Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem, State of the Field, and Performance of Statistical Methods

Abstract

BackgroundDropouts 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 FindingsWe 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−λt) where λ 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/SignificanceOur 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 Influence of Weight Loss on Arterial Stiffness in Obese and Overweight Subjects

“…This corresponds to a dropout rate of 35.7 %, comparable with that already reported in other studies [13]. Out of the patients that dropped-out, a greater percentage was represented by females (40%).…”

Predicting dropout in dietary weight loss trials using demographic and early weight change characteristics: Implications for trial design

“…A recent pooled analysis of individual patient data from 10 large obesity RCTs in the US showed a significantly increased risk of dropout with advancing age (HR 1.02, 95% CI 1.01,1.02) 20 however weight loss was not the primary outcome in most of these studies therefore it is possible that the subjects in these studies differ from those in studies focussed primarily on weight loss, the dropout rates were low (3.7-17.2%) compared with the averages reported in previous research (23%, 26.3%) 21,22 and the range in the systematic review (7-90%) 2 . It is also important to consider that these papers generally represent secondary, unplanned, post hoc analyses and particularly with respect to the papers showing no effect there is a high risk of type 2 error, that is the studies have insufficient power to demonstrate a significant effect.…”

Effects of omega-3 supplementation in combination with diet and exercise on weight loss and body composition

“…The retention rate in this study was 63% after 6 mo. The issue of the dropout rate in weight-loss trials is problematic, with a recent review predicting that a good retention rate would be 63% at 12 mo (35). Thus, the retention rate for this study was acceptable.…”