A unified model for estimating and testing familial aggregation

A population-wide analysis of the familial risk of suicide in Utah, USA

BACKGROUND

The degree to which suicide risk aggregates in US families is unknown. The authors aimed to determine the familial risk of suicide in Utah, and tested whether familial risk varies based on the characteristics of the suicides and their relatives.

METHODS

A population-based sample of 12 160 suicides from 1904 to 2014 were identified from the Utah Population Database and matched 1:5 to controls based on sex and age using at-risk sampling. All first through third- and fifth-degree relatives of suicide probands and controls were identified (N = 13 480 122). The familial risk of suicide was estimated based on hazard ratios (HR) from an unsupervised Cox regression model in a unified framework. Moderation by sex of the proband or relative and age of the proband at time of suicide (<25 v. ⩾25 years) was examined.

RESULTS

Significantly elevated HRs were observed in first- (HR 3.45; 95% CI 3.12-3.82) through fifth-degree relatives (HR 1.07; 95% CI 1.02-1.12) of suicide probands. Among first-degree relatives of female suicide probands, the HR of suicide was 6.99 (95% CI 3.99-12.25) in mothers, 6.39 in sisters (95% CI 3.78-10.82), and 5.65 (95% CI 3.38-9.44) in daughters. The HR in first-degree relatives of suicide probands under 25 years at death was 4.29 (95% CI 3.49-5.26).

CONCLUSIONS

Elevated familial suicide risk in relatives of female and younger suicide probands suggests that there are unique risk groups to which prevention efforts should be directed - namely suicidal young adults and women with a strong family history of suicide.

Risk of multiple myeloma and other malignancies among first- and second-degree relatives of patients with multiple myeloma: A population-based study

Objectives

We conducted a population‐based study to assess the risk for multiple myeloma (MM) and other cancers in first‐ and second‐degree relatives of MM patients, and to investigate whether evidence of anticipation is present in familial MM.

Methods

We retrieved 24 845 first‐degree relatives and 41 008 second‐degree relatives of 7847 MM patients, and 86 984 first‐degree relatives, and 138 660 second‐degree relatives of 26 511 matched controls. A Cox model was used to assess the risk for MM and other cancers in relatives of MM patients. Anticipation was assessed by a Cox model, where all parents and offspring of MM patients were included in the risk set.

Results

In second‐degree relatives of MM patients, no overall significant association with an MM diagnosis was observed (HR 1.99; 95%CI:0.86–4.57). In parents and offspring of MM patients, we found no significant difference in the ages at onset of MM (HR 1.28;95% CI:0.50–3.28). In affected parent‐offspring pairs, we observed no statistically significant difference in overall survival between the generations (HR 0.74; 95%CI:0.20–2.69).

Conclusions

Overall, second‐degree relatives of MM patients were not associated with an increased risk for MM. Our study supports that genetic anticipation is not present in familial MM.

Valid and efficient subgroup analyses using nested case-control data

BACKGROUND

It is not uncommon for investigators to conduct further analyses of subgroups, using data collected in a nested case-control design. Since the sampling of the participants is related to the outcome of interest, the data at hand are not a representative sample of the population, and subgroup analyses need to be carefully considered for their validity and interpretation.

METHODS

We performed simulation studies, generating cohorts within the proportional hazards model framework and with covariate coefficients chosen to mimic realistic data and more extreme situations. From the cohorts we sampled nested case-control data and analysed the effect of a binary exposure on a time-to-event outcome in subgroups defined by a covariate (an independent risk factor, a confounder or an effect modifier) and compared the estimates with the corresponding subcohort estimates. Cohort analyses were performed with Cox regression, and nested case-control samples or restricted subsamples were analysed with both conditional logistic regression and weighted Cox regression.

RESULTS

For all studied scenarios, the subgroup analyses provided unbiased estimates of the exposure coefficients, with conditional logistic regression being less efficient than the weighted Cox regression.

CONCLUSIONS

For the study of a subpopulation, analysis of the corresponding subgroup of individuals sampled in a nested case-control design provides an unbiased estimate of the effect of exposure, regardless of whether the variable used to define the subgroup is a confounder, effect modifier or independent risk factor. Weighted Cox regression provides more efficient estimates than conditional logistic regression.