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可否通过 OR 的统计学调整最小化病例对照研究荟萃分析中的潜在混杂偏倚?一项二次数据分析。

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

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China.

Center for Clinical Epidemiology & Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, No. 56 Nanlishi Road, Beijing, 100045, China.

出版信息

BMC Med Res Methodol. 2017 Dec 29;17(1):179. doi: 10.1186/s12874-017-0454-x.

Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

摘要

背景

病例对照研究中采用了不同的混杂因素调整策略来估计优势比(OR),即原始研究调整了多少混杂因素以及调整的变量是什么。本二次数据分析旨在检测病例对照研究中混杂因素调整策略的差异是否会导致潜在的偏倚,以及这种偏倚是否会影响荟萃分析的汇总效应量。

方法

我们纳入了所有关注非吸烟女性乳腺癌与被动吸烟之间关联的荟萃分析,以及这些荟萃分析中包含的每个原始病例对照研究。计算每个原始研究的相对偏差(RD),以检测与粗 OR 相比,调整幅度对 OR 估计的影响程度。同时,绘制散点图描述具有不同数量调整混杂因素的调整 OR 的分布。

结果

病例对照研究荟萃分析中存在实质性不一致,这会影响汇总效应量的精度。首先,大多数荟萃分析中使用混合未调整和调整的 OR 来合并个体 OR。其次,合并了具有不同混杂因素调整策略的原始研究,即每个原始研究中调整的混杂因素数量和不同的调整因素。第三,调整并没有使原始研究的效应量趋于收敛,这表明模型拟合可能未能纠正混杂引起的系统误差。

结论

病例对照研究中混杂因素调整策略的异质性可能导致荟萃分析中汇总效应量进一步偏倚,尤其是对于弱或中等关联,从而导致因果推断的方向甚至反转。因此,需要进一步的方法学研究,参考混杂因素调整策略的评估,以及在基于荟萃分析的汇总估计得出结论时如何考虑这种偏倚。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9dab/5747180/5ac3a9f0acea/12874_2017_454_Fig1_HTML.jpg

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