Iyassu Ashagrie Sharew, Fenta Haile Mekonnen, Dessie Zelalem G, Zewotir Temesgen T
College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Debremarkos University, Debre Markos, Ethiopia.
BMC Med Inform Decis Mak. 2024 Dec 27;24(1):406. doi: 10.1186/s12911-024-02827-2.
In causal analyses, some third factor may distort the relationship between the exposure and the outcome variables under study, which gives spurious results. In this case, treatment groups and control groups that receive and do not receive the exposure are different from one another in some other essential variables, called confounders.
Place of birth was used as exposure variable and age-specific childhood vaccination status was used as outcome variables. Three approaches of confounder selection techniques such as all pre-treatment covariates, outcome cause covariates, and common cause covariates were proposed. Multiple logistic regression was used to estimate the propensity score for inverse probability treatment weighting (IPTW) confounder adjustment techniques. The proportional odds model was used to estimate the causal effect of place of birth on age-specific childhood vaccination. To validate the result obtained from observed data, we used a plasmode simulation of resampling 1000 samples from actual data 500 times.
Outcome cause and common cause confounder identification techniques gave comparable results in terms of treatment effect in the plasmode data. However, outcome causes that contain common causes and predictors of the outcome confounder identification gave relatively better treatment effect results. The treatment effect result in the IPTW confounder adjustment method was better than that of the regression adjustment method. The effect of place of birth on log odds of cumulative probability of age-specific childhood vaccination was 0.36 with odds ratio of 1.43 for higher level vaccination status.
It is essential to use plasmode simulation data to validate the reproducibility of the proposed methods on the observed data. It is important to use outcome-cause covariates to adjust their confounding effect on the outcome. Using inverse probability treatment weighting gives unbiased treatment effect results as compared to the regression method of confounder adjustment. Institutional delivery increases the likelihood of childhood vaccination at the recommended schedule.
在因果分析中，某些第三因素可能会扭曲所研究的暴露因素与结果变量之间的关系，从而得出虚假结果。在这种情况下，接受和未接受暴露的治疗组和对照组在一些其他关键变量（称为混杂因素）上彼此不同。
将出生地用作暴露变量，将特定年龄的儿童疫苗接种状况用作结果变量。提出了三种混杂因素选择技术方法，即所有治疗前协变量、结果原因协变量和共同原因协变量。使用多元逻辑回归来估计逆概率治疗加权（IPTW）混杂因素调整技术的倾向得分。使用比例优势模型来估计出生地对特定年龄儿童疫苗接种的因果效应。为了验证从观察数据中获得的结果，我们使用了从实际数据中重采样1000个样本、共500次的模拟模型。
在模拟模型数据中，结果原因和共同原因混杂因素识别技术在治疗效果方面给出了可比的结果。然而，包含共同原因和结果混杂因素预测因素的结果原因给出了相对更好的治疗效果结果。IPTW混杂因素调整方法的治疗效果结果优于回归调整方法。出生地对特定年龄儿童疫苗接种累积概率的对数优势的影响为0.36，较高水平疫苗接种状况的优势比为1.43。
使用模拟模型数据来验证所提出方法在观察数据上的可重复性至关重要。使用结果原因协变量来调整它们对结果的混杂效应很重要。与混杂因素调整的回归方法相比，使用逆概率治疗加权可得出无偏的治疗效果结果。机构分娩增加了按推荐时间表进行儿童疫苗接种的可能性。
