A class of Covariate-Adjusted Response-Adaptive Allocation Designs for Multitreatment Binary Response Trials

A class of covariate-adjusted response-adaptive randomization procedures is developed for binary treatment outcomes in a phase III clinical trial set up involving multiple treatments. The target allocation is developed by combining the ethical aspects with statistical precision under the existence of treatment covariate interaction. Relevant measures of the performance for the proposed allocation designs are studied and compared.     

Optimal Adaptive Designs for Binary Response Trials With Three Treatments

A fundamental question in response–adaptive randomization is: What allocation proportion should we target to achieve required power while resulting in fewer treatment failures? For comparing two treatments, such optimal allocations are well studied in the literature. However, few authors address the question for multiple treatments and the generalization of optimal allocations is necessary in practice. We are interested in finding the optimal allocation proportion, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time. We propose such an optimal allocation for three treatments by giving an analytical solution for the optimization problem. Numerical studies show that a response–adaptive randomization procedure that targets proposed optimal allocation is superior to complete randomization. We also discuss some future research topics and additional issues on optimal adaptive designs.     

Response-Adaptive Allocation for Circular Data

Response-adaptive designs are used in phase III clinical trials to allocate a larger proportion of patients to the better treatment. Circular data is a natural outcome in many clinical trial setup, e.g., some measurements in opthalmologic studies, degrees of rotation of hand or waist, etc. There is no available work on response-adaptive designs for circular data. With reference to a dataset on cataract surgery we provide some response-adaptive designs where the responses are of circular nature and propose some test statistics for treatment comparison under adaptive data allocation procedure. Detailed simulation study and the analysis of the dataset, including redesigning the cataract surgery data, are carried out.     

Adaptive Allocation and Failure Saving in Randomised Clinical Trials

In the present work, we develop a randomized two-treatment single period response adaptive design by combining two contrasting aspects (i.e., ethics and optimality), where optimality is defined in a meaningful way. We compare this rule with some of the existing rules by computing various performance measures of the rules.     

An Outcome-Adaptive Allocation Method for Clinical Trials With Dual Binary Objectives

This article presents an outcome-adaptive allocation method for dual-outcome clinical trials, where the allocation probabilities for each treatment depend on the relative performance of those treatments with respect to both outcomes. The method for computing such allocation weights is presented for cases where treatment performance is based on intertreatment comparisons, hypothetical standards, and a combination of the two. Simulation studies show that this method allocates patients away from inferior, ineffective, or harmful treatments and toward superior or otherwise beneficial treatments, at the cost of a moderate power loss when compared with balanced designs. This method also performs well in the presence of dependent outcomes. An example from a study of stem cell transplant patients with two objectives is presented to show how this method works in practice, where patients are allocated most frequently into the most efficacious treatment and away from those that do not work as well or are relatively more futile.     

Dose Finding Designs for Continuous Responses and Binary Utility

Often in clinical trials the observed responses are continuous but a regulatory agency will approve the drug only if the probability is sufficiently large that the efficacy measure exceeds a predefined threshold and the toxicity does not exceed another given threshold. Thus the measure of interest (utility) is based on dichotomized responses. We consider normally distributed correlated responses and build a utility function using the probit transform. Locally optimal designs are used as benchmarks for more practical designs such as composite and adaptive designs. We focus on D -criterion (i.e., all parameters of the dose-response model are of interest) and consider only two-stage designs. It is shown that the practice of reporting dichotomized responses leads to a substantial loss in the precision of estimated parameters (or in the power loss).     

An Evaluation of a Bayesian Method of Dose Escalation Based on Bivariate Binary Responses

Abstract

Recently, various approaches have been suggested for dose escalation studies based on observations of both undesirable events and evidence of therapeutic benefit. This article concerns a Bayesian approach to dose escalation that requires the user to make numerous design decisions relating to the number of doses to make available, the choice of the prior distribution, the imposition of safety constraints and stopping rules, and the criteria by which the design is to be optimized. Results are presented of a substantial simulation study conducted to investigate the influence of some of these factors on the safety and the accuracy of the procedure with a view toward providing general guidance for investigators conducting such studies. The Bayesian procedures evaluated use logistic regression to model the two responses, which are both assumed to be binary. The simulation study is based on features of a recently completed study of a compound with potential benefit to patients suffering from inflammatory diseases of the lung.     

Optimal Designs for a Probit Model With a Quadratic Term

This article studies optimal designs to analyze dose-response functions with a downturn. Two interesting challenges are estimating the entire dose-response curve and estimating the ED₅₀. Here, I obtain and compare optimal designs for these objectives, separately and together in a two-stage design. I adopt a probit model with a quadratic term to describe the dose-response. Under the probit model, Yang's method is used to obtain the minimal number of support points that maximize any concave function of the Fisher information matrix. Optimal designs are obtained based on the minimal number of support points, and their efficiencies are compared.     

Comparisons of Methods for Analysis of Repeated Binary Responses with Missing Data

It is important yet challenging to choose an appropriate analysis method for the analysis of repeated binary responses with missing data. The conventional method using the last observation carried forward (LOCF) approach can be biased in both parameter estimates and hypothesis tests. The generalized estimating equations (GEE) method is valid only when missing data are missing completely at random, which may not be satisfied in many clinical trials. Several random-effects models based on likelihood or pseudo-likelihood methods and multiple-imputation-based methods have been proposed in the literature. In this paper, we evaluate the random-effects models with full- or pseudo-likelihood methods, GEE, and several multiple-imputation approaches. Simulations are used to compare the results and performance among these methods under different simulation settings.     

Optimal Design for Dose Response Using Beta Distributed Responses

Abstract

Whenever a response is naturally confined to a finite interval (such as a visual analog scale for pain severity), the beta distribution provides a simple and flexible probability distribution to model such a response. The parameters of the distribution can then be related to covariates, such as dose, in a clinical trial through the generation of a beta regression model. In this article, we explore locally optimal designs for this class of regression models, focusing mainly on minimization of the generalized variance of maximum likelihood estimators (D-optimality). Optimal designs and sensitivity to misspecification of model parameters are examined using a candidate points searching algorithm. Although formally the model assumes that the response is continuous, it provides a parsimonious approximation for ordinal data when there is a relatively large number of categories. The resulting estimators and optimal designs are simpler and may offer more ease in interpretation than those derived from models for ordered categorical outcomes. The proposed methods are applied to data from a clinical trial.     

A Simulation Study to Compare New Adaptive Dose–Ranging Designs

The main goals in an adaptive dose-ranging study are to detect dose response, to determine if any doses(s) meets clinical relevance, to estimate the dose-response, and then to decide on the dose(s) (if any) to take into the confirmatory Phase III. Adaptive dose-ranging study designs may result in power gains to detect dose response and higher precision in estimating the target dose and the dose response curve. In this article, we complement the library of available methods with five new adaptive dose-ranging designs. Due to their inherent complexity, the operating characteristics can be assessed only through intensive simulations. We present here results of a comprehensive simulation study that compares and contrasts these designs for a variety of different scenarios.     

Adjusting for Baseline on the Analysis of Repeated Binary Responses With Missing Data

Little research has been done to evaluate the effect of adjusting for baseline in the analysis of repeated incomplete binary data through simulation study. In this article, covariate adjusted and unadjusted implementations of the following methods were compared in analyzing incomplete repeated binary data when the outcome at the study endpoint is of interest: logistic regression with the last observation carried forward (LOCF), generalized estimating equations (GEE), weighted GEE (WGEE), generalized linear mixed model (GLMM), and multiple imputation (MI) with analyses via GEE. Incomplete data mimicking several clinical trial scenarios were generated using missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) mechanisms. Across the various analytic methods and scenarios covariate adjusted analyses generally yielded larger, less biased treatment effect estimates and larger standard errors compared with their unadjusted counterpart. The net result of these factors was increased power from the covariate adjusted analyses without increasing Type I error rates. Although all methods were biased in at least some of the MNAR scenarios, the Type I error rates from LOCF exceeded 20% whereas the highest rate from any other method in any scenario was less than 10%. LOCF also yielded biased results in MCAR and MAR data whereas the other methods were not biased or had smaller biases than LOCF. These results support longitudinal modeling of repeated binary data over LOCF logistic regression of the study endpoint only. These results also support covariate adjustment for baseline severity in these longitudinal models.     

Optimal Treatment for Maori with Alcohol and Drug-Use-Related Problems: An Investigation of Cultural Factors in Treatment

There is an increasing emphasis on taking account of the diversity of social, psychological, and cultural factors in the assessment and treatment of alcohol and drug-use-related problems. In New Zealand the increasing use of customary Maori values, beliefs, and practices in the treatment of Maori with alcohol and drug-use-related problems has also been accompanied by the adaptation and integration of Western approaches to fit contemporary Maori sociocultural needs. This paper reports on an investigation of cultural factors and cultural identity in the alcohol and drug-user treatment of a clinical sample of Maori. The essential finding was a very high endorsement of the importance of cultural factors in treatment-irrespective of age, gender, mood, level of dependence, previous admissions, cultural connectedness, or whether they were treated in a Maori dedicated program or not. A significant number believed that a sense of belonging to an Iwi (tribe), identifying as a Maori and having pride in being Maori were also important in the recovery/healing process. The findings of this study support the need to investigate the relationship between specific “cultural factors” and other clinical components of effective treatment for Maori.     

Impact of Time Allocation Practices on Academic Outcomes for Students from a 2-Campus Pharmacy School

Objective: To assess how students from 2 campuses spent their time during P1-P3 (first through third) years, and whether that time allocation impacted their APPE grades and NAPLEX performance.Methods: Data from 2 graduating classes were gathered, including baseline student demographics, academic performance, licensing examination scores and pass rates, and an annual internal student survey. For the survey, students were asked how much time they spent each week on class attendance, watching recorded lectures, studying and course-related activities, school-sponsored extracurricular activities, and work. Data was analyzed by campus for the 3 years (P1-P3) and then evaluated separately as individual academic years.Results: There were statistical differences between campuses in attending class, watching recorded lectures, and participating in school activities. However, there was no statistical difference between the 2 campuses in APPE grades, NAPLEX scores, or pass rates.Conclusion: How students from these 2 campuses spent their time during pharmacy school was not predictive of academic success.     

住院患者口服药物调配方式的调查及应对措施

目的 促进基层医院口服药物调配方式更加科学、合理,最大限度地确保患者药疗质量.方法 对我市二级以上医院住院患者口服药物调配方式进行调查,分析基层医院口服药物调配方式的不足.结果 基层医院目前无法做到单剂量调剂配发.结论 结合实际工作,提出服药卡与提示相结合,药师与护士共同监督患者按照时辰药理学服用药物,切实保证药疗质量与安全.     

昆明市药品宅配业务市场浅析

目的:了解昆明市药品宅配业务的市场需求。方法:对拟建的药品宅配业务进行市场问卷调查。结果与结论:初步确定了药品宅配业务的目标市场,药品宅配业务在昆明的发展前景可观。     

Covariate-Adjusted Adaptive Designs for Continuous Responses in a Phase III Clinical Trial: Recommendation for Practice

In this paper we propose a Bayesian method to combine safety data collected from two separate drug development programs using the same active drug substance but for different indications, formulations, or patient populations. The objective of combining the data across the programs is to better define the level of safety risk associated with the new indication or target population. There may be adverse events (AEs) observed in the new program that represent new safety signals. Our method is to explore the AEs using data from both development programs. Our approach utilizes data collected previously to assist in analyzing safety data from the new program. It is assumed that the frequency of a certain AE follows a distribution with a parameter that characterizes the safety risk level. The parameter is assumed to follow a distribution function. In the Bayesian framework, this distribution function is called a prior distribution in the absence of data and posterior distribution when updated by real data. The key concept behind our method is to use data from the previous program to construct a posterior distribution that will in turn serve as a prior distribution for the new program. The construction of this updated prior down weights data from the previous program to emphasize the new program and thus avoids simple pooling of the data across programs. Such “soft use” of previous information minimizes the potential for undue influence of previous data on the analysis. Data from the new program are used to update the prior distribution and compute the posterior distribution for the new program. Key statistics are then calculated from the posterior distribution to quantify the risk level for the new program. We have tested the proposed approach using data from a real Phase 2 study that was conducted as part of a clinical development program for a new indication of an approved drug. The results indicate that the estimated risk level was affected both by the observed event rates and the extents of exposure across the two development programs. This approach appropriately characterizes the safety profile across the two development programs and properly contextualizes new safety signals from the new program.     

基于分布式网络资源的网络带宽分配方法

本文针对计算机网络资源的带宽分配问题提出一种分配求解方法,该方法根据用户提出的带宽资源需求和系统可用资源的状况,按用户满意度最佳和系统效率最佳的原则制订价格,并通过用户的价格参数进行带宽资源的协商与分配.分析表明,该方法可以达到优化系统效率和提高用户满意度的目标.