Publicación:
Current practices in missing data handling for interrupted time series studies performed on individual-level data: A scoping review in health research
Current practices in missing data handling for interrupted time series studies performed on individual-level data: A scoping review in health research
| dc.contributor.author | Bazo-Alvarez J.C. | es_PE |
| dc.contributor.author | Morris T.P. | es_PE |
| dc.contributor.author | Carpenter J.R. | es_PE |
| dc.contributor.author | Petersen I. | es_PE |
| dc.date.accessioned | 2024-05-30T23:13:38Z | |
| dc.date.available | 2024-05-30T23:13:38Z | |
| dc.date.issued | 2021 | |
| dc.description | This study was funded by the National Institute for Health Research (NIHR) School for Primary Care Research, project number 444. JCB was sponsored by FONDECYT- CONCYTEC (grant contract number 231-2015- FONDECYT). TPM, TMP and JRC were supported by the Medical Research Council (grant numbers MC_UU_12023/ 21 and MC_UU_12023/29). The study sponsors only had a funding role in this research. Thus, the researchers worked with total independence from their sponsors. | |
| dc.description.abstract | Objective: Missing data can produce biased estimates in interrupted time series (ITS) analyses. We reviewed recent ITS investigations on health topics for determining 1) the data management strategies and statistical analysis performed, 2) how often missing data were considered and, if so, how they were evaluated, reported and handled. Study Design and Setting: This was a scoping review following standard recommendations from the PRISMA Extension for Scoping Reviews. We included a random sample of all ITS studies that assessed any intervention relevant to health care (eg, policies or programmes) with individual-level data, published in 2019, with abstracts indexed on MEDLINE. Results: From 732 studies identified, we finally reviewed 60. Reporting of missing data was rare. Data aggregation, statistical tools for modelling population-level data and complete case analyses were preferred, but these can lead to bias when data are missing at random. Seasonality and other time-dependent confounders were rarely accounted for and, when they were, missing data implications were typically ignored. Very few studies reflected on the consequences of missing data. Conclusion: Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best practice. © 2021 Bazo-Alvarez et al. | |
| dc.description.sponsorship | Fondo Nacional de Desarrollo Científico y Tecnológico - Fondecyt | |
| dc.identifier.doi | https://doi.org/10.2147/CLEP.S314020 | |
| dc.identifier.scopus | 2-s2.0-85111702945 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12390/2988 | |
| dc.language.iso | eng | |
| dc.publisher | Dove Medical Press Ltd | |
| dc.relation.ispartof | Clinical Epidemiology | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Segmented regression | |
| dc.subject | Interrupted time series analysis | es_PE |
| dc.subject | Missing data | es_PE |
| dc.subject | Multiple imputation | es_PE |
| dc.subject | Scoping review | es_PE |
| dc.subject.ocde | https://purl.org/pe-repo/ocde/ford#2.02.04 | |
| dc.title | Current practices in missing data handling for interrupted time series studies performed on individual-level data: A scoping review in health research | |
| dc.type | info:eu-repo/semantics/review | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# |