Nelson Scott D, Malone Daniel, Lafleur Joanne
Department of Veterans Affairs, Salt Lake City, UT, USA,
Pharmacoeconomics. 2015 Sep;33(9):887-92. doi: 10.1007/s40273-015-0283-x.
Economic and epidemiological models need various inputs to estimate the occurrence of events in different subsets of the population, such as the incidence of events for patients with risk factors compared with those without. However, the baseline event incidence for patients without risk factors (incidence_no_risk) may not be reported in the literature, therefore the event incidence in the population (incidence_pop) is commonly used in its place as the baseline. However, this is problematic because incidence_pop is a weighted average of a heterogeneous population. We therefore developed a method for deriving the incidence for persons without risk factors (incidence_no_risk) by adjustment of incidence_pop. We calculated incidence_no_risk using the relative risk for events due to risk factors (RR_risk), incidence_pop, and the prevalence of the risk factor (pRF), which are typically available in the literature. Since the incidence for patient with risk factors (incidence_risk) can be expressed as incidence_risk = incidence_no_risk × RR_risk, we found that incidence_no_risk = incidence_pop/((RR_risk × pRF) + (1 - pRF)). We validated the equation by modeling the fracture incidence in high-risk patients in an osteoporosis transition-state model. With incidence_pop used as the baseline fracture incidence, the model overestimated hip fractures in the study population (10.72 fractures/1000 patient-years). After adjustment of incidence_pop using incidence_no_risk as the baseline incidence, the model accurately predicted hip fractures (2.27/1000 patient-years). Therefore, incidence_no_risk can be calculated using this method based on the event incidence for the study population, the relative risk increase associated with the risk factor, and the prevalence of the risk factor.
经济和流行病学模型需要各种输入数据来估计不同人群亚组中事件的发生率,例如有风险因素的患者与无风险因素的患者相比事件的发生率。然而,无风险因素患者的基线事件发生率(incidence_no_risk)可能在文献中未被报道,因此通常用人群中的事件发生率(incidence_pop)来替代作为基线。然而,这存在问题,因为incidence_pop是一个异质人群的加权平均值。因此,我们开发了一种通过调整incidence_pop来推导无风险因素人群发生率(incidence_no_risk)的方法。我们使用文献中通常可获得的风险因素导致事件的相对风险(RR_risk)、incidence_pop和风险因素的患病率(pRF)来计算incidence_no_risk。由于有风险因素患者的发生率(incidence_risk)可以表示为incidence_risk = incidence_no_risk × RR_risk,我们发现incidence_no_risk = incidence_pop / ((RR_risk × pRF) + (1 - pRF))。我们通过在骨质疏松症过渡状态模型中对高危患者的骨折发生率进行建模来验证该方程。将incidence_pop用作基线骨折发生率时,该模型高估了研究人群中的髋部骨折发生率(10.72例骨折/1000患者年)。使用incidence_no_risk作为基线发生率对incidence_pop进行调整后,该模型准确预测了髋部骨折发生率(2.27/1000患者年)。因此,可以基于研究人群的事件发生率、与风险因素相关的相对风险增加以及风险因素的患病率,使用此方法计算incidence_no_risk。