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There has been an increase in the number of chronic pain clinical trials in which the treatments being evaluated did not differ significantly from placebo in the primary efficacy analyses despite previous research suggesting that efficacy could be expected. These findings could reflect a true lack of efficacy or methodological and other aspects of these trials that compromise the demonstration of efficacy. There is substantial variability among chronic pain clinical trials with respect to important research design considerations, and identifying and addressing any methodological weaknesses would enhance the likelihood of demonstrating the analgesic effects of new interventions. An IMMPACT consensus meeting was therefore convened to identify the critical research design considerations for confirmatory chronic pain trials and to make recommendations for their conduct. We present recommendations for the major components of confirmatory chronic pain clinical trials, including participant selection, trial phases and duration, treatment groups and dosing regimens, and types of trials. Increased attention to and research on the methodological aspects of confirmatory chronic pain clinical trials has the potential to enhance their assay sensitivity and ultimately provide more meaningful evaluations of treatments for chronic pain.

Since the early 1980s, the diffusion of Just-In-Time (JIT) manufacturing from Japanese manufacturers to U.S. manufacturers has progressed at an accelerated rate. At this stage of the diffusion process, JIT implementations are more common and more advanced in large U.S. manufacturers than in small; consequently, U.S. businessmen's understanding of issues associated with JIT implementations in large manufacturers is more developed than that of small manufacturers. When small manufacturers represent about 96 percent of all U.S. manufacturers, investigation of JIT implementations in small, as well as large, manufacturers is warranted. This survey study investigates JIT implementation differences between small and large U.S. manufacturers. Ten management practices that constitute the JIT concept are used to examine implementation of JIT manufacturing systems. Odds ratio were constructed to determine if an association exists between implemented versus not implemented and manufacturer size for each JIT practice. Ten changes in performance attributed to JIT implementation are also assessed and examined in the study. Logistic regression models are used to examine the relationships between implementation status of each of the JIT practices and of each of the changes in performance in small and large manufacturers. The results of the study show that the frequencies of the 10 JIT management practices implemented differ between the two groups of manufacturer size, and an association exists between the JIT practices implemented and manufacturer size. Moreover, the changes in performance attributed to JIT implementation vary, depending on implementation status of specific JIT management practices and manufacturer size.empirical study, just-in-time manufacturing, performance, manufacturer size, logistic regression

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The increasing complexity of randomized clinical trials and the practice of obtaining a wide variety of measurements from study participants have made the consideration of multiple endpoints a critically important issue in the design, analysis, and interpretation of clinical trials. Failure to consider important outcomes can limit the validity and utility of clinical trials; specifying multiple endpoints for the evaluation of treatment efficacy, however, can increase the rate of false positive conclusions about the efficacy of a treatment. We describe the use of multiple endpoints in the design, analysis, and interpretation of pain clinical trials, and review available strategies and methods for addressing multiplicity. To decrease the probability of a Type I error (i.e., the likelihood of obtaining statistically significant results by chance) in pain clinical trials, the use of gatekeeping procedures and other methods that correct for multiple analyses is recommended when a single primary endpoint does not adequately reflect the overall benefits of treatment. We emphasize the importance of specifying in advance the outcomes and clinical decision rule that will serve as the basis for determining that a treatment is efficacious and the methods that will be used to control the overall Type I error rate.

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