Biomedical subjects

George Y H Chi

Publications and source records attributed to George Y H Chi.

5 recordsLinked to original sources

PubMed2006

A ratio test in active control non-inferiority trials with a time-to-event endpoint.

There are essentially two kinds of non-inferiority hypotheses in an active control trial: fixed margin and ratio hypotheses. In a fixed margin hypothesis, the margin is a prespecified constant and the hypothesis is defined in terms of a single parameter that represents the effect of the active treatment relative to the control. The statistical inference for a fixed margin hypothesis is straightforward. The outstanding issue for a fixed margin non-inferiority hypothesis is how to select the margin, a task that may not be as simple as it appears. The selection of a fixed non-inferiority margin has been discussed in a few articles (Chi et al., 2003; Hung et al., 2003; Ng, 1993). In a ratio hypothesis, the control effect is also considered as an unknown parameter, and the noninferiority hypothesis is then formulated as a ratio in terms of these two parameters, the treatment effect and the control effect. This type of non-inferiority hypothesis has also been called the fraction retention hypothesis because the ratio hypothesis can be interpreted as a retention of certain fraction of the control effect. Rothmann et al. (2003) formulated a ratio non-inferiority hypothesis in terms of log hazards in the time-to-event setting. To circumvent the complexity of having to deal with a ratio test statistic, the ratio hypothesis was linearized to an equivalent hypothesis under the assumption that the control effect is positive. An associated test statistic for this linearized hypothesis was developed. However, there are three important issues that are not addressed by this method. First, the retention fraction being defined in terms of log hazard is difficult to interpret. Second, in order to linearize the ratio hypothesis, Rothmann's method has to assume that the true control effect is positive. Third, the test statistic is not powerful and thus requires a huge sample size, which renders the method impractical. In this paper, a ratio hypothesis is defined directly in terms of the hazard. A natural ratio test statistic can be defined and is shown to have the desired asymptotic normality. The demand on sample size is much reduced. In most commonly encountered situations, the sample size required is less than half of those needed by either the fixed margin approach or Rothmann's method.

Clinical Trials as Topic↗

PubMed2005

Some issues with composite endpoints in clinical trials.

This article discusses some important issues that may arise in the current usage of composite endpoints as primary endpoints for demonstrating the efficacy of new drugs in clinical trials. The discussion focuses on time-to-event composite endpoints. Issues discussed include validity of a composite endpoint, the often lack of follow-up of patients beyond first event, the analysis of a composite endpoint, its sub-composite and individual component endpoints and their interpretation. Actual published examples in the literature are used to illustrate some of these problems. It is recommended that a clinical trial using a composite endpoint as the primary endpoint should be designed to include patient follow-up beyond the first event if possible. For data collected from such trials, basic formats for tabular presentation of trial data and for results of analysis of the composite endpoint, its sub-composite and individual component endpoints are proposed for transparency and ease of interpretation.

Clinical Trials as Topic↗

PubMed2004

Evaluation of health-related quality-of-life measures in oncology drug product applications: issues and concerns.

Health-related quality-of-life outcomes as reported by patients are valuable data and ideally should be critical to evaluating clinical benefit. The unblinded or open-label designs commonly adapted in oncology trials have the potential to introduce selection bias, reporting bias, and analyses bias. In this paper, issues surrounding use of patient reported outcomes to evaluate oncology drug products, including definition of hypothesis, study design, analysis, and interpretation of patient reported outcome data, are reported.

Antineoplastic Agents↗

PubMed2004

Hypotheses and type I error in active-control noninferiority trials.

A fundamental assumption in the design and analysis of an active-control noninferiority trial is that the active control is truly effective. If this assumption does not hold, i.e., the active control is not effective, a harmful drug may be approved based on the result of a noninferiority trial. The assessment of the assumption is usually based on statistically significant results of historical randomized clinical trials on the active control, in which the conclusion may be falsely positive. This false positive rate, however, is not taken into consideration in current noninferiority trial test procedures. In this paper, various possible hypotheses for noninferiority trials are presented. The noninferiority hypotheses correctly associated with the objective of noninferiority trials are suggested. However, since there are no data collected for placebo in a noninferiority trial, this hypothesis cannot be directly tested by using data from noninferiority studies alone. The claim of noninferiority is based on the significant test for the control effect in historical trials and the significant test for noninferiority in a current noninferiority trial with a given fraction retention or margin. The false positive rate associated with such noninferiority test procedure is defined in this paper. The simulation result demonstrates the magnitude of the false positive rate inflation associated with the noninferiority test procedure.

Clinical Trials as Topic↗

PubMed2003

Design and analysis of non-inferiority mortality trials in oncology.

The recent revision of the Declaration of Helsinki and the existence of many new therapies that affect survival or serious morbidity, and that therefore cannot be denied patients, have generated increased interest in active-control trials, particularly those intended to show equivalence or non-inferiority to the active-control. A non-inferiority hypothesis has historically been formulated in terms of a fixed margin. This margin was historically designed to exclude a 'clinically meaningful difference', but has become recognized that the margin must also be no larger than the assured effect of the control in the new study. Depending on how this 'assured effect' is determined or estimated, the selected margin may be very small, leading to very large sample sizes, especially when there is an added requirement that a loss of some specified fraction of the assured effect must be ruled out. In cases where it is appropriate, this paper proposes non-inferiority analyses that do not involve a fixed margin, but can be described as a two confidence interval procedure that compares the 95 per cent two-sided CI for the difference between the treatment and the control to a confidence interval for the control effect (based on a meta-analysis of historical data comparing the control to placebo) that is chosen to preserve a study-wide type I error rate of about 0.025 (similar to the usual standard for a superiority trial) for testing for retention of a prespecified fraction of the control effect. The approach assumes that the estimate of the historical active-control effect size is applicable in the current study. If there is reason to believe that this effect size is diminished (for example, improved concomitant therapies) the estimate of this historical effect could be reduced appropriately. The statistical methodology for testing this non-inferiority hypothesis is developed for a hazard ratio (rather than an absolute difference between treatments, because a hazard ratio seems likely to be less population dependent than the absolute difference). In the case of oncology, the hazard ratio is the usual way of comparing treatments with respect to time to event (time to progression or survival) endpoints. The proportional hazards assumption is regarded as reasonable (approximately holding). The testing procedures proposed are conditionally equivalent to two confidence interval procedures that relax the conservatism of two 95 per cent confidence interval testing procedures and preserve the type I error rate at a one-sided 0.025 level. An application of this methodology to Xeloda, a recently approved drug for the treatment of metastatic colorectal cancers, is illustrated. Other methodologies are also described and assessed - including a point estimate procedure, a Bayesian procedure and two delta-method confidence interval procedures. Published in 2003 by John Wiley & Sons, Ltd.

Antimetabolites, Antineoplastic↗