2008
DOI: 10.1136/bmj.39465.451748.ad
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Empirical evidence of bias in treatment effect estimates in controlled trials with different interventions and outcomes: meta-epidemiological study

Abstract: Objective To examine whether the association of inadequate or unclear allocation concealment and lack of blinding with biased estimates of intervention effects varies with the nature of the intervention or outcome. Design Combined analysis of data from three metaepidemiological studies based on collections of metaanalyses. Data sources 146 meta-analyses including 1346 trials examining a wide range of interventions and outcomes. Main outcome measures Ratios of odds ratios quantifying the degree of bias associat… Show more

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Cited by 2,231 publications
(1,223 citation statements)
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References 25 publications
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“…We used central randomisation stratifying for important predictive factors,51 52 53 54 used blinded outcome assessors,51 52 53 54 assessed several register based outcomes that are likely less influenced by knowledge about intervention group affiliation than other outcomes, included stratification factors in our main analyses,55 56 conducted our main analyses on the data where missingness was controlled by multiple imputation,57 58 conducted our analyses blinded for intervention group,59 and drew our conclusions without knowledge of intervention group 59. Moreover, although we based our sample size calculation on a power of 80%, by inflating our sample by 30% and analysing all participants using multiple imputations, we actually had a power of 91% to detect the a priori defined least significant difference regarding the primary outcome.…”
Section: Discussionmentioning
confidence: 99%
“…We used central randomisation stratifying for important predictive factors,51 52 53 54 used blinded outcome assessors,51 52 53 54 assessed several register based outcomes that are likely less influenced by knowledge about intervention group affiliation than other outcomes, included stratification factors in our main analyses,55 56 conducted our main analyses on the data where missingness was controlled by multiple imputation,57 58 conducted our analyses blinded for intervention group,59 and drew our conclusions without knowledge of intervention group 59. Moreover, although we based our sample size calculation on a power of 80%, by inflating our sample by 30% and analysing all participants using multiple imputations, we actually had a power of 91% to detect the a priori defined least significant difference regarding the primary outcome.…”
Section: Discussionmentioning
confidence: 99%
“…Lack of blinding is unlikely to influence the objectively measured primary outcome 22, although bias could be introduced in the self‐reported measurement of process variables such as reported receiving and reading of booklets. Possible recall bias is unlikely to be a major concern for the validity of estimated treatment effect, but it could affect analyses of process and mediating variables.…”
Section: Discussionmentioning
confidence: 99%
“…Data synthesis-We undertook meta-analyses only if the treatments, participants, and the underlying clinical questions in the studies were similar enough for pooling to be appropriate (Wood 2008). If an I 2 was less than or equal to 50%, then we used a fixed-effect model, whereas if the I 2 was greater than 50%, then we used a random-effects model (Higgins 2011).…”
Section: Other Potential Sources Of Biasmentioning
confidence: 99%