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Re: st: Repeated Measures ANOVA and Missing Values


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Repeated Measures ANOVA and Missing Values
Date   Mon, 17 Oct 2011 00:58:11 -0400

Do you really believe that the missing values are MCAR in this case (as you must assume with listwise deletion/ignoring cases)? I think you run the risk of biased estimates if you go that route. Why not use multiple imputation instead? 


Marchenko, Y. (2010). Multiple-imputation analysis using Stata’s mi command. Stata Conference, Boston 2010.
http://www.stata.com/meeting/boston10/boston10_marchenko.pdf

 
Marchenko, Y. (2011). Chained equations and more in multiple imputation in Stata 12. 2011 UK Stata Users Group Meeting.
http://repec.org/usug2011/UK11_Marchenko.pdf

 

Royston, P. (2009). Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. The Stata Journal 9(3), 466–477.
http://www.stata-journal.com/article.html?article=st0067_4http://ideas.repec.org/c/boc/bocode/s446602.html


van Buuren, S. (2010). Item Imputation Without Specifying Scale Structure. Methodology, 6(1), 31–36.
http://www.stefvanbuuren.nl/publications/Item%20imputation%20-%20Methodology%202010.pdf


van Buuren, S. & Groothuis-Oudshoorn, K. (March 26, 2011). Multivariate Imputation by Chained Equations: Package ‘mice’, Version 2.8.
http://cran.r-project.org/web/packages/mice/mice.pdfhttp://cran.r-project.org/web/packages/mice/index.html

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming. 

http://www.stefvanbuuren.nl/publications/MICEinR-Draft.pdf

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., & Rubin, D.B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064. 
http://www.stefvanbuuren.nl/publications/FCSinmultivariateimputation-JSCS2006.pdf

Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16, 3, 219–242. http://www.stefvanbuuren.nl/publications/MIbyFCS-SMMR2007.pdf

Lee, K.J., & Carlin, J.B. (2010). Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation. American Journal of Epidemiology, 171(5), 624-632. 


Cam

> Date: Sun, 16 Oct 2011 16:27:37 -0700
> Subject: Re: st: Repeated Measures ANOVA and Missing Values
> From: ender97@gmail.com
> To: statalist@hsphsun2.harvard.edu
> 
> I'm trying to run a oneway repeated measures ANOVA (the variable is =
> session, with three levels). I have organized my data in long format, =
> with one column for subject ID, one column for session number and one =
> column for the dependent variable. Here is a listing of part of my data =
> file:
> ...
> There are obviously several cases that are missing observations at =
> session 2 or session 3. Does wsanova or Stata's anova command =
> automatically drop cases with missing values at any of the three =
> sessions? My first pass with the analysis seems to suggest it does not.
> 
> What is the best way to deal with these missing values (either deleting =
> cases with missing values or telling wsanova or anova to ignore them in =
> the analysis)?
> -------------------------------
> John,
> 
> You are correct, -wsanova- does not drop cases that have missing
> observations. If
> want complete case analysis you will have to drop observations manually.
> 
> However, if you want to use all of the observations I would recommend
> -xtmixed-. It
> would look something like this in Stata 12:
> xtmixed DV i.session || ID:, reml
> testparm i.session
> 
> If you have lots of subjects you can use the chi-square value as is.
> If your sample is on
> the small size, you can rescale the chi-square to F by dividing it by
> its df (in your case 2).
> You can use the residual df from the -wsanova- as denominator df. The
> -Ftail- function
> will get you the p-value.
> 
> This approach is not universally approved depending upon what
> discipline you come from.
> My Chicago Stata Users Group presentation on this topic can be found at
> http://www.stata.com/meeting/chicago11/materials/chi11_ender.pdf
> 
> -- 
> Phil Ender
> UCLA Statistical Consulting Group
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