[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Missing outcome variables - how to deal with these? |

Date |
Fri, 22 May 2009 09:52:08 +0000 (GMT) |

--- On Thu, 21/5/09, Tomas M wrote: > I have a set of data, with missing outcome variables. > I plan to use multinomial logistic regression to > analyze the association of my predictor variables with > the outcomes. <snip> How would I deal with these missing > outcome values in Stata? > > Would I drop the missing values from analyses? Or > would I include them as a separate outcome in my multinomial > logistic regression models? > > Would it also be possible to impute the missing outcome > values using some function to predict the outcome based on a > given set of independent predictor variables (derived based > on the set of full and complete data)? If so, how > would I go about this in Stata? Most of the remidies for this type of problem assume that the probability of having a missing value only depedends on observed characteristics of the respondent (this is the so- called Missing At Ramdom or MAR assumption) If you have only missing observations on the dependent variable then just leaving the missing observations out of the analysis will lead to unbiased estimates whenever the remedies are appropriate (see for example Allison 2002, footnote 1). So, in these cases these remedies add very little, except that you use information from more observations. However, this is only helpful if you have few observations to begin with. So, in your case I would just declare those missing values as missing (=.) and do nothing else, unless you have a very small sample size. The missing values will than be automatically ignored. If you want to do an imputation I would use Patrick Royston's (2004, 2005a, 2005b, 2007) -ice- command. Hope this helps, Maarten Paul D. Allison (2002) Missing Data, Quantitative Applications in the Social Sciences, nr. 136. Thousand Oaks: Sage. Royston, P. 2004. Multiple imputation of missing values. Stata Journal 4(3): 227--241. Royston, P. 2005a. Multiple imputation of missing values: update. Stata Journal 5(2): 188--201. Royston, P. 2005b. Multiple imputation of missing values: Update of ice. Stata Journal 5(4): 527--536. Royston, P. 2007. Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring. Stata Journal 7(4): 445--464. ----------------------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://home.fsw.vu.nl/m.buis/ ----------------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: Missing outcome variables - how to deal with these?***From:*Tomas M <anon556656@live.ca>

- Prev by Date:
**st: Forest plot of hazard ratios** - Next by Date:
**st: AW: Forest plot of hazard ratios** - Previous by thread:
**st: Missing outcome variables - how to deal with these?** - Next by thread:
**RE: st: Missing outcome variables - how to deal with these?** - Index(es):

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