Inverse Probability Weighted Cox Regression for Doubly Truncated Data
Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models h...
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| Published in | Biometrics Vol. 74; no. 2; pp. 481 - 487 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley-Blackwell
01.06.2018
Blackwell Publishing Ltd |
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| Online Access | Get full text |
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| Abstract | Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. |
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| AbstractList | Summary Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. Summary Doubly truncated data arise when event times are observed only if they fall within subject‐specific, possibly random, intervals. While non‐parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this paper, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson’s disease. |
| Author | Betensky, Rebecca A. Simon, David K. Mandel, Micha de Uña-Álvarez, Jacobo |
| AuthorAffiliation | 2 Department of Statistics and OR and Center for Biomedical Research (CINBIO), University of Vigo, Vigo, Spain 3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 1 Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel 4 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA |
| AuthorAffiliation_xml | – name: 1 Department of Statistics, The Hebrew University of Jerusalem, Jerusalem, Israel – name: 4 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA – name: 2 Department of Statistics and OR and Center for Biomedical Research (CINBIO), University of Vigo, Vigo, Spain – name: 3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA |
| Author_xml | – sequence: 1 givenname: Micha surname: Mandel fullname: Mandel, Micha – sequence: 2 givenname: Jacobo surname: de Uña-Álvarez fullname: de Uña-Álvarez, Jacobo – sequence: 3 givenname: David K. surname: Simon fullname: Simon, David K. – sequence: 4 givenname: Rebecca A. surname: Betensky fullname: Betensky, Rebecca A. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28886206$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1017/CBO9780511802843 10.1080/01621459.1999.10474187 10.2307/2532891 10.1016/j.stamet.2015.11.002 10.1089/ars.2008.2241 10.1080/01621459.2012.746073 10.1093/hmg/ddm159 10.1080/01621459.1989.10478780 10.1016/j.csda.2014.03.017 10.1016/j.cell.2011.02.010 10.1007/s10985-013-9287-z 10.1093/biomet/64.2.225 10.1007/s10463-008-0192-2 10.1093/biomet/asp026 10.1198/jasa.2009.tm08614 10.1214/aos/1176346585 10.1007/s00180-012-0318-0 10.1186/1471-2350-12-69 10.1111/j.2517-6161.1976.tb01597.x 10.18637/jss.v037.i07 10.1093/biomet/asr072 10.1002/sim.3938 10.1111/j.1541-0420.2009.01287.x 10.2307/3316134 10.1016/j.spl.2008.09.026 10.1214/aos/1176346584 |
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| Keywords | U statistic Biased data Right truncation Inverse weighting |
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| Snippet | Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods... Summary Doubly truncated data arise when event times are observed only if they fall within subject‐specific, possibly random, intervals. While non‐parametric... Summary Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric... Doubly truncated data arise when event times are observed only if they fall within subject‐specific, possibly random, intervals. While non‐parametric methods... |
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| SubjectTerms | Age of Onset Biased data BIOMETRIC METHODOLOGY: DISCUSSION PAPER Biometry - methods Humans Inverse weighting Movement disorders Neurodegenerative diseases Parkinson Disease - genetics Parkinson's disease Polymorphism, Single Nucleotide Probability Proportional Hazards Models Regression Analysis Regression models Right truncation Single-nucleotide polymorphism Software Statistical analysis U statistic |
| Title | Inverse Probability Weighted Cox Regression for Doubly Truncated Data |
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