Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Center for Primary Care, Harvard Medical School, Boston, MA, USA.
Med Decis Making. 2019 Nov;39(8):998-1009. doi: 10.1177/0272989X19884927. Epub 2019 Nov 9.
Network meta-analyses (NMAs) that compare treatments for a given condition allow physicians to identify which treatments have higher or lower probabilities of reducing the risks of disease complications or increasing the risks of treatment side effects. Translating these data into personalized treatment plans requires integration of NMA data with patient-specific pretreatment risk estimates and preferences regarding treatment objectives and acceptable risks. We introduce a modeling framework to integrate data probabilistically from NMAs with data on individualized patient risk estimates for disease outcomes, treatment preferences (such as willingness to incur greater side effects for increased life expectancy), and risk preferences. We illustrate the modeling framework by creating personalized plans for antipsychotic drug treatment and evaluating their effectiveness and cost-effectiveness. Compared with treating all patients with the drug that yields the greatest quality-adjusted life-years (QALYs) on average (amisulpride), personalizing the selection of antipsychotic drugs for schizophrenia patients over the next 5 years would be expected to yield 0.33 QALYs (95% credible interval [crI]: 0.30-0.37) per patient at an incremental cost of $4849/QALY gained (95% crI: dominant-$12,357), versus 0.29 and 0.04 QALYs per patient when accounting for only risks or preferences, respectively, but not both. The analysis uses a linear, additive utility function to reflect patient treatment preferences and does not consider potential variations in patient time discounting. Our modeling framework rigorously computes what physicians normally have to do mentally. By integrating 3 key components of personalized medicine-evidence on efficacy, patient risks, and patient preferences-the modeling framework can provide personalized treatment decisions to improve patient health outcomes.
网络荟萃分析（NMAs）比较了针对特定疾病的治疗方法，使医生能够确定哪些治疗方法降低疾病并发症风险或增加治疗副作用风险的可能性更高或更低。将这些数据转化为个性化治疗计划需要将 NMA 数据与患者特定的预处理风险估计以及对治疗目标和可接受风险的偏好相结合。我们引入了一个建模框架，将来自 NMAs 的数据与个体化患者疾病结局风险估计、治疗偏好（例如，为增加预期寿命而愿意承担更大的副作用）以及风险偏好的数据进行概率整合。我们通过创建抗精神病药物治疗的个性化计划并评估其有效性和成本效益来演示建模框架。与用平均产生最大质量调整生命年（QALYs）的药物治疗所有患者（氨磺必利）相比，在接下来的 5 年内对精神分裂症患者选择抗精神病药物进行个性化选择，预计每个患者会产生 0.33 QALYs（95%可信区间[crI]：0.30-0.37），增量成本为 4849 美元/QALY（95% crI：占优-12357 美元），而仅考虑风险或偏好时，每个患者分别为 0.29 和 0.04 QALYs，但两者都不考虑。该分析使用线性、加性效用函数来反映患者的治疗偏好，并且不考虑患者时间贴现的潜在变化。我们的建模框架严格计算了医生通常需要进行的思维过程。通过整合个性化医学的 3 个关键组成部分——疗效证据、患者风险和患者偏好——该建模框架可以提供个性化的治疗决策，以改善患者的健康结果。
