Dynamic borrowing from a single prior data source using the conditional power prior

The conditional power prior is a popular method to borrow information from a single prior data source. The amount of borrowing is controlled by the power parameter which is fixed before running the new study. However, fixing this parameter before running a new study is often difficult and may be unwise because if the outcomes in the current study are much different from the prior data outcomes, the power parameter cannot be changed to reflect a more appropriate degree of borrowing. On the other hand, treating the power parameter as a random variable to be updated via Bayes theorem may relinquish control over how much to borrow in cases where regulatory oversight recommends a conservative approach.Previous authors have determined the power parameter at the end of the current study based on "stochastic" similarity in the outcomes between the current study and the prior data. In this paper, we introduce some modifications to those methods. First, we determine the power parameter based on similarity between a percentage of the current study outcome data available at an interim look and the prior outcome data. This may limit potential for operational bias resulting from the determination of the power parameter after the current study is complete. Next, we introduce a new measure of similarity between the current (interim) and prior data that limits similarity by a pre-specified clinical margin. The proposed clinical similarity region may be readily understood by clinicians who need to assess when such borrowing is clinically appropriate. Through simulations, we show that our approach has low bias and good power, while reducing type I error rate in areas outside of the "similarity region". An example of a hypothetical medical device study illustrates its potential use in practice.

Noninferiority testing for matched-pair ordinal data with misclassification

New treatments that are noninferior or equivalent to-but not necessarily superior to-the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient-reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.

Non-inferiority margins employed in clinical trials in Japan

WHAT IS KNOWN AND OBJECTIVE

Determination of the non-inferiority margin is one of the major and most difficult considerations when planning a non-inferiority clinical trial. This article aims to list the non-inferiority margins employed in recent clinical drug-development trials in Japan.

METHODS

We investigated non-inferiority margins by reviewing new drug-development dossiers for drugs approved between January 2010 and December 2012 in Japan.

RESULTS AND DISCUSSION

We identified 174 non-inferiority trials, where the efficacy of the test drug was compared to that of a control drug. We have described 70 clinical endpoints and the corresponding non-inferiority margins. In antidiabetes drug trials, a margin of 0·4% mean difference in haemoglobin A1c level was used most frequently. In trials for glaucoma and ocular hypertension, 1·5 mmHg mean difference in intra-ocular pressure value was the commonest margin. A 10% margin of proportion difference was the most frequently chosen in trials of anti-infection drugs. We have provided a short description of the methods used to determine the non-inferiority margin.

WHAT IS NEW AND CONCLUSION

We report on the non-inferiority margins used for a range of endpoints in recent drug-development trials for a number of different diseases. We hope that the details would be helpful to those appraising, reporting or designing non-inferiority trials.

A note on partial covariate-adjustment and design considerations in noninferiority trials when patient-level data are not available

The traditional fixed margin approach to evaluating an experimental treatment through an active-controlled noninferiority trial is simple and straightforward. However, its utility relies heavily on the constancy assumption of the experimental data. The recently developed covariate-adjustment method permits more flexibility and improved discriminatory capacity compared to the fixed margin approach. However, one major limitation of this covariate-adjustment methodology is its adherence on the patient-level data, which may not be accessible to investigators in practice. In this article, under some assumptions, we examine the feasibility of a partial covariate-adjustment approach based on data typically available from journal publications or other public data when the patient-level data are unavailable. We illustrate the usefulness of this approach through two real examples. We also provide design considerations on the efficiency of the partial covariate-adjustment approach.

Bayesian approach to non-inferiority trials for normal means

Regulatory framework recommends that novel statistical methodology for analyzing trial results parallels the frequentist strategy, e.g. the new method must protect type-I error and arrive at a similar conclusion. Keeping these in mind, we construct a Bayesian approach for non-inferiority trials with normal response. A non-informative prior is assumed for the mean response of the experimental treatment and Jeffrey's prior for its corresponding variance when it is unknown. The posteriors of the mean response and variance of the treatment in historical trials are then assumed as priors for its corresponding parameters in the current trial, where that treatment serves as the active control. From these priors, a Bayesian decision criterion is derived to determine whether the experimental treatment is non-inferior to the active control. This criterion is evaluated and compared with the frequentist method using simulation studies. Results show that both Bayesian and frequentist approaches perform alike, but the Bayesian approach has a higher power when the variances are unknown. Both methods also arrive at the same conclusion of non-inferiority when applied on two real datasets. A major advantage of the proposed Bayesian approach lies in its ability to provide posterior probabilities for varying effect sizes of the experimental treatment over the active control.

A covariate-adjustment regression model approach to noninferiority margin definition

To maintain the interpretability of the effect of experimental treatment (EXP) obtained from a noninferiority trial, current statistical approaches often require the constancy assumption. This assumption typically requires that the control treatment effect in the population of the active control trial is the same as its effect presented in the population of the historical trial. To prevent constancy assumption violation, clinical trial sponsors were recommended to make sure that the design of the active control trial is as close to the design of the historical trial as possible. However, these rigorous requirements are rarely fulfilled in practice. The inevitable discrepancies between the historical trial and the active control trial have led to debates on many controversial issues. Without support from a well-developed quantitative method to determine the impact of the discrepancies on the constancy assumption violation, a correct judgment seems difficult. In this paper, we present a covariate-adjustment generalized linear regression model approach to achieve two goals: (1) to quantify the impact of population difference between the historical trial and the active control trial on the degree of constancy assumption violation and (2) to redefine the active control treatment effect in the active control trial population if the quantification suggests an unacceptable violation. Through achieving goal (1), we examine whether or not a population difference leads to an unacceptable violation. Through achieving goal (2), we redefine the noninferiority margin if the violation is unacceptable. This approach allows us to correctly determine the effect of EXP in the noninferiority trial population when constancy assumption is violated due to the population difference. We illustrate the covariate-adjustment approach through a case study.

Issues with Statistical Risks for Testing Methods in Noninferiority Trial Without a Placebo ARM

Noninferiority trials without a placebo arm often require an indirect statistical inference for assessing the effect of a test treatment relative to the placebo effect or relative to the effect of the selected active control treatment. The indirect inference involves the direct comparison of the test treatment with the active control from the noninferiority trial and the assessment, via some type of meta-analyses, of the effect of the active control relative to a placebo from historical studies. The traditional within-noninferiority-trial Type I error rate cannot ascertain the statistical risks associated with the indirect inference, though this error rate is of the primary consideration under the frequentist statistical framework. Another kind of Type I error rate, known as across-trial Type I error rate, needs to be considered in order that the statistical risks associated with the indirect inference can be controlled at a small level. Consideration of the two kinds of Type I error rates is also important for defining a noninferiority margin. For the indirect statistical inference, the practical utility of any method that controls only the across-trial Type I error rate at a fixed small level is limited.

Establishing efficacy of a new experimental treatment in the 'gold standard' design

Provided that there are no ethical concerns, the comparison of an active drug with placebo in a randomized two-arm clinical trial provides the most convincing way to demonstrate the efficacy of a new experimental treatment. However, in a placebo-controlled clinical trial it is not sufficient to demonstrate merely a statistically significant treatment difference. Regulatory authorities strongly recommend to assess additionally whether the observed treatment difference is also of clinical relevance. The inherent issue is the necessity of the a priori definition of what constitutes a clinically relevant difference in efficacy. This problem can be solved in a three-arm study by including an active control group. We address the necessary conditions in the gold standard design which allow the claim of efficacy for the new treatment with particular focus on assay sensitivity.

A regulatory perspective on choice of margin and statistical inference issue in non-inferiority trials

Without a placebo arm, any non-inferiority inference involving assessment of the placebo effect under the active control trial setting is difficult. The statistical risk for falsely concluding non-inferiority cannot be evaluated unless the constancy assumption approximately holds that the effect of the active control under the historical trial setting where the control effect can be assessed carries to the noninferiority trial setting. The constancy assumption cannot be checked because of missing the placebo arm in the non-inferiority trial. Depending on how serious the violation of the assumption is thought to be, one may need to seek an alternative design strategy that includes a cushion for a very conservative non-inferiority analysis or shows superiority of the experimental treatment over the control. Determination of the non-inferiority margin depends on what objective the non-inferiority analysis is intended to achieve. The margin can be a fixed margin or a margin functionally defined. Between-trial differences always exist and need to be properly considered.

The effects of non-compliance on intent-to-treat analysis of equivalence trials

The standard approach for analysing a randomized clinical trial is based on intent-to-treat (ITT) where subjects are analysed according to their assigned treatment group regardless of actual adherence to the treatment protocol. For therapeutic equivalence trials, it is a common concern that an ITT analysis increases the chance of erroneously concluding equivalence. In this paper, we formally investigate the impact of non-compliance on an ITT analysis of equivalence trials with a binary outcome. We assume 'all-or-none' compliance and independence between compliance and the outcome. Our results indicate that non-compliance does not always make it easier to demonstrate equivalence. The direction and magnitude of changes in the type I error rate and power of the study depend on the patterns of non-compliance, event probabilities, the margin of equivalence and other factors.

Choosing an equivalence limit for noninferiority or equivalence studies

Studies that compare treatments with the purpose of demonstrating that the treatments are similar require an a priori definition of an equivalence limit, how different the treatments can be before the difference is of concern. Defining such an equivalence limit is one of the most difficult aspects of planning the study. Three principles are proposed for setting such limits, depending on the objective of the study: a putative placebo calculation, an approach based on clinically important differences, and methods based on statistical properties. All methods will be useful for many studies, but the study objective should determine the final choice of an equivalence limit. The statistician must play an integral role in determining the final equivalence limit. Advice is offered for helping the statistician participate in the decision on the equivalence limits.