Supplementary methods
Coding of variables in the risk model
All variables used in the analysis were categorical. Nevi were counted in these studies by trained personnel, but according to
different protocols. To reduce the effect of consequent variation in absolute nevus counts among studies, nevus number was
ranked within each study and classified into four centile groups (0-50%, 50-75%, 75-90%, >90%, with intervals closed on the
right and open on the left). As nevus count is an age-dependent variable (1), this was done separately for the under 50 and 50
and over age groups then combined as one variable. Clinically atypical nevi are an important component of the at-risk
phenotype but were variably defined. In the current analyses we used a count of large nevi, categorized as 0, 1-2, and 3+ large
nevi present on the body. Hair color was grouped as brown/black, red or blonde. Sunburn history was recorded as either no
episodes or one or more episodes of sunburn. Freckling was recorded as present or absent, based upon assessment of
freckling coverage on the face, arms and shoulders by trained nurses. Skin type was recorded using the Fitzpatrick scale (2, 3)
that was scored from I to IV (I: always burns, never tans to IV: never burns, tans very easily) and grouped into two categories;
I+II, III+IV. Family history of melanoma in a first-degree relative was recorded as either present or absent.
Recruitment of Leeds Case-Control data and coding of nevus variables
Population-ascertained incident melanoma cases were recruited to a case–control study in a geographically defined area of the
UK (Yorkshire and the Northern region south of the River Tyne) (67% participation rate) between September 2000 and
December 2005. The 513 population-ascertained controls were identified by the cases’ family doctors as not having cancer,
and were randomly invited from individuals with the same sex and within the same 5-year age group as a case (55% response
rate).
To generate the nevus variables we used nevus count data produced by trained research nurses. Cases were classified using
the total count of nevi between 2mm and 5mm as above, using threshold values for this classification from the distribution of
counts in the controls (0-50%, 50-75%, 75-90%, >90%). Data on these variables were complete for 731 cases and 425
controls. To test whether missing variables in the Leeds data affected the results, separate analyses were also run on the full
Leeds dataset with missing values imputed using MICE (15 chains with 25 iterations) in the same manner as above.
Imputing missing values for the pooled controls.
To calculate risk scores for the controls in the pooled datasets it was necessary to fill in missing data. To impute missing
values, we assumed that the data could be treated as missing at random (MAR) and implemented Multiple Imputation by
Chained Equations using the “mice” library in R 3.0.2(4). We ran the imputation analysis for 15 iterations and the imputation
process was run separately in 30 independent chains. Each chain is a sequence of successive imputations that uses the results
of the previous iteration to inform the next. Each of the model variables was included as a predictor in the imputation analysis
as well as age, sex, large moles on the arms (classified as large moles on the body as above), number of atypical nevi (classified
as 0, 1+), number of raised nevi (classified as 0,1-2,3+) and an additional variable representing the study. Nevus counts were
measured on the whole body in some studies and on the arm in others. Both variables were included in the imputation model
but only the body nevus variable is used in the risk model. Sunburn status was measured by two variables representing burns
before 15 years of age and after 15 years of age. Both variables were imputed and a composite sunburn variable (ever/never)
was calculated from the imputed results.
The quality of the imputation runs was finvestigated by examining diagnostic plots produced by the MICE routine. These plot
the mean and standard deviation of each imputed variable at each iteration point and for each imputation chain. We observed
good mixing between different imputation chains and found no evidence of bias or a trend across the iterations (data not
shown), suggesting the imputations are of an acceptable quality.
Comparing self reported and nurse derived mole counts and freckling in the risk model
Self-reported freckling status was determined as a binary (yes/no) variable from a question regarding the number of freckles
the interviewee thought were present at the end of summer as an adult. This was compared to the nurse-derived freckling
variable as detailed in the methods section. Questionnaire responders were asked to provide a count by a friend/partner of the
number of moles on their back from the base of the neck to the waist. This score was directly compared with the nurses’ count
of the same region using Bland-Altman plots in the control group. They were also asked to classify their mole count into one of
four groups (None; Few; Some; Many) using “cartoon” diagrams as a guideline. This classification was compared to the
professionally determined mole count variable (described above) in the cases and controls.
We then substituted the self-reported freckling variable and cartoon mole count classification variable into the risk model and
recalculated the risk classification status to check for agreement with the original model. The net reclassification index (NRI)
was calculated to show if there was a net improvement in classification of cases and controls from using the professionally
derived mole and freckling variables compared to the self-reported variables.
Estimating absolute risk for a UK individual using the risk score
We present an example using the Leeds cohort data as a basis of how the risk score algorithm could be used to calculate
absolute risk using the methods of Gail et al. and Fears et al. (5, 6).
If we assume our example is a 30 year old woman, from the UK with red hair, Fitzpatrick skin type I, freckling, but has no large
nevi, a low nevus count, who has not been severely sunburnt and has no family history of melanoma in a first-degree relative
would have a risk score of 4.62 (1.76x1.66x1.58 from estimates in table 1)
The baseline rate of melanoma incidence in the UK for 30-34 year old women between 2009-2011 was 0.00014 (source CRUK
website). As the UK is a reasonably small with only one climate there is no need to adapt this rate by region, but for larger
countries such as the USA a specific regional rate would likely be necessary. We estimate the attributable risk for this group as
0.866 using the subset of the case data in the pooled analysis that meet these criteria. Using the method of Gail et al. we can
estimate a baseline hazard for this group as approximately 0.00002 (5). The risk of death for any cause in this group is 0.0004
(ONS) and we assume that 5% of all deaths in this age group in the UK are from melanoma based on estimates provided from
the CRUK website (CRUK http://www.cancerresearchuk.org/cancer-info/cancerstats/mortality/age/#Adults).
If we assume that the rare disease assumption holds (given the lifetime risk of melanoma in the UK is currently approximately
1 in 55), and thus the ORs in the model are equivalent to RRs, then the relative risk of a 30 year old woman from the UK with
the risk factors discussed earlier is 4.62. Using this information then using the method of Fears et al. (6) 5 year absolute risk
can be estimated using the following equation:
𝑷(𝒂, 𝒓) = {𝒉𝟏 𝒓 /(𝒉𝟏 𝒓 + 𝒉𝟐} [𝟏 − 𝒆𝒙𝒑{−𝟓(𝒉𝟏 𝒓 + 𝒉𝟐)}]
Where h1 is the baseline hazard rate, h2 the death rate form causes not related to melanoma and r the relative risk for the
subject. For our example the absolute risk of developing melanoma can be estimated as 0.04% over the next 5 years.
Supplementary table 1. Missingness of variables across each of the studies and each of the seven variables. Variable missing
because they were not recorded are marked in red, variables missing for other reasons in blue. 92.9% of all missing data is
due to the variable not being recorded.
Study
Bataille 1996
Berwick 1996
Elwood 1985
Elwood 1990
Green 1985
Holly 1995
Holman 1984
Kanetsky 2001
Kennedy 2003
LeMarchand 2006
Mossner 2007
Osterlind 1988
Swerdlow 1986
Titus-Ernstoff
2005
Westerdahl 1994
Westerdahl 2000
TOTAL
%missing
(variable)
Total systematic
missing
Total not
systematic missing
n
855
1199
1190
390
464
1387
1022
513
508
556
661
829
377
Hair
colour
4
0
9
0
0
0
11
3
4
0
1
42
19
1101
1041
1484
13577
Fitzpatrick
4
27
15
0
0
36
2
9
0
1
0
6
1
Family
History
1
0
24
390
0
63
0
0
508
0
661
44
11
Study
missing
total
23
1324
3632
393
464
1491
2057
12
1022
114
2669
1071
424
% missing
(study)
0.4
15.8
43.6
14.4
14.3
15.4
28.8
0.3
28.7
2.9
57.7
18.5
16.1
354
0
0
3906
33
1041
53
1228
0
1041
0
2743
1503
3177
1742
21118
19.5
43.6
16.8
22.2
64.0
28.8
9.0
20.2
41.9
661
8670
3406
1041
2600
19628
92.9
352
18
500
187
143
1490
7.1
Freckling
14
45
1190
3
0
5
1022
0
2
2
661
2
377
Sunburn
(present)
0
0
14
0
0
0
0
0
0
42
661
145
1
Largemole
(present)
0
1199
1190
0
464
0
1022
0
508
0
661
3
15
Molecount
(present)
0
53
1190
0
0
1387
0
0
0
69
24
829
0
1
12
36
142
0
4
71
3398
14
38
98
1013
1101
1041
1484
8688
1.0
25.0
7.5
0
3250
142
148
Supplementary table 2: Number and percentage of cases and controls in each risk band for the Leeds dataset including
imputed cases 1. AUC=0.77 (0.75-0.78, DeLong 3 imputations.)†.
Case (%)
Control (%)
Pooled controls*
(%)
Low, relative to
1776(12.3)
3284 (42.7)
2309 (30)
3070 (21.3)
2553 (33.2)
2309 (30)
5276 (36.6)
1395 (18.1)
2309 (30)
4278 (29.7)
463 (6.0)
770 (10)
peers**
Medium-Low,
relative to peers
Medium-High,
relative to peers
High,
Relative to peers
1.
Imputed using MICE over 15 independent chains for 25 iterations.
* Expectation of how controls in the Leeds case-control data would be distributed if they followed the distribution pattern of
the controls in the pooled analyses.
** Peers are defined as individuals of the same age and sex drawn from the same population.
† Chi squared test p<2.2x10-16
Supplementary table 3: Comparison of self-classification and nurse-classification of mole count in the Leeds controls.
Nurses mole count1
Self assessment mole
0-50%
50-75%
75-90%
>90%
None
79
14
5
0
Few
149
62
22
21
Some
33
33
33
19
Many
3
12
9
10
count2
1. The nurses mole count were classified using the total count of nevi between 2mm and 5mm into four groups using the
following threshold values; 0-50%, 50-75%, 75-90%, >90%.
2. Self-counts were determined by asking individuals to pick from four diagrams with varying mole counts the one that
best represented themselves (supplementary figure 4).
Supplementary table 4. Comparison of self-reported freckling status with nurse-assessed freckling in Leeds controls.
Nurses freckling1
Self assessment freckling2
Yes
No
Yes
261
29
No
153
52
1. Professionally trained nurses assessed freckling as the percentage of freckling on the arms, face and shoulders.
Presence of any freckling was collapsed into a single variable in both cases.
2. Individuals were asked to assess which of six figures that represented various degrees of freckling best represented
them.
Supplementary table 5: Differences in classification of cases and controls from the Leeds dataset into risk groups
when using freckling and mole count variables derived from professional examination compared with variables
derived from individuals self reporting their freckling and mole counts. The model that includes the nurse-derived
variables is better at reclassifying cases into higher risk groups and controls into lower risk groups than the model that uses
self-reported variables (NRI=0.29).
Nurse reported freckling/mole count
Cases
Controls
Self reported
Low, relative
Medium-Low,
Medium,
High,
Low,
Medium-Low,
Medium,
High,
freckling/mole count.
to peers
relative to
relative
relative
relative to
relative to
relative
relative
peers
to peers
to peers
peers
peers
to peers
to peers
Low, relative to peers
61
35
16
0
133
20
4
0
Medium-Low, relative to
32
82
92
13
46
83
23
1
Medium, relative to peers
0
43
147
102
2
34
42
18
High, relative to peers
0
0
10
98
0
0
8
11
peers
Supplementary figure 1. Distribution of risk scores in controls from analyses
of the 16 pooled studies over 30 multiple imputation runs (n=226,830 or
30x7561).
Supplementary figure 2. Distribution of risk scores in Leeds melanoma cases
and controls (n=1156).
Supplementary figure 3. Bland Altman plot of nurse-assessed mole count of
the back compared with self-reported mole count of the back in controls from
the Leeds dataset (N=470). Dotted lines mark the 95% limits of agreement for
the difference in mole counts (mean=2.95).
Supplementary figure 4 Diagram used by individuals from the Leeds casecontrol dataset to self declare mole count. Individuals were asked, ”Which one
of these diagrams best describes how moley you are?”
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