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首页> 外文期刊>Statistics in medicine >Sequential analysis of latent variables using mixed-effect latent variable models: Impact of non-informative and informative missing data.
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Sequential analysis of latent variables using mixed-effect latent variable models: Impact of non-informative and informative missing data.

机译：使用混合效应潜在变量模型对潜在变量进行顺序分析：非信息性和信息性缺失数据的影响。

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摘要

Sequential methods allowing for early stopping of clinical trials are widely used in various therapeutic areas. These methods allow for the analysis of different types of endpoints (quantitative, qualitative, time to event) and often provide, in average, substantial reductions in sample size as compared with single-stage designs while maintaining pre-specified type I and II errors. Sequential methods are also used when analysing particular endpoints that cannot be directly measured, such as depression, quality of life, or cognitive functioning, which are often measured through questionnaires. These types of endpoints are usually referred to as latent variables and should be analysed with latent variable models. In addition, in most clinical trials studying such latent variables, incomplete data are not uncommon and the missing data process might also be non-ignorable. We investigated the impact of informative or non-informative missing data on the statistical properties of the double triangular test (DTT), combined with the mixed-effects Rasch model (MRM) for dichotomous responses or the traditional method based on observed patient's scores (S) to the questionnaire. The achieved type I errors for the DTT were usually close to the target value of 0.05 for both methods, but increased slightly for the MRM when informative missing data were present. The DTT was very close to the nominal power of 0.95 when the MRM was used, but substantially underpowered with the S method (reduction of about 23 per cent), irrespective of whether informative missing data were present or not. Moreover, the DTT using the MRM allowed for reaching a conclusion (under H(0) or H(1)) with fewer patients than the S method, the average sample number for the latter increasing importantly when the proportion of missing data increased. Incorporating MRM in sequential analysis of latent variables might provide a more powerful method than the traditional S method, even in the presence of non-informative or informative missing data.
机译：允许早期停止临床试验的序贯方法广泛用于各个治疗领域。这些方法可用于分析不同类型的终点（定量，定性，事件发生时间），并且与单阶段设计相比，通常可以平均减少样本数量，同时保持预先指定的I和II型误差。当分析无法直接测量的特定终点时，例如抑郁，生活质量或认知功能，通常使用问卷调查法来测量时，也使用顺序方法。这些类型的端点通常称为潜在变量，应使用潜在变量模型进行分析。此外，在大多数研究此类潜在变量的临床试验中，不完整的数据并不罕见，丢失的数据过程也可能不可忽略。我们调查了信息性或非信息性缺失数据对双三角检验（DTT）的统计特性的影响，并结合了基于二分法反应的混合效应Rasch模型（MRM）或基于观察患者得分的传统方法（S ）的问卷。两种方法的DTT的I型误差通常接近0.05的目标值，但是当存在信息缺失的数据时，MRM的I型误差略有增加。使用MRM时，DTT非常接近0.95的标称功效，但无论是否存在提供情报的缺失数据，S方法的功效都大大不足（减少了约23％）。而且，使用MRM的DTT可以得出比S方法更少的患者结论（在H（0）或H（1）下），后者的平均样本数在丢失数据的比例增加时非常重要。甚至在存在非信息性或信息性缺失数据的情况下，将MRM合并到潜在变量的顺序分析中也可能提供比传统S方法更强大的方法。

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