Many statistical methods for truncated data rely on the assumption that the failure and truncation time are independent, which can be unrealistic in applications. The study cohorts obtained from bone marrow transplant (BMT) registry data are commonly recognized as truncated samples, the time-to-failure is truncated by the transplant time. There are clinical evidences that a longer transplant waiting time is a worse prognosis of survivorship. Therefore, it is reasonable to assume the dependence between transplant and failure time. To better analyze BMT registry data, we utilize a Cox analysis in which the transplant time is both a truncation variable and a predictor of the time-to-failure. An inverse-probability-weighted (IPW) estimator is proposed to estimate the distribution of transplant time. Usefulness of the IPW approach is demonstrated through a simulation study and a real application.

Thèse diffusée initialement dans le cadre d'un projet pilote des Presses de l'Université de Montréal/Centre d'édition numérique UdeM (1997-2008) avec l'autorisation de l'auteur.

Use of progression-free survival in the evaluation of clinical interventions is hampered by a variety of issues, including censoring patterns not addressed in the usual methods for survival analysis. Progression can be right-censored before survival or interval-censored between inspection times. Current practice calls for imputing events to their time of detection. Such an approach is prone to bias, underestimates standard errors and makes inefficient use of the data at hand. Moreover a composite outcome prevents inference about the actual treatment effect on the risk of progression. This thesis develops semiparametric and sieve maximum likelihood estimators to more formally analyze progression-related endpoints. For the special case where death rarely precedes progression, a Cox-Aalen model is proposed for regression analysis of time-to-progression under intermittent inspection. The general setting considering both progression and survival is examined with a Markov Cox-type illness-death model under various censoring schemes. All of the resulting estimators globally converge to the truth slower than the parametric rate, but their finite-dimensional components are asymptotically efficient. Numerical studies suggest that the new methods perform better than their imputation-based alternatives under moderate to large samples having higher rates of censoring.

This paper analyzes the effect of political and economic factors on the risk of regime change in countries between 1975 and 2010, using survival analysis with time-dependent covariates. The findings show that negative economic growth increases the risk of regime change in the following year, and that a higher level of GDP per Capita, as well as international trade, has an inhibiting effect on the risk of regime change in democracies. The results also show that countries with young regimes are more likely to experience a regime change, and that countries with a long tradition of democratic governance suffer virtually no risk of experiencing a regime failure. These findings lend heavy support to the democratic consolidation theory, while giving mixed support to other theories of economic and political causes of regime change. The more generalized approach to regime change used in this paper provides a stepping stone for opening up a greater understanding of the mechanisms which cause regime change in all types of governments, and regardless of the direction of the change in relation to democracy.

<p>We explore the posterior inference available for Bayesian spatial point process models. In the literature, discussion of such models is usually focused on model fitting and rejecting complete spatial randomness, with model diagnostics and posterior inference often left as an afterthought. Posterior predictive point patterns are shown to be useful in performing model diagnostics and model selection, as well as providing a wide array of posterior model summaries. We prescribe Bayesian residuals and methods for cross-validation and model selection for Poisson processes, log-Gaussian Cox processes, Gibbs processes, and cluster processes. These novel approaches are demonstrated using existing datasets and simulation studies.</p> / Dissertation

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This dissertation is a historically comparative study of adult mortality between Taiwan and the United States throughout the 20th century. The 20th century was characterized by the largest rise in life expectancy at birth and the most rapid decrease in mortality in recorded human history. This dissertation aims not only to examine and compare the trends and levels of life expectancy in Taiwan and the United States over an extended period of time, but also to evaluate the extent to which smoking behavior and obesity play an important role in the recent levels of adult mortality in the United States. I used logistic models of mortality to examine and compare the trends and levels of life expectancy in Taiwan from 1906 to 2008 and in the United States from 1933 to 2007. Second, I re-estimated life expectancy by introducing smoking-attributable mortality to further compare the levels of life expectancy between the two countries. Third, I estimated event history models to investigate whether and how smoking behavior and obesity are related to mortality in the United States in the 1990 to 2006 and the 2000 to 2006 periods. At the end of the 20th century, the level of life expectancy at birth for females in the U.S. was higher than in Taiwan, but they were close. In this century, however, the level of life expectancy at birth in Taiwan has increased to a higher level than in the U.S. The levels of male life expectancy at birth for the two countries are similar in this century, but there were significant differences in the 20th century. The great improvements in juvenile, background and senescent mortality rates in Taiwan may be used to explain this correspondence of life expectancy between the two countries today. Besides, higher smoking-attributed mortality can also serve as another possible reason for the stagnant levels of life expectancy in the U.S. Finally, smoking-related and obesity-related mortality have become progressively more important as predictors of adult mortality in the U.S. in past decades.

共變數的值會隨著時間而改變時，我們稱之為時間相依之共變數。時間相依之共變數往往具有重複測量的特性，也是長期資料裡最常見到的一種共變數形態；在對時間相依之共變數進行重複測量時，可以考慮每次測量的間隔時間相同或是間隔時間不同兩種情形。在間隔時間相同的情形下，我們可以忽略間隔時間所產生的效應，利用分組的Cox模式或是合併的羅吉斯迴歸模式來分析，而合併的羅吉斯迴歸是一種把資料視為“對象 時間單位”形態的分析方法；此外，分組的Cox模式和合併的羅吉斯迴歸模式也都可以用來預測存活機率。在某些條件滿足下，D’Agostino等六人在1990年已經證明出這兩個模式所得到的結果會很接近。 當間隔時間為不同時，我們可以用計數過程下的Cox模式來分析，在計數過程下的Cox模式中，資料是以“對象 區間”的形態來分析。2001年Bruijne等人則是建議把間隔時間也視為一個時間相依之共變數，並將其以B-spline函數加至模式中分析；在我們論文的實證分析裡也顯示間隔時間在延伸的Cox模式中的確是個很顯著的時間相依之共變數。延伸的Cox模式為間隔時間不同下的時間相依之共變數提供了另一個分析方法。至於在時間相依之共變數的預測方面，我們是以指數趨勢平滑法來預測其未來時間點的數值；利用預測出來的時間相依之共變數值再搭配延伸的Cox模式即可預測未來的存活機率。 / It is so called “time-dependent covariates” that the values of covariates change over time. Time-dependent covariates are measured repeatedly and often appear in the longitudinal data. Time-dependent covariates can be regularly or irregularly measured. In the regular case, we can ignore the TEL(time elapsed since last observation) effect and the grouped Cox model or the pooled logistic regression model is employed to anlalyze. The pooled logistic regression is an analytic method using the“person-period”approach. The grouped Cox model and the pooled logistic regression model also can be used to predict survival probablity. D’Agostino et al. (1990) had proved that pooled logistic regression model is asymptotically equivalent to the grouped Cox model. If time-dependent covariates are observed irregularly, Cox model under counting process may be taken into account. Before making the prediction we must turn the original data into“person-interval”form, and this data form is also suitable for the prediction of grouped Cox model in regular measurements. de Bruijne et al.(2001) first considered TEL as a time-dependent covariate and used B-spline function to model it in their proposed extended Cox model. We also show that TEL is a very significant time-dependent covariate in our paper. The extended Cox model provided an alternative for the irregularly measured time-dependent covariates. On the other hand, we use exponential smoothing with trend to predict the future value of time-dependent covariates. Using the predicted values with the extended Cox model then we can predict survival probablity.

Thesis (M.A. in anthropology))--Washington State University, December 2007. / Includes bibliographical references (leaves 157-180).

Thesis (M.A. in anthropology)--Washington State University, May 2009. / Title from PDF title page (viewed on Apr. 2, 2009). "Department of Anthropology." Includes bibliographical references (p. 61-68).