Biomedical subjects

H M James Hung

Publications and source records attributed to H M James Hung.

13 recordsLinked to original sources

PubMed2006

A regulatory view on adaptive/flexible clinical trial design.

Recently there is growing interest in use of adaptive or flexible designs for development of pharmaceutical products. Statistical methodology has been greatly advanced in the literature. However, there are still some important issues with the methodology and application. In addition, there are many other challenges with these designs, including efficiency of these designs in the entire development program, trial conduct and logistics, the infrastructure of an adaptive trial, the regulatory evaluation of trial results and trial conduct, etc. Up till now, regulatory experience in these designs is very limited. We share some of the challenges.

Biometry↗

PubMed2005

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.

Biopharmaceutics↗

PubMed2005

Adaptive covariate adjustment in clinical trials.

In analysis of covariance (ANCOVA), as a result of covariate adjustment, the estimated mean difference between the two comparative treatment groups may have a better precision than the unadjusted estimate. The extent of improvement of precision depends on the correlation between the outcome variable and the covariate selected for adjustment. Therefore, for this purpose, it is desirable to apply a proper transformation to this covariate so that the transformed covariate has a stronger correlation with the outcome variable. The best predictor from the covariate for the outcome variable is the conditional expectation of the outcome variable given the covariate. Thus, a viable strategy is using regression modeling approach to search for a statistical model to well approximate the conditional expectation based on external and/or current trial data. We propose an adaptive strategy to achieve this goal if the current data are needed to help the search.

Algorithms↗

PubMed2005

Adaptive statistical analysis following sample size modification based on interim review of effect size.

In designing a comparative clinical trial, the required sample size is a function of the effect size, the value of which is unknown and at best may be estimated from historical data. Insufficiency in sample size as a result of overestimating the effect size can be destructive to the success of the clinical trial. Sample size re-estimation may need to be properly considered as a part of clinical trial planning. This paper is intended to give the motivations for the sample size re-estimation based partly on the effect size observed at an interim analysis and for a resulting simple adaptive test strategy. The performance of this adaptive design strategy is assessed by comparing it with a fixed maximum sample size design that is properly adjusted in anticipation of the possible sample size adjustment.

Algorithms↗

PubMed2004

Multiple testing of noninferiority hypotheses in active controlled trials.

For noninferiority testing with the maximum allowable noninferiority margin being prespecified, one can perform valid statistical testing at the same alpha level for multiple noninferiority hypotheses with margins being smaller than this maximum margin. This is easily comprehensible because only one confidence level is used to assess which margins within the interval bounded by the maximum margin can be ruled out. If different confidence intervals are used, e.g., the interval generated from the intent-to-treat population is used for testing superiority and the interval generated from the per-protocol population is used for testing noninferiority, the problem of multiplicity will surface and the adjustment of alpha for each testing may be needed. All these predicate on the condition that at least a certain element of the maximum allowable noninferiority margin, whether it is the entire margin or the fraction of the active control effect to be retained, must be fixed in advance. None of these elements can be allowed to be influenced directly or indirectly by any analysis of the noninferiority trial data. Otherwise, the noninferiority analysis may be invalid.

Controlled Clinical Trials as Topic↗

PubMed2003

Some fundamental issues with non-inferiority testing in active controlled trials.

In an active controlled non-inferiority trial without a placebo arm, it is often not entirely clear what the primary objective is. In many cases the considered goal is to demonstrate that the experimental treatment preserves at least some fraction of the effect of the active control. The active control effect is a parameter, the value of which is unknown. To test the hypothesis of effect preservation, the classical confidence interval approach requires specification of a non-inferiority margin which is a function of the unknown active control effect. When the margin is estimated, it is also not clear what is the relevant type I error of making a false assertion about preservation of the active control effect. The statistical uncertainty of the estimated margin arguably needs to be incorporated in evaluation of the type I error. In this paper we discuss these fundamental issues. We show that the classical confidence interval approach cannot attain the target type I error exactly since this error varies as the sample size or as the values of the nuisance parameters in the active controlled trial change. In contrast, the preservation tests, as proposed in literature, can attain the target type I error rate exactly, regardless of the sample size and the values of the nuisance parameters, but can do so only at the price of several strong assumptions holding that may not be directly verifiable. One assumption is the constancy condition holding whereby the effect of the active control in the historical trial populations is assumed to carry to the population of the active control trial. When this condition is violated, both the confidence interval approach and the preservation test method may be problematic.

Confidence Intervals↗

PubMed2003

TACT method for non-inferiority testing in active controlled trials.

In active controlled trials without a placebo arm, non-inferiority testing is often considered but has different objectives. For the objective of demonstrating the efficacy of an experimental treatment or retention of a fraction of the control effect by the treatment, there are two types of statistical methods for testing - the synthesis method and the confidence interval method. According to the study of Wang, Hung and Tsong, the former is efficient under the so-called constancy condition but may have the alpha error rate inflate rapidly if the condition does not hold. In contrast, the latter method with careful selection of the non-inferiority margin tends to be conservative if the condition holds and may still have a valid alpha error otherwise unless the effect of the active control is less to a large extent in the active controlled trial than in the historical trials. We developed the TACT method, Two-stage Active Control Testing, as a viable compromise between the two methods. Through the TACT method, the uninterpretable non-inferiority testing may be avoided prior to the end of the trial. The TACT method carefully constructed can have a valid alpha error rate and the power close to the synthesis method if the constancy condition holds. In addition, the TACT method is more powerful than the confidence interval method for testing for the efficacy of the new treatment relative to the putative placebo and for showing that the new treatment is not inferior to the active control comparator.

Confidence Intervals↗

PubMed2003

Assessing treatment efficacy in noninferiority trials.

Often one of the primary objectives of an active-controlled noninferiority trial without a placebo arm is to assert that an experimental treatment would have been more effective than a putative placebo had the placebo been included in the trial. This may be an important consideration for regulatory applications. To achieve this objective, such a noninferiority analysis entails cross-trial statistical inference. Because of the uncertainty and difficulty surrounding cross-trial inference, the noninferiority analysis often aims to demonstrate that the experimental treatment preserves a specified fraction of the effect of the active control. The rationale is that by demonstrating the percent effect retention, the efficacy of the experimental treatment can be established with a great level of confidence. The confidence interval approach and synthesized test approach have been used for inferring the percent effect preservation. In this work we evaluate the type I error rates of these approaches to the cross-trial statistical inference for establishing treatment efficacy. The evaluation provides guidance as to what percentage of the control effect needs to be preserved so that through noninferiority testing of effect retention one can assert the treatment efficacy within a desired level of the error rate.

Clinical Trials as Topic↗

PubMed2003

Statistical issues on objective, design, and analysis of noninferiority active-controlled clinical trial.

In practice, "noninferiority" active-controlled trials have been designed for three different objectives: establishing evidence of efficacy over placebo, preserving a specific percentage of the effect size of the active control, or demonstrating the test treatment is "not much inferior" to the active control. All three objectives can be represented by the same set of statistical hypotheses with the parameters defined differently. The various designs and statistical analysis procedures for active-controlled trials proposed in the literature can be group into two basic types: the historical-controlled trial approach and the cross-study comparison approach. These approaches require some unverifiable constancy assumptions. Under the constancy assumptions, the cross-study comparison uses the estimate effect of active-control treatment as the unbiased estimate of the active/placebo difference in the current noninferiority trial. A normalized Z-statistic is used to test the hypotheses. On the other hand, the historical controlled trial approach uses a conservative confidence limit as if it were a constant to replace the active/placebo difference in the current trial. The two approaches may lead to consistent conclusions only when the constancy assumptions can be supported by a large number of historical studies giving a consistent active-control treatment effect over placebo and that the active-control effect does not change over time.

Controlled Clinical Trials as Topic↗

PubMed2002

Utility and pitfalls of some statistical methods in active controlled clinical trials.

Increasingly often, the study objective in an active controlled clinical trial without a placebo arm is to show that a new treatment is no less effective than the active control treatment within some noninferiority range. Two issues behind this objective are that of whether the new treatment is efficacious relative to a putative placebo and that of whether the new treatment preserves a certain fraction of effect of the active control. To address these issues, two types of statistical analysis methods are employed in recent pharmaceutical applications. In one type of method, a noninferiority margin is determined, and then the relative effect of the new treatment versus the control is compared against the margin to test noninferiority and the efficacy of the new treatment. In the other type of method, a synthetic statistic is constructed to directly estimate or test the effect of the new treatment relative to the putative placebo without resorting to noninferiority argument. Preservation of control effect can also be estimated and tested. These methods carry some crucial assumptions. The effect of active control is often estimated from a collection of historical placebo controlled trials using the random effects modeling of DerSimonian and Laird. In this work we find that statistical validity of the latter method rests highly on the assumptions that control effect is not reduced in the current active controlled trial population compared to the historical trials and that a normal approximation is appropriate in the random effects modeling. This type of method is very sensitive to departure from these assumptions. In contrast, the former method is ultraconservative in terms of type I error when the assumptions are met and can be anticonservative when control effect is substantially less in the active controlled trial than estimated from the historical placebo controlled trials.

Controlled Clinical Trials as Topic↗

PubMed2002

Issues related to subgroup analysis in clinical trials.

Interpretation of subgroup findings is a difficult task. The attempt of this article is to clarify confusions on subgroup analysis and to give some practical suggestions on how to avoid mistakes in interpreting subgroup outcome. We believe that the correct interpretation of subgroup findings is closely related to the intrinsic statistical property and validity of the subgroup analysis. A systematic discussion on subgroup analysis from a statistical point of view will be helpful to clinical trial practitioners.

Bias↗

PubMedDate not supplied

Adapting the sample size planning of a phase III trial based on phase II data.

Traditionally, in clinical development plan, phase II trials are relatively small and can be expected to result in a large degree of uncertainty in the estimates based on which Phase III trials are planned. Phase II trials are also to explore appropriate primary efficacy endpoint(s) or patient populations. When the biology of the disease and pathogenesis of disease progression are well understood, the phase II and phase III studies may be performed in the same patient population with the same primary endpoint, e.g. efficacy measured by HbA1c in non-insulin dependent diabetes mellitus trials with treatment duration of at least three months. In the disease areas that molecular pathways are not well established or the clinical outcome endpoint may not be observed in a short-term study, e.g. mortality in cancer or AIDS trials, the treatment effect may be postulated through use of intermediate surrogate endpoint in phase II trials. However, in many cases, we generally explore the appropriate clinical endpoint in the phase II trials. An important question is how much of the effect observed in the surrogate endpoint in the phase II study can be translated into the clinical effect in the phase III trial. Another question is how much of the uncertainty remains in phase III trials. In this work, we study the utility of adaptation by design (not by statistical test) in the sense of adapting the phase II information for planning the phase III trials. That is, we investigate the impact of using various phase II effect size estimates on the sample size planning for phase III trials. In general, if the point estimate of the phase II trial is used for planning, it is advisable to size the phase III trial by choosing a smaller alpha level or a higher power level. The adaptation via using the lower limit of the one standard deviation confidence interval from the phase II trial appears to be a reasonable choice since it balances well between the empirical power of the launched trials and the proportion of trials not launched if a threshold lower than the true effect size of phase III trial can be chosen for determining whether the phase III trial is to be launched.

Clinical Trials, Phase II as Topic↗

PubMedDate not supplied

Methodological issues with adaptation of clinical trial design.

Adaptation of clinical trial design generates many issues that have not been resolved for practical applications, though statistical methodology has advanced greatly. This paper focuses on some methodological issues. In one type of adaptation such as sample size re-estimation, only the postulated value of a parameter for planning the trial size may be altered. In another type, the originally intended hypothesis for testing may be modified using the internal data accumulated at an interim time of the trial, such as changing the primary endpoint and dropping a treatment arm. For sample size re-estimation, we make a contrast between an adaptive test weighting the two-stage test statistics with the statistical information given by the original design and the original sample mean test with a properly corrected critical value. We point out the difficulty in planning a confirmatory trial based on the crude information generated by exploratory trials. In regards to selecting a primary endpoint, we argue that the selection process that allows switching from one endpoint to the other with the internal data of the trial is not very likely to gain a power advantage over the simple process of selecting one from the two endpoints by testing them with an equal split of alpha (Bonferroni adjustment). For dropping a treatment arm, distributing the remaining sample size of the discontinued arm to other treatment arms can substantially improve the statistical power of identifying a superior treatment arm in the design. A common difficult methodological issue is that of how to select an adaptation rule in the trial planning stage. Pre-specification of the adaptation rule is important for the practicality consideration. Changing the originally intended hypothesis for testing with the internal data generates great concerns to clinical trial researchers.

Clinical Trials as Topic↗