New block bootstrap methods: Sufficient and/or ordered

In this study, we propose sufficient time series bootstrap methods that achieve better results than conventional non-overlapping block bootstrap, but with less computing time and lower standard errors of estimation. Also, we propose using a new technique using ordered bootstrapped blocks, to better preserve the dependency structure of the original data. The performance of the proposed methods are compared in a simulation study for MA(2) and AR(2) processes and in an example. The results show that our methods are good competitors that often exhibit improved performance over the conventional block methods.     

An Analytical Framework for Designing Community‐Based Care for Chronic Diseases

In this study, we propose a methodological framework to provide a road map to clinicians and system planners in developing chronic disease management strategies, and designing community‐based care. We extend the analytical epidemiologic model by utilizing a patient flow approach, in order to model the multiple care‐provider visit patterns of patients with a specific chronic illness. The patterns of care received by a group of patients are represented in compact form by means of a Markov model that is based on a disease‐specific state space. Our framework also reflects the case‐mix biases as well as the care‐provider level clustering of the patients. By using this approach, we identify the patterns of care, determine the care provider and patient characteristics associated with optimal management of care, and estimate the potential influence of various interventions. The framework is applied to the data of 4000+ stroke patients discharged from the acute care hospitals of Quebec to their homes. Our findings provide a basis for designing community‐based care initiatives for stroke survivors in the province.     

Designing Personalized Treatment: An Application to Anticoagulation Therapy

In this study, we develop an analytical framework for personalizing the anticoagulation therapy of patients who are taking warfarin. Consistent with medical practice, our treatment design consists of two stages: (i) the initiation stage, modeled using a partially‐observable Markov decision process, during which the physician learns through systematic belief updates about the unobservable patient sensitivity to warfarin, and (ii) the maintenance stage, modeled using a Markov decision process, during which the physician relies on his formed belief about patient sensitivity to determine the stable, patient‐specific, warfarin dose to prescribe. We develop an expression for belief updates in the POMDP, establish the optimality of the myopic policy for the MDP, and derive conditions for the existence and uniqueness of a myopically optimal dose. We validate our models using a real‐life patient data set gathered at the Hematology Clinic of the Jewish General Hospital in Montreal. The proposed analytical framework and case study enable us to develop useful clinical insights, for example, concerning the length of the initiation period and the importance of correctly assessing patient sensitivity.