Using Numerical Methods to Design Simulations: Revisiting the Balancing Intercept.
Using Numerical Methods to Design Simulations: Revisiting the Balancing Intercept.
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使用数值方法设计模拟：重新审视平衡截距。
DOI：
10.1093/aje/kwab264
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发表时间：
2022
影响因子：
5
通讯作者：
Dahabreh,IssaJ
中科院分区：
文献类型：
--
作者：
Robertson,SarahE;Steingrimsson,JonA;Dahabreh,IssaJ
在本文中，我们考虑生成二元随机变量绘制的方法，其期望以协变量为条件遵循具有已知协变量系数的逻辑回归模型。我们检查近似值以找到“平衡截距”，即导致二元随机变量期望边际期望的逻辑模型截距值。我们表明，最近提出的分析近似可能会产生不准确的结果，特别是当针对更极端的边际期望或回归模型的线性预测变量具有高方差时。然后，我们将平衡截距公式化为积分方程的解，并实现基于蒙特卡罗方法求解方程的数值近似，并表明该近似在实践中效果良好。我们对平衡截距基本问题的方法提供了一个广泛适用的策略的例子，用于制定和解决用于评估或教授流行病学方法的模拟研究设计中出现的问题。
In this paper, we consider methods for generating draws of a binary random variable whose expectation conditional on covariates follows a logistic regression model with known covariate coefficients. We examine approximations for finding a “balancing intercept,” that is, a value for the intercept of the logistic model that leads to a desired marginal expectation for the binary random variable. We show that a recently proposed analytical approximation can produce inaccurate results, especially when targeting more extreme marginal expectations or when the linear predictor of the regression model has high variance. We then formulate the balancing intercept as a solution to an integral equation, implement a numerical approximation for solving the equation based on Monte Carlo methods, and show that the approximation works well in practice. Our approach to the basic problem of the balancing intercept provides an example of a broadly applicable strategy for formulating and solving problems that arise in the design of simulation studies used to evaluate or teach epidemiologic methods.