Partial Residual Plots as an Integrated Model Diagnostic Tool in Model-Based Meta-Analysis.

IF 6.3 2区 医学 Q1 PHARMACOLOGY & PHARMACY Clinical Pharmacology & Therapeutics Pub Date : 2024-09-08 DOI:10.1002/cpt.3418
John Maringwa, Paul Matthias Diderichsen, Chandni Valiathan
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Abstract

The use of partial residual plots (PRPs) was explored as a model diagnostic tool in Model-based Meta-Analysis (MBMA). Mathematical derivations illustrating the concepts were followed by an MBMA example using publicly available literature data of anti-depressive treatments with fluoxetine and venlafaxine. An Emax dose-response model was identified for venlafaxine while a constant drug effect combining all dose levels vs. placebo was identified for fluoxetine. The larger the mean baseline Hamilton Depression Rating (HAMD) score, the larger the expected drug effect (P = 0.0122), based on the likelihood ratio test. Mean baseline HAMD score (range) was 25.4 (23.5, 29.4) and 20.8 (15, 26) while mean placebo change from baseline (range) was -9.02 (-12.2, -4.8) and - 6.22 (-10.9, -1.3) for venlafaxine and fluoxetine, respectively. Average baseline HAMD score appeared larger for venlafaxine compared to fluoxetine, albeit a wider range for fluoxetine. Placebo response seemed lower but also more variable in fluoxetine compared to venlafaxine studies. Observed data points tended to deviate from model predictions when the mean baseline HAMD and placebo response values associated with those data points differed substantially from the corresponding values used for the model prediction. Normalizing observed data addressed this, providing a "like-to-like" comparison with model predictions in PRP when assessing the effect of one covariate (dose) after normalizing for other covariates/effects (placebo response and mean baseline). PRPs provide a robust integrated diagnostic tool in MBMA that uses all data to show the correlation between response and any covariate while controlling for other covariates included in the model.

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将部分残差图作为基于模型的 Meta 分析中的综合模型诊断工具》(Partial Residual Plots as an Integrated Model Diagnostic Tool in Model-Based Meta-Analysis)。
在基于模型的元分析(MBMA)中,部分残差图(PRP)作为一种模型诊断工具得到了探讨。在对概念进行数学推导之后,使用公开文献数据对氟西汀和文拉法辛的抗抑郁治疗进行了 MBMA 示例。为文拉法辛确定了一个最大剂量反应模型,而为氟西汀确定了一个结合所有剂量水平与安慰剂的恒定药物效应模型。根据似然比检验,平均基线汉密尔顿抑郁评分(HAMD)越高,预期药物效应越大(P = 0.0122)。文拉法辛和氟西汀的平均基线 HAMD 评分(范围)分别为 25.4(23.5,29.4)和 20.8(15,26),而安慰剂与基线相比的平均变化(范围)分别为-9.02(-12.2,-4.8)和-6.22(-10.9,-1.3)。与氟西汀相比,文拉法辛的平均基线HAMD评分似乎更高,尽管氟西汀的评分范围更广。与文拉法辛研究相比,氟西汀的安慰剂反应似乎更低,但变化也更大。当与数据点相关的 HAMD 和安慰剂反应平均基线值与模型预测所用的相应值相差很大时,观察数据点往往会偏离模型预测值。对观察到的数据进行归一化处理可解决这一问题,在对其他协变量/效应(安慰剂反应和平均基线)进行归一化处理后,在 PRP 中评估一个协变量(剂量)的效应时,可与模型预测进行 "相似 "比较。PRP 在 MBMA 中提供了一个强大的综合诊断工具,它使用所有数据显示响应与任何协变量之间的相关性,同时控制模型中包含的其他协变量。
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来源期刊
CiteScore
12.70
自引率
7.50%
发文量
290
审稿时长
2 months
期刊介绍: Clinical Pharmacology & Therapeutics (CPT) is the authoritative cross-disciplinary journal in experimental and clinical medicine devoted to publishing advances in the nature, action, efficacy, and evaluation of therapeutics. CPT welcomes original Articles in the emerging areas of translational, predictive and personalized medicine; new therapeutic modalities including gene and cell therapies; pharmacogenomics, proteomics and metabolomics; bioinformation and applied systems biology complementing areas of pharmacokinetics and pharmacodynamics, human investigation and clinical trials, pharmacovigilence, pharmacoepidemiology, pharmacometrics, and population pharmacology.
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