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From |
Yuval Arbel <yuval.arbel@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: How to proceed a landmark survival analysis (tests and plots)? |

Date |
Wed, 26 Oct 2011 17:19:18 +0200 |

Hi Anna, Fortunately, I recently completed a paper draft based on this methodology , and I explored it thoroughly, so I might be able to help you.I also apologize for the long e-mail and hope you can follow the whole stages In my opinion what you did so far is only the very elementary stage of the research - which is more or less equivalent to presenting a summary statistics of variables. Assuming that you have only one kind of failure (the patient either survives or dies), what you need to do next is to use the -stcox- command in order to make a regression analysis. In other words: you need to control for other variables, which might cause patients not to survive in order to isolate the impact of therapy1 and therapy2. In this context you should look stata manual for -stcox-. Also see the example I give below The final stage of the analysis (which is the most interesting in my opinon) is to simulate how the survival rates will be affected by modifying the dosage of the different treatments. Here you should look at the manual for -postestimation after stcox- and the example I give below. Let me show you the following example (keep in mind, however, that I'm working with stata 11.2): Suppose lung cancer patients are exposed to saturated fats (lets call this variable "mean_reduct") and smoking (lets call this variable "max_red") during the sample period. Suppose further you were able to measure the amounts each patient were exposed to. The outcomes of -stcox- command is the following: . stcox mean_reduct max_red reductcurrent_max_reduct rent_net8 diff_stdmadadarea permanentincomeestimate82 diff_mor > tgage appreciation,nohr failure _d: fail == 1 analysis time _t: time_index id: appt Iteration 0: log likelihood = -78368.249 Iteration 1: log likelihood = -74694.532 Iteration 2: log likelihood = -74538.881 Iteration 3: log likelihood = -74533.372 Iteration 4: log likelihood = -74533.352 Iteration 5: log likelihood = -74533.352 Refining estimates: Iteration 0: log likelihood = -74533.352 Cox regression -- Breslow method for ties No. of subjects = 9547 Number of obs = 499393 No. of failures = 9547 Time at risk = 547035 LR chi2(8) = 7669.79 Log likelihood = -74533.352 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- mean_reduct | .0129407 .0006221 20.80 0.000 .0117214 .0141599 max_red | .1713769 .020039 8.55 0.000 .1321011 .2106527 red~x_reduct | .0223149 .0005149 43.34 0.000 .0213057 .023324 rent_net8 | .0025795 .0001659 15.55 0.000 .0022543 .0029047 diff_stdma~a | -.4692028 .0457971 -10.25 0.000 -.5589636 -.3794421 permanent~82 | -.0004599 .0000689 -6.67 0.000 -.0005949 -.0003248 diff_mortg~e | -7.166604 .9474631 -7.56 0.000 -9.023597 -5.309611 appreciation | 9.514355 3.162537 3.01 0.003 3.315895 15.71281 ------------------------------------------------------------------------------ What is important here is that the coefficients of "mean_reduct" (0.0129407) and "max_red" (0.1713769) are positive and highly significant. They imply that if you increase the dosage of these harmful substances by the same amount, they both increase the hazard for survival, but compared to saturated fats, smoke is more risky to lung-cancer patients. Next, and based on this model, we would like to predict what would be the survival rates for a dosage mean_reduct=20, max_red=10 at the sample mean and for time_index=100. We can run the following command: . margins if time_index==100, at(mean_reduct=20 max_red=10) atmeans predict(nohr) Adjusted predictions Number of obs = 1262 Model VCE : OIM Expression : Relative hazard, predict(nohr) at : mean_reduct = 20 max_red = 10 red~x_reduct = -32.56141 (mean) rent_net8 = 70.61078 (mean) diff_stdma~a = -.4000001 (mean) permanent~82 = 1116.988 (mean) diff_mortg~e = -.0280243 (mean) appreciation = 0 (mean) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _cons | 3.680264 .8105245 4.54 0.000 2.091666 5.268863 ------------------------------------------------------------------------------ Unfortunately, the -margins- command does not give me the survival rates even if I put predict (basesurv). I can run the following commands, which constract a vector of survival rates for each sample-period: . predict full,basesurv (8405 missing values generated) . collapse (mean) full if fail==1,by(time_index) The problem is that if we run these commands on the original model, the projected survival rates will be valid for zero values of all the variables. Therefore, we need to define the variables of the model again where from each variable we subtract the value we would like to predict. For example: gen max_red1=max_red-20 gen mean_reduct1=mean_reduct-10 etc. Then we run the model again and construct the projected survival rates. I hope that was helpful On Wed, Oct 26, 2011 at 11:54 AM, Änne Glass <aenne.glass@uni-rostock.de> wrote: > Hello Statalist, > > we are interested in doing a landmark survival analysis with Stata(10.1), > comparing therapy1 vs therapy2, before and after a landmark (t=24 months). > Our data table consists of id, survivalTime, survivalStatus, therapyGroup. > > Following your blog at > http://www.stata.com/statalist/archive/2011-02/msg00207.html we did > 1) -stset- for declaring data to be survival-time data with _st, _d, __t, > _t0. (done) > 2) -stsplit- to split data into 2 time-span records with the landmark t=24, > that means one pre-landmark and one post-landmark. This step modified our > data table by adding one line for those ids going over 24 months. (done) > 3) -sts graph- command plotted the Kaplan-Meier failure function for the > whole time-span (0-102 months) for both treatments. (done, but not desired) > > A step-by-step approach would be great, as we are not yet that proficient in > Stata. > Many thanks in advance - Aenne. > * > * 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/ >On Wed, Oct 26, 2011 at 11:54 AM, Änne Glass <aenne.glass@uni-rostock.de> wrote: > Hello Statalist, > > we are interested in doing a landmark survival analysis with Stata(10.1), > comparing therapy1 vs therapy2, before and after a landmark (t=24 months). > Our data table consists of id, survivalTime, survivalStatus, therapyGroup. > > Following your blog at > http://www.stata.com/statalist/archive/2011-02/msg00207.html we did > 1) -stset- for declaring data to be survival-time data with _st, _d, __t, > _t0. (done) > 2) -stsplit- to split data into 2 time-span records with the landmark t=24, > that means one pre-landmark and one post-landmark. This step modified our > data table by adding one line for those ids going over 24 months. (done) > 3) -sts graph- command plotted the Kaplan-Meier failure function for the > whole time-span (0-102 months) for both treatments. (done, but not desired) > > A step-by-step approach would be great, as we are not yet that proficient in > Stata. > Many thanks in advance - Aenne. > * > * 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/ > -- Dr. Yuval Arbel School of Business Carmel Academic Center 4 Shaar Palmer Street, Haifa, Israel e-mail: yuval.arbel@gmail.com * * 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: How to proceed a landmark survival analysis (tests and plots)?***From:*Änne Glass <aenne.glass@uni-rostock.de>

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