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.
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
Can J Cardiol. 2018 Jun;34(6):709-716. doi: 10.1016/j.cjca.2018.01.013. Epub 2018 Jan 31.
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.
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.
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).
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.
在心血管研究中，院前死亡率是选择偏倚的一个重要潜在来源。逆概率 censoring 权重是一种用于解释这种偏倚来源的方法。本文的目的是检查和纠正由于院前死亡率导致的选择偏倚对急性心脏事件后心血管危险因素与全因死亡率之间关系的影响。
利用社区动脉粥样硬化风险研究（ARIC）的数据，检查心血管疾病（CVD）危险因素（0-5；吸烟状况、糖尿病、高血压、血脂异常和肥胖）数量与全因死亡率之间的关系。为了说明选择偏倚的程度，比较了未加权广义线性模型（对数链接和二项分布）和逆概率 censoring 权重模型的估计值。
在未加权多变量分析中，死亡率的风险比估计值从 1 个 CVD 危险因素的 1.09（95%置信区间 [CI]，0.98-1.21）到 5 个 CVD 危险因素的 1.95（95%CI，1.41-2.68）。在逆概率 censoring 权重加权分析中，风险比范围从 1.14（95%CI，0.94-1.39）到 4.23（95%CI，2.69-6.66）。
逆概率 censoring 权重模型的估计值大大高于所有危险因素类别中未加权调整后的估计值。这表明了由于院前死亡率导致的选择偏倚的程度以及对 CVD 危险因素对死亡率影响的估计的影响。此外，结果突出了使用这种方法解决心血管研究中常见偏倚形式的效用。
