Dear Statalist members,
Recently I did a Cox regression with restricted cubic spline (mvrs) in order to graph the non-linear association between hazard ratio and my Xvar1.
Although the graph showed exactly an U-shaped association, the reviewer requied a test for non-linearity (which is actually a P-value in most articles, as far as I have learnt).
Is there any code to test such hypothesis in Stata? Based on my knowledge, it seems like what I am testing is whether the coefficient for cubicly transformed Xvar (Xvar_0, Xvar_1, Xvar_2) is equal to 0 (see the results below). Is that enough for proving the non-linearity?
Thx a lot.
Recently I did a Cox regression with restricted cubic spline (mvrs) in order to graph the non-linear association between hazard ratio and my Xvar1.
Although the graph showed exactly an U-shaped association, the reviewer requied a test for non-linearity (which is actually a P-value in most articles, as far as I have learnt).
Is there any code to test such hypothesis in Stata? Based on my knowledge, it seems like what I am testing is whether the coefficient for cubicly transformed Xvar (Xvar_0, Xvar_1, Xvar_2) is equal to 0 (see the results below). Is that enough for proving the non-linearity?
Thx a lot.
Code:
xi: mvrs stcox Xvar age sex stemi hbp dm hf_2 pci_his ldl crea i.culp timi_0 d2btime thrombo iabp tiro asp
Final multivariable spline model for _t
------------------------------------------------------------------------------
Variable | -----Initial----- -----Final-----
| df Select Alpha Status df Knot positions
-------------+----------------------------------------------------------------
Xvar | 4 1.0000 0.0500 in 3 [lin] 2.79 12.07
age | 4 1.0000 0.0500 in 1 Linear
sex | 1 1.0000 0.0500 in 2 Linear
stemi | 1 1.0000 0.0500 in 2 Linear
hbp | 1 1.0000 0.0500 in 2 Linear
dm | 1 1.0000 0.0500 in 2 Linear
hf_2 | 1 1.0000 0.0500 in 2 Linear
pci_his | 1 1.0000 0.0500 in 2 Linear
ldl | 4 1.0000 0.0500 in 1 Linear
crea | 4 1.0000 0.0500 in 1 Linear
_Iculp_2 | 1 1.0000 0.0500 in 2 Linear
_Iculp_3 | 1 1.0000 0.0500 in 2 Linear
_Iculp_4 | 1 1.0000 0.0500 in 2 Linear
_Iculp_5 | 1 1.0000 0.0500 in 2 Linear
timi_0 | 1 1.0000 0.0500 in 2 Linear
d2btime | 4 1.0000 0.0500 in 1 Linear
thrombo | 1 1.0000 0.0500 in 2 Linear
iabp | 1 1.0000 0.0500 in 2 Linear
tiro | 1 1.0000 0.0500 in 2 Linear
asp | 1 1.0000 0.0500 in 2 Linear
------------------------------------------------------------------------------
Cox regression -- Breslow method for ties
Entry time _t0 Number of obs = 3980
LR chi2(22) = 235.83
Prob > chi2 = 0.0000
Log likelihood = -935.89368 Pseudo R2 = 0.1119
------------------------------------------------------------------------------
_t | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Xvar_0 | .0350324 .087242 0.40 0.688 -.1359588 .2060236
Xvar_1 | -.1742873 .077077 -2.26 0.024 -.3253555 -.0232192
Xvar_2 | .2408546 .0900958 2.67 0.008 .0642702 .4174391
age | .0468714 .0086012 5.45 0.000 .0300133 .0637295
sex | -.513448 .1966388 -2.61 0.009 -.898853 -.1280431
stemi | -.2819774 .2841655 -0.99 0.321 -.8389316 .2749769
hbp | .3275591 .2001628 1.64 0.102 -.0647527 .7198709
dm | .0735698 .1830752 0.40 0.688 -.2852511 .4323906
hf_2 | 1.216276 .1854233 6.56 0.000 .8528525 1.579699
pci_his | .3591419 .2500795 1.44 0.151 -.1310049 .8492887
ldl | -.042943 .1036864 -0.41 0.679 -.2461646 .1602787
crea | .0104941 .0021757 4.82 0.000 .0062299 .0147584
_Iculp_2 | .7382852 .3876987 1.90 0.057 -.0215903 1.498161
_Iculp_3 | .9029866 .3816996 2.37 0.018 .1548691 1.651104
_Iculp_4 | 1.6113 .47715 3.38 0.001 .6761029 2.546497
_Iculp_5 | 1.771119 .7053499 2.51 0.012 .3886584 3.153579
timi_0 | .4584349 .2198601 2.09 0.037 .0275171 .8893528
d2btime | 1.42e-06 9.61e-06 0.15 0.883 -.0000174 .0000203
thrombo | .0092184 .2040014 0.05 0.964 -.3906169 .4090538
iabp | .6062582 .2079598 2.92 0.004 .1986645 1.013852
tiro | -.0185661 .2624105 -0.07 0.944 -.5328812 .495749
asp | -.1059745 .4885523 -0.22 0.828 -1.063519 .8515703
------------------------------------------------------------------------------
Deviance: 1871.787.
. testparm Xvar_*
( 1) Xvar_0 = 0
( 2) Xvar_1 = 0
( 3) Xvar_2 = 0
chi2( 3) = 10.60
Prob > chi2 = 0.0141
