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Re: Multiple imputation in cox regression

To: "GIORGI Roch" <roch.giorgi@mail.ap-hm.fr>
Subject: Re: Multiple imputation in cox regression
From: Tim Hesterberg <timh@insightful.com>
Date: 27 Nov 2006 09:01:14 -0800
Cc: <s-news@lists.biostat.wustl.edu>
In-reply-to: <56A80768E1273C42B9C60D53285D50150153C6C1@phoceo07.aphm.ap-hm.fr> (roch.giorgi@mail.ap-hm.fr)
References: <56A80768E1273C42B9C60D53285D50150153C6C1@phoceo07.aphm.ap-hm.fr>
The missing data library (library(missing)) provides general capabilities
for 
* creating multiple imputations
* running analyses for each set of imputations
  (this supports arbitrary analyses, including Cox regression)
* combining inferences.

At bottom is an excerpt from the readme file for the library, that gives
an overview of the capabilities.

>Dear all,
>
>I have to analyse survival using Cox regression. There is some missing 
>covariates in my data set and I would like to perform multiple imputation. The 
>MICE package doesn't allows to combine multiple imputation for a Cox model.
>Is there an SPLUS/R code to do this?
>
>Regards,
>
>Roch
>
>
>_____________________________________________________
>  Roch GIORGI, MD, PhD
>  Service de Santé Publique et d'Information Médicale
>  Hôpital de la Timone
>  264, rue St Pierre 
>  13385 Marseille cedex 5 
>  Tel: +33 (0)491 384 949
>  Fax: +33 (0)491 385 749 
>  email: roch.giorgi@ap-hm.fr 
>  Web: http://cybertim.timone.univ-mrs.fr/


Here is an excerpt from the readme file
(do library(help = "missing") to see the readme file):

To use the library, first load it, e.g. from the command line use:

> library(missing)

To summarize and view patterns of missing data, see functions:
        miss
        numberMissing

To estimate parameters using EM or data augmentation, see functions:
        mdGauss
        mdLoglin
        mdCgm

To create multiple imputations using data augmentation, see functions:
        impGauss
        impLoglin
        impCgm

To convert your own multiple imputations into the appropriate S-PLUS
objects for use with this library, see functions:
        miVariable
        miList

To perform analyses in parallel on multiple imputations, see functions:
        miApply
        miEval

To consolidate numerical results and standard errors from statistical
analyses from multiple imputations, see functions:
        miMean         miChiSquareTest
        miMeanSE       miLikelihoodTest
        miSummary      miFTest

The help files for these functions contain examples and references
to additional functions.


Extensive documentation is provided in the manual, file
        Missing.pdf


Tim Hesterberg

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