Ischemic heart disease (IHD) is a leading cause of global mortality. Emerging evidence suggests a potential role of the gut microbiome in IHD development. The present study explored the association between alpha diversity, representing microbial richness and evenness, and IHD, as well as the relationship between specific gut microbial species and IHD risk. The study uses a longitudinal design with data from the two cohorts within the Swedish Infrastructure for Medical Population-based Life-course and Environmental Research (SIMPLER). Survival analysis is employed to calculate the hazard rate for all associations. Because of thecompositionality of the gut microbiome species-level data a centered log-ratio (CLR) transformation is applied before the main analysis. We find no associations between alpha diversity and risk of IHD in the age and sex-adjusted model as well as in the main model adjusted for age, sex, smoking, education, physical activity, hypertension, high cholesterol, and intakes of fish, red and processed meat, fermented dairy, fruit and vegetables and alcohol. Five species are found to be statistically significantly associated with IHD in the main model before correction for multiple testing, which is statistically non-significant after the correction.

Previous studies have paid little attention to analyzing the impact of agglomeration externalities on a firm’s survival, especially for developing countries. This study applies various survival analysis methods to examine the relationship of three main types of agglomeration externalities: specialization, diversification (related variety and unrelated variety), and urbanization on the survival of firms in Vietnam, with respect to different types of firms ownership: state-owned firms, foreign-owned firms, and domestic private firms. This study confirms the positive role of specialization in reducing the exit probability for firms, especially for foreign-owned firms. The survival probability of foreign-owned firms and domestic private firms is insensitive to any type of diversification, and only related variety increases the survival chance for state-owned firms. In contrast, urbanization has a small negative impact on domestic private firms.

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.

The following dissertation contains two distinct empirical essays which contribute to the overall field of Financial Economics. Chapter 1, entitled “The Determinants of Dynamic Dependence: An Analysis of Commodity Futures and Equity Markets,” examines the determinants of the dynamic equity-commodity return correlations between five commodity futures sub-sectors (energy, foods and fibers, grains and oilseeds, livestock, and precious metals) and a value-weighted equity market index (S&P 500). The study utilizes the traditional DCC model, as well as three time-varying copulas: (i) the normal copula, (ii) the student’s t copula, and (iii) the rotated-gumbel copula as dependence measures. Subsequently, the determinants of these various dependence measures are explored by analyzing several macroeconomic, financial, and speculation variables over different sample periods. Results indicate that the dynamic equity-commodity correlations for the energy, grains and oilseeds, precious metals, and to a lesser extent the foods and fibers, sub-sectors have become increasingly explainable by broad macroeconomic and financial market indicators, particularly after May 2003. Furthermore, these variables exhibit heterogeneous effects in terms of both magnitude and sign on each sub-sectors’ equity-commodity correlation structure. Interestingly, the effects of increased financial market speculation are found to be extremely varied among the five sub-sectors. These results have important implications for portfolio selection, price formation, and risk management. Chapter 2, entitled, “US Community Bank Failure: An Empirical Investigation,” examines the declining, but still pivotal role, of the US community banking industry. The study utilizes survival analysis to determine which accounting and macroeconomic variables help to predict community bank failure. Federal Deposit Insurance Corporation and Federal Reserve Bank data are utilized to compare 452 community banks which failed between 2000 and 2013, relative to a sample of surviving community banks. Empirical results indicate that smaller banks are less likely to fail than their larger community bank counterparts. Additionally, several unique bank-specific indicators of failure emerge which relate to asset quality and liquidity, as well as earnings ratios. Moreover, results show that the use of the macroeconomic indicator of liquidity, the TED spread, provides a substantial improvement in modeling predictive community bank failure.

Lam Yuk Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 41-43). / Abstracts in English and Chinese. / Chapter 1 --- Basic Concepts --- p.9 / Chapter 1.1 --- Survival data --- p.9 / Chapter 1.1.1 --- An example --- p.9 / Chapter 1.2 --- Some important functions --- p.11 / Chapter 1.2.1 --- Survival function --- p.12 / Chapter 1.2.2 --- Hazard function --- p.12 / Chapter 1.3 --- Cox Proportional Hazards Model --- p.13 / Chapter 1.3.1 --- A special case --- p.14 / Chapter 1.3.2 --- An example (continued) --- p.15 / Chapter 1.4 --- Extension of the Cox Proportional Hazards Model --- p.16 / Chapter 1.5 --- Bootstrap --- p.17 / Chapter 2 --- A New Method --- p.19 / Chapter 2.1 --- Introduction --- p.19 / Chapter 2.2 --- Definition of the test --- p.20 / Chapter 2.2.1 --- Our test statistic --- p.20 / Chapter 2.2.2 --- The alternative test statistic I --- p.22 / Chapter 2.2.3 --- The alternative test statistic II --- p.23 / Chapter 2.3 --- Variations of the test --- p.24 / Chapter 2.3.1 --- Restricted test --- p.24 / Chapter 2.3.2 --- Adjusting for other covariates --- p.26 / Chapter 2.4 --- Apply with bootstrap --- p.28 / Chapter 2.5 --- Examples --- p.29 / Chapter 2.5.1 --- Male mice data --- p.34 / Chapter 2.5.2 --- Stanford heart transplant data --- p.34 / Chapter 2.5.3 --- CGD data --- p.34 / Chapter 3 --- Large Sample Properties and Discussions --- p.35 / Chapter 3.1 --- Large sample properties and relationship to goodness of fit test --- p.35 / Chapter 3.1.1 --- Large sample properties of A and Ap --- p.35 / Chapter 3.1.2 --- Large sample properties of Ac and A --- p.36 / Chapter 3.2 --- Discussions --- p.37

Yu, Kai Shing. / "November 2010." / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 103-114). / Abstracts in English and Chinese. / Abstract --- p.2 / 中文摘要 --- p.6 / List of Contents --- p.9 / List of Table --- p.12 / Abbreviation --- p.13 / Acknowledgement --- p.14 / Chapter Chapter 1: --- Introduction --- p.15 / Chapter 1.1 --- Epidemiology of lung cancer --- p.15 / Chapter 1.2 --- Overview of significant prognostic factors for patients with NSCLC --- p.18 / Chapter 1.2.1 --- Tumor related factors --- p.19 / Chapter 1.2.2 --- Patient related factors --- p.21 / Chapter 1.3 --- Overview of significant prognostic factors for SCLC patients --- p.22 / Chapter Chapter 2: --- Literature Review --- p.25 / Chapter 2.1 --- Prevalence of co-morbidity among lung cancer patients --- p.25 / Chapter 2.2 --- Impact of co-morbidity on non small cell lung cancer patients --- p.28 / Chapter 2.3 --- Impact of co-morbidity on small cell lung cancer patients --- p.36 / Chapter 2.4 --- Summary of evidence from literature review --- p.40 / Chapter Chapter 3: --- Aim and Objectives --- p.42 / Chapter 3.1 --- General aim --- p.42 / Chapter 3.2 --- Specific objectives --- p.42 / Chapter 3.3 --- Main hypothesis --- p.42 / Chapter Chapter 4: --- Methodology --- p.43 / Chapter 4.1 --- Research design --- p.43 / Chapter 4.2 --- Study population --- p.43 / Chapter 4.3 --- Sample size estimation --- p.45 / Chapter 4.4 --- Data collection --- p.47 / Chapter 4.4.1 --- Demographic information --- p.47 / Chapter 4.4.2 --- Co-morbidity --- p.51 / Chapter 4.4.3 --- Adverse symptoms --- p.51 / Chapter 4.4.4 --- Disease characteristics --- p.52 / Chapter 4.4.5 --- Baseline laboratory findings --- p.53 / Chapter 4.4.6 --- Treatment data --- p.53 / Chapter 4.4.7 --- Follow up --- p.53 / Chapter 4.5 --- Statistical analyses --- p.54 / Chapter Chapter 5: --- Results --- p.56 / Chapter 5.1 --- Description of cohort --- p.56 / Chapter 5.2 --- Baseline characteristics --- p.58 / Chapter 5.3 --- Symptom presentation --- p.62 / Chapter 5.4 --- Histological characteristics --- p.64 / Chapter 5.5 --- Treatment characteristics --- p.67 / Chapter 5.6 --- Haematological characteristics of study population --- p.69 / Chapter 5.7 --- Prevalence of co-morbidity --- p.71 / Chapter 5.8 --- Overall survival --- p.74 / Chapter 5.8.1 --- Univariate and multivariate survival analysis for SCLC patients --- p.75 / Chapter 5.8.2 --- Univariate and multivariate survival analysis for NSCLC patients --- p.77 / Chapter 5.8.3 --- In-depth analyses for the Impact of co-morbidity on lung cancer survival --- p.79 / Chapter 5.8.4 --- Selected underlying causes of death --- p.84 / Chapter Chapter 6: --- Discussion --- p.85 / Chapter 6.1 --- Prognostic factors --- p.85 / Chapter 6.2 --- Prevalence of co-morbidity --- p.89 / Chapter 6.3 --- Impact of co-morbidity on lung cancer survival --- p.92 / Chapter 6.4 --- Strengths and limitations of this study --- p.97 / Chapter Chapter 7: --- Conclusions --- p.101 / Chapter Chapter 8: --- Implications and Recommendations for medial practice --- p.102 / References --- p.103

Thesis advisor: Henry I. Braun / The City Connects intervention is motivated by the belief that out-of-school factors act as barriers to student thriving in cognitive and non-cognitive domains. It seeks to address these barriers first by identifying each student’s strengths and needs and then by providing a tailored set of prevention, intervention, and enrichment programs. Underlying the program is the assumption that provision of high-quality resources and individualized services will enable children to be cognitively, socio-emotionally, and physically prepared to thrive in school. This study’s purpose was to estimate the effects of the City Connects intervention on English learners’ (EL) likelihood of exiting Limited English Proficiency (LEP) status. ELs comprise a student subpopulation most at-risk to fail academically, and exposure to the program was hypothesized to improve their likelihood of exiting LEP status earlier than otherwise. A series of one- and two-level discrete-time event history analyses were conducted on the main analytic sample as well as two sub-samples. As participation in City Connects is at the school-level, school-level matching was used for sub-samples 1 and 2, and propensity score weights were applied at the student-level for all three samples. Additionally, hazard probabilities, survival probabilities, cumulative hazard rates, and median lifetimes were estimated. Lastly, a sensitivity analysis was conducted to examine whether effects were robust to unobserved selection bias. The results indicated that ELs participating in the City Connects intervention were significantly more likely to exit LEP status earlier than their peers in comparison schools. The median time in LEP status in City Connects schools was shorter and translated into a gain of at least one half of a year in grade in mainstream classes. Also, all the fitted models indicated that approximately 10 percent more City Connects students exited LEP status by the end of fifth grade than comparison students. Findings highlight the impact of the City Connects intervention, as ELs entering mainstream classes earlier could translate into important academic and non-academic gains, such as improved academic achievement and increased self-confidence. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Migrationsverket är en myndighet som prövar ansökningar från personer som vill söka skydd, ha medborgarskap, studera eller vill jobba i Sverige. Då det på senare tid varit en stor ökning i dessa ansökningar har tiden för vilket ett beslut tar ökat. Varje typ av ansökning (exempelvis medborgarskap) är en process som består av flera steg. Hur beslutet går igenom dessa steg kallas för flöde. Migrationsverket vill därför öka sin flödeseffektivitet. När beslutet är klart och personen tagit del av det men inte är nöjd kan denne överklaga. Detta är en av de mest komplexa processerna på Migrationsverket. Syftet är analysera hur lång tid denna process tar och vilka steg i processen som påverkar tiden. Ett steg (som senare visar sig ha en stor effekt på tiden) är yttranden. Det är när domstolen begär information om vad personen som överklagar har att säga om varför denne överklagar. För att analysera detta var två metoder relevanta, accelerated failure time (AFT) och \multi-state models (MSM). Den ena kan predicera tid till händelse (AFT) medan den andra kan analysera effekten av tidspåverkan (MSM) i stegen. Yttranden tidigt i processen har stor betydelse för hur snabbt en överklagan får en dom samtidigt som att antal yttranden ökar tiden enormt. Det finns andra faktorer som påverkar tiden men inte i så stor grad som yttranden. Då yttranden tidigt i processen samtidigt som antal yttranden har betydelse kan flödeseffektiviteten ökas med att ta tid på sig att skriva ett informativt yttrande som gör att domstolen inte behöver begära flera yttranden. / The Swedish Migration Board is an agency that review applications from individuals who wish to seek shelter, have citizenship, study or want to work in Sweden. In recent time there has been a large increase in applications and the time for which a decision is made has increased. Each type of application (such as citizenship) is a process consisting of several stages. How the decision is going through these steps is called flow. The Swedish Migration Board would therefore like to increase their flow efficiency. When the decision is made and the person has take part of it but is not satisfied, he can appeal. This is one of the most complex processes at the Board. The aim is to analyze how long this process will take and what steps in the process affects the time. One step (which was later found to have a significant effect on time) is opinions. This is when the court requests information on what the person is appealing has to say about why he is appealing. To analyze this, two methods were relevant, accelerated failure time (AFT) and the multi-state models (MSM). One can predict time to event (AFT), the other to analyze the effect of time-manipulation (MSM) in the flow. Opinions early in the process is crucial to how quickly an appeal get judgment while the number of opinions increases the time enormously. There are other factors that affect the time but not so much as opinions. The flow efficiency can be increased by taking time to write an informative opinion which allows the court need not to ask for more opinions.

Survival analysis pertains to a statistical approach designed to take into account the amount of time an experimental unit contributes to a study. A Mayo Clinic study of 418 Primary Biliary Cirrhosis patients during a ten year period was used. The Kaplan-Meier Estimator, a non-parametric statistic, and the Cox Proportional Hazard methods were the tools applied. Kaplan-Meier results include total values/censored values.

Survival analysis is applied on mechanical components installed in gas turbines. We use field experience data collected from repair inspection reports. These data are highly censored since the exact time-to-event is unknown. We only know that it lies before or after the repair inspection time. As event we consider irreparability level of the mechanical components. The aim is to estimate survival functions that depend on the different environmental attributes of the sites where the gas turbines operate. Then, the goal is to use this information to obtain optimal time points for preventive maintenance. Optimal times are calculated with respect to the minimization of a cost function which considers expected costs of preventive and corrective maintenance. Another aim is the investigation of the effect of five different failure modes on the component lifetime. The methods used are based on the Weibull distribution, in particular we apply the Bayesian Weibull AFT model and the Bayesian Generalized Weibull model. The latter is preferable for its greater flexibility and better performance. Results reveal that components from gas turbines located in a heavy industrial environment at a higher distance from sea tend to have shorter lifetime. Then, failure mode A seems to be the most harmful for the component lifetime. The model used is capable of predicting customer-specific optimal replacement times based on the effect of environmental attributes. Predictions can be also extended for new components installed at new customer sites.