On the Assumption of Bivariate Normality in Selection Models: A Copula Approach Applied to Estimating HIV Prevalence

Heckman-type selection models have been used to adjust HIV prevalence estimates for selection bias, which arises when participation in HIV testing and HIV status are correlated after controlling for observed variables. These models typically rely on the assumption that the error terms in the participation and outcome equations are distributed as bivariate normal. We introduce a novel approach for relaxing this parametric assumption using copulae. Here we describe our simulation study and provide the R code for evaluating the performance of copula based selection models for binary outcomes.

赫克曼型选择模型（Heckman-type selection models）已被用于校正由选择偏差引发的HIV感染率估计偏误——此类选择偏差产生于控制观测变量后，HIV检测参与行为与HIV感染状态仍存在相关性的场景。此类模型通常预设，参与方程与结局方程中的误差项服从二元正态分布。我们提出了一种基于连接函数（copulae）的创新性方法，以放宽该参数化假设。本文将详细阐述我们开展的模拟研究，并提供用于评估基于连接函数的二分类结局选择模型性能的R代码。