Adjusting for covariates in non-inferiority studies with margins defined as risk differences

Adjusting for covariates makes efficient use of data and can improve the precision of study results or even reduce sample sizes. There is no easy way to adjust for covariates in a non-inferiority study for which the margin is defined as a risk difference. Adjustment is straightforward on the logit scale, but reviews of clinical studies suggest that the analysis is more often conducted on the more interpretable risk-difference scale. We examined four methods that allow for adjustment on the risk-difference scale: stratified analysis with Cochran-Mantel-Haenszel (CMH) weights, binomial regression with an identity link, the use of a Taylor approximation to convert results from the logit to the risk-difference scale and converting the risk-difference margin to the odds-ratio scale. These methods were compared using simulated data based on trials in HIV. We found that the CMH had the best trade-off between increased efficiency in the presence of predictive covariates and problems in analysis at extreme response rates. These results were shared with regulatory agencies in Europe and the USA, and the advice received is described.     

Endothelial progenitor cell therapy for atherosclerosis: the philosopher's stone for an aging population?

Much of the increased risk for atherosclerosis progression with age may be a result of age-related declines in the capacity of precursor cells to repair damage in the arterial endothelium. To estimate the impact of progenitor cell therapy for atherosclerosis on cardiovascular disease (CVD) mortality, life expectancy, and survival, as compared with the lifetime control of conventional risk factors, we modeled the health effects of bone marrow-derived endothelial progenitor cell therapy using data from the 1950 to 1996 follow-up of the Framingham Heart Study. To model cardiovascular disease mortality, we assumed that progenitor cell therapy was applied at age 30, with the effect assumed to be a 10-year delay in atherosclerosis progression. Age projections were constructed analytically using the stochastic process model for risk factor dynamics and mortality and microsimulation techniques. We considered three types of interventions: (i) keeping risk factors within selected limits to model current clinical recommendations; (ii) an age shift of 10 years to model the effects of progenitor cell therapy; and (iii) elimination of a competing risk (such as cancer). Our study suggests that progenitor cell therapy might increase life expectancy in the population as much as the complete elimination of cancer (in females, an additional 3.67 versus 3.37 years; in males, an additional 5.94 versus 2.86 years, respectively).     

When is a seamless study desirable? Case studies from different pharmaceutical sponsors

Background: Inferentially seamless studies are one of the best‐known adaptive trial designs. Statistical inference for these studies is a well‐studied problem. Regulatory guidance suggests that statistical issues associated with study conduct are not as well understood. Some of these issues are caused by the need for early pre‐specification of the phase III design and the absence of sponsor access to unblinded data. Before statisticians decide to choose a seamless IIb/III design for their programme, they should consider whether these pitfalls will be an issue for their programme. Methods: We consider four case studies. Each design met with varying degrees of success. We explore the reasons for this variation to identify characteristics of drug development programmes that lend themselves well to inferentially seamless trials and other characteristics that warn of difficulties. Results: Seamless studies require increased upfront investment and planning to enable the phase III design to be specified at the outset of phase II. Pivotal, inferentially seamless studies are unlikely to allow meaningful sponsor access to unblinded data before study completion. This limits a sponsor's ability to reflect new information in the phase III portion. Conclusions: When few clinical data have been gathered about a drug, phase II data will answer many unresolved questions. Committing to phase III plans and study designs before phase II begins introduces extra risk to drug development. However, seamless pivotal studies may be an attractive option when the clinical setting and development programme allow, for example, when revisiting dose selection. Copyright © 2014 John Wiley & Sons, Ltd.     

Towards flexible organizations: A multidimensional design

The author considers one way by which the readiness, willingness, and ability of organizations to change themselves can be increased. Specifically, he considers how organizations can be designed to be more flexible and, therefore, more capable of being changed and changing themselves. Flexibility does not guarantee adaptability but it is essential for it.     

University efforts to address confidentiality issues for STI services

Abstract

Objective: This study assessed university policies for addressing confidentiality issues for students seeking STI services. Participants: Universities with sponsored health insurance plans (SHIP) and/or wellness centers were selected from a university health services survey in 2017. Methods: STI service coverage and polices for addressing confidentiality issues related to explanation of benefit (EOB) forms were stratified by institution type (4-year versus 2-year) and minority serving institution (MSI) status. Rao-Scott chi-square tests were used to assess for differences in STI service coverage and polices. Results: More non-MSIs (61.6%) had SHIPs compared to MSIs (40.0%, p?<?.001). Only 40.8% of health centers had a policy for addressing EOB-related confidently issues. Of those, the most reported policy was that students could pay out-of-pocket to avoid generating an EOB (36.2%). Conclusions: Reducing confidentiality barriers are important for STI prevention in students. Universities may consider establishing policies for addressing EOB-related confidentiality concerns.     

The statistician's role in the prevention of missing data

Considerable statistical research has been performed in recent years to develop sophisticated statistical methods for handling missing data and dropouts in the analysis of clinical trial data. However, if statisticians and other study team members proactively set out at the trial initiation stage to assess the impact of missing data and investigate ways to reduce dropouts, there is considerable potential to improve the clarity and quality of trial results and also increase efficiency. This paper presents a Human Immunodeficiency Virus (HIV) case study where statisticians led a project to reduce dropouts. The first step was to perform a pooled analysis of past HIV trials investigating which patient subgroups are more likely to drop out. The second step was to educate internal and external trial staff at all levels about the patient types more likely to dropout, and the impact this has on data quality and sample sizes required. The final step was to work collaboratively with clinical trial teams to create proactive plans regarding focused retention efforts, identifying ways to increase retention particularly in patients most at risk. It is acknowledged that identifying the specific impact of new patient retention efforts/tools is difficult because patient retention can be influenced by overall study design, investigational product tolerability profile, current standard of care and treatment access for the disease under study, which may vary over time. However, the implementation of new retention strategies and efforts within clinical trial teams attests to the influence of the analyses described in this case study. Copyright © 2012 John Wiley & Sons, Ltd.     

Marketing segmentation research—An application to bank services

The research reported here analyses data relating to bank services obtained by a stratified random sampling technique using stepwise linear regression and canonical correlation for the purpose of illustrating the application of segmentation principles to marketing research.     

CAN SCHOOL SPORTS REDUCE RACIAL GAPS IN TRUANCY AND ACHIEVEMENT?

While existing research supports that participation in high‐school athletics is associated with better education and labor‐market outcomes, the mechanisms through which these benefits accrue are not well established. Using individual microdata collected daily, and team‐specific schedules, we retrieve estimates of the causal effect of high‐school athletic participation on absenteeism, suggesting that participation decreases absences, driven primarily by reductions in unexcused absences in boys. There are also strong game‐day effects in truancy, in both boys and girls, with truancy declines on game days more than offset by subsequent absenteeism. Important heterogeneity by race, gender, and family structure may serve to substantially reduce racial gaps in truancy and achievement. (JEL I21, L83)     

Informing the selection of futility stopping thresholds: case study from a late-phase clinical trial

In an environment where (i) potential risks to subjects participating in clinical studies need to be managed carefully, (ii) trial costs are increasing, and (iii) there are limited research resources available, it is necessary to prioritize research projects and sometimes re-prioritize if early indications suggest that a trial has low probability of success. Futility designs allow this re-prioritization to take place. This paper reviews a number of possible futility methods available and presents a case study from a late-phase study of an HIV therapeutic, which utilized conditional power-based stopping thresholds. The two most challenging aspects of incorporating a futility interim analysis into a trial design are the selection of optimal stopping thresholds and the timing of the analysis, both of which require the balancing of various risks. The paper outlines a number of graphical aids that proved useful in explaining the statistical risks involved to the study team. Further, the paper outlines a decision analysis undertaken which combined expectations of drug performance with conditional power calculations in order to produce probabilities of different interim and final outcomes, and which ultimately led to the selection of the final stopping thresholds.