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For debate: substituting placebo controls in long-term Alzheimer's prevention trials

机译：辩论：在长期的阿尔茨海默氏症预防试验中替代安慰剂对照

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Introduction Novel compounds with potential to attenuate or stop the progression of Alzheimer's disease (AD) from its presymptomatic stage to dementia are being tested in man. The study design commonly used is the long-term randomized, placebo-controlled trial (RPCT), meaning that many patients will receive placebo for 18 months or longer. It is ethically problematic to expose presymptomatic AD patients, who by definition are at risk of developing dementia, to prolonged placebo treatment. As an alternative to long-term RPCTs we propose a novel clinical study design, termed the placebo group simulation approach (PGSA), using mathematical models to forecast outcomes of presymptomatic AD patients from their own baseline data. Forecasted outcomes are compared with outcomes observed on candidate drugs, thus replacing a concomitant placebo group. Methods First models were constructed using mild cognitive impairment (MCI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. One outcome is the Alzheimer Disease Assessment Scale - cognitive subscale (ADAScog) score after 24 months, predicted in a linear regression model; the other is the trajectory over 36 months of a composite neuropsychological test score (Neuro-Psychological Battery (NP-Batt)), using a mixed model. Demographics and clinical, biological and neuropsychological baseline values were tested as potential predictors in both models. Results ADAScog scores after 24 months are predicted from gender, obesity, Functional Assessment Questionnaire (FAQ) and baseline scores of Mini-Mental State Examination, ADAScog and NP-Batt with an R 2 of 0.63 and a residual standard deviation of 0.67, allowing reasonably precise estimates of sample means. The model of the NP-Batt trajectory has random intercepts and slopes and fixed effects for body mass index, time, apolipoprotein E4, age, FAQ, baseline scores of ADAScog and NP-Batt, and four interaction terms. Estimates of the residual standard deviation range from 0.3 to 0.5 on a standard normal scale. If novel drug candidates are expected to diminish the negative slope of scores with time, a change of 0.04 per year could be detected in samples of 400 with a power of about 80%. Conclusions First PGSA models derived from ADNI MCI data allow prediction of cognitive endpoints and trajectories that correspond well with real observed values. Corroboration of these models with data from other observational studies is ongoing. It is suggested that the PGSA may complement RPCT designs in forthcoming long-term drug studies with presymptomatic AD individuals.
机译：引言目前正在人类中测试新型化合物，这些化合物具有减轻或阻止阿尔茨海默氏病（AD）从症状发生前阶段到痴呆的进程的潜力。常用的研究设计是长期随机，安慰剂对照试验（RPCT），这意味着许多患者将接受安慰剂18个月或更长时间。从症状上说，有症状的AD患者（据定义有患痴呆症的风险）要接受长期的安慰剂治疗，这在伦理上是有问题的。作为长期RPCT的替代方案，我们提出了一种新颖的临床研究设计，称为安慰剂组模拟方法（PGSA），它使用数学模型从自身的基线数据预测症状前AD患者的结局。将预测的结果与在候选药物上观察到的结果进行比较，从而替代了伴随的安慰剂组。方法使用来自阿尔茨海默氏病神经影像学倡议（ADNI）数据库的轻度认知障碍（MCI）数据构建第一个模型。一种结果是线性回归模型中预测的24个月后的阿尔茨海默病疾病评估量表-认知子量表（ADAScog）得分;另一个是使用混合模型的36个月的综合神经心理学测验分数（神经心理学电池（NP-Batt））的轨迹。人口统计学和临床​​，生物学和神经心理学基线值均作为两个模型中的潜在预测指标进行了测试。结果24个月后的ADAScog得分是根据性别，肥胖，功能评估问卷（FAQ）和小精神状态检查，ADAScog和NP-Batt的基线得分预测的，R 2 为0.63，且残留标准偏差为0.67，可以合理准确地估计样本均值。 NP-Batt轨迹的模型具有随机截距和斜率，并且对体重指数，时间，载脂蛋白E4，年龄，FAQ，ADAScog和NP-Batt的基线分数以及四个交互作用项具有固定影响。残留标准偏差的估计值在标准标准范围内为0.3到0.5。如果预期新候选药物会随着时间的推移而降低分数的负斜率，则可以在400个样本中检测到每年0.04的变化，功效约为80％。结论从ADNI MCI数据得出的第一个PGSA模型可以预测与实际观测值非常吻合的认知终点和轨迹。这些模型与其他观察研究的数据正在得到证实。建议PGSA可能在即将出现症状前AD个体的长期药物研究中补充RPCT设计。

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