Biomedical subjects
Publications and source records attributed to John D Cook.
We examine stochastic inequality probabilities of the form P (X > Y) and P (X > max (Y, Z)) where X, Y, and Z are random variables with beta, gamma, or inverse gamma distributions. We discuss the applications of such inequality probabilities to adaptively randomized clinical trials as well as methods for calculating their values.
The purpose of this paper is to describe and illustrate an outcome-adaptive Bayesian procedure, proposed by Thall and Cook (2004), for assigning doses of an experimental treatment to successive cohorts of patients. The method uses elicited (efficacy, toxicity) probability pairs to construct a family of trade-off contours that are used to quantify the desirability of each dose. This provides a basis for determining a best dose for each cohort. The method combines the goals of conventional Phase I and Phase II trials, and thus may be called a "Phase I-II" design. We first give a general review of the probability model and dose-finding algorithm. We next describe an application to a trial of a biologic agent for treatment of acute myelogenous leukemia, including a computer simulation study to assess the design's average behavior. To illustrate how the method may work in practice, we present a cohort-by-cohort example of a particular trial. We close with a discussion of some practical issues that may arise during implementation.
The highly ordered arrangement of sarcomeric myosin during striated muscle development requires spontaneous calcium (Ca(2+)) transients. Here, we show that blocking transients also compromises patterned assembly of actin thin filaments, titin, and capZ. Because a conserved temporal assembly pattern has been described for these proteins, selective inhibitors of either thick or thin filament formation were used to determine their relative temporal interdependencies. For example, inhibition of myosin light chain kinase (MLCK) by application of a specific inhibitory peptide or phorbol myistate acetate (PMA) disrupts myosin assembly without significantly affecting formation of actin bands. The MLCK inhibitor ML-7, however, disrupted actin as well as myosin. Surprisingly, agents that interfere with actin dynamics, such as cytochalasin D, produced only minor organizational disruptions in actin, capZ, and titin staining. However, cytochalasin D and other actin disrupting compounds significantly perturbed myosin organization. The results indicate that (1) Ca(2+) transients regulate one or more of the earliest steps in sarcomere formation, (2) mature actin filaments can assemble independently of myosin band formation, and (3) myosin thick filament assembly is extremely sensitive to disruption of either the actin or titin filament systems.
We present an adaptive Bayesian method for dose-finding in phase I/II clinical trials based on trade-offs between the probabilities of treatment efficacy and toxicity. The method accommodates either trinary or bivariate binary outcomes, as well as efficacy probabilities that possibly are nonmonotone in dose. Doses are selected for successive patient cohorts based on a set of efficacy-toxicity trade-off contours that partition the two-dimensional outcome probability domain. Priors are established by solving for hyperparameters that optimize the fit of the model to elicited mean outcome probabilities. For trinary outcomes, the new algorithm is compared to the method of Thall and Russell (1998, Biometrics 54, 251-264) by application to a trial of rapid treatment for ischemic stroke. The bivariate binary outcome case is illustrated by a trial of graft-versus-host disease treatment in allogeneic bone marrow transplantation. Computer simulations show that, under a wide rage of dose-outcome scenarios, the new method has high probabilities of making correct decisions and treats most patients at doses with desirable efficacy-toxicity trade-offs.