Analysis of Melanoma Onset: Assessing Familial Aggregation by Using Estimating Equations and Fitting Variance Components via Bayesian Random Effects Models

We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.

Skin cancer clinics in Australia: workload profile and performance indicators from an analysis of billing data

Objective: To describe the workload profile in a network of Australian skin cancer clinics. Design and setting: Analysis of billing data for the first 6 months of 2005 in a primary-care skin cancer clinic network, consisting of seven clinics and staffed by 20 doctors, located in the Northern Territory, Queensland and New South Wales. Main outcome measures: Consultation to biopsy ratio (CBR); biopsy to treatment ratio (BTR); number of benign naevi excised per melanoma (number needed to treat [NNT]). Results: Of 69780 billed activities, 34 622 (49.6%) were consultations, 19 358 (27.7%) biopsies, 8055 (11.5%) surgical excisions, 2804 (4.0%) additional surgical repairs, 1613 (2.3%) non-surgical treatments of cancers and 3328 (4.8%) treatments of premalignant or non-malignant lesions. A total of 6438 cancers were treated (116 melanomas by excision, 4709 non-melanoma skin cancers [NMSCs] by excision, and 1613 NMSCs non-surgically); 5251 (65.2%) surgical wounds were repaired by direct suture, 2651 (32.9%) by a flap (of which 44.8% were simple flaps), 42 (0.5%) by wedge excision and 111 (1.4%) by grafts. The CBR was 1.79, the BTR was 3.1 and the NNT was 28.6. Conclusions: In this network of Australian skin cancer clinics, one in three biopsies identified a skin cancer (BTR, 3.1), and about 29 benign lesions were excised per melanoma (NNT, 28.6). The estimated NNT was similar to that reported previously in general practice. More data are needed on health outcomes, including effectiveness of treatment and surgical repair.