A Practical Guide to Family Studies with Lifetime Data

Department of Public Health

A Practical Guide to Family Studies with Lifetime Data

Research output: Contribution to journal › Journal article › Research › peer-review

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A Practical Guide to Family Studies with Lifetime Data. / Scheike, Thomas H.; Holst, Klaus Kähler.

In: Annual Review of Statistics and Its Application, Vol. 9, 2022, p. 47-69.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Scheike, TH & Holst, KK 2022, 'A Practical Guide to Family Studies with Lifetime Data', Annual Review of Statistics and Its Application, vol. 9, pp. 47-69. https://doi.org/10.1146/annurev-statistics-040120-024253

APA

Scheike, T. H., & Holst, K. K. (2022). A Practical Guide to Family Studies with Lifetime Data. Annual Review of Statistics and Its Application, 9, 47-69. https://doi.org/10.1146/annurev-statistics-040120-024253

Vancouver

Scheike TH, Holst KK. A Practical Guide to Family Studies with Lifetime Data. Annual Review of Statistics and Its Application. 2022;9:47-69. https://doi.org/10.1146/annurev-statistics-040120-024253

Author

Scheike, Thomas H. ; Holst, Klaus Kähler. / A Practical Guide to Family Studies with Lifetime Data. In: Annual Review of Statistics and Its Application. 2022 ; Vol. 9. pp. 47-69.

Bibtex

@article{a3cedb8803cf47d4b1db192029b5b842,

title = "A Practical Guide to Family Studies with Lifetime Data",

abstract = "Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death mdash that is, competing risks mdash and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model.",

author = "Scheike, {Thomas H.} and Holst, {Klaus K{\"a}hler}",

year = "2022",

doi = "10.1146/annurev-statistics-040120-024253",

language = "English",

volume = "9",

pages = "47--69",

journal = "Annual Review of Statistics and Its Application",

issn = "2326-8298",

publisher = "Annual Reviews, inc.",

}

RIS

TY - JOUR

T1 - A Practical Guide to Family Studies with Lifetime Data

AU - Scheike, Thomas H.

AU - Holst, Klaus Kähler

PY - 2022

Y1 - 2022

N2 - Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death mdash that is, competing risks mdash and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model.

AB - Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death mdash that is, competing risks mdash and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model.

U2 - 10.1146/annurev-statistics-040120-024253

DO - 10.1146/annurev-statistics-040120-024253

M3 - Journal article

AN - SCOPUS:85126561155

VL - 9

SP - 47

EP - 69

JO - Annual Review of Statistics and Its Application

JF - Annual Review of Statistics and Its Application

SN - 2326-8298

ER -

ID: 307731427