A Consistency-Adjusted Strategy for Accommodating an Underpowered Primary Endpoint

A clinical trial might involve more than one clinically important endpoint, each of which can characterize the treatment effect of the experimental drug under investigation. Underlying the concept of using such endpoints interchangeably to establish an efficacy claim, or pooling different endpoints to constitute a composite endpoint, is the assumption that findings from such endpoints are consistent with each other. While such an assumption about consistency of efficacy findings appears to be intuitive, it is seldom considered in the design and analysis literature of clinical trials with multiple endpoints. Failure to account for consistency of efficacy findings of two candidate endpoints to establish efficacy, at the design stage, has led to difficulties in interpreting study findings. This article presents a flexible testing strategy for accommodating findings of an alternative to the designated primary endpoint (or a subgroup) to support an efficacy claim. The method is built on the following two premises: (i) Efficacy findings of the designated primary endpoint, although nonsignificant, need to be supportive of those of the alternative endpoint, and (ii) the significance level allocated for testing the second endpoint is determined adaptively based on the magnitude of the p-value for the designated primary endpoint. The method takes into account the hierarchical ordering of the hypotheses tested and the correlation between the test statistics for the two endpoints to increase the chance of a positive trial. We discuss control of the type I error rate for the proposed test strategy and compare its power with that of other methods. In addition, we consider its application to two clinical trials.     

A consistency-adjusted strategy for accommodating an underpowered primary endpoint

A clinical trial might involve more than one clinically important endpoint, each of which can characterize the treatment effect of the experimental drug under investigation. Underlying the concept of using such endpoints interchangeably to establish an efficacy claim, or pooling different endpoints to constitute a composite endpoint, is the assumption that findings from such endpoints are consistent with each other. While such an assumption about consistency of efficacy findings appears to be intuitive, it is seldom considered in the design and analysis literature of clinical trials with multiple endpoints. Failure to account for consistency of efficacy findings of two candidate endpoints to establish efficacy, at the design stage, has led to difficulties in interpreting study findings. This article presents a flexible testing strategy for accommodating findings of an alternative to the designated primary endpoint (or a subgroup) to support an efficacy claim. The method is built on the following two premises: (i) Efficacy findings of the designated primary endpoint, although nonsignificant, need to be supportive of those of the alternative endpoint, and (ii) the significance level allocated for testing the second endpoint is determined adaptively based on the magnitude of the p-value for the designated primary endpoint. The method takes into account the hierarchical ordering of the hypotheses tested and the correlation between the test statistics for the two endpoints to increase the chance of a positive trial. We discuss control of the type I error rate for the proposed test strategy and compare its power with that of other methods. In addition, we consider its application to two clinical trials.     

Clinical and statistical issues in therapeutic equivalence trials

Summary Absolute proof of efficacy can only be given by placebo controlled trials. It is, however, important to classify a drug within the spectrum of existing therapeutic alternatives and, where effective treatment is available, it may be imperative due to ethical considerations to demonstrate that one drug is as effective as another.The issue of therapeutic equivalence trials is discussed along the lines of the important items which should be defined in the protocol: a) the target parameter, which is the primary endpoint of the trial, b) the reference drug, which should be selected with respect to efficacy (superior to others), and safety (largest amount of data), c) the acceptance range, which depends on the primary endpoint, and its implication for the clinical endpoints of morbidity and mortality (the conventional acceptance range for bioequivalence trials does not apply), and d) the statistical procedures, which must take into consideration the unsuitability of the conventional power approach for confirming equivalence.In an equivalence trial, compared to those that are placebo-controlled, the proof that one drug is as effective as another relies much more upon the quality of conduct of the study according to Good Clinical Practice.     

Addressing multiplicity issues of a composite endpoint and its components in clinical trials

Randomized controlled clinical trials often use a composite endpoint as a primary endpoint especially when treatment effects or frequency of individual components of the composite are likely to be small and combining them makes clinical sense for the disease under study. An advantage of the composite endpoint is that, as it combines multiple endpoints to a single endpoint, it reduces or eliminates the multiplicity problem of testing multiple endpoints. In addition, accumulating evidence from individual endpoints into the composite endpoint can lead to better study power and reduce the study size and the duration of the trial. However, composite endpoints can also lead to ambiguous findings and consequently cause difficulty in interpreting study results, for example, when individual component endpoints of a composite show treatment effects in different directions. Also, multiplicity issues will arise if a study sponsor seeks efficacy claims for specific components of the composite or for a targeted subgroup of patients. This paper visits some of these issues and presents some solutions through applications of multiple testing strategies.     

Endpoints in respiratory diseases

A wide range of outcome measures or endpoints has been used in clinical trials to assess the effects of treatments in paediatric respiratory diseases. This can make it difficult to compare treatment outcomes from different trials and also to understand whether new treatments offer a real clinical benefit for patients. Clinical trials in respiratory diseases evaluate three types of endpoints: subjective, objective and health-related outcomes. The ideal endpoint in a clinical trial needs to be accurate, precise and reliable. Ideally, the endpoint would also be measured with minimal risk and across all ages, easy to perform, and be inexpensive. As for any other disease, endpoints for respiratory diseases must be viewed in the context of the important distinction between clinical endpoints and surrogate endpoints. The association between surrogate endpoints and clinical endpoints must be clearly defined for any disease in order for them to be meaningful as outcome measures. The most common endpoints which are used in paediatric trials in respiratory diseases are discussed. For practical purposes, diseases have been separated into acute (bronchiolitis, acute viral-wheeze, acute asthma and croup) and chronic (asthma and cystic fibrosis). Further development of endpoints will enable clinical trials in children with respiratory diseases with the main objective of improving prognosis and safety.     

Quality-of-life assessment in phase III clinical trials of gemcitabine in non-small-cell lung cancer

Health-related quality-of-life (QOL) endpoints in clinical trials provide decision makers with a more comprehensive picture of a specific treatment than activity-related endpoints alone. Such endpoints are increasingly being reported in cancer clinical trials. We reviewed phase III clinical trials that involved gemcitabine in the treatment of unresectable non-small-cell lung cancer. A systematic literature search was undertaken and 16 phase III clinical trials were found in which gemcitabine therapy was included in a treatment arm and QOL was an endpoint. Twelve of the 16 trials compared a gemcitabine-based treatment with a non-gemcitabine-based treatment. Not all data were reported in the trials, and the findings are mixed. However, a review of these 12 trials generally shows that gemcitabine-containing chemotherapy treatments had either no different or more favourable QOL outcomes than non-gemcitabine-containing chemotherapy treatments. Ten of the 16 trials that were reviewed had a primary endpoint or objective that was not QOL. Of these ten trials, only four concluded that one treatment arm could be therapeutically favoured over another in terms of the non-QOL primary endpoint. Two of the trials reported no difference in QOL and two reported that QOL favoured the arm that was therapeutically favoured. Many more trials will need to be conducted in order to conclude that gemcitabine-containing arms are associated with a more desirable QOL than non-gemcitabine-containing arms and that QOL necessarily favours the therapeutically favoured arm in the treatment of non-small-cell lung cancer.     

Practical Recommendations for Regional Consistency Evaluation in Multi-Regional Clinical Trials with Different Endpoints

In recent years, there is an increasing trend to conduct multi-regional clinical trials (MRCT) for drug development in pharmaceutical industry. A carefully designed MRCT could be used in supporting the new drug's approval in different regions simultaneously. The primary objective of an MRCT is to investigate the drug's overall efficacy across regions while also assessing the drug's performance in some specific regions. In order to claim the study drug's efficacy and get drug approval in some specific region(s), the local regulatory authority may require the sponsors to provide evidence of consistency in the treatment effect between the overall patient population and the local region. Usually, the regional specific consistency requirement needs to be pre-specified before the study conduct and the consistency in treatment effect between the region(s) of interest and overall population will be evaluated at the final analysis. In this article, we evaluate the consistency requirements in multi-regional clinical trials for different endpoints, that is, continuous, binary, and survival endpoints. We also compare the different consistency requirements of the same endpoint/measurement if multiple consistency requirements are enforced and our recommendations for each endpoint/measurement will be made based on the comprehensive consideration.     

Selecting the primary endpoint in a randomized clinical trial: The ARE method

The decision on the primary endpoint in a randomized clinical trial is of paramount importance and the combination of several endpoints might be a reasonable choice. Gómez and Lagakos (2013) have developed a method that quantifies how much more efficient it could be to use a composite instead of an individual relevant endpoint. From the information provided by the frequencies of observing the component endpoints in the control group and by the relative treatment effects on each individual endpoint, the asymptotic relative efficiency (ARE) can be computed. This article presents the applicability of the ARE method as a practical and objective tool to evaluate which components, among the plausible ones, are more efficient in the construction of the primary endpoint. The method is illustrated with two real cardiovascular clinical trials and is extended to allow for different dependence structures between the times to the individual endpoints. The influence of this choice on the recommendation on whether or not to use the composite endpoint as the primary endpoint for the investigation is studied. We conclude that the recommendation between using the composite or the relevant endpoint only depends on the frequencies of the endpoints and the relative effects of the treatment.     

Evaluation of Early Efficacy Endpoints for Proof-of-Concept Trials

A Phase II proof-of-concept (POC) trial usually uses an early efficacy endpoint other than a clinical endpoint as the primary endpoint. Because of the advancement in bioscience and technology, which has yielded a number of new surrogate biomarkers, drug developers often have more candidate endpoints to choose from than they can handle. As a result, selection of endpoint and its effect size as well as choice of type I/II error rates are often at the center of heated debates in design of POC trials. While optimization of the trade-off between benefit and cost is the implicit objective in such a decision-making process, it is seldom explicitly accounted for in practice. In this research note, motivated by real examples from the oncology field, we provide practical measures for evaluation of early efficacy endpoints (E4) for POC trials. We further provide optimal design strategies for POC trials that include optimal Go–No Go decision criteria for initiation of Phase III and optimal resource allocation strategies for conducting multiple POC trials in a portfolio under fixed resources. Although oncology is used for illustration purpose, the same idea developed in this research note also applies to similar situations in other therapeutic areas or in early-stage drug development in that a Go–No Go decision has to rely on limited data from an early efficacy endpoint and cost-effectiveness is the main concern.     

Assessing the impact of safety monitoring on the efficacy analysis in large Phase III group sequential trials with non-trivial safety event rate

In Phase III clinical trials for life-threatening conditions, some serious but expected adverse events, such as early deaths or congestive heart failure, are often treated as the secondary or co-primary endpoint, and are closely monitored by the Data and Safety Monitoring Committee (DSMC). A naïve group sequential design (GSD) for such a study is to specify univariate statistical boundaries for the efficacy and safety endpoints separately, and then implement the two boundaries during the study, even though the two endpoints are typically correlated. One problem with this naïve design, which has been noted in the statistical literature, is the potential loss of power. In this article, we develop an analytical tool to evaluate this negative impact for trials with non-trivial safety event rates, particularly when the safety monitoring is informal. Using a bivariate binary power function for the GSD with a random-effect component to account for subjective decision-making in safety monitoring, we demonstrate how, under common conditions, the power loss in the naïve design can be substantial. This tool may be helpful to entities such as the DSMCs when they wish to deviate from the prespecified stopping boundaries based on safety measures.     

Bivariate or Composite Plots of Endpoints

Clinical trials often identify two endpoints or response measures of interest. Depending on the drugs and the disease, the endpoints may be correlated or uncorrelated, reflect efficacy or safety, or one or both may be considered as primary. For visualization, plots of each endpoint from baseline to the end of the trial are presented for each treatment group. This paper illustrates the usefulness of developing bivariate, composite plots of both endpoints jointly. Two applications are presented: one of a fixed combination drug for treating allergic rhinitis, and the other of a dose comparison trial in duodenal ulcer.     

Multiplicity Issues in Clinical Trials With Multiple Objectives

Modern clinical trials for evaluating efficacy and safety of new treatments frequently include multiple objectives with questions of varying clinical importance. Answering them generally requires performing a number of statistical tests and analyses which raise multiplicity of tests issues. These issues can be complex and multidimensional in nature. For example, one dimension may relate to the assessment of the effects of the treatment on multiple endpoints, the other to the effects of multiple doses of the treatment, and yet another to the type of the tests (e.g., superiority or noninferiority-type tests). Also, the trial may seek claims for treatment benefits either for the total patient population or for targeted subgroups. In addition, there may be interest in finding whether or not a certain consistency of results persists across certain multiple endpoints; in some situations this may be measuring different but critical features of a disorder, or in other situations may be measuring the same underlying pathophysiology of the disorder. Addressing such problems in clinical trials for the purpose of controlling the Type I error rate requires the use of advanced statistical test strategies and methods, some of which have only recently appeared. Actually, the last decade has witnessed a number of novel methods as well as innovative extensions of old methods for addressing complex multiplicity problems in clinical trials. The main purpose of this article is to present—at the conceptual level—how multiplicity issues of confirmatory clinical trials that include multiple objectives can be addressed by using some of these new statistical methods that use α-propagation and gatekeeping concepts. One additional goal of this contribution is to address some issues that often arise in the use of coprimary and composite endpoints in clinical trials.     

Statistical Considerations for Testing Multiple Endpoints in Group Sequential or Adaptive Clinical Trials

Many clinical trials are designed with a fixed sample size or total number of events to detect a postulated size of treatment effect on a primary efficacy endpoint. When the trial is completed and the primary efficacy endpoint achieves statistical significance, formal statistical testing of other clinically important secondary endpoints often follows in order for the statistically and clinically significant results of these endpoints to be included in the label of the test pharmaceutical product. In conventional fixed designs without any interim analysis or trial extension, these endpoints are often tested in a pre-specified hierarchical order, following the closed testing principle. This testing strategy ensures a strong control of the overall type I error. However, when trials are conducted using a group-sequential design with interim analyses or can be extended using an adaptive design with an increase of sample size or total number of events, this conventional hierarchical testing strategy may violate the closure principle and the overall type I error rate may not be controlled in the strong sense.     

Statistical considerations for testing multiple endpoints in group sequential or adaptive clinical trials

Many clinical trials are designed with a fixed sample size or total number of events to detect a postulated size of treatment effect on a primary efficacy endpoint. When the trial is completed and the primary efficacy endpoint achieves statistical significance, formal statistical testing of other clinically important secondary endpoints often follows in order for the statistically and clinically significant results of these endpoints to be included in the label of the test pharmaceutical product. In conventional fixed designs without any interim analysis or trial extension, these endpoints are often tested in a pre-specified hierarchical order, following the closed testing principle. This testing strategy ensures a strong control of the overall type I error. However, when trials are conducted using a group-sequential design with interim analyses or can be extended using an adaptive design with an increase of sample size or total number of events, this conventional hierarchical testing strategy may violate the closure principle and the overall type I error rate may not be controlled in the strong sense.     

A systematic review of the effectiveness of rivastigmine for the treatment of behavioral disturbances in dementia and other neurological disorders

ABSTRACT

Background: Dementia is frequently associated with behavioral disturbances, some of which have a significant impact on patient quality of life and the likelihood of institutionalization. Cholinergic systems, among other neurotransmitters in the brain, appear to be involved with different behaviors, such as psychosis, depression, agitation, and personality changes.

Scope: This paper reviews the clinical data on the effectiveness of rivastigmine, a dual inhibitor of acetylcholinesterase and butyrylcholinesterase, in ameliorating behavioral disturbances in different patient populations. Relevant articles were identified through MEDLINE searches with no date restrictions.

Findings: In particular, rivastigmine has shown efficacy in treating behavioral disturbances in patients with a wide range of dementias –Alzheimer's disease, vascular dementia, fronto-temporal dementia, mixed dementia, Lewy body dementia, Parkinson's disease with dementia, and schizophrenia with dementia. Most of the studies have been open-label clinical trials with behavior as a secondary endpoint. The behavior domains that most consistently showed improvement were apathy/indifference, anxiety, delusions (psychosis), and hallucinations. The major limitation of this review is that the effects on behavioral symptoms were usually secondary endpoints in clinical trials.

Conclusion: The efficacious effects of treatment with rivastigmine on various behavioral disturbances provide supporting evidence that cholinergic mechanisms, among other neurotransmitters, are involved in the manifestation of some behavioral and psychological symptoms of dementia.     

Statistical challenges in a regulatory review of cardiovascular and CNS clinical trials

Abstract

There are several challenging statistical problems identified in the regulatory review of large cardiovascular (CV) clinical outcome trials and central nervous system (CNS) trials. The problems can be common or distinct due to disease characteristics and the differences in trial design elements such as endpoints, trial duration, and trial size. In schizophrenia trials, heavy missing data is a big problem. In Alzheimer trials, the endpoints for assessing symptoms and the endpoints for assessing disease progression are essentially the same; it is difficult to construct a good trial design to evaluate a test drug for its ability to slow the disease progression. In CV trials, reliance on a composite endpoint with low event rate makes the trial size so large that it is infeasible to study multiple doses necessary to find the right dose for study patients. These are just a few typical problems. In the past decade, adaptive designs were increasingly used in these disease areas and some challenges occur with respect to that use. Based on our review experiences, group sequential designs (GSDs) have borne many successful stories in CV trials and are also increasingly used for developing treatments targeting CNS diseases. There is also a growing trend of using more advanced unblinded adaptive designs for producing efficacy evidence. Many statistical challenges with these kinds of adaptive designs have been identified through our experiences with the review of regulatory applications and are shared in this article.     

Statistical considerations for rare diseases drug development

ABSTRACT

One of the most challenges for rare disease clinical trials is probably the availability of a small patient population. It is then a great concern on how to conduct clinical trials with a small number of subjects available for obtaining substantial evidence regarding safety and effectiveness for approval of the rare disease drug product under investigation. FDA, however, does not have the intention to create a statutory standard for approval of orphan drugs that are different from the standard for approval of drugs in common conditions. Thus, it is suggested that innovative trial designs such as a complete n-of-1 trial design or an adaptive design should be used for an accurate and reliable assessment of rare disease drug products under investigation. In this article, basic considerations, innovative trial designs, and statistical methods for data analysis are discussed. In addition, some innovative thinking for the evaluation of rare disease drug products is proposed.     

Surrogate endpoints: A basis for a rational approach

Summary In clinical trials, the clinical endpoint is often replaced by an intermediate endpoint, known in some instances as a “surrogate” endpoint. The reasons for the substitution are often both practical and financial. At present, no theoretical basis or practical guidelines exist to help in the choice of surrogate endpoints. An approach is proposed here, based on three provisos which can be verified using one of a series of equations, if sufficient data on the pathophysiology and epidemiology of the disease are available. It is shown that even a strong statistical correlation is not a sufficient criterion for the definition of a surrogate endpoint. It is apparent that results obtained with the commonly used “surrogate” endpoints should be cautiously considered and that the assessment of treatments should, when possible, be based on clinical rather than intermediate endpoints.