Constructing Inverse Probability Weights for Continuous Exposures A Comparison of Methods
Constructing Inverse Probability Weights for Continuous Exposures A Comparison of Methods
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DOI：
10.1097/ede.0000000000000053
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发表时间：
2014-03-01
期刊：
影响因子：
5.4
通讯作者：
Kaufman, Jay S.
中科院分区：
文献类型：
--
作者：
Naimi, Ashley I.;Moodie, Erica E. M.;Kaufman, Jay S.
具有二次暴露的逆概率加权边际结构模型在流行病学中很常见。由于存在异常值以及需要识别曝光的参数形式并考虑非恒定曝光变化，构建连续曝光的逆概率权重可能会变得复杂。通过蒙特卡罗模拟，我们探索了构造连续曝光的逆概率权重的各种方法的性能。我们使用从一个大型经验队列中抽样的数据，产生了两个连续的暴露和双重结果。第一次暴露服从具有同方差的正态分布。第二次暴露服从污染的泊松分布，异方差等于条件均值。我们评估了六种构造逆概率权重的方法：正态分布、具有异方差的正态分布、具有异方差的截断正态分布、伽马分布、t分布(1、3和5个自由度)和分位数入库方法(基于10、15和20个曝光类别)。我们在回归模型中估计了每个模拟暴露中每增加一个单位的边际优势比，该回归模型由使用每种方法构建的逆概率权重加权，然后计算每种方法的偏差和均方误差。对于同方差曝光，标准的正常、伽马和分位数入库方法表现最好。对于异方差曝光，分位数入库、伽马和异方差正常方法表现最好。我们的结果表明，分位数入库方法是一种简单而通用的方法来构造连续曝光的逆概率权重。
Inverse probability-weighted marginal structural models with binary exposures are common in epidemiology. Constructing inverse probability weights for a continuous exposure can be complicated by the presence of outliers, and the need to identify a parametric form for the exposure and account for nonconstant exposure variance. We explored the performance of various methods to construct inverse probability weights for continuous exposures using Monte Carlo simulation. We generated two continuous exposures and binary outcomes using data sampled from a large empirical cohort. The first exposure followed a normal distribution with homoscedastic variance. The second exposure followed a contaminated Poisson distribution, with heteroscedastic variance equal to the conditional mean. We assessed six methods to construct inverse probability weights using: a normal distribution, a normal distribution with heteroscedastic variance, a truncated normal distribution with heteroscedastic variance, a gamma distribution, a t distribution (1, 3, and 5 degrees of freedom), and a quantile binning approach (based on 10, 15, and 20 exposure categories). We estimated the marginal odds ratio for a single-unit increase in each simulated exposure in a regression model weighted by the inverse probability weights constructed using each approach, and then computed the bias and mean squared error for each method. For the homoscedastic exposure, the standard normal, gamma, and quantile binning approaches performed best. For the heteroscedastic exposure, the quantile binning, gamma, and heteroscedastic normal approaches performed best. Our results suggest that the quantile binning approach is a simple and versatile way to construct inverse probability weights for continuous exposures.