Dataset Information

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies.

ABSTRACT: We report the development of a cutaneous melanoma risk algorithm based upon seven factors; hair color, skin type, family history, freckling, nevus count, number of large nevi, and history of sunburn, intended to form the basis of a self-assessment Web tool for the general public.Predicted odds of melanoma were estimated by analyzing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the distribution of the predicted odds in the controls from these studies. Imputation was used to estimate missing data in the pooled datasets. The 30th, 60th, and 90th centiles were used to distribute individuals into four risk groups for their age, sex, and geographic location. Cross-validation was used to test the robustness of the thresholds for each group by leaving out each study one by one. Performance of the model was assessed in an independent UK case-control study dataset.Cross-validation confirmed the robustness of the threshold estimates. Cases and controls were well discriminated in the independent dataset [area under the curve, 0.75; 95% confidence interval (CI), 0.73-0.78]. Twenty-nine percent of cases were in the highest risk group compared with 7% of controls, and 43% of controls were in the lowest risk group compared with 13% of cases.We have identified a composite score representing an estimate of relative risk and successfully validated this score in an independent dataset.This score may be a useful tool to inform members of the public about their melanoma risk.

SUBMITTER: Davies JR

PROVIDER: S-EPMC4487528 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies.

Davies John R JR Chang Yu-mei YM Bishop D Timothy DT Armstrong Bruce K BK Bataille Veronique V Bergman Wilma W Berwick Marianne M Bracci Paige M PM Elwood J Mark JM Ernstoff Marc S MS Green Adele A Gruis Nelleke A NA Holly Elizabeth A EA Ingvar Christian C Kanetsky Peter A PA Karagas Margaret R MR Lee Tim K TK Le Marchand Loïc L Mackie Rona M RM Olsson Håkan H Østerlind Anne A Rebbeck Timothy R TR Reich Kristian K Sasieni Peter P Siskind Victor V Swerdlow Anthony J AJ Titus Linda L Zens Michael S MS Ziegler Andreas A Gallagher Richard P RP Barrett Jennifer H JH Newton-Bishop Julia J

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20150224 5

<h4>Background</h4>We report the development of a cutaneous melanoma risk algorithm based upon seven factors; hair color, skin type, family history, freckling, nevus count, number of large nevi, and history of sunburn, intended to form the basis of a self-assessment Web tool for the general public.<h4>Methods</h4>Predicted odds of melanoma were estimated by analyzing a pooled dataset from 16 case-control studies using logistic random coefficients models. Risk categories were defined based on the ...[more]

PMID: 25713022

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Project description:Pooling biomarker data across multiple studies allows for examination of a wider exposure range than generally possible in individual studies, evaluation of population subgroups and disease subtypes with more statistical power, and more precise estimation of biomarker-disease associations. However, circulating biomarker measurements often require calibration to a single reference assay prior to pooling due to assay and laboratory variability across studies. We propose several methods for calibrating and combining biomarker data from nested case-control studies when reference assay data are obtained from a subset of controls in each contributing study. Specifically, we describe a two-stage calibration method and two aggregated calibration methods, named the internalized and full calibration methods, to evaluate the main effect of the biomarker exposure on disease risk and whether that association is modified by a potential covariate. The internalized method uses the reference laboratory measurement in the analysis when available and otherwise uses the estimated value derived from calibration models. The full calibration method uses calibrated biomarker measurements for all subjects, including those with reference laboratory measurements. Under the two-stage method, investigators complete study-specific analyses in the first stage followed by meta-analysis in the second stage. Our results demonstrate that the full calibration method is the preferred aggregated approach to minimize bias in point estimates. We also observe that the two-stage and full calibration methods provide similar effect and variance estimates but that their variance estimates are slightly larger than those from the internalized approach. As an illustrative example, we apply the three methods in a pooling project of nested case-control studies to evaluate (i) the association between circulating vitamin D levels and risk of stroke and (ii) how body mass index modifies the association between circulating vitamin D levels and risk of cardiovascular disease.

Dataset Information

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies.

Publications

Development and validation of a melanoma risk score based on pooled data from 16 case-control studies.

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