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<p>In this thesis, inferential methods for one-shot device testing data from accelerated life-test are developed. Due to constraints on time and budget, accelerated life-tests are commonly used to induce more failures within a reasonable amount of test-time for obtaining more lifetime information that will be especially useful in reliability analysis. One-shot devices, which can be used only once as they get destroyed immediately after testing, yield observations only on their condition and not on their real lifetimes. So, only binary response data are observed from an one-shot device testing experiment. Since no failure times of units are observed, we use the EM algorithm for determining the maximum likelihood estimates of the model parameters. Also, inference for the reliability at a mission time and the mean lifetime at normal operating conditions are also developed.</p> <p>The thesis proceeds as follows. Chapter 2 considers the exponential distribution with single-stress relationship and develops inferential methods for the model parameters, the reliability and the mean lifetime. The results obtained by the EM algorithm are compared with those obtained from the Bayesian approach. A one-shot device testing data is analyzed by the proposed method and presented as an illustrative example. Next, in Chapter 3, the exponential distribution with multiple-stress relationship is considered and corresponding inferential results are developed. Jackknife technique is described for the bias reduction in the developed estimates. Interval estimation for the reliability and the mean lifetime are also discussed based on observed information matrix, jackknife technique, parametric bootstrap method, and transformation technique. Again, we present an example to illustrate all the inferential methods developed in this chapter. Chapter 4 considers the point and interval estimation for the one-shot device testing data under the Weibull distribution with multiple-stress relationship and illustrates the application of the proposed methods in a study involving the development of tumors in mice with respect to risk factors such as sex, strain of offspring, and dose effects of benzidine dihydrochloride. A Monte Carlo simulation study is also carried out to evaluate the performance of the EM estimates for different levels of reliability and different sample sizes. Chapter 5 describes a general algorithm for the determination of the optimal design of an accelerated life-test plan for one-shot device testing experiment. It is based on the asymptotic variance of the estimated reliability at a specific mission time. A numerical example is presented to illustrate the application of the algorithm. Finally, Chapter 6 presents some concluding remarks and some additional research problems that would be of interest for further study.</p> / Doctor of Philosophy (PhD)

<p><strong>Background and objectives</strong></p> <p>We investigate three issues related to the adjustment for baseline covariates in late phase clinical trials: (1) the analysis of correlated outcomes in multicentre RCTs, (2) the assessment of the probability and implication of prognostic imbalance in RCTs, and (3) the adjustment for baseline confounding in cohort studies.</p> <p><strong>Methods</strong></p> <p>Project 1: We investigated the properties of six statistical methods for analyzing continuous outcomes in multicentre randomized controlled trials (RCTs) where within-centre clustering was possible. We simulated studies over various intraclass correlation (ICC) values with several centre combinations.</p> <p>Project 2: We simulated data from RCTs evaluating a binary outcome by varying risk of the outcome, effect of the treatment, power and prevalence of a binary prognostic factor (PF), and sample size. We compared logistic regression models with and without adjustment for the PF, in terms of bias, standard error, coverage of confidence interval, and statistical power. A tool to assess sample size requirement to control for chance imbalance was proposed.</p> <p>Project 3: We conducted a prospective cohort study to evaluate the effect of tuberculosis (TB) at the initiation of antiretroviral therapy (ART) on all cause mortality using Cox proportional hazard model on propensity score (PS) matched patients to control for potential confounding. We assessed the robustness of results using sensitivity analyses.</p> <p><strong>Results and conclusions</strong></p> <p>Project 1: All six methods produce unbiased estimates of treatment effect in multicentre trials. Adjusting for centre as a random intercept leads to the most efficient treatment effect estimation, and hence should be used in the presence of clustering.</p> <p>Project 2: The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed.</p> <p>Project 3: After controlling for the important confounding variables, HIV patients who had TB at the initiation of ART have a moderate increase in the risk of overall mortality.</p> / Doctor of Philosophy (PhD)

<p>Correlated data with complex association structures arise from longitudinal studies and cluster randomized trials. However, some methodological challenges in the design and analysis of such studies or trials have not been overcome. In this thesis, we address three of the challenges: 1) <em>Power analysis for population based longitudinal study investigating gene-environment interaction effects on chronic disease:</em> For longitudinal studies with interest in investigating the gene-environment interaction in disease susceptibility and progression, rigorous statistical power estimation is crucial to ensure that such studies are scientifically useful and cost-effective since human genome epidemiology is expensive. However conventional sample size calculations for longitudinal study can seriously overestimate the statistical power due to overlooking the measurement error, unmeasured etiological determinants, and competing events that can impede the occurrence of the event of interest. 2) <em>Comparing the performance of different multiple imputation strategies for missing binary outcomes in cluster randomized trials</em>: Though researchers have proposed various strategies to handle missing binary outcome in cluster randomized trials (CRTs), comprehensive guidelines on the selection of the most appropriate or optimal strategy are not available in the literature. 3) <em>Comparison of population-averaged and cluster-specific models for the analysis of cluster randomized trials with missing binary outcome</em>: Both population-averaged and cluster-specific models are commonly used for analyzing binary outcomes in CRTs. However, little attention has been paid to their accuracy and efficiency when analyzing data with missing outcomes. The objective of this thesis is to provide researchers recommendations and guidance for future research in handling the above issues.</p> / Doctor of Philosophy (PhD)

<p>Recently, the Conway-Maxwell Poisson (COM-Poisson) cure rate model has been proposed which includes as special cases some of the well-known cure rate models discussed in the literature. Data obtained from cancer clinical trials are often right censored and the expectation maximization (EM) algorithm can be efficiently used for the determination of the maximum likelihood estimates (MLEs) of the model parameters based on right censored data.</p> <p>By assuming the lifetime distribution to be exponential, lognormal, Weibull, and gamma, the necessary steps of the EM algorithm are developed for the COM-Poisson cure rate model and some of its special cases. The inferential method is examined by means of an extensive simulation study. Model discrimination within the COM-Poisson family is carried out by likelihood ratio test as well as by information-based criteria. Finally, the proposed method is illustrated with a cutaneous melanoma data on cancer recurrence. As the lifetime distributions considered are not nested, it is not possible to carry out a formal statistical test to determine which among these provides an adequate fit to the data. For this reason, the wider class of generalized gamma distributions is considered which contains all of the above mentioned lifetime distributions as special cases. The steps of the EM algorithm are then developed for this general class of distributions and a simulation study is carried out to evaluate the performance of the proposed estimation method. Model discrimination within the generalized gamma family is carried out by likelihood ratio test and information-based criteria. Finally, for the considered cutaneous melanoma data, the two-way flexibility of the COM-Poisson family and the generalized gamma family is utilized to carry out a two-way model discrimination to select a parsimonious competing cause distribution along with a suitable choice of a lifetime distribution that provides the best fit to the data.</p> / Doctor of Philosophy (PhD)

This paper presents an approach to model the risks associated with defaults, prepayments, and order returns in the context of unsecured consumer credits, specifically in buy-now-pay-later (BNPL) loans. The paper presents a Bayesian competing risk proportional hazard model to model the time to default, prepayment, and order return in BNPL loans. Model parameters are estimated using Markov chain Monte Carlo (MCMC) sampling techniques and Bayesian inference is developed using a unique dataset containing monthly performance data of fixed-duration interest-bearing consumer loans. / I denna rapport presenteras en metod för att modellera riskerna förknippade med fallissemang, förskottsbetalning, och returnering av order i osäkrade konsumentkrediter, mer specifikt i köp-nu-betala-senare (BNPL) krediter. Rapporten presenterar en Bayesiansk konkurrerande utfall-modell (competing risk) för att estimera tiden till fallissemang, förskottsbetalning och retur av order i BNPL-lån. Modellens parametrar estimeras med hjälp av Markov chain Monte Carlo (MCMC) metoder och Bayesiansk inferens uppnås med hjälp av ett unikt dataset med månatlig kassaflödesdata från räntebärande BNPL-lån.

Three essays in this dissertation explore the behavior of individuals in response to the housing crisis and its consequences, and the impact of the pandemic on the short-term rental markets. The first essay examines the economic outcomes of young people who have returned to their parents' home, using data from 2003-2017 waves of the National Longitudinal Survey of Youth 1997 Cohort (NLSY 97). The economic outcomes of boomerang movers did not improve compared to the period of independent living, and the income gap with young people who remained independent widened. The residential movement of young people who make boomerang moves has an impact on their income, but this effect is short-lived. Going back to a parental house changes the region and urban form significantly, and movement of urban form from the central city to the suburban and from the suburban to out of the MSA has a negative impact on income. Findings from the study suggest implications. First, more affordable housing should be provided to reduce boomerang moves. Second, ways to increase job opportunities should be explored to reduce the short-term negative impact of boomerang move. Finally, education and vocational training opportunities must be increased to close the income gap among young people. The second essay seeks to answer the following questions through the experiences of individual households due to the foreclosure. First, did foreclosed households regain homeownership? Second, is there a relationship between socio-demographic characteristics of foreclosed household and regaining homeownership? Third, where do homeowners who have lost their homes migrate? Finally, what characteristics of the neighborhood help foreclosed households recover? While previous studies have focused on the resilience of housing markets and regions, this study explores the link between regional characteristics and individual household recovery. The recovery of financially disadvantaged households is an important issue for communities and states. Identifying the mechanism that is responsible for household recovery has implications for implementing programs to aid household recovery. This study primarily relies on the 2005 -2019 Panel Study of Income Dynamics (PSID). Since 2009, PSID has added survey questions about foreclosure; Whether a foreclosure process has begun, the year and month of the start, the result of the process, and whether a foreclosed home is a primary residence. The findings of this study suggest that the government's recovery assistance program should aim to support relocation to areas with lower poverty rates and higher job and educational opportunities. The final essay explores changes in short-term rentals resulting from the COVID-19 pandemic. To identify the impact of the COVID-19 pandemic, this study uses New York City's Airbnb listing data from Inside Airbnb (IA), as well as supplemental data such as American Community Survey (ACS) data. Change in the number of STRs is divided into (1) the number of units left the platform and (2) the number of new units. The former relates to the survival of existing STR units and, the latter to the location choice of new units. The results show that the impact of several variables on survival and generation mechanisms changed since the COVID-19 pandemic. Since the survival mechanism and the generation mechanism of short-term rentals are different, they should be considered separately in regulating the STR to stabilize local housing markets. / Doctor of Philosophy / Although research has been conducted on the housing crisis and recovery of the housing market, there are still unanswered questions from two aspects. First, have the individuals affected by the crisis recovered? Were the individual decisions in response to the crisis effective? Second, how has the new crisis caused by the COVID-19 pandemic impacted the housing market? Are different characteristics observed from previous housing crises? While the evidence is reported that the relationship between the new crisis and housing demand has changed, the impact of the pandemic on contemporary housing crises such as gentrification and reduced housing stock is unknown. This dissertation explores the trajectories of individual behavior in the housing market, using various data sources and methodologies. Of the three essays in this dissertation, the first two essays explore the behavior of individuals in response to the housing crisis and its consequences, and the final essay explores the impact of the pandemic on the short-term rental markets. The first essay investigates the economic outcomes of young people who return to their parental homes after periods of independent living using NLSY97 data. The second essay investigates the relationship between neighborhoods and the economic recovery of households using Panel Study of Income Dynamics. The third essay explores changes in the survival and generation mechanism of Airbnb units associated with the COVID-19 pandemic using New York City's Airbnb listing data. The results of each study commonly lead to the conclusion that housing affordability should be improved. It also suggests that more affordable housing should be provided in areas of greater opportunities. This dissertation ultimately contributes to identifying individuals at risk from external shocks and suggesting goals and strategies for a healthy housing market.

Radiotherapy treatment-response of cancer patients can vary considerably, even in patients sharing the same diagnosis. Enhancing the degree of treatment personalization might offer a way towards improving curation rates. The recent advancements in the field of deep neural networks provide new directions for the non-invasive extraction of patient-individual biomarkers when applied on diagnostic imaging data. Within this thesis, we explored the potential of image-based deep learning as an enabler for individualized therapy. In a cohort of head and neck cancer patients, we first assessed the suitability of applying convolutional neural networks (CNNs) on pre-treatment computed tomography imaging data for the prediction of loco-regional tumor control in the presence of censored outcomes. We further investigated whether the predictive performance can be improved through the adoption of multitask learning strategies that combine multiple outcome prediction models and a tumor segmentation task, both for CNNs and the recently emerged vision transformer-based network architectures. Subsequently, we applied neural networks on multimodal and longitudinal imaging data collected during the course of radiotherapy and evaluated their potential to further improve outcome models. Finally, in the context of proton-beam radiotherapy of primary brain tumor patients, we applied CNNs for the prediction of the linear energy transfer and examined the feasibility of this approach for estimating treatment-related side-effects considering a variable biological effectiveness of protons.:1. Introduction 2. Theoretical background 3. Convolutional neural networks for outcome prediction 4. Multitask learning and attention-based models for outcome prediction 5. Longitudinal and multimodal models for outcome prediction 6. Prediction of the linear energy transfer in proton-beam radiotherapy / Das Ansprechverhalten von Krebspatienten auf eine Strahlentherapie kann selbst zwischen Patienten mit gleicher Diagnose deutlich variieren. Eine erweiterte Personalisierung der Behandlung stellt einen möglichen Weg dar, um die Heilungschancen zu verbessern. Durch die kürzlich erzielten Fortschritte im Bereich neuronaler Netzwerke bieten sich neue Möglichkeiten zur nicht-invasiven Extraktion patientenspezifischer Merkmale aus diagnostischen Bildgebungsdaten. In dieser Arbeit wurde das Potential tiefer, bildbasierter neuronaler Netzwerke für eine individualisierte Strahlentherapie analysiert. In einer Kohorte von Patienten mit Kopf- und Halstumoren wurde zunächst die Eignung faltender neuronaler Netzwerke (CNNs) zur Vorhersage der lokoregionären Kontrolle im Beisein zensierter Beobachtungen basierend auf prätherapeutischer Computertomographie (CT) evaluiert. Außerdem wurde untersucht, ob sich die Vorhersagequalität von CNNs und den kürzlich vorgestellten vision transformer Architekturen unter Verwendung von Multitask-Lernstrategien, welche mehrere Vorhersagemodelle und eine Segmentierung des Tumors kombinieren, verbessern lässt. Anschließend wurde die Anwendbarkeit neuronaler Netzwerke zur Analyse multimodaler und longitudinaler, während des Behandlungsverlaufes aufgenommener, Bildgebungsdaten aufgezeigt und ihr Potential zur weiteren Verbesserung der Vorhersagen analysiert. Abschließend wurden CNNs im Rahmen der Protonentherapie genutzt, um den linearen Energietransfer bei Patienten mit primären Hirntumoren vorherzusagen. Die Plausibilität dieser Vorhersagen zur Risikoabschätzung behandlungsbedingter Nebenwirkungen wurde unter Berücksichtigung einer variablen relativen biologischen Wirksamkeit von Protonen untersucht.:1. Introduction 2. Theoretical background 3. Convolutional neural networks for outcome prediction 4. Multitask learning and attention-based models for outcome prediction 5. Longitudinal and multimodal models for outcome prediction 6. Prediction of the linear energy transfer in proton-beam radiotherapy

In survival analysis, proportional hazards model is the most commonly used and the Cox model is the most popular. These models are developed to facilitate statistical analysis frequently encountered in medical research or reliability studies. In analyzing real data sets, checking the validity of the model assumptions is a key component. However, the presence of complicated types of censoring such as double censoring and partly interval-censoring in survival data makes model assessment difficult, and the existing tests for goodness-of-fit do not have direct extension to these complicated types of censored data. In this work, we use empirical likelihood (Owen, 1988) approach to construct goodness-of-fit test and provide estimates for the Cox model with various types of censored data. Specifically, the problems under consideration are the two-sample Cox model and stratified Cox model with right censored data, doubly censored data and partly interval-censored data. Related computational issues are discussed, and some simulation results are presented. The procedures developed in the work are applied to several real data sets with some discussion.