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From |
Steven Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Survival analysis question |

Date |
Wed, 3 Nov 2010 18:05:00 -0400 |

Steve - I think there is a communication problem here. The event isa subject reaching a state of presyncopy during an upright tilt.Subjects are given the tilt test with Treatment 1 ("pre"), then oneweek later they are given the test with Treatment 2 ("post").Subjects aren't at risk during the week in between because theyaren't doing the tilt test. But I see there is no way you would knowthis from the data alone. Therefore I would like to claim that ineffect "times" can be considered as building up consecutively. Doesthis make sense?Al

. bys subjectid: gen diff = time[2] - time[1] . preserve . bys subjectid: keep if _n==1 (8 observations deleted) . mean diff //paired analysis Mean estimation Number of obs = 8 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ diff | -281.625 114.6071 -552.6277 -10.62231 -------------------------------------------------------------- . restore reg time treatment, cluster(subjectid)

[output skipped]

------------------------------------------------------------------------------ | Robust

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Steve -----Original Message-----

Sent: Wednesday, November 03, 2010 2:40 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Survival analysis question -- Al, I don't think that the two times are consecutive: they are recorded as seconds, but the the two observations on each subject were separated by a week. Steve On Nov 3, 2010, at 2:50 PM, Feiveson, Alan H. (JSC-SK311) wrote: Steve - In my opinion this is multiple failure data. Each subject is subjected to two consecutive exposures, and a subject can "fail" on none, either, or both of these tests. So the variable ttrxt at a given observation is the total time that the particular subject has been at risk up through that observation. Therefore I think the stset command . stset ttrxt, id(id) failure(fail) exit(time .) id: id failure event: fail != 0 & fail < . obs. time interval: (ttrxt[_n-1], ttrxt] exit on or before: time . ------------------------------------------------------------------------------ 16 total obs. 0 exclusions ------------------------------------------------------------------------------ 16 obs. remaining, representing 8 subjects 13 failures in multiple failure-per-subject data 5607 total analysis time at risk, at risk from t = 0 earliest observed entry t = 0 last observed exit t = 1198 is correct. I agree that ideally, one should try a frailty model on this data, but it doesn't work well with only 8 subjects. Al Feiveson -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu ] On Behalf Of Steven Samuels Sent: Wednesday, November 03, 2010 12:35 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Survival analysis question Chris Westby: You don't have multiple-failure data, because the start time for the two tests should be zero. The correct statement is: stset t, failure(fail) This will change the -stcox- results as well. Also try -stsum, by(treatment)- after the two versions of -stset--. I suggest that you consider the -shared- option in -stcox- to allow for the possibility of person-specific baseline hazards. Note that eight subjects is probably not enough for the standard errors to be reliable. Steve Steven J. Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 On Nov 3, 2010, at 8:35 AM, Westby, Christian Michael. (JSC-SK)[USRA] wrote: Dear Statalisters, I am working on comparing survival times in one group of subjects before and after treatment and am having a hard time with the "stset" code. Using the following data set where testing was separated by 1 week, t is time of task before and after treatment (seconds) and ttrxt is time calculated to prevent time from being treated as continuous and fail is 0=completed, 1=not completed. subjectid treatment fail t ttrxt ----------------------------------------------------------------- 1 pre failed 169 169 1 post failed 141 310 2 pre failed 114 114 2 post failed 84 198 3 pre failed 564 564 3 post failed 296 860 4 pre failed 168 168 4 post failed 332 500 5 pre failed 215 215 5 post failed 50 265 6 pre completed 900 900 6 post failed 196 1096 7 pre completed 900 900 7 post failed 298 1198 8 pre completed 900 900 8 post failed 280 1180 ----------------------------------------------------------------- I used . stset ttrxt, id(subjectid) failure(fail) exit(time .) id: subjectid failure event: fail != 0 & fail < . obs. time interval: (ttrxt[_n-1], ttrxt] exit on or before: time . ------------------------------------------------------------------------------ 16 total obs. 0 exclusions ------------------------------------------------------------------------------ 16 obs. remaining, representing 8 subjects 13 failures in multiple failure-per-subject data 5607 total analysis time at risk, at risk from t = 0 earliest observed entry t = 0 last observed exit t = 1198 I then ran . stcox treatment, cluster(subjectid) failure _d: fail analysis time _t: ttrxt exit on or before: time . id: subjectid Iteration 0: log pseudolikelihood = -20.175132 Iteration 1: log pseudolikelihood = -18.079165 Iteration 2: log pseudolikelihood = -18.026011 Iteration 3: log pseudolikelihood = -18.025935 Refining estimates: Iteration 0: log pseudolikelihood = -18.025935 Cox regression -- no ties No. of subjects = 8 Number of obs = 16 No. of failures = 13 Time at risk = 5607 Wald chi2(1) = 4.22 Log pseudolikelihood = -18.025935 Prob > chi2 = 0.0399 (Std. Err. adjusted for 8 clusters in subjectid) ------------------------------------------------------------------------------ | Robust _t | Haz. Ratio Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------- -------------+------ treatment | 4.610013 3.428317 2.05 0.040 1.073226 19.80218 ------------------------------------------------------------------------------ I believe that the output and results are accurate however, I am unable to get Stata to correctly graph the survival curves using the following code . stcurv, surv at1(treatment=0) at2(treatment=1) the resulting graph incorrectly plots both groups starting at less than 100% at a time=0 and the x-axis scale is incorrect. Any thoughts? Chris * * 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/ * * 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/ * * 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/

**Follow-Ups**:**RE: st: Survival analysis question***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

**References**:**st: Survival analysis question***From:*"Westby, Christian Michael. (JSC-SK)[USRA]" <christian.westby@nasa.gov>

**Re: st: Survival analysis question***From:*Steven Samuels <sjsamuels@gmail.com>

**RE: st: Survival analysis question***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

**Re: st: Survival analysis question***From:*Steven Samuels <sjsamuels@gmail.com>

**RE: st: Survival analysis question***From:*"Feiveson, Alan H. (JSC-SK311)" <alan.h.feiveson@nasa.gov>

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