Reducing bias in parameter estimates from stepwise regression in proportional hazards regression with right-censored data

Lifetime Data Analysis
Chang-Heok SohA M Zaslavsky

Abstract

When variable selection with stepwise regression and model fitting are conducted on the same data set, competition for inclusion in the model induces a selection bias in coefficient estimators away from zero. In proportional hazards regression with right-censored data, selection bias inflates the absolute value of parameter estimate of selected parameters, while the omission of other variables may shrink coefficients toward zero. This paper explores the extent of the bias in parameter estimates from stepwise proportional hazards regression and proposes a bootstrap method, similar to those proposed by Miller (Subset Selection in Regression, 2nd edn. Chapman & Hall/CRC, 2002) for linear regression, to correct for selection bias. We also use bootstrap methods to estimate the standard error of the adjusted estimators. Simulation results show that substantial biases could be present in uncorrected stepwise estimators and, for binary covariates, could exceed 250% of the true parameter value. The simulations also show that the conditional mean of the proposed bootstrap bias-corrected parameter estimator, given that a variable is selected, is moved closer to the unconditional mean of the standard partial likelihood estimator in the cho...Continue Reading

References

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Citations

Nov 3, 2009·Statistics in Medicine·Jerald F Lawless, Yan Yuan
Jul 23, 2016·Journal of the American College of Cardiology·Akhil ParasharSamir R Kapadia
Nov 22, 2013·Journal of Medical Internet Research·Ni ZhangAlessio Signorini

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