Blinded continuous monitoring in clinical trials with recurrent event endpoints

In studies with recurrent event endpoints, misspecified assumptions of event rates or dispersion can lead to underpowered trials or overexposure of patients. Specification of overdispersion is often a particular problem as it is usually not reported in clinical trial publications. Changing event rates over the years have been described for some diseases, adding to the uncertainty in planning. To mitigate the risks of inadequate sample sizes, internal pilot study designs have been proposed with a preference for blinded sample size reestimation procedures, as they generally do not affect the type I error rate and maintain trial integrity. Blinded sample size reestimation procedures are available for trials with recurrent events as endpoints. However, the variance in the reestimated sample size can be considerable in particular with early sample size reviews. Motivated by a randomized controlled trial in paediatric multiple sclerosis, a rare neurological condition in children, we apply the concept of blinded continuous monitoring of information, which is known to reduce the variance in the resulting sample size. Assuming negative binomial distributions for the counts of recurrent relapses, we derive information criteria and propose blinded continuous monitoring procedures. The operating characteristics of these are assessed in Monte Carlo trial simulations demonstrating favourable properties with regard to type I error rate, power, and stopping time, ie, sample size.     

An Interview with Joyce Edward: Exemplary Clinician, Advocate, and Scholar

Over the years, noteworthy social workers have been interviewed for the Clinical Social Work Journal. This article features an interview with Joyce Edward who is recognized for her many extraordinary contributions to clinical social work. Joyce has co-written or co-edited three exceptional books for the social work clinician, she has been an esteemed teacher and a vocal activist for quality mental health care. In this interview Joyce reflects on family influences, her work as a social caseworker, the psychoanalytic luminaries with whom she trained, her concerns about the clinical education of today's social work students, and finally, her perspective on managed mental health care.     

Improving the contributions of technology assessment to the health care system of the U.S.A.

Summary Based on 14 case studies of highly effective therapies and the reasons they succeeded less frequently than they could, we propose a variety of steps to improve the health care system of the U.S.A. Whatever proposal emerges from current national debates until innovations are shown to be safe and effective, they should not be supported; when slightly better technologies are much more expensive than other good ones we need to consider appropriate choices carefully; simplified billing and bookkeping would reduce our costs; when a technology is rapidly introduced cautionnary measures may be needed; tracking immunization and repairing their omissions requires a new system; educational programs such as seen effective in hypertension should be applied in other areas such as vaccination; in organ transplantation the nation should consider “presumed consent”; our payment system sometimes creates perverse incentives and therefore needs review; and the preferences of the public in allocation of health resources need to be discovered once the public is informed about the issues. Research supported by Andrew W. Mellon Foundation.     

提高高等医学院校临床教学质量的若干思考

提高医学院校的临床教学质量应做好四个方面的工作:提高认识、增强教学意识,调动临床教学工作的积极性;加强临床教师队伍的建设,注重提高临床教学效果;加强临床教学管理规范化制度化建设;深化临床教学改革。     

江苏省综合性医院临床医生对新医改认知研究

为了解江苏省综合性医院临床医生对新医改现状的看法,评价新医改在江苏省内的实施进展和效果,该研究选取了新医改部分重点问题,采用自设计问卷,调查江苏省内7家三级综合性医院和7家二级综合性医院临床医生,运用描述性分析和卡方检验辅助分析研究结果。发现66.5%的医生更希望政府医改经费投入能侧重于医务人员劳动收入;对家庭医生签约服务的认知度及成效评价低于分级诊疗及医联体建设;认为医患关系和看病难、看病贵问题略有改善的医生分别占20.3%、24.9%、27.9%。需正确认识医务人员认知差异及原因;重视医务人员权益、优化薪酬管理制度;加强家庭医生签约服务的宣传和推广;丰富医联体间活动形式;建立及时有效的临床医生反馈机制。     

Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing

Abstract

A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald’s development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.     

Schizotypal Personality Disorder: A Clinical Social Work Perspective

Schizotypal personality disorder (SPD) is considered to be a “schizophrenia spectrum disorder” as evidenced in part by its cross-listing in that chapter of the DSM-5. SPD is considered to be a condition with limited potential for positive change because one of its major features is the presence of a biologically based cognitive deficit. This assumption, however, is an example of the medical model’s creating a bias against psychosocial features that are always involved in character development. The social work profession’s bio-psycho-social perspectives focus more comprehensively on all features of the condition and promote a more optimistic view of clients’ change potentials. The purposes of this paper are to examine SPD from a social work perspective and to demonstrate, with a case example, how effective intervention can be organized and delivered.     

Bayes shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

The primary objective of a multi-regional clinical trial is to investigate the overall efficacy of the drug across regions and evaluate the possibility of applying the overall trial result to some specific region. A challenge arises when there is not enough regional sample size. We focus on the problem of evaluating applicability of a drug to a specific region of interest under the criterion of preserving a certain proportion of the overall treatment effect in the region. We propose a variant of James-Stein shrinkage estimator in the empirical Bayes context for the region-specific treatment effect. The estimator has the features of accommodating the between-region variation and finiteness correction of bias. We also propose a truncated version of the proposed shrinkage estimator to further protect risk in the presence of extreme value of regional treatment effect. Based on the proposed estimator, we provide the consistency assessment criterion and sample size calculation for the region of interest. Simulations are conducted to demonstrate the performance of the proposed estimators in comparison with some existing methods. A hypothetical example is presented to illustrate the application of the proposed method.     

The precision of regression-type estimator for incremental cost–effectiveness ratio

The estimation of incremental cost–effectiveness ratio (ICER) has received increasing attention recently. It is expressed in terms of the ratio of the change in costs of a therapeutic intervention to the change in the effects of the intervention. Despite the intuitive interpretation of ICER as an additional cost per additional benefit unit, it is a challenge to estimate the distribution of a ratio of two stochastically dependent distributions. A vast literature regarding the statistical methods of ICER has developed in the past two decades, but none of these methods provide an unbiased estimator. Here, to obtain the unbiased estimator of the cost–effectiveness ratio (CER), the zero intercept of the bivariate normal regression is assumed. In equal sample sizes, the Iman–Conover algorithm is applied to construct the desired variance–covariance matrix of two random bivariate samples, and the estimation then follows the same approach as CER to obtain the unbiased estimator of ICER. The bootstrapping method with the Iman–Conover algorithm is employed for unequal sample sizes. Simulation experiments are conducted to evaluate the proposed method. The regression-type estimator performs overwhelmingly better than the sample mean estimator in terms of mean squared error in all cases.     

Generalizing the standardized hazard ratio to multivariate proportional hazards regression,with an application to clinical~genomic studies

The standardized hazard ratio for univariate proportional hazards regression is generalized as a scalar to multivariate proportional hazards regression. Estimators of the standardized log hazard ratio are developed, with corrections for bias and for regression to the mean in high-dimensional analyses. Tests of point and interval null hypotheses and confidence intervals are constructed. Cohort sampling study designs, commonly used in prospective–retrospective clinical genomic studies, are accommodated.