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Differences in survival for patients with familial and sporadic cancer.

https://arctichealth.org/en/permalink/ahliterature282261
Source
Int J Cancer. 2017 Feb 01;140(3):581-590
Publication Type
Article
Date
Feb-01-2017
Author
Myeongjee Lee
Marie Reilly
Linda Sofie Lindström
Kamila Czene
Source
Int J Cancer. 2017 Feb 01;140(3):581-590
Date
Feb-01-2017
Language
English
Publication Type
Article
Keywords
Female
Humans
Male
Middle Aged
Mothers
Neoplasms - mortality
Proportional Hazards Models
Registries
Risk factors
Siblings
Sweden
Abstract
Family history of cancer is a well-known risk factor but the role of family history in survival is less clear. The aim of this study was to investigate the association between family history and cancer survival for the common cancers in Sweden. Using the Swedish population-based registers, patients diagnosed with the most common cancers were followed for cancer-specific death during 1991-2010. We used multivariate proportional hazards (Cox) regression models to contrast the survival of patients with a family history of cancer (individuals whose parent or sibling had a concordant cancer) to the survival of patients without a family history. Family history of cancer had a modest protective effect on survival for breast cancer (hazard ratio (HR)?=?0.88, 95% confidence interval (95% CI)?=?0.81 to 0.96) and prostate cancer (HR?=?0.82, 95% CI?=?0.75 to 0.90). In contrast, family history of cancer was associated with worse survival for nervous system cancers (HR?=?1.24, 95% CI?=?1.05 to 1.47) and ovarian cancer (HR?=?1.20, 95% CI?=?1.01 to 1.43). Furthermore, the poorer survival for ovarian cancer was consistent with a higher FIGO stage and a greater proportion of more aggressive tumors of the serous type. The better survival for patients with a family history of breast and prostate cancer may be due to medical surveillance of family members. The poor survival for ovarian cancer patients with an affected mother or sister is multifactorial, suggesting that these cancers are more aggressive than their sporadic counterparts.
PubMed ID
27759937 View in PubMed
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A unified model for estimating and testing familial aggregation.

https://arctichealth.org/en/permalink/ahliterature106536
Source
Stat Med. 2013 Dec 30;32(30):5353-65
Publication Type
Article
Date
Dec-30-2013
Author
Myeongjee Lee
Paola Rebora
Maria Grazia Valsecchi
Kamila Czene
Marie Reilly
Author Affiliation
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden.
Source
Stat Med. 2013 Dec 30;32(30):5353-65
Date
Dec-30-2013
Language
English
Publication Type
Article
Keywords
Adult
Family
Female
Genetic Predisposition to Disease
Humans
Incidence
Leukemia - genetics
Lymphoma, Non-Hodgkin - genetics
Male
Pedigree
Proportional Hazards Models
Risk
Sweden
Abstract
Investigations of familial aggregation of disease can provide important clues for genetic mechanisms, and many such studies have been published in the epidemiological literature using various statistical methods. We developed a unified model for familial risk by extending a Cox regression model to enable estimation of the detailed effects of kinship. By appropriate parameterisation of the model, we show how the risks to all specific first-degree kinships can be estimated and formally compared using simple interaction terms and how the model can be extended to accommodate higher-degree relatives. The correlation due to observations from family members and from the potential for repeated observations is accommodated by a robust sandwich variance estimator or a bootstrap estimate. Hazard ratios for different kinships are formally compared using a robust Wald test. We illustrate the method with applications to studies of adult leukemia and non-Hodgkin's lymphoma in the Swedish population and display our results on a pedigree diagram. Our estimates are consistent with published work that used simpler stratified methods, and our model enabled the detection of a number of statistically significant effects of kinship. The recognition of such kindred-specific disease risk could be a first step in the design of more informative genetic biomarker studies.
PubMed ID
24151173 View in PubMed
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