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Group-randomized trials are randomized studies that allocate intact groups of individuals to different comparison arms. A frequent practical limitation to adopting such research designs is that only a limited number of groups may be available, and therefore, simple randomization is unable to adequately balance multiple group-level covariates between arms. Therefore, covariate-based constrained randomization was proposed as an allocation technique to achieve balance. Constrained randomization involves generating a large number of possible allocation schemes, calculating a balance score that assesses covariate imbalance, limiting the randomization space to a prespecified percentage of candidate allocations, and randomly selecting one scheme to implement. When the outcome is binary, a number of statistical issues arise regarding the potential advantages of such designs in making inference. In particular, properties found for continuous outcomes may not directly apply, and additional variations on statistical tests are available. Motivated by two recent trials, we conduct a series of Monte Carlo simulations to evaluate the statistical properties of model-based and randomization-based tests under both simple and constrained randomization designs, with varying degrees of analysis-based covariate adjustment. Our results indicate that constrained randomization improves the power of the linearization F-test, the KC-corrected GEE t-test (Kauermann and Carroll, 2001, Journal of the American Statistical Association 96, 1387-1396), and two permutation tests when the prognostic group-level variables are controlled for in the analysis and the size of randomization space is reasonably small. We also demonstrate that constrained randomization reduces power loss from redundant analysis-based adjustment for non-prognostic covariates. Design considerations such as the choice of the balance metric and the size of randomization space are discussed. [ABSTRACT FROM AUTHOR]

This study aimed to find the set of risk indicators best able to predict root caries (RC) incidence in caries-active adults utilizing data from the Xylitol for Adult Caries Trial (X-ACT). Five logistic regression models were compared with respect to their predictive performance for incident RC using data from placebo-control participants with exposed root surfaces at baseline and from two study centers with ancillary data collection (n = 155). Prediction performance was assessed from baseline variables and after including ancillary variables [smoking, diet, use of removable partial dentures (RPD), toothbrush use, income, education, and dental insurance]. A sensitivity analysis added treatment to the models for both the control and treatment participants (n = 301) to predict RC for the control participants. Forty-nine percent of the control participants had incident RC. The model including the number of follow-up years at risk, the number of root surfaces at risk, RC index, gender, race, age, and smoking resulted in the best prediction performance, having the highest AUC and lowest Brier score. The sensitivity analysis supported the primary analysis and gave slightly better performance summary measures. The set of risk indicators best able to predict RC incidence included an increased number of root surfaces at risk and increased RC index at baseline, followed by white race and nonsmoking, which were strong nonsignificant predictors. Gender, age, and increased number of follow-up years at risk, while included in the model, were also not statistically significant. The inclusion of health, diet, RPD use, toothbrush use, income, education, and dental insurance variables did not improve the prediction performance. [ABSTRACT FROM AUTHOR]

Context: Pragmatic trials lack the relatively tight quality control of traditional efficacy studies and hence may pose added analytic challenges owing to the practical realities faced in carrying them out. Case Description: STOP CRC is a cluster randomized trial testing the effectiveness of automated, electronic medical record (EMR)-driven strategies to raise colorectal cancer (CRC) screening rates in safety net clinics. Screen-eligible participants were accrued during year 1 and followed for 12 months (measurement window) to assess completion of a fecal screening test. Control clinics implemented the intervention in year 2. Implementation Challenges/Analytic Issues: Due to limitations on how we could build the intervention tools, the overlap of the year 1 measurement windows with year 2 intervention rollout posed a potential for contamination of the primary outcome for control participants. In addition, a variety of factors led to a lack of synchronization of the measurement windows with actual intervention delivery. In both cases, the net impact of these factors would be to diminish the estimated impact of the intervention. Proposed Solutions: We dealt with the overlap issue by delaying the start of intervention rollout to control clinics in year 2 by 6 months and by truncating the measurement windows for intervention and control participants at this point. In addition we formulated three sensitivity analyses to help address the issue of asynchronization. Conclusion: This case study might help other investigators facing similar challenges think about such issues and the pros and cons of various strategies for dealing with them. [ABSTRACT FROM AUTHOR]