Case time series: a flexible design for big data epidemiological analyses
Time Series Regression Analysis Theme
Abstract: Biomedical research has been transformed by recent developments in big data technologies. For instance, the collection of health records in linked electronic databases provide information on demographics, health events, medications, and lifestyle factors for large samples of patients. Similarly, portable devices such as mobile phones provide the opportunity to recruit large numbers of participants, and to collect real-time and geo-located individual-level measurements. While these resources offer the possibility to answer research questions that could not be feasibly addressed using traditional studies, they require innovative analytical approaches.
This talk will illustrate a novel analytical design called case time series. This study design offers an adaptable framework that combines the individual-level setting and ability to control for confounders of case-only methods, with the flexibility and temporal structure of time series models. It represents a general tool, applicable in different research areas for investigating short-term associations with environmental exposures, clinical conditions, or medications.
The case time series design is suitable for the analysis of highly-informative big data resources, particularly those providing individual profiles with longitudinal measures of health outcomes and time-varying predictors.