Sensitivity analysis for incomplete continuous data
Models for missing data are necessarily based on untestable assumptions whose effect on the conclusions are usually assessed via sensitivity analysis. To avoid the usual normality assumption and/or hard-to-interpret sensitivity parameters proposed by many authors for such purposes, we consider a simple approach for estimating means, standard deviations and correlations. We do not make distributional assumptions and adopt a pattern-mixture model parameterization which has easily interpreted sensitivity parameters. We use the so-called estimated ignorance and uncertainty intervals to summarize the results and illustrate the proposal with a practical example. We present results for both the univariate and the multivariate cases.
KeywordsIdentifiability Ignorance interval Missing data Pattern-mixture model Uncertainty interval
Mathematics Subject Classification (2000)62F10 62F03
Unable to display preview. Download preview PDF.
- Allison PD (2001) Missing data. Sage, Thousand Oaks Google Scholar
- Daniels MJ, Hogan JW (2007) Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis. Chapman & Hall, London Google Scholar
- Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G (2008) Longitudinal data analysis. Chapman & Hall, Boca Raton Google Scholar
- Poleto FZ, Paulino CD, Molenberghs G, Singer JM (2010) Inferential implications of over-parameterization: a case study in incomplete categorical data. Tech rep, RT-MAE-2010-04, Instituto de Matemática e Estatística, Universidade de São Paulo Google Scholar
- Sen PK, Singer JM, Pedroso de Lima AC (2009) From finite sample to asymptotic methods in statistics. Cambridge University Press, Cambridge Google Scholar