[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: RE: RE: Imputing values for categorical data |

Date |
Fri, 16 Apr 2004 10:08:14 -0400 |

There is a problem with the indicator variable method for missing values. See Jones, MP, (1996) "Indicator and Stratification Methods for Missing Explanatory variables in Multiple Linear Regression," JASA, 91: 222-230.

Rich Goldstein

Dupont, William wrote:

Bill

What you propose sounds reasonable to me. However, I recently submitted

a paper that did this and was trashed by a referee, in part because of

how I was handling missing values. Using an indicator variable for

missing values has the advantage as it gives you some idea as to whether

you are, in fact, dealing with nonignorable missing data. However, this

approach does not appear to be in fashion at this time.

My own approach to data analysis is to attempt to use methods that

1. I think are reasonable,

2. are widely accepted within the biostatistical community, and

3. avoid ignoring or attacking sacred cows that are dear to likely

referees.

My own sense is that, at least in medical statistics, multiple

imputation is becoming a very popular way of dealing with missing data.

I also feel it is a sensible approach, particularly if it is only used

for confounding variables or if your study design gives you reason to

believe that the missing data is missing at random.

I would be interested to know what other Statalisters think about using

indicator variables to model missing values.

Bill

-----Original Message-----

From: bill magee [mailto:magee@chass.utoronto.ca] Sent: Friday, April 16, 2004 5:11 AM

To: Dupont, William

Subject: RE: RE: RE: Imputing values for categorical data

Hi Bill --

Rather than imputing a missing categorical control or confounding variable, such as gender, wouldn't it usually be better to just include a category for missing (e.g. a dummy for female, a dummy for missing, with male as the excluded contrast group)?

bill magee

*

* For searches and help try:

* http://www.stata.com/support/faqs/res/findit.html

* http://www.stata.com/support/statalist/faq

* http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: RE: RE: Imputing values for categorical data***From:*Richard Williams <Richard.A.Williams.5@nd.edu>

**References**:**st: RE: RE: RE: Imputing values for categorical data***From:*"Dupont, William" <william.dupont@vanderbilt.edu>

- Prev by Date:
**st: re: bivariate random effects diagnostic meta-analysis** - Next by Date:
**Re: st: RE: RE: RE: Imputing values for categorical data** - Previous by thread:
**st: RE: RE: RE: Imputing values for categorical data** - Next by thread:
**Re: st: RE: RE: RE: Imputing values for categorical data** - Index(es):

© Copyright 1996–2015 StataCorp LP | Terms of use | Privacy | Contact us | What's new | Site index |