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Ying Kuen Cheung

Publications and source records attributed to Ying Kuen Cheung.

7 recordsLinked to original sources
PubMed2006

Selecting promising ALS therapies in clinical trials.

Riluzole is the only approved medication that extends survival for patients with amyotrophic lateral sclerosis (ALS). While other potential neuroprotective agents have been evaluated in randomized clinical trials, none has shown unequivocal success and none has been approved by regulatory agencies. Few symptomatic therapies have been tested in ALS. Effectiveness for drugs with modest benefit can be established only through large phase III randomized clinical trials. With numerous potential agents but limited resources, priority should be given to agents that show promise in phase II trials before proceeding to evaluation in phase III trials. In this article, we review drug development in early phase ALS trials and introduce novel designs. First, to maximize the therapeutic potential of the test medication, we need to identify the highest dose that produces a tolerable level of side effects. Second, candidate treatments should be ranked by conducting randomized selection trials between competing new treatments. The selection paradigm adopts a statistical viewpoint different from the hypothesis testing framework in conventional trials. We exemplify this approach by describing a group-sequential selection design developed for a phase II, randomized, multicenter trial of two combination treatments in patients with ALS, and illustrate the sample size reduction from a conventional trial.

Amyotrophic Lateral Sclerosis↗
PubMed2006

Continuous Bayesian adaptive randomization based on event times with covariates.

In comparative clinical trials, the randomization probabilities may be unbalanced adaptively by utilizing the interim data available at each patient's entry time to favour the treatment or treatments having comparatively superior outcomes. This is ethically appealing because, on average, more patients are assigned to the more successful treatments. Consequently, physicians are more likely to enroll patients onto trials where the randomization is outcome-adaptive rather than balanced in the conventional manner. Outcome-adaptive methods based on a binary variable may be applied by reducing an event time to the indicator of the event's occurrence within a predetermined time interval. This results in a loss of information, however, since it ignores the censoring times of patients who have not experienced the event but whose evaluation interval is not complete. This paper proposes and compares exact and approximate Bayesian outcome-adaptive randomization procedures based on time-to-event outcomes. The procedures account for baseline prognostic covariates, and they may be applied continuously over the course of the trial. We illustrate these methods by application to a phase II selection trial in acute leukaemia. A simulation study in the context of this trial is presented.

Antineoplastic Agents↗
PubMed2005

Exact two-sample inference with missing data.

When comparing follow-up measurements from two independent populations, missing records may arise due to censoring by events whose occurrence is associated with baseline covariates. In these situations, inferences based only on the completely followed observations may be biased if the follow-up measurements and the covariates are correlated. This article describes exact inference for a class of modified U-statistics under covariate-dependent dropouts. The method involves weighing each permutation according to the retention probabilities, and thus requires estimation of the missing data mechanism. The proposed procedure is nonparametric in that no distributional assumption is necessary for the outcome variables and the missingness patterns. Monte Carlo approximation by the Gibbs sampler is proposed, and is shown to be fast and accurate via simulation. The method is illustrated in two small data sets for which asymptotic inferential procedures may not be appropriate.

Biometry↗
PubMed2002

Monitoring the rates of composite events with censored data in phase II clinical trials.

In many phase II clinical trials, interim monitoring is based on the probability of a binary event, response, defined in terms of one or more time-to-event variables within a time period of fixed length. Such outcome-adaptive methods may require repeated interim suspension of accrual in order to follow each patient for the time period required to evaluate response. This may increase trial duration, and eligible patients arriving during such delays either must wait for accrual to reopen or be treated outside the trial. Alternatively, monitoring may be done continuously by ignoring censored data each time the stopping rule is applied, which wastes information. We propose an adaptive Bayesian method that eliminates these problems. At each patient's accrual time, an approximate posterior for the response probability based on all of the event-time data is used to compute an early stopping criterion. Application to a leukemia trial with a composite event shows that the method can reduce trial duration substantially while maintaining the reliability of interim decisions.

Bayes Theorem↗
PubMed2002

On the use of nonparametric curves in phase I trials with low toxicity tolerance.

Gasparini and Eisele (2000, Biometrics 56, 609-615) propose a design for phase I clinical trials during which dose allocation is governed by a Bayesian nonparametric estimate of the dose-response curve. The authors also suggest an elicitation algorithm to establish vague priors. However, in situations where a low percentile is targeted, priors thus obtained can lead to undesirable rigidity given certain trial outcomes that can occur with a nonnegligible probability. Interestingly, improvement can be achieved by prescribing slightly more informative priors. Some guidelines for prior elicitation are established using a connection between this curve-free method and the continual reassessment method.

Bayes Theorem↗
PubMed2002

A simple technique to evaluate model sensitivity in the continual reassessment method.

The continual reassessment method (CRM) is a sequential design used in phase I cancer trials to determine the maximal dose with acceptable toxicity. It has been established that the CRM is consistent under model misspecification but not generally. When the method does not converge to the target percentile, some dose-response models will be more sensitive than others in terms of how close the converged recommendation is to the target. In this article, we interpret the main condition under which the CRM is consistent and apply it to evaluate the sensitivity of the model used with the CRM. The technique presented is found to be a useful supplement to simulation when planning a phase I trial.

Antineoplastic Agents↗