构建我国医院药学服务体系的评价指标体系

目的：构建一套用于评价我国医院药学服务体系建设水平的指标体系。方法：在SPO医疗服务质量评价理论模型的基础上根据国内外药学服务评价理论和方法构建第一级和第二级指标，随后采用借鉴德尔菲法原理的专家调研构建第三级指标，并运用层次分析法（AHP）确定各级指标下各项目权重，完成指标体系构建。结果：研究构建出了一套包含3个一级指标、9个二级指标和34个三级指标的我国医院药学服务评价指标体系。结论：我国医院药学服务体系的发展应将药学服务团队建设、服务规章制度设计和最终临床产出的提升作为首要任务。     

Utility of Covariate-Adjusted Response-Adaptive Randomization in Survival Trials

Covariate-adjusted response-adaptive (CARA) randomization is used in clinical trials to balance the competing goals of assigning a greater number of study subjects to the better treatment and achieving high statistical efficiency in estimating treatment effects in the presence of covariates, while maintaining randomness in treatment assignments. In this article, we propose CARA randomization procedures for survival intervention trials with two treatment arms when the primary outcomes follow an exponential regression model. We demonstrate, through extensive simulation, that our CARA procedures can achieve some reduction in the number of events in the study without compromising power and Type I error, compared with balanced randomization designs, including in situations when the exponential model is misspecified. We conclude that the proposed CARA procedures can be suitable alternatives to the traditional balanced randomization designs in survival trials, provided response data are available during the recruitment phase to enable adaptations in the design. We illustrate the proposed methodology by redesigning two survival trials from the literature.     

基于ISO9001：2015的药物临床试验机构质量管理评价体系的构建

目的 构建基于质量管理体系要求ISO9001：2015的药物临床试验机构的质量管理评价体系。方法 采用专家调查法邀请14名专家进行2轮咨询,确定各级指标,使用层次分析法计算各指标的权重。结果 建立了4个一级指标、15个二级指标和46个三级指标的药物临床试验机构质量管理评价体系;一级指标领导与策划、支持与运行、效果评估、持续改进的权重分别为0.2103、0.3303、0.1835、0.2759;二级指标中组合权重排前3位的分别为预防和纠正（0.1554）、领导力（0.1143）、机构定期开展质控检查（0.0806）;三级指标中组合权重排前3位的为定期进行预防和纠正效果评估（0.0696）、有对应的数据或举措体现改进效果（0.0487）、具有药物临床试验质量管理要求和指引（0.0378）。结论 构建的质量管理评价体系具有一定的科学性、实用性,可用于评估药物临床试验机构的质量管理能力。     

Impact of Subject Attrition on Sample Size Determinations for Longitudinal Cluster Randomized Clinical Trials

Subject attrition is a ubiquitous problem in any type of clinical trial and, thus, needs to be taken into consideration at the design stage particularly to secure adequate statistical power. Here, we focus on longitudinal cluster randomized clinical trials (cluster-RCT) that aim to test the hypothesis that an intervention has an effect on the rate of change in the outcome over time. In this setting, the cluster-RCT assumes a three-level hierarchical data structure in which subjects are nested within a higher level unit such as clinics and are evaluated for outcome repeatedly over the study period. Furthermore, the subject-specific slopes can be modeled in terms of fixed or random coefficients in a mixed-effects linear model. Closed-form sample size formulas for testing the preceding hypothesis have been developed under an assumption of no attrition. In this article, we propose closed-form approximate samples size determinations with anticipated attrition rates by modifying those existing sample size formulas. With extensive simulations, we examine performances of the modified formulas under three attrition mechanisms: attrition completely at random, attrition at random, and attrition not at random. In conclusion, the proposed modification is very effective under fixed-slope models but yields biased, perhaps substantially so, statistical power under random slope models.     

Adaptive designs for comparative effectiveness research trials

Abstract

Medical and health policy decision-makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a “minimum clinically meaningful difference”. The objective of this article is to explore the use of a particular form of ADs for comparing treatments within the CER trial context. To achieve this, the authors review the current state of clinical CER. They also identify areas of CER as particularly strong candidates for application of novel AD and illustrate the potential usefulness of the designs and methods for two group comparisons. The authors found that ADs can stabilize power. Furthermore, the designs ensure adequate power for true effects are at least at clinically significant pre-planned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned. The authors conclude that ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons.     

A Clinical Trial Design With Covariate-Adjusted Response-Adaptive Randomization Using Superiority Confidence of Treatments

Abstract

Adaptive randomization using response outcome or covariate is commonly used in the literature. However, the performance of these designs has not been thoroughly studied, especially when there are various interactions between the covariate and treatment. We have conducted simulations to evaluate the performance of commonly used designs under two-arm and multiple-arm situations. When a predictive factor exists, in the phase II trial conduction using adaptive designs, such as the BATTLE-1, BATTLE-2 trial, and ISPY-2 trials, researchers evaluate the operating characteristics using the traditional power assessment. In this article, new criteria are used in a general modeling frame work to incorporate the complicated interaction. Based on our evaluation, the covariate-adjusted and response-adaptive randomization (Sc-ca) results in a greater total number of responders. Additionally, the design can detect the treatment effect difference in subgroups, and consistently assign patients to the most beneficial treatment according to their covariate profiles. This translates into a higher proportion of individuals receiving optimized treatments compared with other commonly used designs. This adaptive design is a step toward personalized therapy to benefit each patient enrolled in a prospective clinical trial, when there is the strong evidence that predictive factors exist.     

Clinical studies with traditional Chinese medicine in the past decade and future research and development

Traditional Chinese medicine (TCM) is currently considered a complementary or alternative medical system in most Western countries and has been increasingly accepted worldwide. More and more clinical trials on TCM have been conducted internationally, and scientists worldwide are becoming increasingly interested in the evaluation of clinical efficacy of TCM based on clinical trials. This paper reviews the situation of clinical trials on TCM in the past decade, including systematic reviews about clinical trials either focusing on the treatment of disease with TCM approaches or focusing on one herbal product, conduction of clinical trials on TCM either with randomization and controlled methods or general observation. Some general issues on the conduct of clinical trials on TCM, such as randomization, control, quality of life (QOL), patient reported outcomes (PROs) and biomarkers, quality control, safety evaluation and case studies, are discussed, and accordingly some suggestions are proposed.     

Pharmacogenetics in randomized controlled trials: considerations for trial design

Pharmacogenetic analyses of clinical trials aim to either detect whether a subgroup of patients identified by genetic characteristics responds differently to the treatment or to verify whether a proposed genotype-guided treatment is beneficial over standard care. This article describes three different trial designs, differing in the timing of randomization and genotyping. Each design has its own advantages, and the objectives and conditions under which each one is most suited are discussed.     

Two-Arm Comparisons in Two-Stage Designs for Stratified Randomized Phase II Trials

Abstract

Two-stage designs have been widely used in phase II clinical trials to evaluate whether a new treatment shows sufficient evidences of effectiveness to justify being tested in a phase III trial. The common primary endpoint in phase II trials is a binary response, such as tumor response. The distribution of patients’ response for a phase II clinical trial is often heterogeneous, making it desirable to stratify patients into subgroups according to different prognostic factors. In this article, for a two-arm stratified randomized phase II clinical trial, we consider two-stage designs and propose three testing procedures to compare the response rates between two treatments. The three procedures are based on different types of criteria, namely, the weighted average of the stratum-specific differences between treatment response rates, the estimated relative risk and odds ratio under the assumption of a common odds ratio over the strata. We consider conditional approach and present a simulation-based algorithm by modifying the algorithm in London and Chang to determine the parameters in designs for achieving the desired power at the nominal level. A numerical study is conducted to investigate the performance of the proposed procedure. Simulation results show that the split-levels of Type I and Type II errors and randomization ratio have a crucial impact on the overall sample size required. Decreasing the split-level or increasing the randomization ratio at the first-stage can result in a smaller total sample size if early termination after the first-stage does not occur. In terms of the total sample size required, the INVAR-weighted test outperforms the other tests when the odds ratio or the true difference between two response rates is constant across strata. When neither odd ratio nor the difference between two response rates is constant across the strata, the INVAR-weighted test also performs well when the randomization ratio for the first stage is large.     

On Enrichment Strategies for Biomarker Stratified Clinical Trials

ABSTRACT

In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.     

帐篷医院在地震救援二级救治中的作用

目的：探讨地震灾害二级救治的策略与方法。方法：回顾分析汶川地震在救援中北川灾区的二级救治过程。结果：帐篷医院二级救治诊治伤员近10000例次,协助转运伤员6000例次左右。10%～20%搜救成功伤员,主要是严重伤伤员,以重度脑伤及多发伤（胸、腹腔损伤）、开放伤为主,死于现场或转运途中;开放伤口的感染率达70%以上。结论：大规模灾害事故救援中,二级救治作用重大;就近配备良好的帐篷医院是重要的救治单元。     

Utilization of health care databases for pharmacoepidemiology

Pharmacoepidemiology is the study of the utilization and effects of drugs in clinical and population settings, and the outcomes of drug therapy. The growing trend of recording computerized data that will increasingly be automated into health care delivery is making the use of large datasets more and more common in pharmacoepidemiologic research. Most retrospective databases offer large populations and longer observation periods with real-world practice and can answer a variety of research questions quickly and cost-effectively. Observational studies, specifically using large databases, can complement findings from randomized clinical trials (RCTs) by assessing treatment effectiveness in patients encountered in daily clinical practice, although they are more exposed to bias and certainly are lower on the hierarchy of evidence than RCTs. Furthermore, careful defining of the research question with appropriate design and application of advanced statistical techniques, e.g., propensity-score analysis or marginal structural models, can yield findings with validity and improve causal inference of treatment effects. Some existing guidelines for comparative effectiveness help decision makers to evaluate the quality of observational studies comparing the effectiveness of various medical products and services. Thus, the trend for utilization of databases for pharmacoepidemiology will continue to grow in coming years.     

The Epidemiologic Approach to Pharmacogenomics

The epidemiologic approach enables the systematic evaluation of potential improvements in the safety and efficacy of drug treatment which might result from targeting treatment on the basis of genomic information. The main epidemiologic designs are the randomized control trial, the cohort study, and the case-control study, and derivatives of these proposed for investigating gene-environment interactions. However, no one design is ideal for every situation, and methodological issues, notably selection bias, information bias, confounding and chance, all play a part in determining which study design is best for a given situation. There is also a need to employ a range of different designs to establish a portfolio of evidence about specific gene-drug interactions.In view of the complexity of gene-drug interactions, pooling of data across studies is likely to be needed in order to have adequate statistical power to test hypotheses. We suggest that there may be opportunities (i) to exploit samples from trials already completed to investigate possible gene-drug interactions; (ii) to consider the use of the case-only design nested within randomized controlled trials as a possible means of reducing genotyping costs when dichotomous outcomes are being investigated; and (iii) to make use of population-based disease registries that can be linked with tissue samples, treatment information and death records, to investigate gene-treatment interactions in survival.     

An Adaptive Dose-Finding Design Based on Both Safety and Immunologic Responses in Cancer Clinical Trials

Dose-finding in cancer clinical trials has been dominated by algorithmic designs on the principle that the highest tolerable dose is also the most effective dose. This assumption no longer applies to the biologic treatments that are characterized by different toxicity and/or efficacy profiles to the extent that the best therapeutic dose might be well below any dose that produces serious toxicity. As such, we propose a two-stage design with focus on immunotherapy trials, incorporating both safety and efficacy information. The first stage establishes the safety profile of each dose, with escalation decisions based on likelihood principles. Continuous immunologic outcomes are used to evaluate the relative efficacy of the doses. The second stage employs an adaptive randomization to assign patients to doses showing higher efficacy. Safety is being continuously monitored throughout Stage 2, where some doses may be ‘closed’ due to unacceptable toxicity. The proposed design is compared to the modified toxicity probability interval (mTPI) design using percent dose allocation and estimation of outcomes under different scenarios. We show that by using an efficacy-driven adaptive randomization with safety constraints, the allocation distribution is skewed towards more efficacious doses, and thus limit the number of patients exposed to toxic or non-therapeutic doses. Supplementary materials for this article are available online.     

利用补充真实世界数据研究获得更深层次的认识

摘 要随着患者、医护人员、药品注册人员和政策决定者对现实世界中各种诊疗手段的安全性和有效性相关证据的迫切需求,我们需要新的研究设计来高效有效地对此类问题进行评估。与此同时,越来越多的二手数据可以用来提供此种相关证据,常用的二手数据包括电子医疗档案和医疗保险数据等。补充真实世界数据研究同时利用了现有二手数据和通过医生/患者收集的一手数据,并将两者进行结合。在现有数据的基础上,补充真实世界数据研究可以集中精力收集缺失的重要信息来满足研究目的。本文将对补充真实世界数据研究进行介绍,包括研究设计要素,研究的优点和局限性,实施要点以及应用实例等。     

Practical Bayesian design and analysis for drug and device clinical trials

Perhaps the most valuable contribution of Bayesian methods to health care evaluation involves study design. Drug and medical device clinical trialists are increasingly confronted with data that feature complex correlation structures, and are costly and difficult to obtain. In such settings, Bayesian trial designs are attractive since they can incorporate historical data or information from published literature, thus saving time and expense and minimizing the number of subjects exposed to an inferior treatment. Bayesian designs can also adapt to unexpected changes in the protocol, and allow the investigator to explore the plausibility of various outcome scenarios before any patients are enrolled in the trial. Recently, the FDA Center for Devices has encouraged hierarchical Bayesian statistical approaches which allow for the incorporation of such valuable historical data into the design and analysis of new device trials. The practical application of these methods has only become feasible in the last decade due to advances in computing via Markov chain Monte Carlo (MCMC) methods, especially as implemented in the popular BUGS software package. In this paper we illustrate Bayesian analysis and sample size calculations using BRugs, a function for calling BUGS from R. We provide illustrations in two applied settings where incorporation of available historical information is crucial, one concerning an AIDS drug trial and the other a comparison of left ventricular assist devices (LVADs).     

2018年荆门地区10家医院剖宫产围术期预防用抗菌药物的合理性分析

目的 对2018年荆门地区10家医院剖宫产围术期预防用抗菌药物的合理性进行分析,加强荆门地区医院剖宫产围术期预防性抗菌药物的合理使用与管理。方法 回顾性抽取荆门地区2018年10家医院（三级综合医院4家,二级综合医院3家,专科医院3家）剖宫产手术患者病历968份,对围术期抗菌药物的品种选择、首剂给药时机、给药疗程和联合用药情况进行统计分析。结果 参与调查的10家医院抗菌药物使用率均达100%,品种选择合理率三级医院79.5%,二级医院75.4%,专科医院87.8%;首剂给药时间三级医院和专科医院均在脐带结扎后,二级医院69.4%在脐带结扎后,30.6%在术后给药;平均给药疗程三级医院为2.7 d,二级医院为3.5 d,专科医院为2.3 d。抗菌药物的使用大部分为单用,但也存在少数患者有二联用药,无三联使用抗菌药物的情况。结论 荆门地区医院剖宫产围术期抗菌药物品种的选择趋于合理,用法用量逐步规范合理,但首剂给药时机和用药疗程仍存在不合理之处。