The analysis of survey data is a frequently arising issue in clinical trials, particularly when capturing quantities which are difficult to measure. Typical examples are questionnaires about patient's well-being, pain, or consent to an intervention. In these, data is captured on a discrete scale containing only a limited number of possible answers, from which the respondent has to pick the answer which fits best his/her personal opinion. This data is generally located on an ordinal scale as answers can usually be arranged in an ascending order, e.g., "bad", "neutral", "good" for well-being. Since responses are usually stored numerically for data processing purposes, analysis of survey data using ordinary linear regression models are commonly applied. However, assumptions of these models are often not met as linear regression requires a constant variability of the response variable and can yield predictions out of the range of response categories. By using linear models, one only gains insights about the mean response which may affect representativeness. In contrast, ordinal regression models can provide probability estimates for all response categories and yield information about the full response scale beyond the mean. In this work, we provide a concise overview of the fundamentals of latent variable based ordinal models, applications to a real data set, and outline the use of state-of-the-art-software for this purpose. Moreover, we discuss strengths, limitations and typical pitfalls. This is a companion work to a current vignette-based structured interview study in paediatric anaesthesia.

翻译：调查数据的分析在临床试验中是一个频繁出现的问题，特别是在捕捉难以测量的量时。典型例子包括关于患者幸福感、疼痛或对干预措施的同意度的问卷。在这些问卷中，数据是在一个仅包含有限数量可能答案的离散量表上收集的，受访者必须从中选择最符合其个人观点的答案。这些数据通常位于序数量表上，因为答案通常可以按升序排列，例如，对于幸福感而言，可以是“差”、“一般”、“好”。由于出于数据处理目的，回答通常以数值形式存储，因此通常使用普通线性回归模型分析调查数据。然而，这些模型的假设通常无法满足，因为线性回归要求因变量具有恒定的变异性，并且可能产生超出响应类别范围的预测。使用线性模型仅能获得关于平均响应的见解，这可能会影响代表性。相比之下，序数回归模型可以为所有响应类别提供概率估计，并生成超出平均值之外的完整响应量表信息。在这项工作中，我们简要概述了基于潜变量的序数模型的基本原理、在真实数据集上的应用，并概述了为这一目的使用最新软件的方法。此外，我们讨论了其优势、局限性和常见陷阱。这是对当前一项关于儿科麻醉中基于小插曲的结构化访谈研究的配套工作。