Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Cambridge Health Alliance, Harvard Medical School, Cambridge, MA, USA.
Med Decis Making. 2019 Jul;39(5):583-592. doi: 10.1177/0272989X19856884. Epub 2019 Aug 2.
Modeling dose-response relationships of drugs is essential to understanding their safety effects on patients under realistic circumstances. While intention-to-treat analyses of clinical trials provide the effect of assignment to a particular drug and dose, they do not capture observed exposure after factoring in nonadherence and dropout. We develop a Bayesian method to flexibly model the dose-response relationships of binary outcomes with continuous treatment, permitting multiple evidence sources, treatment effect heterogeneity, and nonlinear dose-response curves. In an application, we examine the risk of excessive weight gain for patients with schizophrenia treated with the second-generation antipsychotics paliperidone, risperidone, or olanzapine in 14 clinical trials. We define exposure as total cumulative dose (daily dose × duration) and convert to units equivalent to 100 mg of olanzapine (OLZ doses). Averaging over the sample population of 5891 subjects, the median dose ranged from 0 (placebo randomized participants) to 6.4 OLZ doses (paliperidone randomized participants). We found paliperidone to be least likely to cause excessive weight gain across a range of doses. Compared with 0 OLZ doses, at 5.0 OLZ doses, olanzapine subjects had a 15.6% (95% credible interval: 6.7, 27.1) excess risk of weight gain; corresponding estimates for paliperidone and risperidone were 3.2% (1.5, 5.2) and 14.9% (0.0, 38.7), respectively. Moreover, compared with nonblack participants, black participants had a 6.8% (1.0, 12.4) greater risk of excessive weight gain at 10.0 OLZ doses of paliperidone. Nevertheless, our findings suggest that paliperidone is safer in terms of weight gain risk than risperidone or olanzapine for all participants at low to moderate cumulative OLZ doses.
建立药物剂量-反应关系模型对于理解药物在实际情况下对患者的安全效应至关重要。虽然临床试验的意向治疗分析提供了分配给特定药物和剂量的效果，但它们没有考虑到在不依从和退出因素后观察到的暴露情况。我们开发了一种贝叶斯方法，可以灵活地对具有连续治疗的二分类结局进行剂量-反应关系建模，允许使用多个证据来源、治疗效果异质性和非线性剂量-反应曲线。在一个应用中，我们研究了 14 项临床试验中精神分裂症患者使用第二代抗精神病药帕利哌酮、利培酮或奥氮平治疗时体重过度增加的风险。我们将暴露定义为总累积剂量（每日剂量×持续时间），并转换为相当于 100 毫克奥氮平的单位（OLZ 剂量）。在 5891 名受试者的样本人群中平均计算，中位数剂量范围从 0（安慰剂随机参与者）到 6.4 OLZ 剂量（帕利哌酮随机参与者）。我们发现帕利哌酮在一系列剂量下最不可能导致体重过度增加。与 0 OLZ 剂量相比，5.0 OLZ 剂量时，奥氮平受试者体重增加的风险增加了 15.6%（95%可信区间：6.7，27.1）；帕利哌酮和利培酮的相应估计值分别为 3.2%（1.5，5.2）和 14.9%（0.0，38.7）。此外，与非黑人参与者相比，黑人参与者在接受 10.0 OLZ 剂量的帕利哌酮治疗时，体重过度增加的风险增加了 6.8%（1.0，12.4）。然而，我们的研究结果表明，对于所有参与者而言，在低至中等累积 OLZ 剂量下，与利培酮或奥氮平相比，帕利哌酮在体重增加风险方面更安全。
