Using causal diagrams to guide analysis in missing data problems.
Daniel, RM; Kenward, MG; Cousens, S; de Stavola, BL; (2012) Using causal diagrams to guide analysis in missing data problems. Statistical methods in medical research, 21 (3). pp. 243-56. ISSN 0962-2802 DOI: 10.1177/0962280210394469
|PDF - Accepted Version |
Restricted to Repository staff only until 01 July 2013.
: Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR)) and the circumstances in which causal effects can be estimated without bias by analysing only the subjects with complete data. In doing so, we formally extend the back-door criterion of Pearl and others for use in incomplete data examples. These ideas are illustrated with an example drawn from an occupational cohort study of the effect of cosmic radiation on skin cancer incidence.<br/>
|Faculty and Department:||Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology|
Faculty of Epidemiology and Population Health > Dept of Medical Statistics
|Research Centre:||Centre for Statistical Methodology|
|Web of Science ID:||304231900003|
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