Daisy A Shepherd, David J Amor, Margarita Moreno-Betancur
{"title":"Statistical analysis of observational studies in disability research.","authors":"Daisy A Shepherd, David J Amor, Margarita Moreno-Betancur","doi":"10.1111/dmcn.15948","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":50587,"journal":{"name":"Developmental Medicine and Child Neurology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Developmental Medicine and Child Neurology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/dmcn.15948","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/9 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Wiley-Blackwell is pleased to publish Developmental Medicine & Child Neurology (DMCN), a Mac Keith Press publication and official journal of the American Academy for Cerebral Palsy and Developmental Medicine (AACPDM) and the British Paediatric Neurology Association (BPNA).
For over 50 years, DMCN has defined the field of paediatric neurology and neurodisability and is one of the world’s leading journals in the whole field of paediatrics. DMCN disseminates a range of information worldwide to improve the lives of disabled children and their families. The high quality of published articles is maintained by expert review, including independent statistical assessment, before acceptance.