Results of soy-based meal replacement formula on weight, anthropometry, serum lipids & blood pressure during a 40-week clinical weight loss trial

BACKGROUND: To evaluate the intermediate-term health outcomes associated with a soy-based meal replacement, and to compare the weight loss efficacy of two distinct patterns of caloric restriction. METHODS: Ninety overweight/obese (28 < BMI ≤ 41 kg/m(2)) adults received a single session of dietary counseling and were randomized to either 12 weeks at 1200 kcal/day, 16 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 12/15/18 diet group), or 28 weeks at 1500 kcal/d and 12 weeks at 1800 kcal/d (i.e., the 15/18 diet group). Weight, body fat, waist circumference, blood pressure and serum lipid concentrations were measured at 4-week intervals throughout the 40-week trial. RESULTS: Subjects in both treatments showed statistically significant improvements in outcomes. A regression model for weight change suggests that subjects with larger baseline weights tended to lose more weight and subjects in the 12/15/18 group tended to experience, on average, an additional 0.9 kg of weight loss compared with subjects in the 15/18 group. CONCLUSION: Both treatments using the soy-based meal replacement program were associated with significant and comparable weight loss and improvements on selected health variables

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Background: Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. Methods: A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. Results: QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). Conclusion: The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31)

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Peer reviewedPublisher PD

Evaluating Statistical Methods Using Plasmode Data Sets in the Age of Massive Public Databases: An Illustration Using False Discovery Rates

Plasmode is a term coined several years ago to describe data sets that are derived from real data but for which some truth is known. Omic techniques, most especially microarray and genomewide association studies, have catalyzed a new zeitgeist of data sharing that is making data and data sets publicly available on an unprecedented scale. Coupling such data resources with a science of plasmode use would allow statistical methodologists to vet proposed techniques empirically (as opposed to only theoretically) and with data that are by definition realistic and representative. We illustrate the technique of empirical statistics by consideration of a common task when analyzing high dimensional data: the simultaneous testing of hundreds or thousands of hypotheses to determine which, if any, show statistical significance warranting follow-on research. The now-common practice of multiple testing in high dimensional experiment (HDE) settings has generated new methods for detecting statistically significant results. Although such methods have heretofore been subject to comparative performance analysis using simulated data, simulating data that realistically reflect data from an actual HDE remains a challenge. We describe a simulation procedure using actual data from an HDE where some truth regarding parameters of interest is known. We use the procedure to compare estimates for the proportion of true null hypotheses, the false discovery rate (FDR), and a local version of FDR obtained from 15 different statistical methods

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

Phone and e-mail counselling are effective for weight management in an overweight working population: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>The work setting provides an opportunity to introduce overweight (i.e., Body Mass Index ≥ 25 kg/m²) adults to a weight management programme, but new approaches are needed in this setting. The main purpose of this study was to investigate the effectiveness of lifestyle counselling by phone or e-mail on body weight, in an overweight working population. Secondary purposes were to establish effects on waist circumference and lifestyle behaviours, and to assess which communication method is the most effective.</p> <p>Methods</p> <p>A randomized controlled trial with three treatments: intervention materials with phone counselling (phone group); a web-based intervention with e-mail counselling (internet group); and usual care, i.e. lifestyle brochures (control group). The interventions used lifestyle modification and lasted a maximum of six months. Subjects were 1386 employees, recruited from seven companies (67% male; mean age 43 (SD 8.6) y; mean BMI 29.6 (SD 3.5) kg/m²). Body weight was measured by research personnel and by questionnaire. Secondary outcomes fat, fruit and vegetable intake, physical activity and waist circumference were assessed by questionnaire. Measurements were done at baseline and after six months. Missing body weight was multiply imputed.</p> <p>Results</p> <p>Body weight reduced 1.5 kg (95% CI -2.2;-0.8, p < 0.001) in the phone group and 0.6 kg (95% CI -1.3; -0.01, p = 0.045) in the internet group, compared with controls. In completers analyses, weight and waist circumference in the phone group were reduced with 1.6 kg (95% CI -2.2;-1.0, p < 0.001) and 1.9 cm (95% CI -2.7;-1.0, p < 0.001) respectively, fat intake decreased with 1 fatpoint (1 to 4 grams)/day (95% CI -1.7;-0.2, p = 0.01) and physical activity increased with 866 METminutes/week (95% CI 203;1530, p = 0.01), compared with controls. The internet intervention resulted in a weight loss of 1.1 kg (95% CI -1.7;-0.5, p < 0.001) and a reduction in waist circumference of 1.2 cm (95% CI -2.1;-0.4, p = 0.01), in comparison with usual care. The phone group appeared to have more and larger changes than the internet group, but comparisons revealed no significant differences.</p> <p>Conclusion</p> <p>Lifestyle counselling by phone and e-mail is effective for weight management in overweight employees and shows potential for use in the work setting.</p> <p>Trial registration</p> <p>ISCRTN04265725.</p

Randomized, multi-center trial of two hypo-energetic diets in obese subjects: high- versus low-fat content

Objective:To investigate whether a hypo-energetic low-fat diet is superior to a hypo-energetic high-fat diet for the treatment of obesity.Design:Open-label, 10-week dietary intervention comparing two hypo-energetic (-600 kcal/day) diets with a fat energy percent of 20-25 or 40-45.Subjects:Obese (BMI >/=30 kg/m(2)) adult subjects (n=771), from eight European centers.Measurements:Body weight loss, dropout rates, proportion of subjects who lost more than 10% of initial body weight, blood lipid profile, insulin and glucose.Results:The dietary fat energy percent was 25% in the low-fat group and 40% in the high-fat group (mean difference: 16 (95% confidence interval (CI) 15-17)%). Average weight loss was 6.9 kg in the low-fat group and 6.6 kg in the high-fat group (mean difference: 0.3 (95% CI -0.2 to 0.8) kg). Dropout was 13.6% (n=53) in the low-fat group and 18.3% (n=70) in the high-fat group (P=0.001). Among completers, more subjects lost >10% in the low-fat group than in the high-fat group ((20.8%, n=70) versus (14.7%, n=46), P=0.02). Fasting plasma total, low-density lipoprotein- and high-density lipoprotein-cholesterol decreased in both groups, but more so in the low-fat group than in the high-fat group. Fasting plasma insulin and glucose were lowered equally by both diets.Conclusions:The low-fat diet produced similar mean weight loss as the high-fat diet, but resulted in more subjects losing >10% of initial body weight and fewer dropouts. Both diets produced favorable changes in fasting blood lipids, insulin and glucose.International Journal of Obesity advance online publication, 6 December 2005; doi:10.1038/sj.ijo.0803186