How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change?
How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change?
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DOI：
10.1186/s12874-023-02093-2
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发表时间：
2023-11-21
影响因子：
4
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对于某些情况，治疗的目的是减少随着时间的推移恶化。试验结局可以是连续测量的变化，使用随机斜率模型进行分析，每个治疗组的斜率不同。可以使用两阶段过程获得具有特定访视时间表（例如，每年一次，持续三年）的试验的样本量。首先，根据预先存在的数据集（例如，在类似环境中进行的观察性研究）估计相关（协）方差。其次，使用标准公式计算样本量。然而，随机斜率模型假设线性轨迹，组平均值的任何差异均随随访时间成比例增加。这些假设失败的影响尚不清楚。我们使用模拟来评估非线性轨迹和/或非比例治疗效应对拟议试验功效的影响。我们使用了四种轨迹，包括线性和非线性，并模拟观察性研究来计算样本量。然后模拟这种规模的试验，治疗效果与时间成比例或不成比例。对于比例治疗效应和与观察性研究匹配的试验访视时间表，即使对于非线性轨迹，把握度也接近标称值。但是，如果时间表与观察性研究不匹配，则把握度可能高于或低于标称水平，其程度取决于参数，如残差方差。对于非比例治疗效应，使用随机斜率模型可能导致功效远离标称水平。如果怀疑轨迹为非线性，则用于通知把握度计算的观察数据应尽可能与拟定试验具有相同的访视时间表。此外，如果预期治疗效应为非比例性，则不应使用随机斜率模型。可以使用允许轨迹随时间自由变化的模型，作为第二线分析方法（请记住，功率将丢失）或为试验供电时。在线版本包含补充材料，可通过10.1186/s12874-023-02093-2获得。
For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear. We used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial’s power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time. For a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels. If trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial. The online version contains supplementary material available at 10.1186/s12874-023-02093-2.
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DOI：
10.1177/1536867x211045512
发表时间：
2021-09
期刊：
The Stata journal
影响因子：
--
作者：
Nash S;Morgan KE;Frost C;Mulick A
通讯作者：
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影响因子：
158.5
作者：
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DOI：
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发表时间：
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期刊：
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影响因子：
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作者：
Spain R;Powers K;Murchison C;Heriza E;Winges K;Yadav V;Cameron M;Kim E;Horak F;Simon J;Bourdette D
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影响因子：
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影响因子：
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