Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research

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 evalua...

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Bibliographic Details
Published inClinical epidemiology Vol. 13; pp. 603 - 613
Main Authors Bazo-Alvarez, Juan Carlos, Morris, Tim P, Carpenter, James R, Petersen, Irene
Format Journal Article
LanguageEnglish
Published New Zealand Dove Medical Press Limited 01.01.2021
Dove
Dove Medical Press
Subjects
Analysis
Information management
interrupted time series analysis
Management science
missing data
multiple imputation
Review
scoping review
segmented regression
Strategic planning (Business)
United Kingdom
multiple imputation
interrupted time series analysis
missing data
segmented regression
scoping review
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Summary: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. 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. 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. Handling and reporting of missing data in recent ITS studies performed for health research have many shortcomings compared with best practice.
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ISSN:1179-1349
1179-1349
DOI:10.2147/CLEP.S314020