A note on tree gatekeeping procedures in clinical trials

Dmitrienko et al. (Statist. Med. 2007; 26:2465-2478) proposed a tree gatekeeping procedure for testing logically related hypotheses in hierarchically ordered families, which uses weighted Bonferroni tests for all intersection hypotheses in a closure method by Marcus et al. (Biometrika 1976; 63:655-660). An algorithm was given to assign weights to the hypotheses for every intersection. The purpose of this note is to show that any weight assignment algorithm that satisfies a set of sufficient conditions can be used in this procedure to guarantee gatekeeping and independence properties. The algorithm used in Dmitrienko et al. (Statist. Med. 2007; 26:2465-2478) may fail to meet one of the conditions, namely monotonicity of weights, which may cause it to violate the gatekeeping property. An example is given to illustrate this phenomenon. A modification of the algorithm is shown to rectify this problem.     

A sequential procedure for monitoring clinical trials against historical controls

In this paper, we develop a sequential procedure to monitor clinical trials against historical controls. When there is a strong ethical concern about randomizing patients to existing treatment because biological and medical evidence suggests that the new treatment is potentially superior to the existing one, or when the enrollment is too limited for randomization of subjects into experimental and control groups, one can monitor the trial sequentially against historical controls if the historical data with required quality and sample size are available to form a valid reference for the trial. This design of trial is sometimes the only alternative to a randomized phase III trial design that is intended but not feasible in situations such as above. Monitoring this type of clinical trial leads to a statistical problem of comparing two population means in a situation in which data from one population are sequentially collected and compared with all data from the other population at each interim look. The proposed sequential procedures is based on the sequential conditional probability ratio test (SCPRT) by which the conclusion of the sequential test would be virtually the same as that arrived at by a non-sequential test based on all data at the planned end of the trial. We develop the sequential procedure by proposing a Brownian motion that emulates the test statistic, and then proposing an SCPRT that is adapted to the special properties of the trial.     

多中心临床随机对照试验的Meta分析

目的探讨解决多中心临床随机对照试验中心效应差异的统计分析问题。方法以两项多中心临床随机对照试验数据为例,运用协方差分析及Meta分析。结果协方差分析中心间效应值差异均有统计学意义,行Meta分析,项目1异质性检验差异无统计学意义(P>0.05),采用固定效应模型分析合并效应值组间差异有统计学意义(P<0.05)。项目2异质性检验差异有统计学意义(P<0.05),采用随机效应模型分析合并效应值组间差异无统计学意义(P>0.05)。结论多中心临床随机对照试验研究中,如果存在中心间以及中心与分组间交互效应差异有统计学意义时,可根据Meta分析异质性检验结果选择适合的模型进行合并效应值的组间比较,如果协方差与Meta分析结果不一致时,建议选择Meta分析的结果较为稳妥。     

Cluster randomized controlled trials in primary care: An introduction

Background: Cluster randomized trials occur when groups or clusters of individuals, rather than the individuals themselves, are randomized to intervention and control groups and outcomes are measured on individuals within those clusters. Within primary care, between 1997 and 2000, there has been a virtual doubling in the number of published cluster randomized trials. A recent systematic review, specifically within primary care, found study quality to be both generally lower than that reported elsewhere and not to have shown any recent quality improvement. Objective: To discuss the design, conduct and analysis of cluster randomized trials within primary care in terms of the appropriate expertise required, potential bias, ethical considerations and expense. Discussion: Compared with trials that involve the randomization of individual participants, cluster randomized trials are more complex to design and analyse and, for a given sample size, have decreased power and a broadening of confidence intervals. Cluster randomized trials are specifically prone to potential bias at two levels—the cluster and individual. Regarding the former, it is recommended that cluster allocation be undertaken by a party independent to the research team and careful consideration be given to ensure minimal cluster attrition. Bias at the individual level can be overcome by identifying trial participants before randomization and at this time obtaining consent for intervention, data collection or both. A unique ethical aspect to cluster randomized trials is that cluster leaders may consent to the trial on behalf of potential cluster members. Additional costs of cluster randomized trials include the increased number of patients required, the complexity in their design and conduct and, usually, the need to recruit clusters de novo.

Conclusion: Cluster randomized trials are a powerful and increasingly popular research tool. They are uniquely placed for the conduct of research within primary-care clusters where intracluster contamination can occur. Associated methodological issues are straightforward and surmountable and just need careful consideration and management.     

临床营养研究中随机对照研究质量评价

目的评价两种主要临床营养期刊中随机对照试验（RCT）的质量。方法查阅2000～2008年《中国临床营养杂志》和《肠外与肠内营养》发表的RCT研究,按Cochrane协作网标准评价,并进行Jadad评分。结果两种期刊共发表238篇RCT研究,Jadad评分为（1．65±0．82）分。高质量RCT仅28篇（11．76％）,评分为满分5分的仅5篇（2．10％）。随机分组的方法、组间可比性、纳入排除标准、盲法、撤除和退出的数量和理由、样本含量等方面存在各种问题。结论国内临床营养领域RCT研究的设计和质量控制还存在不足或欠缺,水平尚待提高。     

Assessing apparent treatment--covariate interactions in randomized clinical trials

In the context of clinical trials, a qualitative treatment--covariate interaction occurs when a patient's preferred treatment depends on his covariates. In this paper I review the nature and interpretation of various kinds of interactions, compare the use of overall tests for interaction to subset analysis, present some examples of apparent treatment-covariate interactions that have arisen in actual randomized clinical trials, and discuss some recent work by others related to significance testing, estimation and assessment of apparent treatment-covariate interactions.     

Obtaining medically meaningful answers from an overview of randomized clinical trials

Answers that have medical value can often be obtained from overviews of randomized clinical trials if care is taken in formulating a biologically sensible question and unbiased and careful methods are used in collecting, extracting and analysis the results. This article discusses some of the pitfalls that are encountered during this process, outlines some solutions and emphasizes the need for a conservative interpretation of the results.     

Tree-structured gatekeeping tests in clinical trials with hierarchically ordered multiple objectives

This paper discusses a new class of multiple testing procedures, tree-structured gatekeeping procedures, with clinical trial applications. These procedures arise in clinical trials with hierarchically ordered multiple objectives, for example, in the context of multiple dose-control tests with logical restrictions or analysis of multiple endpoints. The proposed approach is based on the principle of closed testing and generalizes the serial and parallel gatekeeping approaches developed by Westfall and Krishen (J. Statist. Planning Infer. 2001; 99:25-41) and Dmitrienko et al. (Statist. Med. 2003; 22:2387-2400). The proposed testing methodology is illustrated using a clinical trial with multiple endpoints (primary, secondary and tertiary) and multiple objectives (superiority and non-inferiority testing) as well as a dose-finding trial with multiple endpoints.     

Sensitivity analysis for clinical trials with missing continuous outcome data using controlled multiple imputation: A practical guide

Missing data due to loss to follow-up or intercurrent events are unintended, but unfortunately inevitable in clinical trials. Since the true values of missing data are never known, it is necessary to assess the impact of untestable and unavoidable assumptions about any unobserved data in sensitivity analysis. This tutorial provides an overview of controlled multiple imputation (MI) techniques and a practical guide to their use for sensitivity analysis of trials with missing continuous outcome data. These include δ- and reference-based MI procedures. In δ-based imputation, an offset term, δ, is typically added to the expected value of the missing data to assess the impact of unobserved participants having a worse or better response than those observed. Reference-based imputation draws imputed values with some reference to observed data in other groups of the trial, typically in other treatment arms. We illustrate the accessibility of these methods using data from a pediatric eczema trial and a chronic headache trial and provide Stata code to facilitate adoption. We discuss issues surrounding the choice of δ in δ-based sensitivity analysis. We also review the debate on variance estimation within reference-based analysis and justify the use of Rubin's variance estimator in this setting, since as we further elaborate on within, it provides information anchored inference.     

Community readiness to promote Latinas' participation in breast cancer prevention clinical trials

The high breast cancer (BC) mortality rates that exist among Hispanic women (Latinas) are a health disparity burden that needs to be addressed. Prevention clinical trials are a burgeoning area of cancer prevention efforts and may serve to promote parity. Unfortunately, Latinas, along with other ethnic minority women, continue to be under-represented in this form of research. Previous studies have examined individual barriers to ethnic minorities' participation, but none have assessed community factors contributing to Latinas' under-representation in these studies. The present study addressed these limitations from a community perspective by exploring which factors might inhibit Latinas' participation in clinical trials, specifically BC prevention trials. Using the Community Readiness Model (CRM), 19 key informants were interviewed in four communities, two rural and two urban, in Colorado, USA. The key informant assessment involved a semistructured interview that measured the level of community readiness to encourage participation in BC prevention activities. The results reflected a community climate that did not recognise BC as a health problem that affected Latinas in participating communities. Compared to other healthcare priorities, participation in BC prevention clinical trials was considered a low priority in these communities. Overall, leadership and community resources were not identified or allocated to encourage the participation of Latinas. The results highlight the lack of awareness regarding clinical trials among both community members and leaders. According to the CRM, strategies to enhance awareness at multiple levels in the community are necessary. This study demonstrates how the CRM can be used to better understand a community's perspective on BC, and specifically, the under-representation of Latinas in clinical trials.     

Calibrated phase II clinical trials in oncology

This paper proposes the use of calibrated designs in phase II oncological clinical trials and evaluates their statistical properties in terms of power recovery and cost. A calibrated phase II design for a new cancer treatment for a specific tumour, e.g. colo-rectal, consists of random allocation of patients to receive either the investigational treatment or a standard treatment known to have activity at a certain level in phase II trials (e.g. 5 FU, expected response proportion = 0.20). Patients assigned to the standard treatment form the calibration group. The calibration group is not a control group in the traditional sense and one does not conduct a formal efficacy comparison between the investigational treatment group and the calibration group. Instead, one uses the calibration group to evaluate whether the sample of patients who receive the investigational treatment has the capability of showing a response. If the data do not support the hypothesis that the expected response proportion prevails in the calibration group, one declares the investigational group results suspect and recommends a second trial. Assuming acceptable results of the second trial, we use binomial calculations to find the effect of the calibration design on power recovery and relative cost. We show that when an unrepresentative sample occurs, calibration designs generally recover 90 per cent or more of nominal power at a cost of three to fivefold increase in sample size. We recommend for calibrated phase II trials a 'master protocol' approach in which several investigational treatment arms share one concurrent calibration group.     

A hierarchical testing approach for detecting safety signals in clinical trials

Detecting safety signals in clinical trial safety data is known to be challenging due to high dimensionality, rare occurrence, weak signal, and complex dependence. We propose a new hierarchical testing approach for analyzing safety data from a typical randomized clinical trial. This approach accounts for the hierarchical structure of adverse events (AEs), that is, AEs are categorized by system organ class (SOC). Our approach contains two steps: the first step tests, for each SOC, whether any AEs within this SOC are differently distributed between treatment arms; and the second step identifies signal AEs from SOCs passing the first step tests. We show the superiority, in terms of power of detecting safety signals given controlled false discovery rate, of the new approach comparing with currently available approaches through simulation studies. We also demonstrate this approach with two real data examples.     

Participation of epidemiologists and/or biostatisticians and methodological quality of published controlled clinical trials

STUDY OBJECTIVE: This study assessed several methodological aspects related to the quality of published controlled clinical trials (CCTs) in relation to the participation of an epidemiologist/biostatistician (E/B). DESIGN: Handsearch of CCTs published in four medical leading journals for 1993-1995. METHODS: Quality variables, abstracted from a review, were related to authors' specialties. Five hundred and ninety four CCTs were identified via a hand search. The department/unit membership was used to attribute authors' specialties. Of 594 CCTs identified, in 127 the authors' specialties could not be known, leaving 467 trials for analysis. RESULTS: E/B participation occurred in 178 trials (38.1%). This participation was more frequent in multicentric, bigger, and in those trials describing any funding agency. These factors were controlled for in the analysis. E/B participation was positively associated with pre-study sample size estimation (OR = 1.5, 95% confidence intervals (CI) 1.0, 2.3), with reporting the dates for starting/ending the study (OR = 2.1, 95% CI 1.4, 3.3), with using an objectively assessed outcome (OR = 2.4, 95% CI 1.2, 4.6) and with the intention to treat principle (OR = 2.0, 95% CI 1.3, 3.0). The overall quality score was higher in trials where E/B participated. CONCLUSIONS: The results suggest that E/B improve the quality (at least of reports) of clinical trials. Given that quality of research is frequently used to evaluate potential sources of heterogeneity between trials, these results are relevant for meta-analysis.     

Models for patients' recruitment in clinical trials and sensitivity analysis

Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.     

Adjusting for observable selection bias in block randomized trials

In this paper, we propose a model-based approach to detect and adjust for observable selection bias in a randomized clinical trial with two treatments and binary outcomes. The proposed method was evaluated using simulations of a randomized block design in which the investigator favoured the experimental treatment by attempting to enroll stronger patients (with greater probability of treatment success) if the probability of the next treatment being experimental was high, and enroll weak patients (with less probability of treatment success) if the probability of the next treatment being experimental was low. The method allows not only testing for the presence of observable selection bias, but also testing for a difference in treatment effects, adjusting for possible selection bias.     

Semi-parametric and non-parametric methods for clinical trials with incomplete data

Last observation carried forward (LOCF) and analysis using only data from subjects who complete a trial (Completers) are commonly used techniques for analysing data in clinical trials with incomplete data when the endpoint is change from baseline at last scheduled visit. We propose two alternative methods. The semi-parametric method, which cumulates changes observed between consecutive time points, is conceptually similar to the familiar life-table method and corresponding Kaplan-Meier estimation when the primary endpoint is time to event. A non-parametric analogue of LOCF is obtained by carrying forward, not the observed value, but the rank of the change from baseline at the last observation for each subject. We refer to this method as the LRCF method. Both procedures retain the simplicity of LOCF and Completers analyses and, like these methods, do not require data imputation or modelling assumptions. In the absence of any incomplete data they reduce to the usual two-sample tests. In simulations intended to reflect chronic diseases that one might encounter in practice, LOCF was observed to produce markedly biased estimates and markedly inflated type I error rates when censoring was unequal in the two treatment arms. These problems did not arise with the Completers, Cumulative Change, or LRCF methods. Cumulative Change and LRCF were more powerful than Completers, and the Cumulative Change test provided more efficient estimates than the Completers analysis, in all simulations. We conclude that the Cumulative Change and LRCF methods are preferable to LOCF and Completers analyses. Mixed model repeated measures (MMRM) performed similarly to Cumulative Change and LRCF and makes somewhat less restrictive assumptions about missingness mechanisms, so that it is also a reasonable alternative to LOCF and Completers analyses.     

Difficulties in recruiting older people in clinical trials: An examination of barriers and solutions

Limited information exists regarding optimal methods for the recruitment and retention of older people in clinical trials. The aim of this review is to identify common barriers to the recruitment of older people in clinical trials and to propose solutions to overcome these barriers. A review of literature was performed to identify common difficulties in recruiting older people. This in combination with our experience during recruitment for a randomized control trial, have highlighted numerous barriers. Population-specific recruitment strategies, simple informed-consent processes, and effective communication between the researcher and subject are effective strategies to overcome these barriers.     

Traditional multiplicity adjustment methods in clinical trials

This tutorial discusses important statistical problems arising in clinical trials with multiple clinical objectives based on different clinical variables, evaluation of several doses or regiments of a new treatment, analysis of multiple patient subgroups, etc. Simultaneous assessment of several objectives in a single trial gives rise to multiplicity. If unaddressed, problems of multiplicity can undermine integrity of statistical inferences. The tutorial reviews key concepts in multiple hypothesis testing and introduces main classes of methods for addressing multiplicity in a clinical trial setting. General guidelines for the development of relevant and efficient multiple testing procedures are presented on the basis of application‐specific clinical and statistical information. Case studies with common multiplicity problems are used to motivate and illustrate the statistical methods presented in the tutorial, and software implementation of the multiplicity adjustment methods is discussed. Copyright © 2013 John Wiley & Sons, Ltd.