Estimation of treatment effect following a clinical trial with adaptive design.

Parameter estimation following an adaptive design or group sequential design has been extremely challenging due to potential random high from its face value estimate. In this paper, we introduce a new framework to model clinical trial data flow based on a marked point process (MPP). The MPP model allows us to use methods of stochastic calculus for analyses of any adaptive clinical trial. As an example, we apply this method to a two stage treatment selection design and derive a procedure to estimate the treatment effect. Numerical examples will be used to evaluate the performance of the proposed procedure.

Deflected mirage mediation: a phenomenological framework for generalized supersymmetry breaking.

We present a general phenomenological framework for dialing between gravity mediation, gauge mediation, and anomaly mediation. The approach is motivated from recent developments in moduli stabilization, which suggest that gravity mediated terms can be effectively loop suppressed and thus comparable to gauge and anomaly mediated terms. The gauginos exhibit a mirage unification behavior at a "deflected" scale, and gluinos are often the lightest colored sparticles. The approach provides a rich setting in which to explore generalized supersymmetry breaking at the CERN Large Hadron Collider.

Estimating treatment efficacy over time: a logistic regression model for binary longitudinal outcomes.

This paper presents a case study in longitudinal data analysis where the goal is to estimate the efficacy of a new drug for treatment of a severe chronic constipation. Data consist of long sequences of binary outcomes (relief/no relief) on each of a large number of patients randomized to treatment (low and high dose) or placebo. Data characteristics indicate: (1) the treatment effects vary non-linearly with time; (2) there is substantial heterogeneity across subjects in their responses to treatment; and (3) there is a high proportion of subjects who never experience any relief (the non-responders). To overcome these challenges, we develop a hierarchical model for binary longitudinal data with a mixture distribution on the probability of response to account for the high frequency of non-responders. While the model is specified conditionally on subject-specific latent variables, we also draw inferences on key population-average parameters for the assessment of the treatments' efficacy in a population. In addition we employ a model-checking method to compare the goodness-of-fit for our model against simpler modelling approaches for aggregated counts, such as the zero-inflated Poisson and zero-inflated negative binomial models. We estimate subject-specific and population-average rate ratios of relief for the treatment with respect to the placebo as functions of time (RR(t)), and compare them with the rate ratios estimated from the models for aggregated counts. We find that: (1) the treatment is effective with respect to the placebo with higher efficacy at the beginning of the study; (2) the estimated rate ratios from the models for aggregated counts appear to be similar to the average across time of the population-average rate ratios estimated under our model; and (3) model-checking suggests that the hierarchical and zero-inflated negative binomial model fit the data best. If we are mainly interested to establish the overall efficacy (or safety) of a new drug, it is appropriate to aggregate the longitudinal data over time and analyse the count data by use of standard statistical methods. However, the models for aggregated counts cannot capture time trend of treatment such as the initial treatment benefit or the development of tolerance during the early stage of the treatment which may be important information to physicians to predict the treatment effects for their patients.

Dunnett's many-to-one test and least square means.

Dunnett's many-to-one test is used frequently today, especially in dose-finding studies. Using Dunnett's test, the Type I error level for the comparison between the raw mean of the control and the raw means of the study drug groups can be exactly calculated for the normal data. However, this computability depends on the independence of the raw means. Unfortunately, this independence does not exist for the model-based likelihood estimates (least square means) in the cases of ANCOVA and two-way ANOVA models without interaction for unbalanced data. This paper investigates this dependence between the least square means and derives some new procedures to calculate the joint distribution of the statistic for Dunnett's test.

Controlling type I error rate for fast track drug development programmes.

The U.S. Food and Drug Administration (FDA) Modernization Act of 1997 has a Section (No. 112) entitled 'Expediting Study and Approval of Fast Track Drugs' (the Act). In 1998, the FDA issued a 'Guidance for Industry: the Fast Track Drug Development Programs' (the FTDD programmes) to meet the requirement of the Act. The purpose of FTDD programmes is to 'facilitate the development and expedite the review of new drugs that are intended to treat serious or life-threatening conditions and that demonstrate the potential to address unmet medical needs'. Since then many health products have reached patients who suffered from AIDS, cancer, osteoporosis, and many other diseases, sooner by utilizing the Fast Track Act and the FTDD programmes. In the meantime several scientific issues have also surfaced when following the FTDD programmes. In this paper we will discuss the concept of two kinds of type I errors, namely, the 'conditional approval' and the 'final approval' type I errors, and propose statistical methods for controlling them in a new drug submission process.