Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach

BMC Medical Research Methodology

Wen-Chung Lee¹ 

[1] Research Center for Genes, Environment and Human Health, College of Public Health, National Taiwan University, Rm. 536, No. 17, Xuzhou Rd., Taipei 100, Taiwan

关键词: Standardization;    Bias;    Effect modification;    Data mining;    Confounding;    Epidemiologic methods;   

Others  :  866468
DOI  :  10.1186/1471-2288-14-18

 received in 2013-05-08, accepted in 2014-01-27,  发布年份 2014

【 摘 要 】

Background

The randomized controlled study is the gold-standard research method in biomedicine. In contrast, the validity of a (nonrandomized) observational study is often questioned because of unknown/unmeasured factors, which may have confounding and/or effect-modifying potential.

Methods

In this paper, the author proposes a perturbation test to detect the bias of unmeasured factors and a perturbation adjustment to correct for such bias. The proposed method circumvents the problem of measuring unknowns by collecting the perturbations of unmeasured factors instead. Specifically, a perturbation is a variable that is readily available (or can be measured easily) and is potentially associated, though perhaps only very weakly, with unmeasured factors. The author conducted extensive computer simulations to provide a proof of concept.

Results

Computer simulations show that, as the number of perturbation variables increases from data mining, the power of the perturbation test increased progressively, up to nearly 100%. In addition, after the perturbation adjustment, the bias decreased progressively, down to nearly 0%.

Conclusions

The data-mining perturbation analysis described here is recommended for use in detecting and correcting the bias of unmeasured factors in observational studies.

【 授权许可】

   
2014 Lee; licensee BioMed Central Ltd.

【 图 表 】

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