Statistics Seminar: Joanna Wang (UTS) -- Non-ignorable missingness in logistic regression: an application to the 45 and Up Study

Abstract:

Non-responses and missing data are common in observational studies. Ignoring or 
inadequately handling missing data may lead to biased parameter estimates, 
incorrect standard errors and as a consequence, incorrect statistical inference 
and conclusions. We present a strategy for modelling non-ignorable missing where 
the probability of nonresponse depends on the outcome. Using a simple case of 
logistic regression, we quantity the bias in regression estimates and show the 
observed likelihood is non-identifiable under non-ignorable missing data mechanism. 

We then adopt a selection model factorisation of the joint distribution as the 
basis for a sensitivity analysis to study changes in estimated parameters and the 
robustness of study conclusions against different assumptions.

Using simulated data, we explore the performance of the Bayesian selection model 
in correcting for bias in a logistic regression. We then implement our strategy 
using survey data from the 45 and Up Study to investigate factors associated with 
dwelling type change from baseline to follow-up survey. Our findings have practical 
implications for the use of the 45 and Up Study data to answer important health 
and quality-of-life questions.