### COMMON MISTEAKS
MISTAKES IN
USING STATISTICS: Spotting and Avoiding Them

# Dealing with Missing Data

Many methods have been proposed for dealing with missing
data^{1},
but these typically make assumptions that may be difficult or
impossible to verify. Michael Daniels and Joseph Hogan summarize some
of the problems as follows:

"When
data are incomplete, inference about parameters
of
interest cannot be carried out without the benefit of subjecive
assumptions about the distribution of missing responses. They are
subjective because data cannot be used to critique them. Some of these
assumptions are used with such regularity that we forget they are being
made; for example, when commercial software such as SAS or Stat is used
to analyze incomplete longitudinal data using a random effects
model, the missing at random (MAR)
assumption is being used; when the Kaplan-Meier estimator is used to
summarize a survival curve from censored event times, non-informative censoring is being
assumed. Neither assumption can be formally checked, so the validity of
inferences relies on subjective judgment."^{2}

Thus dealing with missing data is a real problem in statistics. There
are at least a couple of types of active research in this area:

- Daniels and Hogan
^{3} propose using
Bayesian
methods, incorporating relevant information available as a prior
distribution.
- Researchers are using data bases of
medical
information to test out different ways of dealing with missing data
and, more generally, observational data.
^{4}

Footnotes:

1. See C. K. Enders and A. C. Gottschall (2011). The Impact of
Missing Data on the Ethical Quality of a Research Study, Chapter
14 in A.T. Panter and S. K. Sterba, Handbook
of Ethics in Quantitative Methodology, Routledge for discussion
of some such methods.

2.
M. Daniels and J. Hogan (2008). Missing Data in Longitudinal
Studies:
Strategies for Bayesian Modeling and Sensitivity Analysis, Chapman and
Hall/CRC, pp. xvii - xviii.

3. See Note 3

4. See, for example, D. Madigan and P. Ryan (2011), What can we really
learn from observational studies? Epidemiology
vol 22, pp. 629 - 631, available at http://scholar.google.com/scholar?hl=en&as_sdt=0,44&cluster=5198548572751487812

Last updated February 4, 2013