Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.

Tisane是一种混合式互动系统，用于构建包括GLMMs在内的广义线性模型。Tisane引入了一种研究设计规范语言，用于表达和探究变量之间的关系。通过交互式编译过程，Tisane能够推断候选统计模型，并进一步澄清用户查询，以构建合法的模型。在案例研究中，研究人员使用Tisane可以更好地专注于他们的目标和假设，从而避免过去的错误。

通过概念和数据关系的形式推理编写统计模型的Tisane