification, be it differential or non-differential. Specifically, we develop parametric pseudo-likelihood-based approaches for modeling cause-specific hazards while adjusting for misclassification information that is obtained either through data internal or external to the current study (respectively, internal or external-validation sampling). Data from either type of validation sampling are used to model predictive values or misclassification probabilities, which, in turn, are used to adjust the cause-specific hazard models. We show that the resulting pseudo-likelihood estimates are consistent and asymptotically normal, and verify these theoretical properties using simulation studies. Lastly, we illustrate the proposed methods using data from a study involving people living with HIV/AIDS (PLWH)in the East-African consortium of the International Epidemiologic Databases for the Evaluation of HIV/AIDS (IeDEA EA). In this example, death is frequently misclassified as disengagement from care as many deaths go unreported to health facilities caring for these patients. In this application, we model the cause-specific hazards of death and disengagement from care among PLWH after they initiate anti-retroviral treatment, while adjusting for death misclassification. / 2021-03-10

This dissertation has three parts. The first part proposes a two-stage estimation procedure for a copula-based model with semi-competing risks data, where the nonterminal event is subject to dependent censoring by the terminal event. Under a copula-based model, the marginal survival functions of individual event times are specified by semiparametric transformation models, and a parametric copula function specifies the between-event dependence. The parameters associated with the marginal of the terminal event are first estimated, and the marginal parameters for the non-terminal event time and the copula parameter are second estimated via maximizing a pseudo-likelihood function based on the joint distribution of the bivariate event times. We derived the asymptotic properties of the proposed estimator and provided an analytic variance estimator for inference. We showed that our approach leads to consistent estimates with less computational cost and more robustness compared to the one-stage procedure developed by Chen (2012). In addition, our approach demonstrates more desirable finite-sample performances over another existing two-stage estimation method proposed by Zhu et al. (2021). The second part develops a goodness-of-fit t est f or t he copula specification under semi-parametric copula models with semi-competing risks data. We constructed an information ratio (IR) statistic by comparing consistent estimates of the two information matrices, the sensitivity matrix and the variability matrix. The information matrices are derived from the log-likelihood function, which is a function of the marginal distribution of the terminal event time, the marginal distribution of the time to the first event, and the copula parameter. We established the asymptotic distribution of the IR statistic and examined the finite-sample performance of the IR test via a simulation study. The third part develops a class of models to characterize the effects of factors that vary with the age at baseline and the age at the event. This project is motivated by the Childhood Cancer Survivor Study. The age-specific effects of the covariates are estimated via an inverse probability weighted kernel smoothing method. We conducted simulation studies to evaluate the performance of the proposed estimator.