Biomedical subjects

Y H Joshua Chen

Publications and source records attributed to Y H Joshua Chen.

5 recordsLinked to original sources

PubMed2006

Testing for crossover of two hazard functions using Gail and Simon's method.

Crossover of two hazard functions is sometimes called qualitative nonproportionality where the hazard ratio could be >1 in some time intervals but <1 in some other intervals. Investigators often wish to know whether a beneficial treatment effect exists over a long period of time (i.e., no crossover). This information is important for the management of safety and efficacy of a new treatment in long-term use. Also, if crossover occurs, the commonly used statistical methods, such as Cox proportional hazards model or linear rank tests, may not be appropriate. Graphical display may be used to visually examine whether there are crossovers in the observed hazard functions. A relevant question is whether the observed crossover of two hazard functions is due to chance variation. In this article, we propose a class of tests for crossover of two hazard functions. The study follow-up period is divided into nonoverlapping time intervals, and the weighted linear rank statistic, including logrank and generalized Wilcoxon statistics, can be calculated from each interval. These statistics are asymptotically independent and have normal distributions. Treating each interval as a "patient subset," qualitative tests of interactions between treatment and patient subsets can naturally apply. For our purpose, we consider the likelihood ratio test proposed by Gail and Simon. Two examples are used to illustrate this approach. The proposed test procedures are also studied through simulations.

Antineoplastic Agents↗

PubMed2005

Treatment comparisons for a partially categorical outcome applied to a biomarker with assay limit.

The plasma level of HIV-RNA has been shown to be a strong prognostic biomarker for clinical progression and death in HIV infected patients and is widely used as the primary outcome in clinical trials to evaluate antiretroviral treatments. Currently approved assays to measure HIV-RNA levels have a lower limit of reliable quantification (LoQ). Current regulatory guidelines recommend using the proportion of patients achieving HIV-RNA levels below the assay limit at a certain time point (e.g. 24 or 48 weeks) as the primary endpoint for regulatory approval. However, a substantial decrease in HIV-RNA that does not go below the LoQ still is considered clinically beneficial for patients with advanced diseases who have failed many other therapies and are unlikely to maximally suppress the virus and achieve HIV-RNA levels below the LoQ. An experimental treatment may not be distinguishable from a control solely in terms of the proportions of patients whose HIV-RNA levels fall below the LoQ. The sensitivity of the comparison between the experimental treatment and the control could be increased by considering as well the difference between the treatments with respect to the HIV-RNA reductions of patients not achieving HIV-RNA levels below the LoQ. In this paper, we introduce a best-rank analysis which assigns the best rank to patients who achieve the HIV-RNA levels below the LoQ and applies the Mann-Whitney-Wilcoxon rank test to compare the two treatment groups. The Mann-Whitney-Wilcoxon statistic is shown to be a weighted sum of two statistics: one to compare the proportions of patients achieving the HIV-RNA levels below the LoQ and one to compare the viral reductions in patients with HIV-RNA levels above the LoQ. The corresponding statistical null and alternative hypotheses and the clinical interpretations of this best-rank test procedure are also discussed. An example is used to illustrate this approach and a simulation study is used to compare this approach with other methods.

Anti-HIV Agents↗

PubMed2004

Increasing the sample size when the unblinded interim result is promising.

Increasing the sample size based on unblinded interim result may inflate the type I error rate and appropriate statistical adjustments may be needed to control the type I error rate at the nominal level. We briefly review the existing approaches which allow early stopping due to futility, or change the test statistic by using different weights, or adjust the critical value for final test, or enforce rules for sample size recalculation. The implication of early stopping due to futility and a simple modification to the weighted Z-statistic approach are discussed. In this paper, we show that increasing the sample size when the unblinded interim result is promising will not inflate the type I error rate and therefore no statistical adjustment is necessary. The unblinded interim result is considered promising if the conditional power is greater than 50 per cent or equivalently, the sample size increment needed to achieve a desired power does not exceed an upper bound. The actual sample size increment may be determined by important factors such as budget, size of the eligible patient population and competition in the market. The 50 per cent-conditional-power approach is extended to a group sequential trial with one interim analysis where a decision may be made at the interim analysis to stop the trial early due to a convincing treatment benefit, or to increase the sample size if the interim result is not as good as expected. The type I error rate will not be inflated if the sample size may be increased only when the conditional power is greater than 50 per cent. If there are two or more interim analyses in a group sequential trial, our simulation study shows that the type I error rate is also well controlled.

Anti-HIV Agents↗

PubMed2004

Incorporating durability information in the comparison of proportions of patients with HIV suppression.

In some HIV clinical trials, the proportion of patients who achieve treatment success at a clinically meaningful time point (e.g., 16 or 24 weeks) and the subsequent durability of the treatment success after that time point are collected from two exclusive followup intervals. Two treatments are usually compared in terms of the proportion of patients achieving treatment success at the pre-defined time point and the subsequent durability information is ignored. However, combining the failure/success proportion at the pre-defined time point and the subsequent durability information in one test statistic could be more powerful if the experimental treatment is more efficacious than the control in that either fewer patients fail the experimental treatment at this time point or the responding patients have longer duration of viral suppression. In this paper, we propose a time-to-event type potency/durability endpoint which captures the information from the two exclusive followup intervals. A linear rank statistic to compare the two treatments in terms of this potency/durability endpoint can be interpreted as a weighted statistic to incorporate the potency information at a clinically meaningful time point (e.g., 16 or 24 weeks) and the durability information after that time point. The statistical hypotheses being tested by using this potency/durability endpoint and their clinical interpretations are discussed. A clinical endpoint study in anti-retroviral treatment naive patients is used to illustrate this method. Simulation studies show that this method is more powerful than comparing the success rates alone when the experimental treatment is also more durable in maintaining long term success.

Algorithms↗

PubMed2003

Monitoring mortality at interim analyses while testing a composite endpoint at the final analysis.

Mortality is often used as the clinical endpoint in clinical trials for acute diseases and takes precedence over any other outcome. A composite outcome such as death plus disease occurrence (or recurrence) or death plus hospitalization may also be considered, sometimes even as the primary outcome due to practical sample size issues. That is, a composite endpoint should have a higher event rate and thus a smaller sample size than for mortality alone to reach the same power. Two different scenarios are considered: in Scenario 1, the composite outcome is the primary endpoint and the mortality outcome is secondary; in Scenario 2, the mortality outcome is the primary endpoint and the composite outcome is secondary. In either scenario, the trial will be stopped if the simple mortality outcome shows an adverse effect or a significant benefit at an interim analysis, while the composite outcome will be tested at the final analysis if the mortality outcomes fails to show significance. These scenarios are typical in many trials sponsored by industry for regulatory approval. We refer to them as a switching the primary endpoint process. Two switching-endpoint procedures are proposed to calculate the efficacy boundary for the composite test statistic at the final analysis. The Bonferroni method is used in Method 1. In Method 2, the calculation is based upon the joint distribution of the test statistics for the simple mortality and the composite outcomes. A completed clinical trial, prospective randomized amlodipine survival evaluation (PRAISE-1), is used to illustrate the two switching-endpoint procedures. A simulation study shows that the two switching-endpoint procedures allow a trial to be stopped early due to a clinically relevant benefit in the mortality while preserving the overall alpha level.

Clinical Trials as Topic↗