Open AccessCurrent Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research
Author(s) -
Juan Carlos BazoAlvarez,
Tim P. Morris,
James Carpenter,
Inge Petersen
Publication year - 2021
Publication title -
clinical epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.868
H-Index - 58
ISSN - 1179-1349
DOI - 10.2147/clep.s314020
Subject(s) - missing data , imputation (statistics) , medicine , confounding , statistics , data collection , data mining , data science , computer science , mathematics
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.