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I would like to simulate survival meta-analysis in clinical trials on R but I'm not pretty sure of what would be the best way to do it and what would be fitting more the reality. The data would include a random treatment effect.

I made an attempt (inspired by the article "Individual patient data meta-analysis of survival data using Poisson regression models" Crowther and al.).

After simulating n patients, allocated in k trials (each trial is divided in 50% assigned to treatment, 50% assigned to control), I have simulated :

  • k beta1; beta1 being the coefficient associated with the treatment effect (coming from a Normal distribution, the mean of it being the mean log HR and the standard error being the random effect)
  • (k-1) beta0; beta0 being the coefficient associated with the trial effect (coming from a Normal distribution, the mean of it being 0 and the standard error being the trial effect). The beta0 for the trial number 1 equals 0.

The survival times are then taken from a Weibull distribution with the shape = 0.5 (for example) and the scale = exp(beta0_j+beta1_j*z_arm_j) (j being the number of the trial).

z_arm is the covariate associated with the arm, equals to -0.5 or 0.5.

What do you think of this ? Does this seem ok ?

Thanks for any help !

Flora

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closed as off-topic by mkt, Peter Flom Jan 16 at 10:52

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    $\begingroup$ Thank you for your edits. In my view, this is close to an understandable and answerable question, but I haven't any clear idea what you might mean by the phrases "simulated - k beta1" and "- (k-1) beta0." I think you will need to explain what these are intended to represent in your post. $\endgroup$ – whuber Jan 16 at 12:48
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    $\begingroup$ It's hard to say whether that results in reasonable within trial survival functions. You could simulate and plot a couple of hundred of them to see how reasonable they look. You may also bee able to enforce certain behaviours by using some of the alternative parametrizations in section 4.1 of this dissertation: nbn-resolving.de/urn:nbn:de:gbv:ma9:1-10711 $\endgroup$ – Björn Jan 17 at 10:16