Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia.
Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
Dev Med Child Neurol. 2024 Nov;66(11):1408-1418. doi: 10.1111/dmcn.15948. Epub 2024 May 9.
Observational studies have a critical role in disability research, providing the opportunity to address a range of research questions. Over the past decades, there have been substantial shifts and developments in statistical methods for observational studies, most notably for causal inference. In this review, we provide an overview of modern design and analysis concepts critical for observational studies, drawing examples from the field of disability research and highlighting the challenges in this field, to inform the readership on important statistical considerations for their studies. WHAT THIS PAPER ADDS: Descriptive research questions have specific analytical complexities, so careful statistical design before analysis is critical. Prediction research aims to produce a model with good predictive ability and requires thorough statistical design prior to analysis. Causal research requires careful statistical analysis planning, facilitated by modern causal inference concepts and analytical methods. Adopting these approaches will strengthen the quality of observational studies addressing a range of research questions in the disability space.
观察性研究在残疾研究中具有重要作用,为解决一系列研究问题提供了机会。在过去的几十年中,观察性研究的统计方法发生了重大转变和发展,尤其是在因果推断方面。在这篇综述中,我们提供了对观察性研究至关重要的现代设计和分析概念的概述,从残疾研究领域举例说明,并强调了该领域的挑战,以使读者了解其研究中重要的统计考虑因素。本文的新增内容:描述性研究问题具有特定的分析复杂性,因此在分析之前进行仔细的统计设计至关重要。预测研究旨在生成具有良好预测能力的模型,并且需要在分析之前进行彻底的统计设计。因果研究需要仔细的统计分析计划,这得益于现代因果推理概念和分析方法。采用这些方法将提高残疾领域解决一系列研究问题的观察性研究的质量。