Implementation of maximin efficient designs in dose‐finding studies

This paper considers the maximin approach for designing clinical studies. A maximin efficient design maximizes the smallest efficiency when compared with a standard design, as the parameters vary in a specified subset of the parameter space. To specify this subset of parameters in a real situation, a four‐step procedure using elicitation based on expert opinions is proposed. Further, we describe why and how we extend the initially chosen subset of parameters to a much larger set in our procedure. By this procedure, the maximin approach becomes feasible for dose‐finding studies. Maximin efficient designs have shown to be numerically difficult to construct. However, a new algorithm, the H‐algorithm, considerably simplifies the construction of these designs. We exemplify the maximin efficient approach by considering a sigmoid Emax model describing a dose–response relationship and compare inferential precision with that obtained when using a uniform design. The design obtained is shown to be at least 15% more efficient than the uniform design. © 2014 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.     

Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

A popular choice when analyzing ordinal data is to consider the cumulative proportional odds model to relate the marginal probabilities of the ordinal outcome to a set of covariates. However, application of this model relies on the condition of identical cumulative odds ratios across the cut-offs of the ordinal outcome; the well-known proportional odds assumption. This paper focuses on the assessment of this assumption while accounting for repeated and missing data. In this respect, we develop a statistical method built on multiple imputation (MI) based on generalized estimating equations that allows to test the proportionality assumption under the missing at random setting. The performance of the proposed method is evaluated for two MI algorithms for incomplete longitudinal ordinal data. The impact of both MI methods is compared with respect to the type I error rate and the power for situations covering various numbers of categories of the ordinal outcome, sample sizes, rates of missingness, well-balanced and skewed data. The comparison of both MI methods with the complete-case analysis is also provided. We illustrate the use of the proposed methods on a quality of life data from a cancer clinical trial.     

Adaptive truncated weighting for improving marginal structural model estimation of treatment effects informally censored by subsequent therapy

Randomized clinical trials are designed to estimate the direct effect of a treatment by randomly assigning patients to receive either treatment or control. However, in some trials, patients who discontinued their initial randomized treatment are allowed to switch to another treatment. Therefore, the direct treatment effect of interest may be confounded by subsequent treatment. Moreover, the decision on whether to initiate a second‐line treatment is typically made based on time‐dependent factors that may be affected by prior treatment history. Due to these time‐dependent confounders, traditional time‐dependent Cox models may produce biased estimators of the direct treatment effect. Marginal structural models (MSMs) have been applied to estimate causal treatment effects even in the presence of time‐dependent confounders. However, the occurrence of extremely large weights can inflate the variance of the MSM estimators. In this article, we proposed a new method for estimating weights in MSMs by adaptively truncating the longitudinal inverse probabilities. This method provides balance in the bias variance trade‐off when large weights are inevitable, without the ad hoc removal of selected observations. We conducted simulation studies to explore the performance of different methods by comparing bias, standard deviation, confidence interval coverage rates, and mean square error under various scenarios. We also applied these methods to a randomized, open‐label, phase III study of patients with nonsquamous non‐small cell lung cancer. Copyright © 2015 John Wiley & Sons, Ltd.     

临床师资队伍培养路径探讨

针对目前高校医学教育中临床师资队伍数量不足、教学能力欠佳、教学意识薄弱、师资培训体系不完善、师资队伍管理制度及机构不完善等问题,提出通过外部引进及内部培养扩大临床师资队伍规模,建立完善的临床师资培训体系提高临床教师教学能力,建立完善的激励机制调动临床教师参与教育教学的积极性,定期召开临床教学会议以交流临床教学工作经验,加强制度建设为临床教师培养提供制度保障,健全临床师资管理机构为临床教师培养提供组织保障等措施来促进临床师资队伍的培养与发展。     

CBL教学方法在口腔临床见习教学中的应用

口腔专业临床见习是口腔医学生进入临床的第一课,但在多数情况下学生仅是走马观花,训练成效有限.将以病例为基础的学习(CBL)模式引入学生的见习课中,使学生在见习中带着问题学习,见习后回归讨论课时通过讨论和学习获得答案,真正发挥见习课的作用,做好理论与实践教学的衔接,取得较好的教学效果.     

实习医生临床带教中交流技能培训的应用

临床实习是衔接医学基础教育与临床实践的桥梁。临床带教的质量直接关系到实习医生整体培养素质。医患交流能力被认为是医生的一项核心临床技能,其重要性越来越受到医学教育界的重视。实习医生临床带教中加强交流能力的培训可以有效提高实习医生的临床工作、沟通能力,最终培养出实践能力较强、基本功扎实、沟通顺畅的综合性高素质医学人才。     

Assessment of clinical relevance by considering point estimates and associated confidence intervals

For the proof of efficacy of a new drug in a placebo‐controlled clinical trial it is not sufficient merely to demonstrate a statistically significant treatment difference. In recent years, regulatory authorities have strongly recommended assessing additionally whether the observed effect size is also of clinical relevance. This opinion is reflected in various guidelines which are of the utmost importance for the successful approval of a new drug. Clinical relevance can be investigated by responder analyses or by considering the point estimates on the original scale together with the associated confidence intervals. In this paper, we focus on the latter approach and discuss the suitability of different criteria which are commonly applied in medical research. Copyright © 2005 John Wiley & Sons, Ltd.     

Decision‐making in a phase II clinical trial: a new approach combining Bayesian and frequentist concepts

The aim of a phase II clinical trial is to decide whether or not to develop an experimental therapy further through phase III clinical evaluation. In this paper, we present a Bayesian approach to the phase II trial, although we assume that subsequent phase III clinical trials will have standard frequentist analyses. The decision whether to conduct the phase III trial is based on the posterior predictive probability of a significant result being obtained. This fusion of Bayesian and frequentist techniques accepts the current paradigm for expressing objective evidence of therapeutic value, while optimizing the form of the phase II investigation that leads to it. By using prior information, we can assess whether a phase II study is needed at all, and how much or what sort of evidence is required. The proposed approach is illustrated by the design of a phase II clinical trial of a multi‐drug resistance modulator used in combination with standard chemotherapy in the treatment of metastatic breast cancer. Copyright © 2005 John Wiley & Sons, Ltd     

Assurance in clinical trial design

Conventional clinical trial design involves considerations of power, and sample size is typically chosen to achieve a desired power conditional on a specified treatment effect. In practice, there is considerable uncertainty about what the true underlying treatment effect may be, and so power does not give a good indication of the probability that the trial will demonstrate a positive outcome. Assurance is the unconditional probability that the trial will yield a ‘positive outcome’. A positive outcome usually means a statistically significant result, according to some standard frequentist significance test. The assurance is then the prior expectation of the power, averaged over the prior distribution for the unknown true treatment effect. We argue that assurance is an important measure of the practical utility of a proposed trial, and indeed that it will often be appropriate to choose the size of the sample (and perhaps other aspects of the design) to achieve a desired assurance, rather than to achieve a desired power conditional on an assumed treatment effect. We extend the theory of assurance to two‐sided testing and equivalence trials. We also show that assurance is straightforward to compute in some simple problems of normal, binary and gamma distributed data, and that the method is not restricted to simple conjugate prior distributions for parameters. Several illustrations are given. Copyright © 2005 John Wiley & Sons, Ltd.     

A permutation test approach to phase II historical control trials

In this note we outline 15 years of Gynecologic Oncology Group (GOG) experience conducting a series of phase II second-line intraperitoneal trials in the treatment of ovarian cancer. Using this information, the goal is to define a new permutation approach to historical control phase II trials in ovarian cancer. We utilize seven previous phase II GOG trials in our database to illustrate our methodology.