[R-sig-ME] Multiple random slopes in coxme
Patrick (Malone Quantitative)
m@|one @end|ng |rom m@|onequ@nt|t@t|ve@com
Mon Dec 7 01:38:37 CET 2020
Huh. I'll think about how that generalizes to the bigger use case. Thanks!
On Sun, Dec 6, 2020 at 6:50 PM David Duffy <David.Duffy using qimrberghofer.edu.au>
wrote:
> > I have placed a masked sample of my data as a .csv at
> > https://github.com/psmalone/reprex/blob/main/coxme_test.csv .
>
> I don't know how much it helps, but I think parametric survival models are
> more robust if you want slopes
> eg for your data above, a Weibull model:
>
> survreg(formula = Surv(FailTime, Event) ~ x1 + x2 + (x1 + x2) *
> frailty(cluster), data = x)
>
> coef se(coef) se2 Chisq DF p
> (Intercept) 7.47245 0.038162 0.030435 38340.62 1.0 0.0e+00
> x1 -0.04780 0.047612 0.044369 1.01 1.0 3.2e-01
> x2 0.06143 0.038344 0.038165 2.57 1.0 1.1e-01
> frailty(cluster) 157.43 71.1 1.8e-08
> x1:frailty(cluster) 0.00104 0.000746 0.000659 1.95 1.0 1.6e-01
> x2:frailty(cluster) -0.00171 0.000642 0.000638 7.08 1.0 7.8e-03
>
> Scale= 0.552
>
> Iterations: 10 outer, 30 Newton-Raphson
> Variance of random effect= 0.0522 I-likelihood = -5351.6
> Degrees of freedom for terms= 0.6 0.9 1.0 71.1 0.8 1.0 1.0
> Likelihood ratio test=224 on 74.3 df, p=<2e-16 n= 5000
>
>
>
--
Patrick S. Malone, Ph.D., Malone Quantitative
NEW Service Models: http://malonequantitative.com
He/Him/His
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