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Can J Cardiol. 2018 Jun;34(6):709-716. doi: 10.1016/j.cjca.2018.01.013. Epub 2018 Jan 31.

Accounting for Selection Bias in Studies of Acute Cardiac Events.

Author information

1
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada; Department of Epidemiology and Environmental Health, University at Buffalo, The State University of New York, Buffalo, New York, USA. Electronic address: hrbanack@buffalo.edu.
2
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.

Abstract

BACKGROUND:

In cardiovascular research, pre-hospital mortality represents an important potential source of selection bias. Inverse probability of censoring weights are a method to account for this source of bias. The objective of this article is to examine and correct for the influence of selection bias due to pre-hospital mortality on the relationship between cardiovascular risk factors and all-cause mortality after an acute cardiac event.

METHODS:

The relationship between the number of cardiovascular disease (CVD) risk factors (0-5; smoking status, diabetes, hypertension, dyslipidemia, and obesity) and all-cause mortality was examined using data from the Atherosclerosis Risk in Communities (ARIC) study. To illustrate the magnitude of selection bias, estimates from an unweighted generalized linear model with a log link and binomial distribution were compared with estimates from an inverse probability of censoring weighted model.

RESULTS:

In unweighted multivariable analyses the estimated risk ratio for mortality ranged from 1.09 (95% confidence interval [CI], 0.98-1.21) for 1 CVD risk factor to 1.95 (95% CI, 1.41-2.68) for 5 CVD risk factors. In the inverse probability of censoring weights weighted analyses, the risk ratios ranged from 1.14 (95% CI, 0.94-1.39) to 4.23 (95% CI, 2.69-6.66).

CONCLUSION:

Estimates from the inverse probability of censoring weighted model were substantially greater than unweighted, adjusted estimates across all risk factor categories. This shows the magnitude of selection bias due to pre-hospital mortality and effect on estimates of the effect of CVD risk factors on mortality. Moreover, the results highlight the utility of using this method to address a common form of bias in cardiovascular research.

PMID:
29801736
DOI:
10.1016/j.cjca.2018.01.013

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