Common pitfalls in statistical analysis: Logistic regression.
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TLDR
In this article, a statistical technique to evaluate the relationship between various predictor variables and an outcome which is binary is discussed.Abstract:
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.read more
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References
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A simulation study of the number of events per variable in logistic regression analysis.
Peter Peduzzi,John Concato,John Concato,Elizabeth Kemper,Elizabeth Kemper,Theodore R. Holford,Alvan R. Feinstein,Alvan R. Feinstein +7 more
TL;DR: Findings indicate that low EPV can lead to major problems, and the regression coefficients were biased in both positive and negative directions, and paradoxical associations (significance in the wrong direction) were increased.
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Relaxing the Rule of Ten Events per Variable in Logistic and Cox Regression
TL;DR: A large simulation study of other influences on confidence interval coverage, type I error, relative bias, and other model performance measures found a range of circumstances in which coverage and bias were within acceptable levels despite less than 10 EPV.
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Common pitfalls in statistical analysis: Odds versus risk.
TL;DR: The meaning of risk and odds and the difference between the two are explained.
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Common pitfalls in statistical analysis: Linear regression analysis
TL;DR: This article deals with linear regression analysis which predicts the value of one continuous variable from another and discusses the assumptions and pitfalls associated with this analysis.
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Development and validation of a prediction model for gestational hypertension in a Ghanaian cohort.
Edward Antwi,Rolf H.H. Groenwold,Joyce L. Browne,Arie Franx,Irene Akua Agyepong,Kwadwo A. Koram,Kerstin Klipstein-Grobusch,Kerstin Klipstein-Grobusch,Diederick E. Grobbee +8 more
TL;DR: The prediction model showed adequate performance after validation in an independent cohort and can be used to classify women into high, moderate or low risk of developing GH, contributing to efforts to provide clinical decision-making support to improve maternal health and birth outcomes.