Table 1.

Overview of Analytical Approaches, Advantages, and Limitations of the Population-based, Case-Control-Family Design

Population-based, Case-Control-Family Design
Analytical Comparison	Types of Analysis and Uses	Advantages	Limitations
Traditional case-control comparisons of case probands with population-based controls and spouse/friend controls	For examining environmental factors, family history, and common genetic factors	More extensive and greater validity of family history data compared with traditional case-control studies, because information is gathered directly from relatives where possible	Poor participation of population-based controls could lead to selection bias and biased estimates, at least for environmental exposures. (This is becoming a major limitation of population-based, case-control studies in general.)
	Logistic regression	In the current context, parents’ reports of childhood sun exposure can be compared with probands’ reports as a measure of reliability.
		Spouse controls have typically high participation.
		Can contribute to genome-wide association studies
Case-control comparisons between case probands and sibling controls	For examining rare and common genetic factors, environmental factors, and gene-environment interactions	Sibling controls avoid confounding due to population stratification and/or unmeasured familial risk factors, and they are often well matched for other potential confounders.	Some cases do not have any siblings.
	Correlated exposures between the proband and sibling should be taken into account, e.g., by using conditional logistic regression or other methods, such as generalized estimating equations.	Sibling controls have relatively high participation.	For some exposures, overmatching within the family for both genetic and shared environmental factors could result in reduced power per subject (1).
		A statistically efficient method for studying gene-environment interactions (54)
Comparisons involving probands and relatives	For examining rare and common genetic factors, environmental factors, and gene-environment interactions	Increases statistical power for estimating genetic associations by making use of data on all family members	Can be resource intensive to recruit relatives
	Modified segregation analysis (55, 56) can be used to estimate the associations of melanoma with known (measured) and unmeasured genes, as well as environmental and phenotypic factors, allowing for other familial causes of disease (both measured and unmeasured), using polygenic (57) or regressive logistic models (55).	Allows examination of both rare and common genetic factors, environmental factors, and their interaction	Recruitment of relatives of controls might be more difficult than that of relatives of cases.
	Prospective and retrospective cohort approaches can be used to estimate the risk of melanoma for different relative groups, including the incidence of disease in relatives of cases or mutation carriers, and comparison of risks between those for relatives of cases and those for relatives of controls (58, 59)	Enables estimation of age-specific cumulative risks and hazard ratios for melanoma associated with family history or with being a carrier of a genetic mutation	Older-onset diseases might result in smaller family sizes; however, in this study, all case probands were aged <40 years at diagnosis.
		The associations with environmental and phenotypic factors can be estimated separately for genetically susceptible subgroups and for noncarriers.
		Familial relative risks can be estimated for different relative groups.
		Permits simple adjustment for ascertainment that maximizes use of family data and gives unbiased estimates with direct inference to the population, in contrast to the multiple-case, family-based study design (1, 12)
		Easier to maintain prospective follow-up with family-based recruitment, leading to high cohort retention rates