Competing risks arise in studies when subjects are exposed to more than one cause of event and event due to one cause excludes event due to other causes. If we want real world probabilities of events and separate the probability of event into different causes, competing risks methodology should be used as opposed to standard survival analysis methods. The traditional analysis for competing risks is to perform a separate analysis for each event type, treating other events as censoring (cause-specific approach). The biggest drawback of cause-specific analysis is the requirement that times for different event types be independent, which is often not the case. Although cause-specific approach can give cause-specific hazards, it could not provide actual probability of event. Recently, cumulative incidence function (CIF) and its following sub-distribution approach have been developed to overcome the above two problems by accounting for competing risks. CIF is useful in estimating the probabilities of events, but we cannot examine covariate effects on the CIF. Sub-distribution approach allows us to test covariate effects on the CIF, but sub-distribution hazards are difficult to interpret and so should be used with caution. In summary, the currently available approaches for competing risks have their own advantages and disadvantages. How to analyze compete risks correctly depends on the aim of study and the characteristics of event types.
zhuangyh/Survival_Analysis
The incidence of malignant melanoma has been on the rise at an alarming rate in the United States and attracted great attention (1). The occurrence of melanoma is associated with race, skin color, skin tendency to burn, freckles, blue or green eye color, light hair color, family history, and prevalence of numerous melanocytic nevi (2-4). It has been shown that the presence of numerous melanocytic nevi is the strongest risk factor for melanoma. Nevi are likely to be precursor lesions for 20-60% of melanomas (5). White populations have higher risks for malignant melanoma than other racial/ethnic groups. In the United States, non-Hispanic white individuals had an annual incidence rate of 25.1 per 100 000 population for the period 2000 through 2004 compared with 1.0 per 100 000 for black, 4.5 per 100 000 for Hispanic white (1, 6). Two studies suggested heritability accounts for about 2/3 of the variance in nevus counts (7) (8). Several specific genetic variations have been implicated. The most notable genetic factors implicated in melanoma at present include CDKN2A, MC1R, and OCA2 (9). It has been shown that the OCA2 rs12913832 SNP is associated with Caucasian populations (10). OCA2 was also strongly related with hair color, with 36% of those homozygous for the g form (gg) having blonde hair compared to 8% of homozygotes for the form (aa) (10). Given the strong relationship between nevus density and melanoma risk, melanoma risk genes are likely candidate genes for nevus formation. Thus, we hypothesized that OCA2 has association with nevus and that population with certain OCA2 variant are susceptible to developing nevus. To determine the influence of OCA2 on total nevus counts and changes on children, we also take gender and race into account in this study during estimating the effect of OCA2 on nevus development.
SAS