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“Statistical opportunities and challenges in observational health data: Addressing missingness using two-phase sampling for nonresponse: methods and benefits"

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If you have a question about this talk, please contact Alison Quenault .

Health data are now routinely collected across different parts of the health system, and linked at the patient level across data sources. This suggests an extremely rich resource that we can use to make accurate outcome predictions for patients – hence stratification and treatment targeting. However, these data are ‘messy’, potentially unstructured, and subject to ‘informative observation’ (since patients only generate data when interacting with healthcare services). Therefore, to fully exploit the vast data being generated in health, careful statistical planning and methodological development is required.

In this talk I will describe recent work using observational health data – including some work in which we have begun to unpick informative observation, and other work around using, combining and updating predictive models. I will describe the emerging opportunity of a learning health system, in which linked data across the health system will be available for research in real time, demanding dynamic prediction, and an opportunity to reduce the ‘data action latency’ the time period between data being available and appropriate action being taken.

This talk is part of the MRC Biostatistics Unit Seminars series.

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Lecture Theatre 6, Baker Building, Department of Engineering First-floor Classroom, Department of Architecture, 1-5 Scroope Terrace, Cambridge, CB2 1PX, www.arct.cam.ac.uk Seminar Room S1, 1st Floor, Alison Richard Building

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