Browsing by Subject "Survival analysis"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Applications of ROC Type Curves in Clinical Trials
    (2023-01) Castro-Pearson, Sandra
    Randomized controlled trials are the most reliable form of evidence to drive medical practice and health policy. Yet, challenges remain in terms of understanding possible errors during protocol implementation and interpreting results of complex outcomes such as censored time-to-event outcomes with and without competing risks. In this dissertation, we study receiver operating characteristic (ROC) curves and use ROC concepts to create graphical methods that address these challenges.
  • Thumbnail Image
    Item
    The evolution of collective action business models: applications in fraternal benefit societies and Township Mutual Fire Insurance companies
    (2013-12) White, James Matthew
    Fraternal benefit societies and township mutual fire insurance companies evolved from community-based mutual aid efforts in the 19th century. Both are similar in nature to commercial mutual insurance companies, yet they both incorporate a number of elements of collective action theory. As such, they are a combination of an insurance provider and a community organization.This dissertation examines the history, evolution and survival of fraternal benefit societies ("fraternals") in the United States and township mutual fire insurance companies ("township mutuals") in Minnesota from their inception to 2013. In addition to a managerial economics analysis of the industries in which they operate, this dissertation provides a quantitative analysis of the relative determinants of survival for firms in these industries. This analysis, which primarily takes the form of a survival analysis, includes business drivers, elements of collective action theory and environmental and social factors in addressing the question of what types of firms are most likely to survive and what must the leaders of these organizations focus on to ensure their continued survival. The primary conclusion of this study is that although firms in these industries have a number of factors that contribute to their continued survival, ultimately they must be run as businesses. In other words, although it is appealing to think of the sentimental aspects of collective action organizations, ultimately, and over the long term, economic considerations dominate the discussion of which firms in the industry survive the longest. In particular, in the case of fraternals, economies of scale, growth and customer retention are highly and significantly correlated with survival. In the case of township mutuals, profitability and market size are the covariates most correlated with longer survival.
  • Thumbnail Image
    Item
    Quantile-optimal Treatment Regimes with Censored Data
    (2018-06) ZHOU, YU
    The problem of estimating an optimal treatment regime has received considerable attention recently. However, most of the earlier work in this area has focused on estimating a mean-optimal treatment regime based on completely observed data. We investigate a new quantile criterion for estimating an optimal treatment regime with right-censored survival outcomes. When the outcome distribution is skewed or when the censoring is heavy, the quantile criterion is easy to interpret and provides an attractive measure of treatment effect. In contrast, the mean criterion often cannot be reliably estimated in such settings. We propose a nonparametric approach to robustly estimate the quantile-optimal treatment regime from a class of candidate treatment regimes without imposing an outcome regression model. We derive a nonstandard converge rate and a non-normal limiting distribution for the estimated parameters indexing the optimal treatment regime using advanced empirical processes theory. Such a theory has not been established in any earlier work for survival data. We also extend the method to a two-stage dynamic setting. We illustrate the practical utility of the proposed new method for single-stage estimation through Monte Carlo studies and an application to a clinical trial data set, and we also examine the performance of the proposed method for two-stage estimation through Monte Carlo studies.