Workshop in Biostatistics
|DATE:||June 2, 2016|
|TIME:||1:30 - 3:00 pm|
|LOCATION:||Medical School Office Building, Rm x303|
|TITLE:||A framework for making cheap data useful|
Access to cheap, easy-to-collect data sources is increasing (think: electronic health records). These data sources are coming to dominate many corners of health research. Regrettably, they are not of particularly high fidelity. In this talk we detail a framework for addressing key issues of data quality - measurement error and missingness. The framework is quite general but requires a bit of shoe leather from the researcher. The benefits are valid inference, even in situations where the missingness is informative and errors are differential, even in cases where errors are dependent on the outcome of interest. The seminar will introduce a slight generalization of the usual multiple imputation framework. We'll then motivate the problem using work done in the Veteran's Affairs to estimate the relationship between HIV status and cancer rates.
Ying Guo, R.J Little, and D.S. McConnell. On using summary statistics from an external calibration sample to correct for vocariate measurement error. Epidemiology, 2012; Vol. 23(1):165-74.
L.S. Freeman, D. Midthune, R.J. Carroll, and V. Kipnis. A comparison of regression calibration, moment reconstruction and imputation for adjusting for covariate measurement error in regression. Stat Med. 2008; 27(25):5195-216.