A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

ABSTRACT: Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one publishe

SUBMITTER: Seibold H 

PROVIDER: S-EPMC8216542 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature