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Abstract:
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Inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods commonly used to deal with missing data from dropout. When missing data arises from dropouts and deaths, one might want to distinguish between reasons for missingness to avoid making inferences about an immortal cohort: a cohort where no one can die. Instead, inferences based on those who are alive at any point in time (mortal cohort inference) might be more interesting. In this talk, I explain the underlying assumptions of the IPW, LI and MI, and describe how these methods can be used to make mortal cohort inference for data that are "missing at random." The importance of clarifying the underlying assumptions can be seen in ageing studies where deaths are common, as results from these methods may be biased if some of their assumptions are not met. Through simulations, I compare the bias and efficiency of methods for making mortal cohort inference: IPW, LI, augmented IPW, and MI, and describe an application of these methods to an ageing study called OCTO. I also discuss methods can be used to make mortal cohort inference for data that are "missing not at random."
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