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Posted to issues@madlib.apache.org by "Pietro Pugni (JIRA)" <ji...@apache.org> on 2016/11/15 11:33:58 UTC
[jira] [Updated] (MADLIB-1040) Survival Analysis - Cox regression
model for time-dependent covariates
[ https://issues.apache.org/jira/browse/MADLIB-1040?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Pietro Pugni updated MADLIB-1040:
---------------------------------
Description:
This JIRA follows a discussion opened on the user mailing list ( http://mail-archives.apache.org/mod_mbox/incubator-madlib-user/201611.mbox/browser ).
The actual Cox model implented in MADlib ( https://madlib.incubator.apache.org/docs/latest/group__grp__cox__prop__hazards.html ) only supports time-independent covariates and doesn't provide any structure for time-dependent covariates, where a subject has one or more rows for different time-varying periods. This version of the CPH model is much more useful in survival analysis because it accounts for changes of covariates effect over time.
To provide some input, here are some good reference links:
- "Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model", by T Thernau: https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf
- "Time-dependent Covariates in Cox Regression": http://www.math.ucsd.edu/~rxu/math284/slect7.pdf
- "Time-dependent covariates in the Cox Proportoinal-Hazards Regression Model", by LD Fisher: https://pdfs.semanticscholar.org/f970/7f0dd6ff04899d7a3323668ee9ed1b9ad28e.pdf
This is the article used by Thernau to implement the counting process algorithm in the R survival package:
- "Cox's regression model for counting processes: a large sample study", by Andersen and Gill: https://projecteuclid.org/download/pdf_1/euclid.aos/1176345976
As far as I know, the counting process algorithm is the fastest used in CPH models. The counter parts is that user has to provide a verticalized dataset with a row per time changes within each subject. The formula used in the coxph() function provided with the survival package is the following:
coxph(data = df, formula = Surv(start, stop, event) ~ cluster(subject.id) + covariate.1 + covariate.2 + ... + covariate.n)
where covariates can be factors (categorical variables) or numeric.
In the linked documentation you can find some examples of counting process datasets.
Thank you everyone
Pietro Pugni
PS: this is my first JIRA. I hope to opened it correctly.
was:
This JIRA follows a discussion opened on the user mailing list ( http://mail-archives.apache.org/mod_mbox/incubator-madlib-user/201611.mbox/browser ).
The actual Cox model implented in MadLib ( https://madlib.incubator.apache.org/docs/latest/group__grp__cox__prop__hazards.html ) only supports time-independent covariates and doesn't provide any structure for time-dependent covariates, where a subject has one or more rows for different time-varying periods. This version of the CPH model is much more useful in survival analysis because it accounts for changes of covariates effect over time.
To provide some input, here are some good reference links:
- "Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model", by T Thernau: https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf
- "Time-dependent Covariates in Cox Regression": http://www.math.ucsd.edu/~rxu/math284/slect7.pdf
- "Time-dependent covariates in the Cox Proportoinal-Hazards Regression Model", by LD Fisher: https://pdfs.semanticscholar.org/f970/7f0dd6ff04899d7a3323668ee9ed1b9ad28e.pdf
This is the article used by Thernau to implement the counting process algorithm in the R survival package:
- "Cox's regression model for counting processes: a large sample study", by Andersen and Gill: https://projecteuclid.org/download/pdf_1/euclid.aos/1176345976
As far as I know, the counting process algorithm is the fastest used in CPH models. The counter parts is that user has to provide a verticalized dataset with a row per time changes within each subject. The formula used in the coxph() function provided with the survival package is the following:
coxph(data = df, formula = Surv(start, stop, event) ~ cluster(subject.id) + covariate.1 + covariate.2 + ... + covariate.n)
where covariates can be factors (categorical variables) or numeric.
In the linked documentation you can find some examples of counting process datasets.
Thank you everyone
Pietro Pugni
PS: this is my first JIRA. I hope to opened it correctly.
> Survival Analysis - Cox regression model for time-dependent covariates
> ----------------------------------------------------------------------
>
> Key: MADLIB-1040
> URL: https://issues.apache.org/jira/browse/MADLIB-1040
> Project: Apache MADlib
> Issue Type: Wish
> Components: Module: Cox Proportional Hazards
> Reporter: Pietro Pugni
> Fix For: v2.0
>
>
> This JIRA follows a discussion opened on the user mailing list ( http://mail-archives.apache.org/mod_mbox/incubator-madlib-user/201611.mbox/browser ).
> The actual Cox model implented in MADlib ( https://madlib.incubator.apache.org/docs/latest/group__grp__cox__prop__hazards.html ) only supports time-independent covariates and doesn't provide any structure for time-dependent covariates, where a subject has one or more rows for different time-varying periods. This version of the CPH model is much more useful in survival analysis because it accounts for changes of covariates effect over time.
> To provide some input, here are some good reference links:
> - "Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model", by T Thernau: https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf
> - "Time-dependent Covariates in Cox Regression": http://www.math.ucsd.edu/~rxu/math284/slect7.pdf
> - "Time-dependent covariates in the Cox Proportoinal-Hazards Regression Model", by LD Fisher: https://pdfs.semanticscholar.org/f970/7f0dd6ff04899d7a3323668ee9ed1b9ad28e.pdf
>
> This is the article used by Thernau to implement the counting process algorithm in the R survival package:
> - "Cox's regression model for counting processes: a large sample study", by Andersen and Gill: https://projecteuclid.org/download/pdf_1/euclid.aos/1176345976
> As far as I know, the counting process algorithm is the fastest used in CPH models. The counter parts is that user has to provide a verticalized dataset with a row per time changes within each subject. The formula used in the coxph() function provided with the survival package is the following:
> coxph(data = df, formula = Surv(start, stop, event) ~ cluster(subject.id) + covariate.1 + covariate.2 + ... + covariate.n)
> where covariates can be factors (categorical variables) or numeric.
> In the linked documentation you can find some examples of counting process datasets.
> Thank you everyone
> Pietro Pugni
> PS: this is my first JIRA. I hope to opened it correctly.
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