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Article: A hierarchical frailty model applied to two-generation melanoma data

TitleA hierarchical frailty model applied to two-generation melanoma data
Authors
KeywordsStatistics
Statistics for Life Sciences, Medicine and Health Sciences
Quality Control, Reliability, Safety and Risk
Statistics for Business, Economics, Mathematical Finance and Insurance
Operation Research and Decision Theory
Issue Date2010
PublisherSpringer Netherlands
Citation
Lifetime Data Analysis, 2010, v. 17, n. 3, p. 445-460 How to Cite?
AbstractWe present a hierarchical frailty model based on distributions derived from non-negative Lévy processes. The model may be applied to data with several levels of dependence, such as family data or other general clusters, and is an alternative to additive frailty models. We present several parametric examples of the model, and properties such as expected values, variance and covariance. The model is applied to a case-cohort sample of age at onset for melanoma from the Swedish Multi-Generation Register, organized in nuclear families of parents and one or two children. We compare the genetic component of the total frailty variance to the common environmental term, and estimate the effect of birth cohort and gender. © 2010 The Author(s).
Persistent Identifierhttp://hdl.handle.net/10722/145073
ISSN
2015 Impact Factor: 0.81
2015 SCImago Journal Rankings: 0.717
ISI Accession Number ID
Funding AgencyGrant Number
Statistics for Innovation (sfi)2460739
Funding Information:

We are grateful to Professor Yudi Pawitan at Karolinska Institutet in Stockholm, Sweden, for getting access to the melanoma data and discussions on the paper. We also wish to thank the associate editor and the referee for valuable comments. Marion Haugen was supported by Statistics for Innovation (sfi)2<INF>,</INF> project number 460739.

References

Aalen OO, Hjort NL (2002) Frailty models that yield proportional hazards. Stat Probab Lett 58: 335–342 doi: 10.1016/S0167-7152(02)00090-1

Aalen OO, Borgan Ø, Gjessing HK (2008) Survival and event history analysis. A process point of view. Springer, New York doi: 10.1007/978-0-387-68560-1

Ford D, Bliss JM, Swerdlow AJ et al (1995) Risk of cutaneous melanoma associated with a family history of the disease: the International Melanoma Analysis Group (IMAGE). Int J Cancer 62: 377–381 doi: 10.1002/ijc.2910620403

Gjessing HK, Lie RT (2008) Biometrical modelling in genetics: are complex traits too complex?. Stat Methods Med Res 17: 75–96 doi: 10.1177/0962280207081241

Gjessing HK, Aalen OO, Hjort NL (2003) Frailty models based on Lévy processes. Adv Appl Probab 35: 532–550 doi: 10.1239/aap/1051201659

Hemminki K, Zhang H, Czene K (2003) Familial and attributable risks in cutaneous melanoma: effects of proband and age. J Invest Dermatol 120: 217–223 doi: 10.1046/j.1523-1747.2003.12041.x

Hougaard P (2000) Analysis of multivariate survival data. Springer, New York doi: 10.1007/978-1-4612-1304-8

Korsgaard IR, Andersen AH (1998) The additive genetic gamma frailty model. Scand J Stat 25: 255–269 doi: 10.1111/1467-9469.00102

Moger TA, Aalen OO (2005) A distribution for multivariate frailty based on the compound Poisson distribution with random scale. Lifetime Data Anal 11: 41–59 doi: 10.1007/s10985-004-5639-z

Moger TA, Pawitan Y, Borgan Ø (2008) Case-cohort methods for survival data on families from routine registers. Stat Med 27: 1062–1074 doi: 10.1002/sim.3004

Pawitan Y, Reilly M, Nilsson E et al (2004) Estimation of genetic and environmental factors for binary traits using family data. Stat Med 23: 449–465 doi: 10.1002/sim.1603

Petersen JH (1998) An additive frailty model for correlated life times. Biometrics 54: 646–661 doi: 10.2307/3109771

Ripatti S, Palmgren J (2000) Estimation of multivariate frailty models using penalized partial likelihood. Biometrics 56: 1016–1022 doi: 10.1111/j.0006-341X.2000.01016.x

Thörn M, Pontén F, Johansson AM et al (1998) Rapid increase in diagnosis of cutaneous melanoma in situ in Sweden, 1968–1992. Cancer Detect Prev 22: 430–437 doi: 10.1046/j.1525-1500.1998.00052.x

Yau KKW (2001) Multilevel models for survival analysis with random effects. Biometrics 57: 96–102 doi: 10.1111/j.0006-341X.2001.00096.x

 

DC FieldValueLanguage
dc.contributor.authorMoger, TAen_US
dc.contributor.authorHaugen, Men_US
dc.contributor.authorYip, BHKen_US
dc.contributor.authorGjessing, HKen_US
dc.contributor.authorBorgan, Øen_US
dc.date.accessioned2012-02-21T05:43:53Z-
dc.date.available2012-02-21T05:43:53Z-
dc.date.issued2010en_US
dc.identifier.citationLifetime Data Analysis, 2010, v. 17, n. 3, p. 445-460en_US
dc.identifier.issn1380-7870en_US
dc.identifier.urihttp://hdl.handle.net/10722/145073-
dc.description.abstractWe present a hierarchical frailty model based on distributions derived from non-negative Lévy processes. The model may be applied to data with several levels of dependence, such as family data or other general clusters, and is an alternative to additive frailty models. We present several parametric examples of the model, and properties such as expected values, variance and covariance. The model is applied to a case-cohort sample of age at onset for melanoma from the Swedish Multi-Generation Register, organized in nuclear families of parents and one or two children. We compare the genetic component of the total frailty variance to the common environmental term, and estimate the effect of birth cohort and gender. © 2010 The Author(s).en_US
dc.languageengen_US
dc.publisherSpringer Netherlandsen_US
dc.relation.ispartofLifetime Data Analysisen_US
dc.rightsThe Author(s)en_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_US
dc.subjectStatisticsen_US
dc.subjectStatistics for Life Sciences, Medicine and Health Sciencesen_US
dc.subjectQuality Control, Reliability, Safety and Risken_US
dc.subjectStatistics for Business, Economics, Mathematical Finance and Insuranceen_US
dc.subjectOperation Research and Decision Theoryen_US
dc.titleA hierarchical frailty model applied to two-generation melanoma dataen_US
dc.typeArticleen_US
dc.identifier.openurlhttp://library.hku.hk:4551/resserv?sid=springerlink&genre=article&atitle=A hierarchical frailty model applied to two-generation melanoma data&title=Lifetime Data Analysis&issn=13807870&date=2011-07-01&volume=17&issue=3& spage=445&authors=Tron Anders Moger, Marion Haugen, Benjamin H. K. Yip, <i>et al.</i>en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1007/s10985-010-9188-3en_US
dc.identifier.pmid21046240-
dc.identifier.scopuseid_2-s2.0-79958272199en_US
dc.relation.referencesAalen OO, Hjort NL (2002) Frailty models that yield proportional hazards. Stat Probab Lett 58: 335–342en_US
dc.relation.referencesdoi: 10.1016/S0167-7152(02)00090-1en_US
dc.relation.referencesAalen OO, Borgan Ø, Gjessing HK (2008) Survival and event history analysis. A process point of view. Springer, New Yorken_US
dc.relation.referencesdoi: 10.1007/978-0-387-68560-1en_US
dc.relation.referencesFord D, Bliss JM, Swerdlow AJ et al (1995) Risk of cutaneous melanoma associated with a family history of the disease: the International Melanoma Analysis Group (IMAGE). Int J Cancer 62: 377–381en_US
dc.relation.referencesdoi: 10.1002/ijc.2910620403en_US
dc.relation.referencesGjessing HK, Lie RT (2008) Biometrical modelling in genetics: are complex traits too complex?. Stat Methods Med Res 17: 75–96en_US
dc.relation.referencesdoi: 10.1177/0962280207081241en_US
dc.relation.referencesGjessing HK, Aalen OO, Hjort NL (2003) Frailty models based on Lévy processes. Adv Appl Probab 35: 532–550en_US
dc.relation.referencesdoi: 10.1239/aap/1051201659en_US
dc.relation.referencesHemminki K, Zhang H, Czene K (2003) Familial and attributable risks in cutaneous melanoma: effects of proband and age. J Invest Dermatol 120: 217–223en_US
dc.relation.referencesdoi: 10.1046/j.1523-1747.2003.12041.xen_US
dc.relation.referencesHougaard P (2000) Analysis of multivariate survival data. Springer, New Yorken_US
dc.relation.referencesdoi: 10.1007/978-1-4612-1304-8en_US
dc.relation.referencesKorsgaard IR, Andersen AH (1998) The additive genetic gamma frailty model. Scand J Stat 25: 255–269en_US
dc.relation.referencesdoi: 10.1111/1467-9469.00102en_US
dc.relation.referencesMoger TA, Aalen OO (2005) A distribution for multivariate frailty based on the compound Poisson distribution with random scale. Lifetime Data Anal 11: 41–59en_US
dc.relation.referencesdoi: 10.1007/s10985-004-5639-zen_US
dc.relation.referencesMoger TA, Pawitan Y, Borgan Ø (2008) Case-cohort methods for survival data on families from routine registers. Stat Med 27: 1062–1074en_US
dc.relation.referencesdoi: 10.1002/sim.3004en_US
dc.relation.referencesPawitan Y, Reilly M, Nilsson E et al (2004) Estimation of genetic and environmental factors for binary traits using family data. Stat Med 23: 449–465en_US
dc.relation.referencesdoi: 10.1002/sim.1603en_US
dc.relation.referencesPetersen JH (1998) An additive frailty model for correlated life times. Biometrics 54: 646–661en_US
dc.relation.referencesdoi: 10.2307/3109771en_US
dc.relation.referencesRipatti S, Palmgren J (2000) Estimation of multivariate frailty models using penalized partial likelihood. Biometrics 56: 1016–1022en_US
dc.relation.referencesdoi: 10.1111/j.0006-341X.2000.01016.xen_US
dc.relation.referencesThörn M, Pontén F, Johansson AM et al (1998) Rapid increase in diagnosis of cutaneous melanoma in situ in Sweden, 1968–1992. Cancer Detect Prev 22: 430–437en_US
dc.relation.referencesdoi: 10.1046/j.1525-1500.1998.00052.xen_US
dc.relation.referencesYau KKW (2001) Multilevel models for survival analysis with random effects. Biometrics 57: 96–102en_US
dc.relation.referencesdoi: 10.1111/j.0006-341X.2001.00096.xen_US
dc.identifier.volume17en_US
dc.identifier.issue3en_US
dc.identifier.spage445en_US
dc.identifier.epage460en_US
dc.identifier.eissn1572-9249en_US
dc.identifier.isiWOS:000291486700007-
dc.description.otherSpringer Open Choice, 21 Feb 2012en_US
dc.identifier.citeulike8240092-

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