![]() ![]() This statistical approach reduces a set of intercorrelated variables into a few dimensions that gather a big amount of the variability of the original variables ( 3). One of the most used methods is the principal component analysis (PCA). ![]() The problem of multicollinearity in clinical studies is ubiquitous, and there are many statistical methods being developed to handle it ( 2). Additionally, the standard errors of the estimations affected by multicollinearity tend to be large, making the inference over such estimations less precise (wider confidence intervals and bigger P values). Although the problem of collinearity will not compromise the predictive ability of a regression model, it can interfere in determining the precise effect of each predictor. These variables frequently correlate with each other, which will introduce multicollinearity in the regression models ( 1). Accepted for publication Jun 28, 2017.Ĭlinical studies utilizing electronic healthcare records (EHR) usually present a large number of variables. Keywords: Principal component analysis multicollinearity regression R Policy of Dealing with Allegations of Research Misconduct.Policy of Screening for Plagiarism Process. ![]()
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