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This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion. [ABSTRACT FROM AUTHOR]

Interprofessional education (IPE) to improve collaborative competencies is essential for delivering high-quality care. Yet creating clinically relevant IPE and linking it to improvements in behaviours remains challenging, and few objective measurement instruments are available. We developed a process for creating IPE and objective observational tools through collaborative care best practice models (CCBPMs). These models describe the professional and interprofessional behaviours needed for specific patient populations, illnesses, and care settings. Four IPE workshops based on CCBPMs were implemented for all medical and nursing students during their clinical/clerkships years. Students in Cohort 1 completed two IPE workshops: rapid response and end-of-life. For Cohort 2, students completed four IPE workshops, adding chronic paediatric illness and transitions for the cognitively impaired. Valid and reliable collaborative behaviors observational assessment tools (CBOATs) derived from CCBPMs for the rapid response and end-of-life workshops were developed. CBOATs were used in the longitudinal assessment of student learning for both cohorts during two Interprofessional Teamwork Objective Structured Clinical Examinations (ITOSCEs) conducted before and after the students completed the IPE workshops. Over a 2-year period, 457 students completed the IPE simulations and ITOSCEs. Both medical and nursing students demonstrated significant improvement in CBOAT scores. Comparisons between the cohorts showed that participation in four versus two IPE experiences did not significantly improve most CBOAT scores. We conclude that undergraduate IPE simulation experiences based on CCBPMs result in measurable improvements in learner behaviours necessary for effective collaborative and team-based practice in specific care areas. [ABSTRACT FROM PUBLISHER]

Proposes a conditional likelihood methods for analyzing the repeated measurement of categorial responses. Conditional likelihood approach for complete data; Extension to include incompletely classified subjects; Estimation with the EM algorithm; Examples; Conclusions.

This article presents a method for analyzing repeated measures with categorical responses based on maximizing a conditional likelihood. Many existing methods for analyzing repeated categorical responses, such as those proposed by Duncan (1980, 1985), Bishop, Fienberg, and Holland (1975), and Cochran (1950) are special cases of the method based on the conditional likelihood approach. This article explores the relationships among the various methods and extends several of the existing methods. The conditional likelihood approach is used to analyze data from a four-wave panel study with trichotomous responses. The data, taken from Fienberg, Bromet, Follmann, Lambert, and May (1985), are from an investigation of the psychological effects of the accident at the Three Mile Island nuclear power plant in the spring of 1979. In this example, methods are presented for checking the assumption of local independence in the model used for the conditional likelihood analysis. With an extension of the results of Tjur (1982), it is demonstrated that the conditional estimates can be obtained with standard statistical software. [ABSTRACT FROM AUTHOR]