UNASSIGNED: We built a simple model using the Cox PH to investigate the impact of specific cytokine profiles in predicting the overall HIV incidence. Further, Kaplan-Meier curves were used to compare HIV incidence rates between the treatment and placebo groups while assessing the overall treatment effectiveness. Utilizing stepwise regression, we developed a series of Cox PH models to analyze 48 longitudinally measured cytokine profiles. We considered three kinds of effects in the cytokine profile measurements: average, difference, and time-dependent covariate. These effects were combined with baseline covariates to explore their influence on predictors of HIV incidence.
UNASSIGNED: Comparing the predictive performance of the Cox PH models developed using the AIC metric, model 4 (Cox PH model with time-dependent cytokine) outperformed the others. The results indicated that the cytokines, interleukin (IL-2, IL-3, IL-5, IL-10, IL-16, IL-12P70, and IL-17 alpha), stem cell factor (SCF), beta nerve growth factor (B-NGF), tumor necrosis factor alpha (TNF-A), interferon (IFN) alpha-2, serum stem cell growth factor (SCG)-beta, platelet-derived growth factor (PDGF)-BB, granulocyte macrophage colony-stimulating factor (GM-CSF), tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), and cutaneous T-cell-attracting chemokine (CTACK) were significantly associated with HIV incidence. Baseline predictors significantly associated with HIV incidence when considering cytokine effects included: age of oldest sex partner, age at enrollment, salary, years with a stable partner, sex partner having any other sex partner, husband\'s income, other income source, age at debut, years lived in Durban, and sex in the last 30 days.
UNASSIGNED: Overall, the inclusion of cytokine effects enhanced the predictive performance of the models, and the PrEP group exhibited reduced HIV incidences compared to the placebo group.
■我们使用CoxPH建立了一个简单的模型,以研究特定细胞因子谱在预测总体HIV发病率中的影响。Further,使用Kaplan-Meier曲线比较治疗组和安慰剂组之间的HIV发病率,同时评估总体治疗效果。利用逐步回归,我们开发了一系列CoxPH模型来分析48个纵向测量的细胞因子谱.我们在细胞因子谱测量中考虑了三种效应:平均,差异,和时间依赖的协变量。将这些效应与基线协变量相结合,以探索它们对HIV发病率预测因子的影响。
■比较使用AIC度量开发的CoxPH模型的预测性能,模型4(具有时间依赖性细胞因子的CoxPH模型)优于其他模型。结果表明,细胞因子,白细胞介素(IL-2,IL-3,IL-5,IL-10,IL-16,IL-12P70和IL-17α),干细胞因子(SCF),β神经生长因子(B-NGF),肿瘤坏死因子α(TNF-A),干扰素(IFN)α-2,血清干细胞生长因子(SCG)-β,血小板衍生生长因子(PDGF)-BB,粒细胞巨噬细胞集落刺激因子(GM-CSF),肿瘤坏死因子相关凋亡诱导配体(TRAIL),皮肤T细胞吸引趋化因子(CTACK)与HIV发病率显著相关.考虑细胞因子效应时,基线预测因子与HIV发病率显着相关,包括:年龄最大的性伴侣的年龄,入学年龄,薪水,多年来有一个稳定的合作伙伴,有其他性伴侣的性伴侣,丈夫的收入,其他收入来源,首次亮相的年龄,在德班生活了几年,在过去的30天里做爱.
■总的来说,纳入细胞因子效应增强了模型的预测性能,与安慰剂组相比,PrEP组的HIV发病率降低。