Observational studies are an indispensable complement to randomized clinical trials (RCT) for comparison of treatment effectiveness. Often RCTs cannot be carried out due to the costs of the trial, ethical questions and rarity of the outcome. When noncompliance and missing data are prevalent, RCTs become more like observational studies. The main problem is to adjust for the selection bias in the observational study. One increasingly used method is propensity-score matching. Compared to traditional multi-covariate matching methods, matching on the propensity score alleviates the curse of dimensionality. It allows investigators to balance multiple covariate distributions between treatment groups by matching on a single score. This thesis focuses on the large sample properties of the matching estimators of the treatment effect. The first part of this thesis deals with problems of the analytic supports of the logit propensity score and various matching methods. The second part of this thesis focuses on the matching estimators of additive and multiplicative treatment effects. We derive the asymptotic order of the biases and asymptotic distributions of the matching estimators. We also derive the large sample variance estimators for the treatment effect estimators. The methods and theoretical results are applied and checked in a series of simulation studies. The third part of this thesis is devoted to a comparison between propensity-score matching and multiple linear regression using simulation.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 45-55).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 137-141).

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This dissertation consists of two essays. First essay investigates the implications of researcher data requirement on the risk-adjusted returns of firms. Using the monthly CRSP data from 1925 to 2013, we present evidence that firms which survive longer have higher average returns and lower standard deviation of annualized returns than the firms which do not. I further demonstrate that there is a positive relation between firms’ survival and average performance. In order to account for the positive correlation between survival and average performance, I model the relation of survival and pricing errors using a Farlie-Gumbel-Morgenstern joint distribution function and fit resulting the moment conditions to the data. Our results show that even a low correlation between firm survival time and pricing errors can lead to a much higher correlation between the survival time and average pricing errors. Failure to adjust for this data selection biases can result in over/under estimates of abnormal returns by 5.73 % in studies that require at least five years of returns data. Second essay examines diversification benefits of commodity futures portfolios in the light of the rapid increase in investor participation in commodity futures market since 2000. Many actively managed portfolios outperform traditional buy and hold portfolios for the sample period from January, 1986 to October, 2013. The evidence documented through traditional intersection test and stochastic discount factor based spanning test indicates that financializaiton has reduced segmentation of commodity market with equity and bond market and has increased the riskiness of investing in commodity futures markets. However, diversifying property of commodity portfolios have not disappeared despite the increased correlation between commodity portfolios returns and equity index returns.

Patient care programs such as wellness, preventive care and specifically disease management programs, which target the chronically ill population, are designed to reduce healthcare costs and improve health, while promoting the efficient use of healthcare resources, and increasing productivity. The organizational form adopted by the health plan for these programs, i.e. in-sourced vs. outsourced is an important factor in the success of these programs and the extent to which the core objectives listed above are fulfilled. Transaction cost economics aims to explain the working arrangement for an organization and to explain why sourcing decisions were made by considering alternate organizational arrangements and comparing the costs of transacting under each. This research aims to understand the nature and sources of transaction costs, how they affect the sourcing decision of disease management and other programs, and its effect on the organization, using current industry data. Predictive models are used to obtain empirical results of the influence of each factor, and also to provide cost estimates for each organizational form available, irrespective of the form currently adopted. The analysis of the primary data obtained by the means of a web-based survey supports and confirms the effect of transaction cost factors on these programs. This implies that in order to reap financial rewards and serve patients better, health plans must aim to minimize transaction costs and select the organizational form that best accomplishes this objective.

The use of diagnostic rules based on microarray gene expression data has received wide attention in bioinformatics research. In order to form diagnostic rules, statistical techniques are needed to form classifiers with estimates for their associated error rates, and to correct for any selection biases in the estimates. There are also the associated problems of identifying the genes most useful in making these predictions. Traditional statistical techniques require the number of samples to be much larger than the number of features. Gene expression datasets usually have a small number of samples, but a large number of features. In this thesis, some new techniques are developed, and traditional techniques are used innovatively after appropriate modification to analyse gene expression data. Classification: We first consider classifying tissue samples based on the gene expression data. We employ an external cross-validation with recursive feature elimination to provide classification error rates for tissue samples with different numbers of genes. The techniques are implemented as an R package BCC (Bias-Corrected Classification), and are applied to a number of real-world datasets. The results demonstrate that the error rates vary with different numbers of genes. For each dataset, there is usually an optimal number of genes that returns the lowest cross-validation error rate. Detecting Differentially Expressed Genes: We then consider the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. The focus is on the use of mixture models to handle the multiplicity issue. The mixture model approach provides a framework for the estimation of the prior probability that a gene is not differentially expressed. It estimates various error rates, including the FDR (False Discovery Rate) and the FNR (False Negative Rate). We also develop a method for selecting biomarker genes for classification, based on their repeatability among the highly differentially expressed genes in cross-validation trials. The latter method incorporates both gene selection and classification. Selection Bias: When forming a prediction rule on the basis of a small number of classified tissue samples, some form of feature (gene) selection is usually adopted. This is a necessary step if the number of features is high. As the subset of genes used in the final form of the rule has not been randomly selected but rather chosen according to some criteria designed to reflect the predictive power of the rule, there will be a selection bias inherent in estimates of the error rates of the rule if care is not taken. Various situations are presented where selection biases arise in the formation of a prediction rule and where there is a consequent need for the correction of the biases. Three types of selection biases are analysed: selection bias from not using external cross-validation, selection bias of not working with the full set of genes, and the selection bias from optimizing the classification error rate over a number of subsets obtained according to a selection method. Here we mostly employ the support vector machine with recursive feature elimination. This thesis includes a description of cross-validation schemes that are able to correct for these selection biases. Furthermore, we examine the bias incurred when using the predicted rather than the true outcomes to define the class labels in forming and evaluating the performance of the discriminant rule. Case Study: We present a case study using the breast cancer datasets. In the study, we compare the 70 highly differentially expressed genes proposed by van 't Veer and colleagues, against the set of the genes selected using our repeatability method. The results demonstrate that there is more than one set of biomarker genes. We also examine the selection biases that may exist when analysing this dataset. The selection biases are demonstrated to be substantial.

<p>This thesis consists of three papers that examine college choice and earnings among university graduates in Sweden.</p><p>Paper [I] analyzes how geographical accessibility to higher education affects university enrollment decisions in Sweden. The empirical findings show that the probability of enrollment in university education increases with accessibility to university education. The results also indicate that accessibility adds to the likelihood of attending a university within the region of residence. Both these findings are robust with regard to different specifications of accessibility. The empirical results furthermore indicate that the enrollment decisions of individuals with a less privileged background are more sensitive to accessibility to university education than are the decisions of individuals from a more favorable background.</p><p>Paper [II] examines the effect on earnings of graduating from five different college groups. The paper relies on selection on observables and linear regression to identify the earnings effect of college choice. Contrary to the majority of previous Swedish studies, we do not find any systematic differences in estimated earnings between college graduates from the different college groups. This finding does not only hold when considering all college graduates, but also when focusing on men and women separately as well as when considering college graduates in two specific fields of education. The results suggest that an estimator of the earnings effects of college choice that does not properly adjust for ability is likely to be substantially biased.</p><p>Paper [III] estimates the causal effect on earnings of graduating from old universities rather than new universities/university colleges. The study compares estimates from several different matching methods and linear regression. We cannot find any significant differences in earnings between graduates from the two groups of colleges. This holds for male and female sub-samples covering all majors, as well as male and female sub-samples covering two broad fields of education. The results are robust with regard to different methods of propensity score matching and regression adjustment. Furthermore, the results indicate little sensitivity with regard to the empirical support in the data and alternative specifications of the propensity scores.</p>

This thesis consists of three papers that examine college choice and earnings among university graduates in Sweden. Paper [I] analyzes how geographical accessibility to higher education affects university enrollment decisions in Sweden. The empirical findings show that the probability of enrollment in university education increases with accessibility to university education. The results also indicate that accessibility adds to the likelihood of attending a university within the region of residence. Both these findings are robust with regard to different specifications of accessibility. The empirical results furthermore indicate that the enrollment decisions of individuals with a less privileged background are more sensitive to accessibility to university education than are the decisions of individuals from a more favorable background. Paper [II] examines the effect on earnings of graduating from five different college groups. The paper relies on selection on observables and linear regression to identify the earnings effect of college choice. Contrary to the majority of previous Swedish studies, we do not find any systematic differences in estimated earnings between college graduates from the different college groups. This finding does not only hold when considering all college graduates, but also when focusing on men and women separately as well as when considering college graduates in two specific fields of education. The results suggest that an estimator of the earnings effects of college choice that does not properly adjust for ability is likely to be substantially biased. Paper [III] estimates the causal effect on earnings of graduating from old universities rather than new universities/university colleges. The study compares estimates from several different matching methods and linear regression. We cannot find any significant differences in earnings between graduates from the two groups of colleges. This holds for male and female sub-samples covering all majors, as well as male and female sub-samples covering two broad fields of education. The results are robust with regard to different methods of propensity score matching and regression adjustment. Furthermore, the results indicate little sensitivity with regard to the empirical support in the data and alternative specifications of the propensity scores.