ArticleClinical epidemiology2020
Handling Missing Values in Interrupted Time Series Analysis of Longitudinal Individual-Level Data.
Article in Clinical epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Impact of centralized volume-based procurement of high-value medical consumables on patients' hospitalization expenses in Gansu, China: an interrupted time series analysis.BMC health services research · 2026Article
- The impact of the COVID-19 pandemic on HIV, malaria, tuberculosis and childhood vaccination services in Guinea: an interrupted time series analysis.BMJ global health · 2025Article
- Changes in sodium levels in Australian packaged foods between 2014 and 2019: an interrupted time series analysis of the impact of the Victorian Salt Reduction Partnership's media advocacy strategy.The international journal of behavioral nutrition and physical activity · 2023Article
- Hypertension care during the COVID-19 pandemic in an integrated health care system.Journal of clinical hypertension (Greenwich, Conn.) · 2023Article
- Elevated Anxious and Depressed Mood Relates to Future Executive Dysfunction in Older Adults: A Longitudinal Network Analysis of Psychopathology and Cognitive Functioning.Clinical psychological science : a journal of the Association for Psychological Science · 2023Article
- Current Practices in Missing Data Handling for Interrupted Time Series Studies Performed on Individual-Level Data: A Scoping Review in Health Research.Clinical epidemiology · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundIn the interrupted time series (ITS) approach, it is common to average the outcome of interest at each time point and then perform a segmented regression (SR) analysis. In this study, we illustrate that such 'aggregate-level' analysis is biased when data are missing at random (MAR) and provide alternative analysis methods.
methodsUsing electronic health records from the UK, we evaluated weight change over time induced by the initiation of antipsychotic treatment. We contrasted estimates from aggregate-level SR analysis against estimates from mixed models with and without multiple imputation of missing covariates, using individual-level data. Then, we conducted a simulation study for insight about the different results in a controlled environment.
resultsAggregate-level SR analysis suggested a substantial weight gain after initiation of treatment (average short-term weight change: 0.799kg/week) compared to mixed models (0.412kg/week). Simulation studies confirmed that aggregate-level SR analysis was biased when data were MAR. In simulations, mixed models gave less biased estimates than SR analysis and, in combination with multilevel multiple imputation, provided unbiased estimates. Mixed models with multiple imputation can be used with other types of ITS outcomes (eg, proportions). Other standard methods applied in ITS do not help to correct this bias problem.
conclusionAggregate-level SR analysis can bias the ITS estimates when individual-level data are MAR, because taking averages of individual-level data before SR means that data at the cluster level are missing not at random. Avoiding the averaging-step and using mixed models with or without multilevel multiple imputation of covariates is recommended.
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