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From |
<carlo.lazzaro@tiscalinet.it> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: R: data prep for survival analysis with time varying covariate |

Date |
Sat, 21 Jan 2012 12:50:03 +0100 |

Dear Kyleigh, I have expanded a bit the chuck of dataset you provided the list with (10 patients, one failure each during the analysis time; patients are gathered in two different groups (drugs, with a 0/1 dummy)), as in its original version was impossible to analyze (too few observations). Two Cox regressions were performed, considering either variable Waittime constant over time (-stcox drugs Waittime-), or variable Waittime varying with time (interacting with a square root function of analysis time: -stcox drugs, tvc(Waittime) texp((_t)^.5)-). It's up to you to verify whether these assumptions (cox regression and tvc, namely) fit your research issue. HTH and Kind Regards, Carlo ...............................code starts....................................... input ID Timetoevent Event Waittime 1 50 0 40 2 73 0 8 3 150 1 100 4 100 0 50 5 70 0 10 6 35 0 60 7 120 1 80 8 20 1 0 9 140 0 100 10 200 1 30 end stset Timetoevent, id(ID) failure(Event==1) g drugs=1 in 1/5 replace drugs=0 if drugs==. stcox drugs Waittime stcox drugs, tvc(Waittime) texp((_t)^.5) ............................code ends................................................. -----Messaggio originale----- Da: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Kyleigh Schraeder Inviato: giovedì 19 gennaio 2012 00:20 A: statalist@hsphsun2.harvard.edu Oggetto: st: data prep for survival analysis with time varying covariate Hi Dr. Gagnon, This is the first time I'm using a survival analysis so I hope my questions make sense. My variables are: id, timetoevent (in days), event, and waittime. In my study, every patient has their own start time or time 0. At time 0, each patient is put on a wait-list. Some patients may still be waiting when the 'event' happens (they should be censored). However, some patients may come off of the wait list and receive services. Thus, some patients are technically 'not waiting' for a period of time before the event occurs. So, as I understand, 'waittime' is a time-varying covariate. I am interested in examining the effect of a patient's wait-time on the outcome (whether the event occurs). In other words, what is the probability of the event occuring for patient X, given their waiting time. Right now my data is currently set up where each participant has their own row of data. ID Timetoevent Event Waittime 1 50 0 40 2 73 0 8 3 150 1 100 I'm not sure how to best arrange the data in STATA since I'm confused as to how to put this data in the wide-person format since I don't have an Event1, Event2, Event3 or a Wait1 Wait2 Wait3.. I have tried creating a vector using loop commands (to give me an Event1 Event2 Event3) but I need to specify the number of variables I create (max 390 days of observation) and this is different for each patient.. Any help or steps in the right direction would be appreciated! Thank you Kyleigh * * 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/ * * 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/

**References**:**st: data prep for survival analysis with time varying covariate***From:*Kyleigh Schraeder <kyschraeder@gmail.com>

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