Home About us Contact
|
Home About us Contact | ||
|
|
||
Comparative Clinical Trials (comparative + clinical_trials)
Selected AbstractsAdaptive Randomization for Multiarm Comparative Clinical Trials Based on Joint Efficacy/Toxicity OutcomesBIOMETRICS, Issue 3 2009Yuan Ji Summary We present an outcome-adaptive randomization (AR) scheme for comparative clinical trials in which the primary endpoint is a joint efficacy/toxicity outcome. Under the proposed scheme, the randomization probabilities are unbalanced adaptively in favor of treatments with superior joint outcomes characterized by higher efficacy and lower toxicity. This type of scheme is advantageous from the patients' perspective because on average, more patients are randomized to superior treatments. We extend the approximate Bayesian time-to-event model in Cheung and Thall (2002,,Biometrics,58, 89,97) to model the joint efficacy/toxicity outcomes and perform posterior computation based on a latent variable approach. Consequently, this allows us to incorporate essential information about patients with incomplete follow-up. Based on the computed posterior probabilities, we propose an AR scheme that favors the treatments with larger joint probabilities of efficacy and no toxicity. We illustrate our methodology with a leukemia trial that compares three treatments in terms of their 52-week molecular remission rates and 52-week toxicity rates. [source] Sequential methods and group sequential designs for comparative clinical trialsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 5 2003Véronique Sébille Abstract Comparative clinical trials are performed to assess whether a new treatment has superior efficacy than a placebo or a standard treatment (one-sided formulation) or whether two active treatments have different efficacies (two-sided formulation) in a given population. The reference approach is the single-stage design and the statistical test is performed after inclusion and evaluation of a predetermined sample size. In practice, the single-stage design is sometimes difficult to implement because of ethical concerns and/or economic reasons. Thus, specific early termination procedures have been developed to allow repeated statistical analyses to be performed on accumulating data and stop the trial as soon as the information is sufficient to conclude. Two main different approaches can be used. The first one is derived from strictly sequential methods and includes the sequential probability ratio test and the triangular test. The second one is derived from group sequential designs and includes Peto, Pocock, and O'Brien and Fleming methods, , and , spending functions, and one-parameter boundaries. We review all these methods and describe the bases on which they rely as well as their statistical properties. We also compare these methods and comment on their advantages and drawbacks. We present software packages which are available for the planning, monitoring and analysis of comparative clinical trials with these methods and discuss the practical problems encountered when using them. The latest versions of all these methods can offer substantial sample size reductions when compared with the single-stage design not only in the case of clear efficacy but also in the case of complete lack of efficacy of the new treatment. The software packages make their use quite simple. However, it has to be stressed that using these methods requires efficient logistics with real-time data monitoring and, apart from survival studies or long-term clinical trials with censored endpoints, is most appropriate when the endpoint is obtained quickly when compared with the recruitment rate. [source] Utilities of the P -value Distribution Associated with Effect Size in Clinical Trials,BIOMETRICAL JOURNAL, Issue 6 2003H.M. James Hung Abstract The P -value, which is widely used for assessing statistical evidence in randomized comparative clinical trials, is a function of the observed effect size of the experimental treatment relative to the control treatment. The relationship of the P -value with the observed effect size at study completion and the effect size anticipated at the design stage has potential usefulness in providing guidance for planning and interpretation of a clinical trial. The post-trial power associated with a statistically significant P -value from a completed study is also a random variable and its use may assist in planning a follow-up trial to confirm the statistically significant findings in an initial study. A measure of robustness is explored to quantify the degree of sensitivity of the observed P -value to potential bias that may be contained in the observed effect size. [source] Adaptive Randomization for Multiarm Comparative Clinical Trials Based on Joint Efficacy/Toxicity OutcomesBIOMETRICS, Issue 3 2009Yuan Ji Summary We present an outcome-adaptive randomization (AR) scheme for comparative clinical trials in which the primary endpoint is a joint efficacy/toxicity outcome. Under the proposed scheme, the randomization probabilities are unbalanced adaptively in favor of treatments with superior joint outcomes characterized by higher efficacy and lower toxicity. This type of scheme is advantageous from the patients' perspective because on average, more patients are randomized to superior treatments. We extend the approximate Bayesian time-to-event model in Cheung and Thall (2002,,Biometrics,58, 89,97) to model the joint efficacy/toxicity outcomes and perform posterior computation based on a latent variable approach. Consequently, this allows us to incorporate essential information about patients with incomplete follow-up. Based on the computed posterior probabilities, we propose an AR scheme that favors the treatments with larger joint probabilities of efficacy and no toxicity. We illustrate our methodology with a leukemia trial that compares three treatments in terms of their 52-week molecular remission rates and 52-week toxicity rates. [source] Patient Protection and Affordable Care Act of 2010: Summary, Analysis, and Opportunities for Advocacy for the Academic Emergency PhysicianACADEMIC EMERGENCY MEDICINE, Issue 7 2010Jeffrey A. Kline MD ACADEMIC EMERGENCY MEDICINE 2010; 17:E69,E74 © 2010 by the Society for Academic Emergency Medicine Abstract The Patient Protection and Affordable Care Bill, commonly referred to as the "Health Care Bill" or the "Health Care Reform Bill," was enacted in March 2010. This article is a review and analysis of the sections of this Act that are relevant to researchers and teachers of emergency care. The purpose of this document is to serve as a citable reference for interested parties and a reference to quickly locate the sections of the Bill relevant to academic emergency physicians. When appropriate, text was copied verbatim from the Bill. The source of the downloaded Act, and the page numbers of the text sections, are provided to help the reader to find the sections described. This review is presented in two parts. Part I presents 11 sections extirpated from the Act, with short interpretations of the significance of each section. Part II presents an analysis of the sections that the authors believe represent opportunities for emergency care researchers and teachers to make the most impact, through active involvement with the various departments and agencies of the federal government that will be charged with interpreting and implementing this Act. The Act contains sections that could lead to new funding opportunities for research in emergency care, especially for comparative clinical trials and clinical studies that focus on integration and efficiency of health care delivery. The Act will establish several new institutes, centers, and committees that will create policies highly relevant to emergency care. The authors conclude that this Act can be expected to have a profound influence on research and training in emergency care. [source] | |||||||||||||