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methods for causal inference using large-scale observational healthcare data. The project will address fundamental methodological challenges in estimating causal treatment effects from longitudinal
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estimated from observed data. The overall aim of the project is to develop statistical theory, methodology, and computational methods for such complex data problems, with a particular focus on models
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interest are not directly observable. These may include, for example, ability, attitudes, well-being, or different dimensions of poverty, which instead must be estimated from observed data. The overall aim
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methods and theory for analysing time-to-event data when a fraction of the population is immune to the event of interest (‘cured’). For example, in oncology the event of interest is cancer relapse/death