PHASE II ADAPTIVE ENRICHMENT DESIGN TO DETERMINE THE POPULATION TO ENROLL IN PHASE III TRIALS, BY SELECTING THRESHOLDS FOR BASELINE DISEASE SEVERITY

We propose and evaluate a two-stage, phase 2, adaptive clinical trial design. Its goal is to determine whether future phase 3 (confirmatory) trials should be conducted, and if so, which population should be enrolled. The population selected for phase 3 enrollment is defined in terms of a disease severity score measured at baseline. We optimize the phase 2 trial design and analysis in a decision theory framework. Our utility function represents a combination of the cost of conducting phase 3 trials and, if the phase 3 trials are successful, the improved health of the future population minus the cost of treatment. Given such a utility function and a discrete prior distribution on the conditional treatment effect, we compute the Bayes optimal adaptive design. The resulting design is compared to simpler designs in simulation studies. We also apply the designs to resampled data from a completed, phase 2 trial evaluating a new surgical intervention for stroke

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code

Simple, Defensible Sample Sizes Based on Cost Efficiency -- With Discussion and Rejoinder

The conventional approach of choosing sample size to provide 80% or greater power ignores the cost implications of different sample size choices. Costs, however, are often impossible for investigators and funders to ignore in actual practice. Here, we propose and justify a new approach for choosing sample size based on cost efficiency, the ratio of a study’s projected scientific and/or practical value to its total cost. By showing that a study’s projected value exhibits diminishing marginal returns as a function of increasing sample size for a wide variety of definitions of study value, we are able to develop two simple choices that can be defended as more cost efficient than any larger sample size. The first is to choose the sample size that minimizes the average cost per subject. The second is to choose sample size to minimize total cost divided by the square root of sample size. This latter method is theoretically more justifiable for innovative studies, but also performs reasonably well and has some justification in other cases. For example, if projected study value is assumed to be proportional to power at a specific alternative and total cost is a linear function of sample size, then this approach is guaranteed either to produce more than 90% power or to be more cost efficient than any sample size that does. These methods are easy to implement, based on reliable inputs, and well justified, so they should be regarded as acceptable alternatives to current conventional approaches

Pharmacodynamic genes do not influence risk of neutropenia in cancer patients treated with moderately high-dose irinotecan

A recent study found that variation in camptothecin pharmacodynamic genes (TOP1, PARP1, TDP1 and XRCC1) correlated with efficacy and risk of neutropenia in irinotecan-treated cancer patients (median dose: 180 mg/m2), which suggests that these genes might predict outcomes to irinotecan-based therapies. The present study was conducted to evaluate previous gene associations using an independent sample of patients receiving irinotecan

Advances in Statistical Approaches to Oncology Drug Development

We describe some recent developments in statistical methodology and practice in oncology drug development from an academic and an industry perspective. Many adaptive designs were pioneered in oncology, and oncology is still at the forefront of novel methods to enable better and faster Go/No-Go decision making while controlling the cost

Characterization of the Raf Kinase Inhibitory Protein (RKIP) Binding Pocket: NMR-Based Screening Identifies Small-Molecule Ligands

Raf kinase inhibitory protein (RKIP), also known as phoshaptidylethanolamine binding protein (PEBP), has been shown to inhibit Raf and thereby negatively regulate growth factor signaling by the Raf/MAP kinase pathway. RKIP has also been shown to suppress metastasis. We have previously demonstrated that RKIP/Raf interaction is regulated by two mechanisms: phosphorylation of RKIP at Ser-153, and occupation of RKIP's conserved ligand binding domain with a phospholipid (2-dihexanoyl-sn-glycero-3-phosphoethanolamine; DHPE). In addition to phospholipids, other ligands have been reported to bind this domain; however their binding properties remain uncharacterized.In this study, we used high-resolution heteronuclear NMR spectroscopy to screen a chemical library and assay a number of potential RKIP ligands for binding to the protein. Surprisingly, many compounds previously postulated as RKIP ligands showed no detectable binding in near-physiological solution conditions even at millimolar concentrations. In contrast, we found three novel ligands for RKIP that specifically bind to the RKIP pocket. Interestingly, unlike the phospholipid, DHPE, these newly identified ligands did not affect RKIP binding to Raf-1 or RKIP phosphorylation. One out of the three ligands displayed off target biological effects, impairing EGF-induced MAPK and metabolic activity.This work defines the binding properties of RKIP ligands under near physiological conditions, establishing RKIP's affinity for hydrophobic ligands and the importance of bulky aliphatic chains for inhibiting its function. The common structural elements of these compounds defines a minimal requirement for RKIP binding and thus they can be used as lead compounds for future design of RKIP ligands with therapeutic potential