Evidence map›Paper›PMID 32698801›Full record

ArticleBMC medical research methodology2020

A novel method for controlling unobserved confounding using double confounders.

Lu Liu, Lei Hou, Yuanyuan Yu, Xinhui Liu, Xiaoru Sun, Fan Yang, Qing Wang, Ming Jing, Yeping Xu, Hongkai Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.3field-weighted citation impact, top 18% of its field
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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
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

11 authors at 1 institution in 1 country.

Lu LiuInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Lei HouInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Yuanyuan YuInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Xinhui LiuInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Xiaoru SunInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Fan YangInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Qing WangInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Ming JingInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China.
Yeping XuSynthesis Electronic Technology Co.Ltd, 250012, Jinan, Shandong, People's Republic of China.
Hongkai LiInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China. lihongkaiyouxiang@163.com.
Fuzhong XueInstitute for Medical Dataology, Shandong University, 250012, Jinan, Shandong, People's Republic of China. xuefzh@sdu.edu.cn.
Shandong University · CN

Funding

National Natural Science Foundation of China 81773547Natural Science Foundation of Shandong Province ZR2019ZD02
6 · The paper itself

Abstract

backgroundControlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method.

methodsWe are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders.

resultsThe simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99-2.93) mmol/L with per 1- kg/m

conclusionsWe propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access.

Indexed as

Models, StatisticalTriglyceridesBlood PressureBody Mass IndexChinaCholesterol, HDLData Interpretation, StatisticalHumansObservational Studies as TopicCholesterol, HDLTriglyceridesCausal effectGeneralized moment estimate modelIdentificationUnobserved confounders

Identifiers

PMID32698801
PMCPMC7374896
OpenAlexW3044585063

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.