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Can statistic adjustment of OR minimize the potential confounding bias for meta-analysis of case-control study? A secondary data analysis

Overview of attention for article published in BMC Medical Research Methodology, December 2017
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Title
Can statistic adjustment of OR minimize the potential confounding bias for meta-analysis of case-control study? A secondary data analysis
Published in
BMC Medical Research Methodology, December 2017
DOI 10.1186/s12874-017-0454-x
Pubmed ID
Authors

Tianyi Liu, Xiaolu Nie, Zehao Wu, Ying Zhang, Guoshuang Feng, Siyu Cai, Yaqi Lv, Xiaoxia Peng

Abstract

Different confounder adjustment strategies were used to estimate odds ratios (ORs) in case-control study, i.e. how many confounders original studies adjusted and what the variables are. This secondary data analysis is aimed to detect whether there are potential biases caused by difference of confounding factor adjustment strategies in case-control study, and whether such bias would impact the summary effect size of meta-analysis. We included all meta-analyses that focused on the association between breast cancer and passive smoking among non-smoking women, as well as each original case-control studies included in these meta-analyses. The relative deviations (RDs) of each original study were calculated to detect how magnitude the adjustment would impact the estimation of ORs, compared with crude ORs. At the same time, a scatter diagram was sketched to describe the distribution of adjusted ORs with different number of adjusted confounders. Substantial inconsistency existed in meta-analysis of case-control studies, which would influence the precision of the summary effect size. First, mixed unadjusted and adjusted ORs were used to combine individual OR in majority of meta-analysis. Second, original studies with different adjustment strategies of confounders were combined, i.e. the number of adjusted confounders and different factors being adjusted in each original study. Third, adjustment did not make the effect size of original studies trend to constringency, which suggested that model fitting might have failed to correct the systematic error caused by confounding. The heterogeneity of confounder adjustment strategies in case-control studies may lead to further bias for summary effect size in meta-analyses, especially for weak or medium associations so that the direction of causal inference would be even reversed. Therefore, further methodological researches are needed, referring to the assessment of confounder adjustment strategies, as well as how to take this kind of bias into consideration when drawing conclusion based on summary estimation of meta-analyses.

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The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 17%
Student > Master 3 9%
Student > Ph. D. Student 3 9%
Student > Bachelor 2 6%
Other 2 6%
Other 4 11%
Unknown 15 43%
Readers by discipline Count As %
Medicine and Dentistry 10 29%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Agricultural and Biological Sciences 2 6%
Neuroscience 2 6%
Other 3 9%
Unknown 14 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 December 2017.
All research outputs
#20,458,307
of 23,015,156 outputs
Outputs from BMC Medical Research Methodology
#1,893
of 2,029 outputs
Outputs of similar age
#377,542
of 441,864 outputs
Outputs of similar age from BMC Medical Research Methodology
#45
of 52 outputs
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