Hepatitis C virus (HCV) infection is a major global health problem. WHO guidelines recommend screening all people living with HIV for hepatitis C. Considering the limited resources for health in low and middle income countries, targeted HCV screening is potentially a more feasible screening strategy for many HIV cohorts. Hence there is an interest in developing clinician-friendly tools for selecting subgroups of HIV patients for whom HCV testing should be prioritized. Several statistical methods have been developed to predict a binary outcome. Multiple studies have compared the performance of different predictive models, but results were inconsistent. A cross-sectional HCV diagnostic study was conducted in the HIV cohort of Sihanouk Hospital Center of Hope in Phnom Penh, Cambodia. We compared the performance of logistic regression, Spiegelhalter-Knill-Jones and CART to predict Hepatitis C co-infection in this cohort. We estimated the number of HCV co-infections that would be missed. To correct for over-optimism, the leave-one-out bootstrap estimator was used for estimating this quantity. Logistic regression misses the fewest HCV co-infections (8%), but would still refer 98% of HIV patients for HCV testing. Spiegelhalter-Knill-Jones (SKJ) and CART respectively miss 12% and 29% of HCV co-infections but would only refer about 30% for HCV testing. In our dataset, logistic regression has the highest log-likelihood and smallest proportions of HCV co-infections missed but Spiegelhalter-Knill-Jones has the highest area under the ROC curve. The likelihood ratios estimated by Spiegelhalter-Knill-Jones might be easier to interpret for clinicians than odds ratios estimated by logistic regression or the decision tree from CART. CART is the most flexible method, and no model has to be specified regarding presence of interactions and form of the relationship between outcome and predictor variables.

中文翻译：

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柬埔寨艾滋病毒患者丙型肝炎共感染预测模型的比较。

丙型肝炎病毒（HCV）感染是全球主要的健康问题。世卫组织准则建议对所有感染HIV的人进行丙型肝炎筛查。考虑到中低收入国家卫生资源有限，针对HCV进行筛查可能是许多HIV人群更可行的筛查策略。因此，有兴趣开发对临床医生友好的工具，以选择应优先进行HCV检测的HIV患者亚组。已经开发了几种统计方法来预测二进制结果。多项研究比较了不同预测模型的性能，但结果不一致。在柬埔寨金边西哈努克希普医院希望中心的艾滋病毒队列中进行了HCV横断面诊断研究。我们比较了Logistic回归的效果，Spiegelhalter-Knill-Jones和CART预测了该人群中的丙型肝炎共感染。我们估计了将错过的HCV合并感染的数量。为了纠正过度乐观，使用了留一法自举估计器来估计此数量。Logistic回归遗漏了最少的HCV合并感染（8％），但仍会将98％的HIV患者转介HCV检测。Spiegelhalter-Knill-Jones（SKJ）和CART分别遗漏了12％和29％的HCV合并感染，但仅占HCV检测的30％。在我们的数据集中，对数回归的对数似然率最高，而漏掉的HCV合并感染的比例最小，但Spiegelhalter-Knill-Jones的ROC曲线下面积最大。由Spiegelhalter-Knill-Jones估计的似然比可能比由logistic回归或CART决策树估计的比值比更容易为临床医生解释。CART是最灵活的方法，无需针对交互的存在以及结果与预测变量之间的关系形式指定模型。