The online version of this article (doi:10.1186/1471-2288-14-18) contains supplementary material, which is available to authorized users.
The author declares that he has no competing interests.
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.
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.
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%.
The data-mining perturbation analysis described here is recommended for use in detecting and correcting the bias of unmeasured factors in observational studies.
Additional file 2: Tables S1-S10: Additional results of the adjustment of one perturbation variable for the hypothetical population in Tables 1-2.(DOC 84 KB)
Additional file 3: Figures S1-S3: Additional results of the perturbation analysis for the hypothetical population in Table 1. (DOC 68 KB)
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- Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach
- BioMed Central
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