Biomedical subjects

Mithat Gönen

Publications and source records attributed to Mithat Gönen.

3 recordsLinked to original sources

PubMed2003

Planning for subgroup analysis: a case study of treatment-marker interaction in metastatic colorectal cancer.

Subgroup analysis is a common secondary objective in clinical trials. In oncology where the outcome is often binary (such as tumor response) or time-to-event (such as survival), subgroup analysis can be formulated using an interaction term in logistic or proportional hazards regression models. We focus on a case study of planning a randomized trial in metastatic colorectal cancer possibly involving a treatment-marker interaction. We present a method that can be used to compute the power of interaction tests for a given sample size or to compute the necessary sample sizes for a desired level of power for the planned subgroup analysis. The principle idea is borrowed from analysis of variance and uses appropriate contrasts after a variance-stabilizing transformation. This method is conceptually and operationally simple. It can be applied to binary- or ordinal-marker measurements, and existing sample size tables or software can be used. The accuracy of the approximation is shown to be reasonable by simulation studies.

Biomarkers, Tumor↗

PubMed2003

Bayesian multiple testing for two-sample multivariate endpoints.

In clinical studies involving multiple variables, simultaneous tests are often considered where both the outcomes and hypotheses are correlated. This article proposes a multivariate mixture prior on treatment effects, that allows positive probability of zero effect for each hypothesis, correlations among effect sizes, correlations among binary outcomes of zero versus nonzero effect, and correlations among the observed test statistics (conditional on the effects). We develop a Bayesian multiple testing procedure, for the multivariate two-sample situation with unknown covariance structure, and obtain the posterior probabilities of no difference between treatment regimens for specific variables. Prior selection methods and robustness issues are discussed in the context of a clinical example.

Bayes Theorem↗

PubMed2002

An optimal two-stage phase II design utilizing complete and partial response information separately.

Phase II clinical trials in oncology are performed to evaluate the therapeutic efficacy of a new treatment regimen. A common measure of efficacy for these trials is the proportion of patients who obtain a response measured by tumor shrinkage. It is standard practice to classify this response into the following categories: (1) complete response (CR); (2) partial response (PR); (3) stable disease; and (4) progression of disease. Tumor response is then treated as a binary variable whereby patients who achieve either a CR or a PR are considered responders and all others nonresponders. A two-stage design that allows for early termination of the trial if the treatment shows little efficacy such as Gehan or Simon gives equal weight to a CR and a PR. However, a CR, defined as complete disappearance of the tumor, is more likely than a PR to signal an important antitumor effect and result in a survival advantage. We argue that CRs and PRs should be considered separately, and hence we propose a two-stage design with a multilevel endpoint (i.e., CR, PR, and nonresponders). This design is an extension of Simon's optimal two-stage design and is based on a trinomial model. For most scenarios the proposed design results in an improvement in expected sample size compared to Simon's optimal design. Design optimization was performed by a direct search based on enumerating exact trinomial probabilities. Sample size tables are provided for parameter sets commonly used in the oncologic setting. Software is available by contacting the authors.

Clinical Trials, Phase II as Topic↗