Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.

The Role of Home Practice Engagement in a Mindfulness-Based Intervention

Over the last three decades, there has been a precipitous rise in curiosity regarding the clinical use of mindfulness meditation for the self-management of a broad range of chronic health conditions. Despite the ever-growing body of evidence supporting the use of mindfulness-based therapies for both medical and psychological concerns, data on the active ingredients of these mind-body interventions are relatively scarce. Regular engagement in formal mindfulness practice is considered by many to be requisite for generating therapeutic change; however, previous investigations of at-home practice in MBIs have produced mixed results. The equivocal nature of these findings has been attributed to significant methodological limitations, including the lack of standardized, systematic practice monitoring tools, and a singular focus on practice time, with little attention paid to the nature and quality of one’s practice. The present study used a prospective, observational design to assess the effects of home-based practice on dispositional mindfulness, self-compassion, and psychological functioning in twenty-eight people enrolled in an MBSR or MBCT program. To address some of the aforementioned limitations, the present study collected detailed weekly accounts of participants’ home-based practice engagement, including information about practice time (i.e., frequency and duration), exercise type, perceived effort and barriers to participation, and practice quality. Hierarchical multiple regression was used to examine the relative contribution of practice time and practice quality on treatment outcomes, and to explore possible predictors of adherence to at-home practice recommendations. As anticipated, practice quality and perceived effort improved with time; however, rather unexpectedly, practice quality was not a significant predictor of treatment-related improvements in psychological health. Home practice engagement, however, was predictive of change in dispositional mindfulness, in the expected direction. Results of our secondary analyses demonstrated that employment status was predictive of home practice engagement, with those who were unemployed completing more at-home practice on average. Mindfulness self-efficacy at baseline and previous experience with meditation or other contemplative practices were independently predictive of mean practice quality. The results of this study suggest that home practice helps generate meaningful change in dispositional mindfulness, which is purportedly a key mechanism of action in mindfulness-based interventions.