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The theme of this dissertation is to develop simple and interpretable individualized treatment rules (ITRs) using statistical learning methods to assist personalized decision making in clinical practice. Considerable heterogeneity in treatment response is observed among individuals with mental disorders. Administering an individualized treatment rule according to patient-specific characteristics offers an opportunity to tailor treatment strategies to improve response. Black-box machine learning methods for estimating ITRs may produce treatment rules that have optimal benefit but lack transparency and interpretability. Barriers to implementing personalized treatments in clinical psychiatry include a lack of evidence-based, clinically interpretable, individualized treatment rules, a lack of diagnostic measure to evaluate candidate ITRs, a lack of power to detect treatment modifiers from a single study, and a lack of reproducibility of treatment rules estimated from single studies. This dissertation contains three parts to tackle these barriers: (1) methods to estimate the best linear ITR with guaranteed performance among the class of linear rules; (2) a tree-based method to improve the performance of a linear ITR fitted from the overall sample and identify subgroups with a large benefit; and (3) an integrative learning combining information across trials to provide an integrative ITR with improved efficiency and reproducibility. In the first part of the dissertation, we propose a machine learning method to estimate optimal linear individualized treatment rules for data collected from single stage randomized controlled trials (RCTs). In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely to be far from optimal, effective methods to construct such rules must guarantee performance, in terms of yielding the best clinical outcome (highest reward) among the class of simple rules under consideration. Furthermore, it is important to evaluate the benefit of the derived rules on the whole sample and in pre-specified subgroups (e.g., vulnerable patients). To achieve both goals, we propose a robust machine learn- ing algorithm replacing zero-one loss with an authentic approximation loss (ramp loss) for value maximization, referred to as the asymptotically best linear O-learning (ABLO), which estimates a linear treatment rule that is guaranteed to achieve optimal reward among the class of all linear rules. We then develop a diagnostic measure and inference procedure to evaluate the benefit of the obtained rule and compare it with the rules estimated by other methods. We provide theoretical justification for the proposed method and its inference procedure, and we demonstrate via simulations its superior performance when compared to existing methods. Lastly, we apply the proposed method to the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial on major depressive disorder (MDD) and show that the estimated optimal linear rule provides a large benefit for mildly depressed and severely depressed patients but manifests a lack-of-fit for moderately depressed patients. The second part of the dissertation is motivated by the results of real data analysis in the first part, where the global linear rule estimated by ABLO from the overall sample performs inadequately on the subgroup of moderately depressed patients. Therefore, we aim to derive a simple and interpretable piece-wise linear ITR to maintain certain optimality that leads to improved benefit in subgroups of patients, as well as the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piece-wise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy (REVAMP) trial for treating major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit. The third part deals with the difficulties in the low power of identifying ITRs and replicating ITRs due to small sample sizes of single randomized controlled trials. In this work, a novel integrative learning method is developed to synthesize evidence across trials and provide an integrative ITR that improves efficiency and reproducibility. Our method does not require all studies to collect a common set of variables and thus allows information to be combined from ITRs identified from randomized controlled trials with heterogeneous sets of baseline covariates collected from different domains with different resolution. Based on the research goal, the integrative learning can be used to enhance a high-resolution ITR by borrowing information from coarsened ITRs or improve the coarsened ITR from a high-resolution ITR. With a simple modification, the proposed integrative learning can also be applied to improve the estimation of ITRs for studies with blockwise missing feature variables. We conduct extensive simulation studies to show that our method has improved performance compared to existing methods where only single-trial ITRs are used to learn personalized treatment rules. Lastly, we apply the proposed method to RCTs of major depressive disorder and other comorbid mental disorders. We found that by combining information from two studies, the integrated ITR has a greater benefit and improved efficiency compared to single-trial rules or universal non-personalized treatment rule.

Background: Agricultural workers in New Zealand have high rates of occupational injury compared to most other occupational groups. They are also over-represented in work-related fatal injury statistics. While it is recognised that the personal and social costs of occupational injuries to agricultural workers are considerable, the ability to develop and evaluate evidence-based injury control strategies for this group has been limited by the lack of quality information on occupational exposures and injury events. Aim: The aim of this thesis was to develop and pilot a comprehensive occupational exposure and acute injury outcome data collection system for agricultural workers which will provide an evidence base for a public health approach to acute occupational injury control within the agricultural sector of New Zealand. The thesis objectives were therefore to: * Develop study methods to collect occupational exposure and injury outcome information. * Assess the likely validity of these study methods. * Determine the feasibility of implementing the study methods. * Suggest modifications to the study methods to enhance their validity and feasibility. Methods: Pastoral farms in the Waitaki region of New Zealand were identified using a database of New Zealand farm owners. The owners and workers on these farms were contacted and asked to participate. Participants were required to complete an Initial Questionnaire which included items on farm and personal characteristics, the farm environment, training, safety perceptions and attitudes and safety behaviour. Participants were then monitored for six months. During the monitoring period each participant completed a monthly log of their work activities during the preceding week. Any work-related injuries to workers on participating farms were also recorded and reported monthly. Participants who were injured were followed up for an interview to obtain detailed injury event information. At the end of the monitoring period a second Questionnaire was administered to assess change during the study. Participants were asked about any occupational injury events during the study as part of one of the monthly logs and the second Questionnaire to provide a comparison measure to the monthly reports. A random third of participating farms were visited at the end of the study to assess the validity of participants� reports on the farm environment. Results: Sixty-two farms were recruited into the study, a recruitment rate of 24%. This resulted in 82 study participants. Fifty-seven farms and 72 participants completed the study, resulting in retention rates of 92% and 88% respectively. Return of study items was high, with the lowest observed level of return being 92%. Levels of response error were low in most of the study items, with exceptions being the recording of the hours spent handling animals (37%) and total hours worked (22%). Most postal items (over 68%) were returned before a reminder call was made. Participants� reports about the farm environment closely matched the observations made during the visits, with little evidence of significant misreporting. The validity of reported injury events during the study could not be determined, as the two methods of capturing injury events identified different events. Conclusions: Within the limitations of the study, most of the study methods appeared to be feasible and have acceptable validity. The low recruitment rate and issues with validating the capture of injury events indicated that modifications to the study design were necessary to achieve acceptable validity and feasibility, however. Recommendations were made on how feasibility and validity might be improved.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 83-86). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 83-86). Also available on the Internet.

Background: Agricultural workers in New Zealand have high rates of occupational injury compared to most other occupational groups. They are also over-represented in work-related fatal injury statistics. While it is recognised that the personal and social costs of occupational injuries to agricultural workers are considerable, the ability to develop and evaluate evidence-based injury control strategies for this group has been limited by the lack of quality information on occupational exposures and injury events. Aim: The aim of this thesis was to develop and pilot a comprehensive occupational exposure and acute injury outcome data collection system for agricultural workers which will provide an evidence base for a public health approach to acute occupational injury control within the agricultural sector of New Zealand. The thesis objectives were therefore to: * Develop study methods to collect occupational exposure and injury outcome information. * Assess the likely validity of these study methods. * Determine the feasibility of implementing the study methods. * Suggest modifications to the study methods to enhance their validity and feasibility. Methods: Pastoral farms in the Waitaki region of New Zealand were identified using a database of New Zealand farm owners. The owners and workers on these farms were contacted and asked to participate. Participants were required to complete an Initial Questionnaire which included items on farm and personal characteristics, the farm environment, training, safety perceptions and attitudes and safety behaviour. Participants were then monitored for six months. During the monitoring period each participant completed a monthly log of their work activities during the preceding week. Any work-related injuries to workers on participating farms were also recorded and reported monthly. Participants who were injured were followed up for an interview to obtain detailed injury event information. At the end of the monitoring period a second Questionnaire was administered to assess change during the study. Participants were asked about any occupational injury events during the study as part of one of the monthly logs and the second Questionnaire to provide a comparison measure to the monthly reports. A random third of participating farms were visited at the end of the study to assess the validity of participants� reports on the farm environment. Results: Sixty-two farms were recruited into the study, a recruitment rate of 24%. This resulted in 82 study participants. Fifty-seven farms and 72 participants completed the study, resulting in retention rates of 92% and 88% respectively. Return of study items was high, with the lowest observed level of return being 92%. Levels of response error were low in most of the study items, with exceptions being the recording of the hours spent handling animals (37%) and total hours worked (22%). Most postal items (over 68%) were returned before a reminder call was made. Participants� reports about the farm environment closely matched the observations made during the visits, with little evidence of significant misreporting. The validity of reported injury events during the study could not be determined, as the two methods of capturing injury events identified different events. Conclusions: Within the limitations of the study, most of the study methods appeared to be feasible and have acceptable validity. The low recruitment rate and issues with validating the capture of injury events indicated that modifications to the study design were necessary to achieve acceptable validity and feasibility, however. Recommendations were made on how feasibility and validity might be improved.

Thesis (Ph.D.)--Baylor University, 2008. / Includes bibliographical references (p. 64-65)

Thesis (Ph. D.)--University of Pennsylvania, 1942. / Reproduced from type-written copy. "References" at end of chapters II-Iv.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Economics." Includes bibliographical references (p. 96-99).