A method for therapeutic dose selection in a phase II clinical trial using contrast statistics

This paper proposes a statistical method for determining the therapeutic dose of a test drug in a confirmatory clinical trial based on a phase II clinical trial using 3 or 4 doses of the drug. This method assumes the primary variable has a normal distribution with a common variance, that a test-drug effect is seen when the population means show a response pattern indicating a monotonic increase with dose, and that there is a prior distribution for the population means. Under the proposed method, multiple contrast statistics are determined, such as contrast statistics for linear increase and plateau, and a response pattern is selected based on the maximum contrast statistic. The posterior probability that the selected response pattern is the true one is evaluated, and if this exceeds the cut-off value a therapeutic dose is selected based on the estimated response pattern. To select the appropriate cut-off value, a simulation study was conducted using a loss function for which the loss due to overestimation is greater than the loss due to underestimation. It was found that, as a rule, the appropriate cut-off value to reduce the expected loss for various response patterns is 0.75 for a 3-group trial and 0.70 for a 4-group trial. Using these cut-off values, the proposed method was applied to a previous clinical trial of a leukotriene receptor antagonist in patients with bronchial asthma. The method enabled the selection of what are considered appropriate response patterns and a therapeutic dose. Thus, the proposed method appears reasonable.     

An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery.

PURPOSE: This article presents an iterative framework for managing the dynamic process of health technology assessment. The framework uses Bayesian statistical decision theory and value of information (VOI) analysis to inform decision making regarding appropriate patient management and to direct future research effort over the lifetime of a technology. Within the article, the framework is applied to a policy decision regarding preoperative patient management before major elective surgery, for which trial data are available. METHOD: The evidence available prior to the trial is used to determine the appropriate method of patient management and to ascertain whether, at the time of commissioning, the trial was potentially worthwhile. The prior information is then updated with the trial data via a Bayesian analysis using informative priors. This post trial information set is then used to reassess the appropriate method for patient management and to determine whether there is a requirement for any further research. RESULTS: Prior to the trial, preoperative optimization with dopexamine is identified as the appropriate method of patient management. The results of the VOI analysis suggest that a short-term trial was potentially worthwhile (population expected value of perfect information [EVPI] = 48 million pounds sterling). Following the trial, the uncertainty surrounding the choice of appropriate patient management and the potential worth of further research had increased (population EVPI = 67 million pounds sterling). CONCLUSIONS: The article demonstrates the value and practicality of applying the iterative framework to the dynamic process of health technology assessment. It is only by formally incorporating all of the information available to decision makers, through informed priors, that the appropriate decisions can be made.     

医疗器械临床试验方案的制定原则与设计方法

阐述了医疗器械临床试验方案的制定原则和具体的设计方法。通过介绍DC-CR2005计算机X线影像板扫描仪临床试验方案,详细论述了医疗器械临床试验的目的、背景、内容、方法、临床评价标准、临床性能的评价方法和统计处理方法、总体设计等,旨在提供临床试验参考方案,进一步规范医疗器械临床试验行为,提高医疗器械临床试验水平,保证临床试验结果真实、可靠。     

Evaluating the interaction between the therapy and the treatment in clinical trials by the propensity score weighting method

In clinical trials for antihypertensive drugs, a combination therapy trial and a monotherapy trial are often conducted simultaneously. In this situation, it can be a clinical concern to know the difference of the safety or efficacy of the new drug between the two therapies, in other words, to investigate the interaction between the therapy (monotherapy or combination therapy) and the treatment (test or control). However, because patients are often registered in either of these trials on the basis of their background characteristics, specific patients may be selected to participate in the monotherapy trial or combination therapy trial and not chosen at random, whereas the treatment is assigned randomly in each trial after registration. If this fact is not considered, the statistical analysis of the interaction may be biased. In this paper, we aim to evaluate the interaction between the two aforementioned factors by adjusting for covariates that may affect registration in the two trials. For this purpose, we apply the propensity score weighting method to suit the problem. The propensity score in this case is decomposed into the usual propensity score for the registration and the assignment probability for the random treatment assignment on the basis of their two-stage structure. We also discuss the augmented estimator known as the doubly robust estimator. In addition, we apply this method to data of a clinical trial for an antihypertensive drug that was conducted in Japan and conduct a simulation study to evaluate the performance of our proposed method.     

Modification of the sample size and the schedule of interim analyses in survival trials based on data inspections.

A method is presented which allows us to adapt the sample size as well as the number and time points of interim analyses to the treatment difference observed at an interim look during the course of a clinical trial with censored survival time as the endpoint. The method allows the inclusion of data inspections during the course of the trial and redesign of the trial on the basis of the observed treatment difference without affecting the type I error risk. Formulae for recalculating the required number of events and the number of further patients to be randomized as a function of the observed hazard rates and the detectable hazard ratio are given.     

A graphical method for exploring heterogeneity in meta-analyses: application to a meta-analysis of 65 trials

Heterogeneity can be a major component of meta-analyses and by virtue of that fact warrants investigation. Classic analysis methods, such as meta-regression, are used to explore the sources of heterogeneity. However, it may be difficult to apply such a method in complex cases or in the absence of an a priori hypothesis. This paper presents a graphical method to identify trials, groups of trials or groups of patients that are sources of heterogeneity. The contribution of these trials to the overall result can also be evaluated with this method. Each trial is represented by a dot on a 2D graph. The X-axis represents the contribution of the trial to the overall Cochran Q-test for heterogeneity. The Y-axis represents the influence of the trial, defined as the standardized squared difference between the treatment effects estimated with and without the trial. This approach has been applied to data from the Meta-Analysis of Chemotherapy in Head and Neck Cancer (MACH-NC) comprising 10,850 patients in 65 randomized trials. The graphical method allowed us to identify trials that contributed considerably to the overall heterogeneity and had a strong influence on the overall result. It also provided useful information for the interpretation of heterogeneity in this meta-analysis. The proposed graphical method identifies trials that account for most of the heterogeneity without having to explore all possible sources of heterogeneity by subgroup analyses. This method can also be applied to identify types of patients that explain heterogeneity in the treatment effect.     

Supplementary analysis of probabilities at the termination of a group sequential phase II trial

We consider estimation of various probabilities after termination of a group sequential phase II trial. A motivating example is that the stopping rule of a phase II oncologic trial is determined solely based on response to a drug treatment, and at the end of the trial estimating the rate of toxicity and response is desirable. The conventional maximum likelihood estimator (sample proportion) of a probability is shown to be biased, and two alternative estimators are proposed to correct for bias, a bias-reduced estimator obtained by using Whitehead's bias-adjusted approach, and an unbiased estimator from the Rao-Blackwell method of conditioning. All three estimation procedures are shown to have certain invariance property in bias. Moreover, estimators of a probability and their bias and precision can be evaluated through the observed response rate and the stage at which the trial stops, thus avoiding extensive computation.     

The methodology and operation of a pilot randomized control trial of the effectiveness of the Bug Busting method against a single application insecticide product for head louse treatment

A Department of Health leaflet suggests two treatment methods for head lice: mechanical removal by wet combing; and insecticide lotion/rinses. However, there are no reports in the literature comparing the effectiveness of these two treatment methods and well controlled clinical trials of insecticide treatments are sparse. A pilot randomized control trial of the effectiveness of a specific method of wet combing, 'Bug Busting', against a single application of a proprietary insecticide product is reported. The difficulties of designing a trial are discussed and modifications that would allow a definitive trial to take place are suggested. The pilot study included enzyme analysis of lice for insecticide resistance status assessment.     

Sample size calculation for the one‐sample log‐rank test

An improved method of sample size calculation for the one‐sample log‐rank test is provided. The one‐sample log‐rank test may be the method of choice if the survival curve of a single treatment group is to be compared with that of a historic control. Such settings arise, for example, in clinical phase‐II trials if the response to a new treatment is measured by a survival endpoint. Present sample size formulas for the one‐sample log‐rank test are based on the number of events to be observed, that is, in order to achieve approximately a desired power for allocated significance level and effect the trial is stopped as soon as a certain critical number of events are reached. We propose a new stopping criterion to be followed. Both approaches are shown to be asymptotically equivalent. For small sample size, though, a simulation study indicates that the new criterion might be preferred when planning a corresponding trial. In our simulations, the trial is usually underpowered, and the aspired significance level is not exploited if the traditional stopping criterion based on the number of events is used, whereas a trial based on the new stopping criterion maintains power with the type‐I error rate still controlled. Copyright © 2014 John Wiley & Sons, Ltd.     

A weighted logistic regression model for estimation of recurrence of adenomas

In a colorectal polyp prevention trial, some participants might have their follow-up colonoscopy conducted before the scheduled time (i.e. at the end of the trial). This results in variable follow-up lengths for participants and the data of recurrence status at the end of the trial can be considered as current status data. In this paper, we use a weighted logistic regression model to estimate recurrence rate of adenoma data at the end of the trial. The weights are used to adjust for variable follow-up. We show that logistic regression tends to underestimate recurrence rate. In a simulation study, we show that Kaplan-Meier estimator derived from the right endpoint of the current status data tends to overestimate recurrence rate in contrast to logistic regression and the weighted logistic regression method can produce reasonable estimates of recurrence rate even under a high non-compliance rate compared to conventional logistic regression and Kaplan-Meier estimator. The method described here is illustrated with an example from a colon cancer study.     

Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed‐effects models

We propose statistical definitions of the individual benefit of a medical or behavioral treatment and of the severity of a chronic illness. These definitions are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine. The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses, including dichotomous and count responses. The method is based on new developments in generalized linear mixed‐effects models, which are introduced in this article. To illustrate, analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are presented. The estimation of individual benefits is also explained. Copyright © 2016 John Wiley & Sons, Ltd.     

Estimating long‐term effects of treatment from placebo‐controlled trials with an extension period,using virtual twins

The best information about the benefits of long‐term treatment is obtained from a long‐term placebo‐controlled trial. However, once efficacy has been demonstrated in relatively brief trials, it may not be possible to conduct long‐term placebo‐controlled trials, for ethical or practical reasons. This paper presents a method for estimating long‐term effects of a treatment from a placebo‐controlled trial in which some participants originally randomized to active‐treatment volunteer to continue on treatment during an extension study, but follow‐up of participants originally assigned to placebo ends with the trial, or they are crossed over to active treatment during the extension. We propose using data from the trial to project the outcomes for a ‘virtual twin’ for each active‐treatment volunteer under the counterfactual placebo condition, and using bootstrap methods for inference. The proposed method is validated using simulation, and applied to data from the Fracture Intervention Trial and its extension, FLEX. Copyright © 2010 John Wiley & Sons, Ltd.     

Conditional power calculations for clinical trials with historical controls

A trial of a new therapy is to be compared to results from a previous trial of patients treated with a standard therapy. For a given sample size for the trial of the new therapy, we desire the power, against a specific alternative hypothesis, for the hypothesis test of the null hypothesis that the therapies are equivalent. Alternatively, the sample size required for the trial of the new therapy is needed for a target power. We explain why a popular method for doing these calculations is wrong, and discuss alternative methods in the context of normal outcomes, binary outcomes, and time-to-event outcomes.     

Bayesian predictive approach to interim monitoring in clinical trials

This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study and a large mortality trial.     

Clinical and prophylactic trials with assured new treatment for those at greater risk: I. A design proposal.

OBJECTIVES: The accepted sine qua non for estimating the difference in efficacy between a new and a standard treatment is a randomized controlled clinical trial. Yet in some situations it is either practically or ethically impossible to conduct such a trial. For example, patients who are desperately ill may decline to participate when they learn they may not receive the new treatment, especially when that treatment is readily available outside the experimental protocol. Likewise, in a prophylactic trial of a promising vaccine, recruitment of persons at greater risk may falter or fail. Our objective is to demonstrate that a rigorous comparison of treatments may still be attainable. METHODS: The features of a controlled clinical or prophylactic trial are reviewed from the perspectives of Food and Drug Administration regulations, ethical considerations, and practical problems. RESULTS: An explicit risk-based allocation method of design and analysis is proposed, one guaranteeing that all subjects at greater risk will receive the new treatment. CONCLUSIONS: Under certain conditions, a risk-based allocation trial can furnish consistent estimates of both standard and experimental treatment effects for those at greater risk while avoiding certain difficulties caused by randomized treatment allocation.     

Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects

ObjectivesWithin epidemiology, a stepped wedge trial design (i.e., a one-way crossover trial in which several arms start the intervention at different time points) is increasingly popular as an alternative to a classical cluster randomized controlled trial. Despite this increasing popularity, there is a huge variation in the methods used to analyze data from a stepped wedge trial design.Study Design and SettingFour linear mixed models were used to analyze data from a stepped wedge trial design on two example data sets. The four methods were chosen because they have been (frequently) used in practice. Method 1 compares all the intervention measurements with the control measurements. Method 2 treats the intervention variable as a time-independent categorical variable comparing the different arms with each other. In method 3, the intervention variable is a time-dependent categorical variable comparing groups with different number of intervention measurements, whereas in method 4, the changes in the outcome variable between subsequent measurements are analyzed.ResultsRegarding the results in the first example data set, methods 1 and 3 showed a strong positive intervention effect, which disappeared after adjusting for time. Method 2 showed an inverse intervention effect, whereas method 4 did not show a significant effect at all. In the second example data set, the results were the opposite. Both methods 2 and 4 showed significant intervention effects, whereas the other two methods did not. For method 4, the intervention effect attenuated after adjustment for time.ConclusionDifferent methods to analyze data from a stepped wedge trial design reveal different aspects of a possible intervention effect. The choice of a method partly depends on the type of the intervention and the possible time-dependent effect of the intervention. Furthermore, it is advised to combine the results of the different methods to obtain an interpretable overall result.     

Bayesian estimation of cost-effectiveness: an importance-sampling approach

We describe a method for estimating the cost-effectiveness of a new treatment compared to a standard, using data from a comparative clinical trial. We quantify the clinical effectiveness as a binary variable indicating success or failure. The underlying statistical model assumes that costs are uncensored and follow separate gamma distributions in each of the groups defined by the four possible combinations of treatment arm and effectiveness outcome. The method is subjectivist, in that it represents prior uncertainty about model parameters with a probability distribution, which we update via Bayes's theorem to produce a posterior distribution. We approximate the posterior by importance sampling, a straightforward simulation method. We illustrate the method with an analysis of cost (derived from resource usage data) and effectiveness (measured by one-year survival) in a clinical trial in heart disease. The example demonstrates that the method is practical and provides for a flexible data analysis.     

Reproducibility probability in clinical trials

For marketing approval of a new drug product, the United States Food and Drug Administration (FDA) requires that substantial evidence of the effectiveness of the drug product be provided through the conduct of at least two adequate and well-controlled clinical trials. The purpose of conducting the second clinical trial is to study whether the clinical result from the first trial is reproducible in the second trial with the same study protocol. Under certain circumstance, the FDA Modernization Act of 1997 includes a provision to allow data from one adequate and well-controlled clinical trial investigation and confirmatory evidence to establish effectiveness for risk/benefit assessment of drug and biological candidates for approval. In this paper, we introduce the concept of reproducibility probability for a given clinical trial, which is useful in providing important information for regulatory agencies in deciding whether a single clinical trial is sufficient and for pharmaceutical companies in adjusting the sample size in a future clinical trial. Three approaches, the estimated power approach, the method of confidence bounds and the Bayesian approach, are studied in evaluating reproducibility probabilities under several study designs commonly used in clinical trials.     

Simulation‐based adjustment after exploratory biomarker subgroup selection in phase II

As part of the evaluation of phase II trials, it is common practice to perform exploratory subgroup analyses with the aim of identifying patient populations with a beneficial treatment effect. When investigating targeted therapies, these subgroups are typically defined by biomarkers. Promising results may lead to the decision to select the respective subgroup as the target population for a subsequent phase III trial. However, a selection based on a large observed treatment effect may potentially induce an upwards‐bias leading to over‐optimistic expectations on the success probability of the phase III trial. We describe how Approximate Bayesian Computation techniques can be used to derive a simulation‐based bias adjustment method in this situation. Recommendations for the implementation of the approach are given. Simulation studies show that the proposed method reduces bias substantially compared with the maximum likelihood estimator. The procedure is illustrated with data from an oncology trial. Copyright © 2017 John Wiley & Sons, Ltd.     

Covariate-adjusted non-parametric survival curve estimation

Kaplan-Meier survival curve estimation is a commonly used non-parametric method to evaluate survival distributions for groups of patients in the clinical trial setting. However, this method does not permit covariate adjustment which may reduce bias and increase precision. The Cox proportional hazards model is a commonly used semi-parametric method for conducting adjusted inferences and may be used to estimate covariate-adjusted survival curves. However, this model relies on the proportional hazards assumption that is often difficult to validate. Research work has been carried out to introduce a non-parametric covariate-adjusted method to estimate survival rates for certain given time intervals. We extend the non-parametric covariate-adjusted method to develop a new model to estimate the survival rates for treatment groups at any time point when an event occurs. Simulation studies are conducted to investigate the model's performance. This model is illustrated with an oncology clinical trial example.