PFIRRMANN et al
PROGNOSIS OF LONG-TERM SURVIVAL IN CML PATIENTS
SUPPLEMENTARY INFORMATION
Table of contents
Supplementary methods – Page 2
Supplementary results – Page 4
Supplementary references – Page 5
Supplementary tables – Page 6
Supplementary figures – Page 10
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Supplementary methods
Multiple regression modelling
As subdistribution hazards provided a direct link between the candidate variables and
the cumulative incidence probabilities of dying of CML, the Fine-Gray model was
preferred to standard Cox regression and cause-specific hazards.1,2 However,
analysis of the cause-specific hazards of the event of interest and of all competing
risks, supports the understanding of the event dynamics.3 Furthermore, for standard
Cox regression, a particular SAS macro in order to model fractional polynomials for
the continuous prognostic candidate variables was available.4 With the SAS macro,
the statistically most suitable polynomial transformation for each variable was
identified when modelling its influence on the cause-specific hazard of dying from
CML. These transformations were then also used for investigating the influence on
the subdistribution hazard with the Fine-Gray model. Interpretation of the results was
based on both hazard models. The need to include interactions in a multiple model
was examined. To assess the assumption of proportional hazards, for both
regression models, Schoenfeld residuals were investigated.5,6
Cutoff identification
For identification of cutoff(s) dividing the score into different risk groups, 1000
bootstrap samples were drawn with replacement from the original dataset. 7 Within
each bootstrap sample, the minimal P value approach8 helped to determine the cutoff
defining two groups with most different cumulative probabilities of dying from CML
according to the Gray test statistic. A kernel density estimator was used as a
smoothing function to visualize the selected cutoffs.9 Only cutoffs with P values <0.05
after Bonferroni adjustment for multiple testing were considered. After the decision on
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PROGNOSIS OF LONG-TERM SURVIVAL IN CML PATIENTS
the cutoff(s) based on the kernel density estimator, the calibration of the resulting risk
groups of a new model was assessed with a calibration plot.10
Supplementary results
Variables with prognostic influence on the cause-specific hazards of dying of
CML
In the situation of competing risks, it is recommended to also investigate causespecific hazards and the hazards of all competing events.3
Applying the Cox model with cause-specific hazards of dying of CML, in addition to
age, spleen size, peripheral blasts, and platelet count, also sex and its interaction
with hemoglobin were identified as significant prognostic factors. Supplementary
Table 3 shows this best model in the 2190 patients with data to all candidate
variables. Using only the four variables of the EUTOS long-term survival (ELTS)
score, results in all 2205 evaluable patients are given in Supplementary Table 3. The
cause-specific hazards ratios of the four variables in Supplementary Table 3 and the
subdistribution hazards ratios reported for the ELTS score in Table 3 of the main
manuscript are largely alike. This concordance suggests that the four variables have
an actual effect on the cumulative incidence probabilities of dying of CML and their
significance is not (just) due to an indirect effect on causes of death unrelated to
CML. According to the Schoenfeld residuals, for the variables of the ELTS score, the
assumption of proportional subdistribution hazards was appropriate. For prognostic
analyses in the presence of competing risks, rather the use of the Fine-Gray model is
recommended and the more parsimonious model of the ELTS score was preferred
over the cause-specific hazards model which additionally identified sex and its
interaction with hemoglobin as prognostic variables.
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Variables with prognostic influence on the hazards of death unrelated to CML
Whether using the Fine-Gray model or the Cox model, age, sex, its interaction with
hemoglobin, and white blood cell count were identified to be influential on the
hazards of death unrelated to CML (Supplementary Table 4). Since the Akaike
information criterion was applied for model selection, P=0.091 (sex, Table S4) was
acceptable. As expected, the hazard for death unrelated to CML increased with age.
Sex needs to be interpreted together with its interaction with hemoglobin; while the
level of hemoglobin had hardly an influence on the hazards for male patients, it was
influential with respect to females. Women with low hemoglobin levels had higher
hazards of death unrelated to CML than males and women with higher levels of
hemoglobin. Starting from hemoglobin levels higher than 10 g/dL, the hazards of
women were lower than the hazards of men. Higher WBC counts corresponded to
reduced hazards of CML-unrelated death. Again, by and large, the outcome of both
hazard models is the same (Supplementary Table 4). In conclusion of all prognostic
modelling, the events “death due to CML” and “death unrelated to CML” seem to be
independent.
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Supplemental references
1.
Andersen PK, Geskus RB, de Witte T, Putter H. Competing risks in
epidemiology: possibilities and pitfalls. Int J Epidemiol. 2012;41(3):861-870.
2.
Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a
Competing Risk. Journal of the American Statistical Association. 1999;94(446):496509.
3.
Latouche A, Allignol A, Beyersmann J, Labopin M, Fine JP. A competing risks
analysis should report results on all cause-specific hazards and cumulative incidence
functions. Journal of Clinical Epidemiology. 2013;66(6):648-653.
4.
Sauerbrei W, Meier-Hirmer C, Benner A, Royston P. Multivariable regression
model building by using fractional polynomials: Description of SAS, STATA and R
programs. Computational Statistics & Data Analysis. 2006;50(12):3464-3485.
5.
Therneau TM, Grambsch, PM. Modeling Survival Data: Extending the Cox
Model. New York: Springer; 2000.
6.
Scrucca L, Santucci A, Aversa F. Regression modeling of competing risk using
R: an in depth guide for clinicians. Bone marrow transplantation. 2010;45(9):13881395.
7.
Davison AC, Hinkley, DV. Bootstrap Methods and Their application.
Cambridge: Cambridge University Press; 1997.
8.
Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of Using
“Optimal” Cutpoints in the Evaluation of Prognostic Factors. Journal of the National
Cancer Institute. 1994;86(11):829-835.
9.
Silverman BW. Density estimation for statistics and data analysis. London:
Chapman and Hall; 1986.
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10.
PROGNOSIS OF LONG-TERM SURVIVAL IN CML PATIENTS
Putter H, Fiocco M, Geskus RB. Tutorial in biostatistics: competing risks and
multi-state models. Statistics in Medicine. 2007;26(11):2389-2430.
Supplementary tables
Table 1. Established baseline prognostic scores in chronic myeloid leukemia.
Formula
Sokal Scorea
Sokal score
Risk group
= exp ( 0.0116 x (age [in years] – 43.4)
Low risk: <0.80
+ 0.0345 x (spleen size [cm below costal margin] – 7.51)
+ 0.1880 x ((platelet count [in 109/L] /700)² – 0.563)
Intermediate risk:
+ 0.0887 x (blasts [% in peripheral blood]– 2.10) )
≥0.80 and ≤1.20
High risk: >1.20
Euro Scoreb
Euro score
=
( 0.6666 x age [0 when age < 50 years; 1, otherwise]
Low risk: ≤780
+ 0.0420 x spleen size [cm below costal margin]
+ 0.0584 x blasts [% in peripheral blood]
Intermediate risk:
+ 0.0413 x eosinophils [%in peripheral blood]
>780 and ≤1480
+ 0.2039 x basophils [0 when basophils [% in peripheral
blood] < 3; 1, otherwise]
High risk: >1480
+ 1.0956 x platelet count [0 when platelets count [in 109/L]
< 1500; 1, otherwise] ) x 1000
EUTOS scorec
EUTOS score
=
7 x basophils [% in peripheral blood]
Low risk: ≤87
+ 4 x spleen size [cm below costal margin]
High risk: >87
EUTOS indicates European Treatment and Outcome Study.
a
Sokal JE, Cox EB, Baccarani M, Tura S, Gomez GA, Robertson JE, et al. Prognostic discrimination in "good-risk" chronic
granulocytic leukemia. Blood 1984; 63(4): 789-799.
b
Hasford J, Pfirrmann M, Hehlmann R, Allan NC, Baccarani M, Kluin-Nelemans JC, et al. A new prognostic score for survival of
patients with chronic myeloid leukemia treated with interferon alfa. Writing Committee for the Collaborative CML Prognostic
Factors Project Group. J Natl Cancer Inst 1998; 90(11): 850-858.
c
Hasford J, Baccarani M, Hoffmann V, Guilhot J, Saussele S, Rosti G, et al. Predicting complete cytogenetic response and
subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score. Blood 2011; 118(3):
686-692.
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Table 2. Causes of death in the in-study registry.
Number of cases
Percent
92
44
Second cancer
50
24
Cardiovascular event
21
10
Infection
12
6
Other causes
21
10
Chronic myeloid leukemia (CML)
Not related to CML (total: 104 cases, 50%)a
Hemorrhagy
3
Old age
3
Acute leukemia unrelated to CMLb
2
Renal failure
2
Bone marrow aplasia
1
Car accident
1
Cerebrovascular adverse event
1
Chronic lung disease
1
CML (no progression reported)c
1
Ernia
1
Liver failure
1
Pemphigo
1
Poisoning
1
Suicide
1
Surgical complication
1
Unknownd
Total
aIn
6
n=208
100%
none of the 104 cases, a “date of CML progression” was reported.
bFurther
information for the assessment “unrelated to CML” was not known.
cFurther
information for the assessment “CML” was not known.
dIn
12
none of the 12 cases, progression of CML was reported.
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Table 3. Cox models with cause of death due to CML as event of interest and with
censoring for death due to other causes.
Significant prognostic factors in 2190 patients with complete data to all nine candidate variables.
Variables
Lower 95%
Upper 95%
Estimation of
Cause-specific
confidence limit
confidence limit
P
coefficient 
hazard ratio
for hazard ratio
for hazard ratio
(Age in years/10)3
0.0029
1.003
1.001
1.005
0.002
Spleen size, cm below costal margin
0.0539
1.055
1.017
1.096
0.005
Blasts, % in peripheral blood
0.0910
1.095
1.015
1.182
0.019
((Platelet count x 109/L)/1000)-0.5
0.3998
1.492
1.073
2.074
0.017
Sex (coding: 0 = male, 1 = female)
2.8272
16.898
2.321
123.013
0.005
Interaction: hemoglobin, g/dl x sex
-0.2581
0.773
0.642
0.930
0.006
Significance of the four prognostic factors part of the EUTOS survival score, now using all 2205 evaluable patients.
Variables
Lower 95%
Upper 95%
Estimation of
Cause-specific
confidence limit
confidence limit
P
coefficient 
hazard ratio
for hazard ratio
for hazard ratio
(Age in years/10)3
0.0027
1.003
1.001
1.005
0.004
Spleen size, cm below costal margin
0.0628
1.065
1.029
1.197
<0.001
Blasts, % in peripheral blood
0.1042
1.110
1.027
1.104
0.007
((Platelet count x 109/L)/1000)-0.5
0.4034
1.497
1.078
2.079
0.016
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Table 4. Significant prognostic factors in all 2190 patients with complete data to all 9
candidate variables when causes of death unrelated to CML were of primary interest.
Fine and Gray model keeping patients with the competing event death due to CML in the risk set.
Variables
Lower 95%
Upper 95%
Estimation of
Subdistribution
confidence limit
confidence limit
P
coefficient 
hazard ratio
for hazard ratio
for hazard ratio
(Age in years/10)3
0.0077
1.008
1.006
1.009
<0.001
Sex (coding: 0 = male, 1 = female)
2.2045
9.066
0.702
117.084
0.091
Interaction: hemoglobin, g/dl x sex
-0.2291
0.795
0.640
0.989
0.039
(White blood cell count x109/L)/100
-0.3915
0.676
0.512
0.893
0.006
Lower 95%
Upper 95%
Cox model with censoring at time of death due to CML.
Variables
Estimation of
Cause-specific
confidence limit
confidence limit
P
coefficient 
hazard ratio
for hazard ratio
for hazard ratio
(Age in years/10)3
0.0078
1.008
1.006
1.009
<0.001
Sex (coding: 0 = male, 1 = female)
2.4272
11.327
1.127
113.830
0.039
Interaction: hemoglobin, g/dl x sex
-0.2470
0.781
0.642
0.951
0.014
(White blood cell count x109/L)/100
-0.3890
0.678
0.514
0.893
0.006
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Supplementary figures
Figure 1. Kernel density of 997 optimal cutoffs according to the minimal P value
approach.
One thousand bootstrap samples with n = 2205 were drawn from the original dataset.
After P value adjustment for multiple testing, in 997 samples, a statistically significant
best cutoff was identified. A smoothing function of these 997 cutoffs resulted in the
three-headed kernel density. The cutoffs at the two highest peaks, 1.5680 and
2.2185 led to the definition of three risk groups for the new score.
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Figure 2. Cumulative incidence probabilities of dying of chronic myeloid leukemia in
1120 patients of the out-study registry.
a Stratification of the risk groups according to the Sokal score.
Number of patients still at risk (n) at different years of observation
Year
0
2
5
8
High risk, n
259
219
142
15
Intermediate risk, n
411
384
261
28
Low risk, n
450
420
298
37
At 2, 5, and 8 years, horizontal crossbars indicate the upper and lower limit of the
95% confidence interval (CI) for the estimated probability.
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b Stratification of the risk groups according to the Euro score.
Number of patients still at risk (n) at different years of observation
Year
0
2
5
8
High risk, n
131
110
67
5
Intermediate risk, n
523
480
336
38
Low risk, n
466
433
298
37
At 2, 5, and 8 years, horizontal crossbars indicate the upper and lower limit of the
95% confidence interval (CI) for the estimated probability.
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c Stratification of the risk groups according to the EUTOS score.
Number of patients still at risk (n) at different years of observation
Year
0
2
5
8
High risk, n
149
128
86
8
Low risk, n
971
895
615
72
At 2, 5, and 8 years, horizontal crossbars indicate the upper and lower limit of the
95% confidence interval (CI) for the estimated probability.
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