Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial.
Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial.
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多元纵向临床实验室安全结果，伴随药物和临床不良事件的联合模型：在怀孕期间对基于青蒿素的治疗的应用。
DOI：
10.1186/s12874-021-01412-9
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发表时间：
2021-10-09
影响因子：
4
通讯作者：
Chirwa T
中科院分区：
文献类型：
--
作者：
Patson N;Mukaka M;D'Alessandro U;Chapotera G;Mwapasa V;Mathanga D;Kazembe L;Laufer MK;Chirwa T
在药物试验中，重复收集临床不良事件（AE）、合并用药和实验室安全性结局，以支持药物安全性证据。尽管这些结局存在潜在相关性，但通常单独分析，由于安全性数据的部分评估，可能导致错误信息和低效估计。使用联合建模，我们研究了临床AE是否因治疗而异，以及实验室结果（丙氨酸氨基转移酶，总胆红素）和合并用药如何与青蒿素为基础的抗疟治疗后随时间推移的临床AE相关。我们使用的数据来自一项妊娠期基于青蒿素的疟疾治疗试验，该试验将870名妇女随机接受蒿甲醚-苯芴醇（AL），阿莫地喹-青蒿琥酯（ASAQ）和双氢青蒿素-哌喹（DHAPQ）。我们拟合了一个联合模型，该模型包含来自四个结局的四个子模型：丙氨酸氨基转移酶的纵向子模型、总胆红素的纵向子模型、合并用药的泊松子模型和临床AE的泊松子模型。由于临床AE是我们的主要结局，因此纵向子模型和合并用药子模型分别通过当前值和随机效应关联结构与临床AE子模型相关联。我们拟合了临床AE的传统泊松模型，以评估治疗对临床AE（即发生率比（IRR））估计值的影响是否在传统泊松模型和联合模型之间存在差异，其中AL为参考治疗。在870名女性中，564名（65%）至少发生1起AE。使用联合模型，AE与合并用药相关（log IRR 1.7487; 95% CI：1.5471，1.9503; p < 0.001），但未检测到总胆红素（log IRR：-0.0288; 95% CI：-0.5045，0.4469; p = 0.906）和丙氨酸氨基转移酶（log IRR：0.1153; 95% CI：-0.0889，0.3194; p = 0.269）。Poisson模型低估了治疗对AE发生率的影响，关节模型中ASAQ的log IRR为0.2118（95% CI：0.0082，0.4154; p = 0.041），Poisson模型为0.1838（95% CI：0.0574，0.3102; p = 0.004）。我们证明，尽管AE在治疗之间没有变化，但与泊松模型相比，联合模型产生了有效的AE发生率估计值。联合模型显示AE与合并用药之间呈正相关，但与实验室结果无关。ClinicalTrials.gov：NCT00852423
In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment. Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p < 0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: − 0.5045, 0.4469; p = 0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: − 0.0889, 0.3194; p = 0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p = 0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p = 0.004) for Poisson model. We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. ClinicalTrials.gov: NCT00852423
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影响因子：
4
作者：
Hickey GL;Philipson P;Jorgensen A;Kolamunnage-Dona R
通讯作者：
Kolamunnage-Dona R
影响因子：
4.2
作者：
Merz, Michael;Lee, Kwan R.;Kullak-Ublick, Gerd A.;Brueckner, Andreas;Watkins, Paul B.
通讯作者：
Watkins, Paul B.
DOI：
10.1093/biostatistics/1.4.465
发表时间：
2000-12-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Henderson, R;Diggle, P;Dobson, A
通讯作者：
Dobson, A
影响因子：
4
作者：
Long JD;Mills JA
通讯作者：
Mills JA
影响因子：
2
作者：
Buu, Anne;Li, Runze;Tan, Xianming;Zucker, Robert A.
通讯作者：
Zucker, Robert A.