Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations.
Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations.
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DOI：
10.1016/j.jclinepi.2010.04.020
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发表时间：
2010-12
影响因子：
7.2
通讯作者：
Schmid, Christopher H.
中科院分区：
文献类型：
--
作者：
Zucker, Deborah R.;Ruthazer, Robin;Schmid, Christopher H.
比较组合N-of-1试验的不同统计模型来估计群体治疗效果。对已发表的比较阿米替林治疗与联合治疗（阿米替林+氟西汀）的N-of-1系列试验的数据进行分析，比较总结和个体参与者数据荟萃分析、重复测量模型、贝叶斯分层模型、单周期、单对和平均结果交叉模型。最佳拟合模型包括随机截距（阿米替林反应）和固定治疗效果（添加氟西汀）。结果支持一个共同的、不相关的患者内部协方差结构，该结构在治疗之间和患者之间是相等的。假设患者内部方差不相等，采用随机效应模型。贝叶斯层次模型提高了精度，并对患者内部方差先验高度敏感。组合N-of-1试验的最佳模型需要考虑目标、数据源以及患者内部和患者之间的相对差异。如果没有足够的病人，病人之间的差异将很难用协变量来解释。N-of-1的数据，每个患者很少观察，可能不支持异质患者变异的模型。有了共同的方差，模型就显得健壮了。贝叶斯模型可以改进参数估计，但对方差成分的先验假设很敏感。在资源有限的情况下，必须通过增加参与者来平衡患者内精确度的提高，以解释人群差异。
To compare different statistical models for combining N-of-1 trials to estimate a population treatment effect. Data from a published series of N-of-1 trials comparing amitriptyline therapy and combination treatment (amitriptyline + fluoxetine ) were analyzed to compare summary and individual participant data meta-analysis, repeated measures models, Bayesian hierarchical models, single-period, single-pair and averaged outcome crossover models. The best fitting model included a random intercept (response on amitriptyline) and fixed treatment effect (added fluoxetine). Results supported a common, uncorrelated within-patient covariance structure that is equal between-treatments and across patients. Assuming unequal within-patient variances, a random effects model was favored. Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors. Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within and between patient variances. Without sufficient patients, between-patient variation will be hard to explain with covariates. N-of-1 data with few observations per patients may not support models with heterogeneous within-patient variation. With common variances, models appear robust. Bayesian models may improve parameter estimation but are sensitive to prior assumptions about variance components. With limited resources, improving within-patient precision must be balanced by increased participants to explain population variation.
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影响因子：
158.5
作者：
GUYATT, G;SACKETT, D;PUGSLEY, S
通讯作者：
PUGSLEY, S
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
影响因子：
2
作者：
Higgins, JPT;Whitehead, A;Thompson, SG
通讯作者：
Thompson, SG
影响因子：
2.7
作者：
Jones, Ashley P.;Riley, Richard D.;Whitehead, Anne
通讯作者：
Whitehead, Anne
影响因子：
2.7
作者：
Ishak, K. Jack;Platt, Robert W.;Caro, J. Jaime
通讯作者：
Caro, J. Jaime