Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs. Review of RCTs published between July and December 2013 in the BMJ, JAMA, Lancet, and New England Journal of Medicine, excluding cluster randomized trials and trials whose primary outcome was survival. Of the 77 identified eligible articles, 73 (95%) reported some missing outcome data. The median percentage of participants with a missing outcome was 9% (range 0 – 70%). The most commonly used method to handle missing data in the primary analysis was complete case analysis (33, 45%), while 20 (27%) performed simple imputation, 15 (19%) used model based methods, and 6 (8%) used multiple imputation. 27 (35%) trials with missing data reported a sensitivity analysis. However, most did not alter the assumptions of missing data from the primary analysis. Reports of ITT or modified ITT were found in 52 (85%) trials, with 21 (40%) of them including all randomized participants. A comparison to a review of trials reported in 2001 showed that missing data rates and approaches are similar, but the use of the term ITT has increased, as has the report of sensitivity analysis. Missing outcome data continues to be a common problem in RCTs. Definitions of the ITT approach remain inconsistent across trials. A large gap is apparent between statistical methods research related to missing data and use of these methods in application settings, including RCTs in top medical journals.

Several studies have investigated the potential role of selenium (Se) in the development of type 2 diabetes (T2D) with disparate findings. We conducted a systematic review and meta-analysis to synthesize the evidence of any association between Se and T2D. PubMed, Embase, and Scopus were searched following the Preferred Reporting Items for Systematic Reviews and Meta-analysis Approach (PRISMA). Sixteen studies from 15 papers met inclusion criteria defined for this review. Of the 13 observational studies included, 8 demonstrated a statistically significant positive association between concentrations of Se and odds for T2D, with odds ratios (95% confidence intervals) ranging from 1.52 (1.01–2.28) to 7.64 (3.34–17.46), and a summary odds ratio (OR) (95% confidence interval (CI)) of 2.03 (1.51–2.72). In contrast, among randomized clinical trials (RCTs) of Se, a higher risk of T2D was not observed for those who received Se compared to a placebo (OR = 1.18, 95% CI 0.95–1.47). Taken together, the results for the relationship between Se and T2D differ between observational studies and randomized clinical trials (RCTs). It remains unclear whether these differences are the result of uncontrolled confounding in the observational studies, or whether there is a modest effect of Se on the risk for T2D that may vary by duration of exposure. Further investigations on the effects of Se on glucose metabolism are needed.

There is an association between cryptogenic stroke and patent foramen ovale (PFO). The optimal treatment strategy for secondary prevention remains unclear. The purpose of this study was to analyze aggregate data examining the safety and efficacy of transcatheter device closure versus standard medical therapy in patients with PFO and cryptogenic stroke.

We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program.

Factors associated with cervical cancer screening in a safety net population