OBJECTIVES: It is possible for baseline imbalances to occur between treatment groups for one or more variables in a randomized controlled trial, although the identification and detection of baseline imbalances remain controversial. If trials with baseline imbalances are combined in a meta-analysis, then this may result in misleading conclusions. STUDY DESIGN AND SETTING: The identification and consequences of baseline imbalances in meta-analyses are discussed. Metaregression using mean baseline scores as a covariate is proposed as a potential method for adjusting baseline imbalances within meta-analysis. We will use a recent systematic review looking at the effect of calcium supplements on weight as an illustrative case study. RESULTS: Meta-analysis conducted using the mean final values of the treatment groups as the outcome resulted in an apparent, statistically significant, treatment effect. However, using a meta-analysis of baseline values, this was shown to be due to the baseline imbalance between treatment groups, rather than as a result of any intervention received by the participants. Applying the method of metaregression demonstrated that there was in fact a smaller, statistically insignificant effect between treatment groups. CONCLUSION: The meta-analyst should always consider the possibility of baseline imbalances and adjustments should be made wherever possible.
OBJECTIVES: It is possible for baseline imbalances to occur between treatment groups for one or more variables in a randomized controlled trial, although the identification and detection of baseline imbalances remain controversial. If trials with baseline imbalances are combined in a meta-analysis, then this may result in misleading conclusions. STUDY DESIGN AND SETTING: The identification and consequences of baseline imbalances in meta-analyses are discussed. Metaregression using mean baseline scores as a covariate is proposed as a potential method for adjusting baseline imbalances within meta-analysis. We will use a recent systematic review looking at the effect of calcium supplements on weight as an illustrative case study. RESULTS: Meta-analysis conducted using the mean final values of the treatment groups as the outcome resulted in an apparent, statistically significant, treatment effect. However, using a meta-analysis of baseline values, this was shown to be due to the baseline imbalance between treatment groups, rather than as a result of any intervention received by the participants. Applying the method of metaregression demonstrated that there was in fact a smaller, statistically insignificant effect between treatment groups. CONCLUSION: The meta-analyst should always consider the possibility of baseline imbalances and adjustments should be made wherever possible.
Authors: Jody D Ciolino; Reneé H Martin; Wenle Zhao; Michael D Hill; Edward C Jauch; Yuko Y Palesch Journal: Stat Methods Med Res Date: 2011-08-24 Impact factor: 3.021
Authors: Susan Armijo-Olivo; Bruno R da Costa; Greta G Cummings; Christine Ha; Jorge Fuentes; Humam Saltaji; Matthias Egger Journal: PLoS One Date: 2015-07-10 Impact factor: 3.240
Authors: Susan Armijo-Olivo; Humam Saltaji; Bruno R da Costa; Jorge Fuentes; Christine Ha; Greta G Cummings Journal: BMJ Open Date: 2015-09-03 Impact factor: 2.692
Authors: Susan Armijo-Olivo; Maria Ospina; Bruno R da Costa; Matthias Egger; Humam Saltaji; Jorge Fuentes; Christine Ha; Greta G Cummings Journal: PLoS One Date: 2014-05-13 Impact factor: 3.240