Hello,
Excuse me for a more methodological than technical question. I'm developing
a Cox model with 10 covariates. One of them is age (named "eta").
I've checked proportionality with cox.zph with age continuous:
> cox.zph(coxph(Surv(TTP,CENSOTTP)~eta))
rho chisq p
eta -0.0154 0.0225 0.88
and categorical (eta<60):
> cox.zph(coxph(Surv(TTP,CENSOTTP)~eta<60))
rho chisq p
eta < 60TRUE 0.0168 0.0255 0.873
it seems ok but when I check graphical pattern of log-minus-log Kaplan Meier I
see a convergent-divergent pattern, which seems violating proportionality:
>
plot(survfit(Surv(TTP,CENSOTTP)~eta<60),fun="cloglog",xlab="log
TTP",ylab="log(-log(S))")
also plotting cox.zph of eta<60 (log time) shows a sinusoidal pattern. I'
m not experienced with plotting cox.zph or log-minus-log Kaplan Meier, so my
question is: should I always rely on significance test from cox.zph or should I
look at graphical pattern? Is age in this example violating proportionality?
I paste below "eta" (age) "TTP" (time to event)
"CENSOTTP" (censor)
Thank you
Pietro Bulian
Clinical and Experimental Hematology Research Unit
Centro di Riferimento Oncologico, I.R.C.C.S.
Via Pedemontana, 12
I-33081 Aviano (PN) - Italy
phone: +39 0434 659 412
fax: +39 0434 659 409
e-mail: pbulian@cro.it
"eta" "TTP" "CENSOTTP"
71 68 0
52 29 0
49 5 1
69 136 0
79 11 0
61 1 1
56 3 1
71 52 1
61 40 1
70 51 1
58 30 0
78 52 0
65 40 0
65 44 1
55 60 1
68 84 1
70 48 1
73 34 1
49 8 0
44 52 1
65 25 0
56 86 1
57 116 0
52 25 0
66 76 0
67 64 0
67 87 0
58 23 0
59 15 1
51 89 0
55 65 1
62 45 1
65 73 0
62 38 0
87 11 0
65 16 1
62 20 0
64 39 1
73 27 1
58 34 0
70 22 1
41 5 0
54 52 0
37 15 1
72 4 1
51 14 1
68 19 1
68 7 0
59 24 1
67 32 1
68 54 0
74 22 0
68 5 1
83 16 1
66 186 0
58 16 1
44 87 1
67 54 1
71 48 0
61 25 1
58 29 0
58 41 0
63 63 1
61 12 1
69 45 1
49 56 1
56 12 1
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81 39 0
68 50 0
46 50 0
63 54 0
64 34 1
66 98 0
45 32 0
74 76 0
66 53 0
83 11 0
42 19 0
72 130 0
60 23 0
55 20 0
57 138 0
77 46 0
59 46 1
58 4 1
80 76 0
73 58 0
59 17 1
47 95 0
54 14 1
66 7 1
66 28 1
64 76 0
77 25 1
77 27 1
45 22 0
78 124 0
67 40 0
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55 70 0
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62 40 0
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64 6 0
56 16 1
50 16 0
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66 89 0
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57 15 1
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54 136 1
69 10 1
65 33 0
66 96 0
72 128 0
39 14 1
78 52 0
84 99 0
58 73 0
73 24 1
60 201 0
59 116 1
68 85 0
63 152 0
68 4 1
72 24 0
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60 12 1
62 25 1
62 82 0
66 28 0
62 9 0
61 27 1
62 90 1
56 85 0
48 14 1
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60 19 0
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73 47 0
66 169 0
66 43 0
63 56 0
55 150 0
57 125 1
61 161 0
56 24 1
63 36 0
61 28 0
74 12 1
67 71 1
70 28 1
78 69 0
65 23 0
58 24 1
70 39 0
71 12 0
58 22 0
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63 46 1
55 20 1
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81 49 0
66 16 0
70 100 0
64 31 0
72 18 0
62 84 1
60 24 1
61 19 0
68 67 0
52 24 1
77 70 0
74 91 0
67 33 1
80 36 1
69 107 0
60 96 1
61 102 0
59 52 0
50 14 0
71 60 1
72 52 0
59 72 0
80 64 1
55 48 0
70 60 1
76 56 0
41 16 1
55 24 1
68 49 1
69 148 0
59 54 1
74 153 0
58 186 0
70 40 0
71 42 1
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