Rigorous control conditions diminish treatment effects in weight loss-randomized controlled trials

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Abstract

<div class="section"> <a class="named-anchor" id="S1">  </a> <h5 class="section-title" id="d417277e164">Background</h5> <p id="P1">It has not been established whether control conditions with large weight losses (WLs) diminish expected treatment effects in WL or prevention of weight gain (PWG) randomized controlled trials (RCTs). </p> </div><div class="section"> <a class="named-anchor" id="S2">  </a> <h5 class="section-title" id="d417277e169">Subjects/Methods</h5> <p id="P2">We performed a meta-analysis of 239 WL/PWG RCTs that include a control group and at least one treatment group. A maximum likelihood meta-analysis framework is used in order to model and understand the relationship between treatment effects and control group outcomes. </p> </div><div class="section"> <a class="named-anchor" id="S3">  </a> <h5 class="section-title" id="d417277e174">Results</h5> <p id="P3">Under the informed model, an increase in control group WL of one kilogram corresponds with an expected shrinkage of the treatment effect by 0.309 kg [95% CI (−0.480, −0.138), p = 0.00081]; this result is robust against violations of the model assumptions. </p> </div><div class="section"> <a class="named-anchor" id="S4">  </a> <h5 class="section-title" id="d417277e179">Conclusions</h5> <p id="P4">We find that control conditions with large weight losses diminish expected treatment effects. Our investigation may be helpful to clinicians as they design future WL/PWG studies. </p> </div>

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An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials.

C Schmid, J. Lau, M. W. McIntosh … (1998)

If the control rate (CR) in a clinical trial represents the incidence or the baseline severity of illness in the study population, the size of treatment effects may tend to very with the size of control rates. To investigate this hypothesis, we examined 115 meta-analyses covering a wide range of medical applications for evidence of a linear relationship between the CR and three treatment effect (TE) measures: the risk difference (RD); the log relative risk (RR), and the log odds ratio (OR). We used a hierarchical model that estimates the true regression while accounting for the random error in the measurement of and the functional dependence between the observed TE and the CR. Using a two standard error rule of significance, we found the control rate was about two times more likely to be significantly related to the RD (31 per cent) than to the RR (13 per cent) or the OR (14 per cent). Correlations between TE and CR were more likely when the meta-analysis included 10 or more trials and if patient follow-up was less than six months and homogeneous. Use of weighted linear regression (WLR) of the observed TE on the observed CR instead of the hierarchical model underestimated standard errors and overestimated the number of significant results by a factor of two. The significant correlation between the CR and the TE suggests that, rather than merely pooling the TE into a single summary estimate, investigators should search for the causes of heterogeneity related to patient characteristics and treatment protocols to determine when treatment is most beneficial and that they should plan to study this heterogeneity in clinical trials.

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Association of run-in periods with weight loss in obesity randomized controlled trials.

O Affuso, K A Kaiser, T L Carson … (2014)

Study-level design characteristics that inform the optimal design of obesity randomized controlled trials (RCTs) have been examined in few studies. A pre-randomization run-in period is one such design element that may influence weight loss. We examined 311 obesity RCTs published between 1 January 2007 and 1 July 2009 that examine d weight loss or weight gain prevention as a primary or secondary end-point. Variables included run-in period, pre-post intervention weight loss, study duration (time), intervention type, percent female and degree of obesity. Linear regression was used to estimate weight loss as a function of (i) run-in (yes/no) and (ii) run-in, time, percent female, body mass index and intervention type. Interaction terms were also examined. Approximately 19% (18.6%) of the studies included a run-in period, with pharmaceutical studies having the highest frequency. Although all intervention types were associated with weight loss (Mean = 2.80 kg, SD = 3.52), the inclusion of a pre-randomization run-in was associated with less weight loss (P = 0.0017) compared with studies that did not include a run-in period. However, this association was not consistent across intervention types. Our results imply that in trials primarily targeting weight loss in adults, run-in periods may not be beneficial for improving weight loss outcomes in interventions.