Accommodating stochastic departures from percentile invariance in causal models

Summary.  Consider a clinical trial in which participants are randomized to a single-dose treatment or a placebo control and assume that the adherence level is accurately recorded. If the treatment is effective, then good adherers in the treatment group should do better than poor ad- herers because they received more drug; the treatment group data follow a dose–response curve. But, good adherers to the placebo often do better than poor adherers, so the observed adherence–response in the treatment group cannot be completely attributed to the treatment. Efron and Feldman proposed an adjustment to the observed adherence–response in the treatment group by using the adherence–response in the control group. It relies on a percentile invariance assumption under which each participant's adherence percentile within their assigned treatment group does not depend on the assigned group (active drug or placebo). The Efron and Feldman approach is valid under percentile invariance, but not necessarily under departures from it. We propose an analysis based on a generalization of percentile invariance that allows adherence percentiles to be stochastically permuted across treatment groups, using a broad class of stochastic permutation models. We show that approximate maximum likelihood estimates of the underlying dose–response curve perform well when the stochastic permutation process is correctly specified and are quite robust to model misspecification.     

Biomarkers in drug development: friend or foe? A personal reflection gained working within oncology

Hopes and expectations for the use and utility of new, emerging biomarkers in drug development have probably never been higher, especially in oncology. Biomarkers are exalted as vital patient selection tools in an effort to target those most likely to benefit from a new drug, and so to reduce development costs, lessen risk and expedite developments times. It is further hoped that biomarkers can be used as surrogate endpoints for clinical outcomes, to demonstrate effectiveness and, ultimately, to support drug approval. However, I perceive that all is not straightforward, and, particularly in terms of the promise of accelerated drug development, biomarker strategies may not in all cases deliver the advances and advantages hoped for.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

A comparison of random balance and two-stage group screening designs: A case study

In this paper we focus on the problem of supersaturated (fewer runs than factors) screening experiments. We consider two major types of designs which have been proposed in this situ¬ation: random balance and two-stage group screening. We discuss the relative merits and demerits of each strategy. In addition, we compare the performance of these strategies by means of a case study in which 100 factors are screened in 20,42,62, and 84 runs.     

Rapid Assessment for Psychopathology in a College Health Clinic: Utility of College Student Specific Questions

Abstract

Objective: The purpose of this study was to determine the utility and unique benefits of employing the College Health Questionnaire (CHQ) in a college health care setting. The CHQ is a newly designed measure to assess psychosocial issues commonly problematic among college students. Participants: One hundred nine patients participated in the study during their medical appointments. Methods: Participants completed the study questionnaires (CHQ and Patient Health Questionnaire) in the waiting room. Their provider had the opportunity to use the responses in treatment decision making. Results: A majority (66.1%) endorsed at least one CHQ item. Patients who indicated traditional mental health problems were more likely to be prescribed psychotropic medications, whereas patients who endorsed psychosocial issues commonly problematic among college students were more likely to be referred for behavioral treatment. Conclusions: Screening for behavioral problems in college primary care settings without the use of college-related questions would result in missing numerous important psychosocial problems.     

Product partition latent variable model for multiple change-point detection in multivariate data

The product partition model (PPM) is a well-established efficient statistical method for detecting multiple change points in time-evolving univariate data. In this article, we refine the PPM for the purpose of detecting multiple change points in correlated multivariate time-evolving data. Our model detects distributional changes in both the mean and covariance structures of multivariate Gaussian data by exploiting a smaller dimensional representation of correlated multiple time series. The utility of the proposed method is demonstrated through experiments on simulated and real datasets.