Detecting and correcting the bias of unmeasured factors using perturbation analysis: a data-mining approach.
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.
Research Center for Genes, Environment and Human Health, College of Public Health, National Taiwan University, Rm, 536, No, 17, Xuzhou Rd,, Taipei 100, Taiwan. wenchung@ntu.edu.tw.
Wen-Chung
WC
Lee
D015982:Bias; D003198:Computer Simulation; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D057225:Data Mining; D004812:Epidemiologic Methods; D006801:Humans; D015233:Models, Statistical; D064887:Observational Studies as Topic / Q000379:methods*; D012107:Research Design