Initial data analysis for longitudinal studies to build a solid foundation for reproducible analysis
PLoS ONE, vol. 19, pp. e0295726
Abstract
Initial data analysis (IDA) is the part of the data pipeline that takes place between the end of data retrieval and the beginning of data analysis that addresses the research question. Systematic IDA and clear reporting of the IDA findings is an important step towards reproducible research. A general framework of IDA for observational studies includes data cleaning, data screening, and possible updates of pre-planned statistical analyses. Longitudinal studies, where participants are observed repeatedly over time, pose additional challenges, as they have special features that should be taken into account in the IDA steps before addressing the research question. We propose a systematic approach in longitudinal studies to examine data properties prior to conducting planned statistical analyses. In this paper we focus on the data screening element of IDA, assuming that the research aims are accompanied by an analysis plan, meta-data are well documented, and data cleaning has already been performed. IDA data screening comprises five types of explorations, covering the analysis of participation profiles over time, evaluation of missing data, presentation of univariate and multivariate descriptions, and the depiction of longitudinal aspects. Executing the IDA plan will result in an IDA report to inform data analysts about data properties and possible implications for the analysis plan-another element of the IDA framework. Our framework is illustrated focusing on hand grip strength outcome data from a data collection across several waves in a complex survey. We provide reproducible R code on a public repository, presenting a detailed data screening plan for the investigation of the average rate of age-associated decline of grip strength. With our checklist and reproducible R code we provide data analysts a framework to work with longitudinal data in an informed way, enhancing the reproducibility and validity of their work.
Authors 7
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Lara Lusa corresponding
University of Primorska · University of Ljubljana
Affiliation as printed
Department of Mathematics, Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Capodistria, Slovenia
Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Inserm · Bordeaux Population Health
Affiliation as printed
Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
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Universitätsmedizin Greifswald · Universität Greifswald
Affiliation as printed
Institute for community Medicine, SHIP-KEF University Medicine of Greifswald, Greifswald, Germany
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The University of Melbourne · Murdoch Children's Research Institute
Affiliation as printed
Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Melbourne, Australia
University of Melbourne, Melbourne, Australia
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Department of Clinical Epidemiology and Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Center for Statistical Training and Consulting, Michigan State University, East Lansing, MI, United States of America
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Affiliation as printed
Center for Statistical Training and Consulting, Michigan State University, East Lansing, MI, United States of America
Department of Statistics and Probability, Michigan State University, East Lansing, MI, United States of America
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