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

Juan Carlos Bazo-Alvarez,1,2 Tim P Morris,3 James R Carpenter,3,4 Irene Petersen1,5 1Research Department of Primary Care and Population Health, University College London (UCL), London, UK; 2School of Medicine, Universidad Cesar Vallejo, Trujillo, Peru; 3MRC Clinical Trials Unit at UCL, London, UK; 4Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK; 5Department of Clinical Epidemiology, Aarhus University, Aarhus, DenmarkCorrespondence: Juan Carlos Bazo-AlvarezResearch Department of Primary Care and Population Health, University College London (UCL), Rowland Hill Street, London, NW3 2PF, UKTel +44 7376076260Email juan.alvarez.16@ucl.ac.ukObjective: 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.Keywords: interrupted time series analysis, segmented regression, missing data, multiple imputation, scoping revie