Evidence map›Paper›PMID 29162569›Full record

ArticleBMJ open2017

Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies.

Tingting Wang, Hongkai Li, Ping Su, Yuanyuan Yu, Xiaoru Sun, Yi Liu, Zhongshang Yuan, Fuzhong Xue

Abstract read
In one paragraph

Article in BMJ open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2019
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Tingting WangDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Hongkai LiDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Ping SuDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Yuanyuan YuDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Xiaoru SunDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Yi LiuDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Zhongshang YuanDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.
Fuzhong XueDepartment of Biostatistics, School of Public Health, Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Through simulation, we focused on six causal diagrams concerning different roles of mediators. Sensitivity analysis was conducted to assess the bias performances of varying across exposure-mediator effects and mediator-outcome effects when adjusting for the mediator.

settingBased on the causal relationships in the real world, we compared the biases of varying across the effects of exposure-mediator with those of varying across the effects of mediator-outcome when adjusting for the mediator. The magnitude of the bias was defined by the difference between the estimated effect (using logistic regression) and the total effect of the exposure on the outcome.

resultsIn four scenarios (a single mediator, two series mediators, two independent parallel mediators or two correlated parallel mediators), the biases of varying across the effects of exposure-mediator were greater than those of varying across the effects of mediator-outcome when adjusting for the mediator. In contrast, in two other scenarios (a single mediator or two independent parallel mediators in the presence of unobserved confounders), the biases of varying across the effects of exposure-mediator were less than those of varying across the effects of mediator-outcome when adjusting for the mediator.

conclusionsThe biases were more sensitive to the variation of effects of exposure-mediator than the effects of mediator-outcome when adjusting for the mediator in the absence of unobserved confounders, while the biases were more sensitive to the variation of effects of mediator-outcome than those of exposure-mediator in the presence of an unobserved confounder.

Indexed as

BiasConfounding Factors, EpidemiologicResearch DesignHumansepidemiologypublic health

Identifiers

PMID29162569
PMCPMC5719285

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.