Randomized clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the inference of the treatment effect in terms of this measure. In a cancer clinical trial with a time to an event as the endpoint, hazard ratio is a popular measure for the relative difference between treatment groups. Most current statistical inference procedures for hazard ratio rely on the proportional hazard assumption, which may not be applicable to practice when it does not hold. Nonparametric confidence intervals for the hazard ratio have been proposed based on the asymptotic normality of the kernel estimate for the hazard ratio, but they were found not very satisfactory in the simulation studies. In the first part of this thesis, the empirical likelihood method is used to construct the confidence interval for the time-dependent hazard ratio. The asymptotic distribution of the empirical likelihood ratio is derived and simulation studies are conducted to evaluate the proposed method. It was also argued that the measure of the relative treatment effect based on the hazard ratio may be difficult to understand by clinicians. An alternative measure called probabilistic index was suggested and the C-index was proposed to estimate this index. However, it was pointed out recently that the expected value of the estimate based on the C-index may be far removed from the true index. In the second part of this thesis, assuming a semi-parametric density ratio model, two new estimates based on respectively the conditional likelihood and weighted empirical likelihood are proposed. Associated confidence intervals are also derived based on the bootstrap re-sampling method. The proposed inference procedures are evaluated by Monte-Carlo simulations and applied to the analysis of data from a clinical trial on early breast cancer. After primary analysis including all patients is completed in clinical trials, analysis by subgroups defined based on covariates of patients is often of interest to assess the homogeneity of treatment effects over these subgroups. The treatment-covariate interaction is usually used for this assessment. In the last part of this thesis, a non-parametric measure is used to quantify the interaction between treatments and binary covariates in the presence of censoring. Asymptotic distribution of the interaction estimates are derived and the bootstrap method is applied to construct the confidence intervals. The proposed approaches are also evaluated and compared by Monte-Carlo simulations and applied to a real data set from clinical trial. / Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2011-05-21 11:07:52.992

One of my previous works introduced a new data mining technique to analyze multiple experiments called TAME: Trained Across Multiple Experiments. TAME detects treatment effects of a randomized controlled experiment by utilizing data from outside of the experiment of interest. TAME with linear regression showed promising result; in all simulated scenarios, TAME was at least as good as a standard method, ANOVA, and was significantly better than ANOVA in certain scenarios. In this work, I further investigated and improved TAME by altering how TAME assembles data and creates subject models. I found that mean-centering “prior� data and treating each experiment as equally important allow TAME to detect treatment effects better. In addition, we did not find Random Forest to be compatible with TAME.

This thesis includes three substantive empirical studies (in Chapters 3, 4 and 5), where each study uses the same econometric methodology, named Regression Discontinuity design, which has an attractive feature - local randomisation. This feature has given the superiority of the method over the other evaluation methods in estimating unbiased treatment effects. Besides, the fuzzy Regression Discontinuity design can control for the endogeneity of the treatment variable, which is another advantage of the method. In each of the studies considered, the endogeneity problem exists. The application of the fuzzy Regression Discontinuity design is itself a contribution in each of the studies. Moreover, each study contributes in its own field. In Chapter 3, we investigate how much the Social Safety Net programs, that provide free food, or cash, or both to the food insecure households in Bangladesh, improve calorie consumption of the beneficiary households. Using Household Income and Expenditure Survey 2005, we find that the effect of the programs is around 843 kilo calorie, which is substantial compared to the previous studies. In Chapter 4, we examine how much was the impact of Education Maintenance Allowance, a program that provided weekly allowance to the young people in Years 12 and 13 in England, on the staying rate in the post compulsory full-time education. The program was abolished in 2010. Using the Longitudinal Survey of Young People in England, we find that the effect of the program was substantial - around 15 percent. The effect of a £1 increase in weekly allowance was around 1 percent. These effects were mainly on the white young people. Using the household survey data - Family Expenditure Survey (1968-2009) - in UK, Chapter 5 establishes that before 1981 consumption substantially fell at the retirement age. This fall is less severe after 1980. However, throughout the data period, consumption fall at the retirement age is fully explained by the expected fall in income, which contradicts the life cycle model, where a consumption growth is independent of an income growth.

The first chapter uses the propensity score matching method to measure the average impact of insurance on health service utilization in terms of office-based physician visits, total number of reported visits to hospital outpatient departments, and emergency room visits. Four matching algorithms are employed to match propensity scores. The results show that insurance significantly increases office-based physician visits, and its impacts on reported visits to hospital outpatient departments and emergency room visits are positive, but not significant. This implies that physician offices will receive a substantial increase in demand if universal insurance is imposed. Government will need to allocate more resources to physician offices relative to outpatient or emergency room services in the case of universal insurance in order to accommodate the increased demand. The second chapter studies the sensitivity of propensity score matching methods to different estimation methods. Traditionally, parametric models, such as logit and probit, are used to estimate propensity score. Current technology allows us to use computationally intensive methods, either semiparametric or nonparametric, to estimate it. We use the Monte Carlo experimental method to investigate the sensitivity of the treatment effect to different propensity score estimation models under the unconfoundedness assumption. The results show that the average treatment effect on the treated (ATT) estimates are insensitive to the estimation methods when index function for treatment is linear, but logit and probit model do better jobs when the index function is nonlinear. The third chapter proposes a Cross-Sectionally Varying (CVC) Coefficient method to approximate individual treatment effects with nonexperimental data, the distribution of treatment effects, the average treatment effect on the treated and the average treatment effect. The CVC method reparameterizes the outcome of no treatment and the treatment effect in terms of observable variables, and uses these observables together with a Bayesian estimator of their coefficients to approximate individual treatment effects. Monte Carlo simulations demonstrate the efficacy and applicability of the proposed estimator. This method is applied to two datasets: data from the U.S. Job Training Partnership ACT (JTPA) program and a dataset that contains firms’ seasoned equity offerings and operating performances.

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Compliance, the extent to which patients follow a medication regimen, has been recognized as one of the most serious problems facing medical practice today. Recent developments in assessing compliance include electronic compliance monitors (ECM), devices that record the date and time of the release of medication from its original container. This allows utilizing ECM compliance data in statistical analyses related to clinical trials. This thesis proposes ways of dealing with the time-varying nature of compliance. We examine the compliance behaviour from real ECM data through statistical analysis of compliance rate, followed by a time-to-event analysis with respect to first noncompliance event. Then, using discrete event simulation and proportional hazards models we compare analyses using a fixed treatment covariate and time-varying compliance covariate based on pharmacokinetic principles in estimating treatment effect. We observe a reduction of up to 40% in EMSE in favour of the latter model for treatment effect estimation.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第20868号 / 経博第563号 / 新制||経||283(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 准教授 山田 憲, 准教授 高野 久紀 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

Thesis advisor: Robert Murphy / The primary goal of the European Union is to promote a high degree of competition between regions in an effort to allow for the creation of the single market. In the year 2004, the EU had allowed ten new member states to enter into the European Union. This paper looks at the potential positive or negative impact from entering into partnership with the EU. It looks at convergence between EU member states and a potential treatment effect in order to determine that this is indeed a localized phenomenon in the EU or is there a general convergence between all countries. The paper uses a fixed effects approach in order to determine the impact of partnership and use of the Euro within the EU. I find evidence of convergence and a positive benefit from partnership; however, using the Euro appears to have a negative impact on countries. / Thesis (BA) — Boston College, 2015. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Economics.

Personalised medicine describes the practice of tailoring medical care to the individual characteristics of each patient. Fundamental to this practice is the identification of markers associated with differential treatment response. Such markers can be identified through the assessment of treatment effect modification using statistical methods. Randomised controlled trials provide the optimal setting for evaluating differential response to treatment. Due to restrictions regarding sample size, study length and ethics, observational studies are more appropriate in many circumstances, particularly for the identification of markers associated with adverse side-effects and long term response to treatments. However, the analysis of observational data raises some additional challenges. The overall aim of this thesis was to develop statistical methodology for the analysis of observational data, specifically primary care databases, to identify and evaluate markers associated with differential treatment response. Three aspects of the assessment of treatment effect modification in an observational setting were addressed. The first aspect related to the assessment of treatment effect modification on the additive measurement scale which corresponds to a comparison of absolute treatment effects across patient subgroups. Various ways in which this can be assessed in an observational setting were reviewed and a novel measure, the ratio of absolute effects, which can be calculated from certain multiplicative regression models, was proposed. The second aspect regarded the confounding adjustment and it was investigated how the presence of interactions between the moderator and confounders on both treatment receipt and outcome can bias estimates of treatment effect modification if unaccounted for using Monte Carlo simulations. It was determined that the presence of bias differed across different confounding adjustment methods and, in the majority of settings, the bias was reduced when the interactions between the moderator and confounders were accounted for in the confounding adjustment model. Thirdly, it has been proposed that patient data in observational studies be organised into and analysed as series of nested nonrandomised trials. This thesis extended this study design to evaluate predictive markers of differential treatment response and explored the benefits of this methodology for this purpose. It was suggested how absolute treatment effect estimates can be estimated and compared across patient subgroups in this setting. A dataset comprising primary care medical records of adults with rheumatoid arthritis was used throughout this thesis. Interest lay in the identification of characteristics predictive of the onset of type II diabetes associated with steroid (glucocorticoid) therapy. The analysis in this thesis suggested older age may be associated with a higher risk of steroid-associated type II diabetes, but this warrants further investigation. Overall, this thesis demonstrates how observational studies can be analysed such that accurate and meaningful conclusions are made within personalised medicine research.

This dissertation consists of three essays. Chapter II examines the possible asymmetric response of gasoline prices to crude oil price changes using an error correction model with GARCH errors. Recent papers have looked at this issue. Some of these papers estimate a form of error correction model, but none of them accounts for autoregressive heteroskedasticity in estimation and testing for asymmetry and none of them takes the response of crude oil price into consideration. We find that time-varying volatility of gasoline price disturbances is an important feature of the data, and when we allow for asymmetric GARCH errors and investigate the system wide impulse response function, we find evidence of asymmetric adjustment to crude oil price changes in weekly retail gasoline prices Chapter III discusses the relationship between fiscal deficit and exchange rate. Economic theory predicts that fiscal deficits can significantly affect real exchange rate movements, but existing empirical evidence reports only a weak impact of fiscal deficits on exchange rates. Based on US dollar-based real exchange rates in G5 countries and a flexible varying coefficient model, we show that the previously documented weak relationship between fiscal deficits and exchange rates may be the result of additive specifications, and that the relationship is stronger if we allow fiscal deficits to impact real exchange rates non-additively as well as nonlinearly. We find that the speed of exchange rate adjustment toward equilibrium depends on the state of the fiscal deficit; a fiscal contraction in the US can lead to less persistence in the deviation of exchange rates from fundamentals, and faster mean reversion to the equilibrium. Chapter IV proposes a kernel method to deal with the nonparametric regression model with only discrete covariates as regressors. This new approach is based on recently developed least squares cross-validation kernel smoothing method. It can not only automatically smooth the irrelevant variables out of the nonparametric regression model, but also avoid the problem of loss of efficiency related to the traditional nonparametric frequency-based method and the problem of misspecification based on parametric model.