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Sue-Jane Wang

Publications and source records attributed to Sue-Jane Wang.

11 recordsLinked to original sources
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

Sample size for gene expression microarray experiments.

MOTIVATION: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size. RESULTS: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays. CONTACT: jchen@nctr.fda.gov.

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↗
PubMed2004

Sample size for identifying differentially expressed genes in microarray experiments.

Microarray technology allows simultaneous comparison of expression levels of thousands of genes under each condition. This paper concerns sample size calculation in the identification of differentially expressed genes between a control and a treated sample. In a typical experiment, only a fraction of genes (altered genes) is expected to be differentially expressed between two samples. Sample size determination depends on a number of factors including the specified significance level (alpha), the desired statistical power (1-beta), the fraction (eta) of truly altered genes out of the total g genes studied, and the effect sizes (Delta) for the altered genes. This paper proposes a method to calculate the number of arrays required to detect at least 100lambda % (where 0 < lambda < or = 1) of the truly altered genes under the model of an equal effect size for all altered genes. The required numbers of arrays are tabulated for various values of alpha, beta, Delta, eta, and lambda for the one-sample and two-sample t-tests for g = 10,000. Based on the proposed approach, to identify up to 90% of truly altered genes among the unknown number of truly altered genes, the estimated numbers of arrays needed appear to be manageable. For instance, when the standardized effect size is at least 2.0, the number of arrays needed is less than or equal to 14 for the two-sample t-test and is less than or equal to 10 for the one-sample t-test. As the cost per array declines, such array numbers become practical. The proposed method offers a simple, intuitive, and practical way to determine the number of arrays needed in microarray experiments in which the true correlation structure among the genes under investigation cannot be reasonably assumed. An example dataset is used to illustrate the use of the proposed approach to plan microarray experiments.

Animals↗
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↗
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

Short of complete abstinence: an analysis exploration of multiple drinking episodes in alcoholism treatment trials.

BACKGROUND: In alcoholism treatment clinical trials, conventional analysis of efficacy outcomes often focuses on the time to a first event, where the event may be "any drinking", "safe (or low risk) drinking", "moderate drinking" or "heavy drinking," in addition to multiple outcomes such as frequency of drinking days, percent abstinence days, etc. METHODS: We consider the multivariate failure time analytic methods. In alcoholism treatment trials, the naturalistic course of drinking behavior during treatment intervention often presents with a gradual change in drinking before the emergence of a more stable drinking or abstinence pattern. Thus, for each subject, evaluation of all drinking events, and incorporating the event times over a defined duration, may give a more comprehensive description of his/her drinking pattern. As a consequence, the efficacy of a new treatment for alcoholism may be elevated with greater statistical sensitivity. RESULTS: The utility of the multiple failure time method is demonstrated via a real case study for evaluation of alcoholism treatments. The multiple event time analyses showed that the risk of having "any drinking days" or "heavy drinking days" during the entire duration of the study was significantly lower with experimental treatment than with placebo. Further explorations showed that the treatment effect was primarily observed in the later relapse events and not the first event with respect to relapse to any drinking episodes. Such effect would have missed using the traditional time to first event analysis approach. The observed effect of treatment with respect to relapse to multiple heavy drinking episodes was shown not only in the first event but also in the later events. CONCLUSION: The multiple failure time approach may be applicable when 'drinking failure' is variously defined as a single drink, one at-risk drinking day, one heavy drinking day, or one alcohol-related social, occupational or medical problem. If "a drinking episode" is properly defined and the design gains statistical efficiency, the multiple event analytic strategy should provide improved statistical power to detect treatment effects.

Alcohol Drinking↗