Power and Sample Size Calculations for SNP Association Studies With Censored Time-to-Event Outcomes
For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single‐nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time‐to‐event outcomes such as time of death...
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| Published in | Genetic epidemiology Vol. 36; no. 6; pp. 538 - 548 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Blackwell Publishing Ltd
01.09.2012
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0741-0395 1098-2272 1098-2272 |
| DOI | 10.1002/gepi.21645 |
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| Abstract | For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single‐nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time‐to‐event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open‐source software package to design SNP association studies with survival outcomes are provided. |
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| AbstractList | For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided. For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating Single Nucleotide Polymorphisms associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided. For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided.For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms (SNPs) associated with clinical outcomes. The primary clinical endpoint for many of these studies are time-to-event outcomes such as time of death or disease progression which are subject to censoring mechanisms. The Cox score test can be readily employed to test the association between a SNP and the outcome of interest. In addition to the effect and sample size, and censoring distribution, the power of the test will depend on the underlying genetic risk model and the distribution of the risk allele. We propose a rigorous account for power and sample size calculations under a variety of genetic risk models without resorting to the commonly used contiguous alternative assumption. Practical advice along with an open-source software package to design SNP association studies with survival outcomes are provided. |
| Author | Jung, Sin-Ho Owzar, Kouros Cox, Nancy Li, Zhiguo |
| AuthorAffiliation | 2 Section of Genetic Medicine, Department of Medicine and Department of Human Genetics, University of Chicago 1 Department of Biostatistics and Bioinformatics, Duke University |
| AuthorAffiliation_xml | – name: 1 Department of Biostatistics and Bioinformatics, Duke University – name: 2 Section of Genetic Medicine, Department of Medicine and Department of Human Genetics, University of Chicago |
| Author_xml | – sequence: 1 givenname: Kouros surname: Owzar fullname: Owzar, Kouros email: kouros.owzar@duke.edu organization: Department of Biostatistics and Bioinformatics, Duke University, North Carolina, Durham – sequence: 2 givenname: Zhiguo surname: Li fullname: Li, Zhiguo organization: Department of Biostatistics and Bioinformatics, Duke University, North Carolina, Durham – sequence: 3 givenname: Nancy surname: Cox fullname: Cox, Nancy organization: Section of Genetic Medicine, Department of Medicine and Department of Human Genetics, University of Chicago, Illinois, Chicago – sequence: 4 givenname: Sin-Ho surname: Jung fullname: Jung, Sin-Ho organization: Department of Biostatistics and Bioinformatics, Duke University, North Carolina, Durham |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/22685040$$D View this record in MEDLINE/PubMed |
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| References_xml | – reference: Spencer C, Su Z, Donnelly P, Marchini J. 2009. Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5(5): e1000477. – reference: Eddelbuettel D, Francois R. 2011. Rcpp: seamless R and C++ integration. J Stat Softw 40(8): 1-18. – reference: De La Vega F, Gordon D, Su X, Scafe C, Isaac H, Gilbert D, Spier E. 2005. Power and sample size calculations for genetic case/control studies using gene-centric SNP maps: application to human chromosomes 6, 21, and 22 in three populations. Hum Hered 60(1): 43-60. – reference: Jung S. 2005. Sample size for FDR-control in microarray data analysis. Bioinformatics 21(14): 3097--3104. – reference: Skol A, Scott L, Abecasis G, Boehnke M. 2006. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38(2): 209-213. – reference: Hsieh F, Lavori P. 2000. Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates. Control Clin Trials 21(6): 552-560. – reference: Kelly W, Halabi S, Carducci M, George D, Mahoney J, Stadler W, Morris M, Kantoff P, Monk JP, Kaplan E, Vogelzang NJ, Small, EJ. 2012. Randomized, double-blind, placebo-controlled phase III trial comparing docetaxel, and prednisone with or without bevacizumab in men with metastatic castration-resistant prostate cancer: CALGB 90401. J Clin Oncol 30(13):1534-1540. – reference: R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. – reference: Galassi M, Davies J, Theiler J, Gough B, Jungman P, Alken P, Booth M, Rossi F. 2009. GNU Scientific Library Reference Manual (3rd ed.) Bristol, UK: Network Theory Ltd. – reference: Schoenfeld D. 1983. Sample-size formula for the proportional-hazards regression model. 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| Snippet | For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single‐nucleotide polymorphisms... For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating single-nucleotide polymorphisms... For many clinical studies in cancer, germline DNA is prospectively collected for the purpose of discovering or validating Single Nucleotide Polymorphisms... |
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| SubjectTerms | Cancer censoring pharmacogenomics Cox score test Deoxyribonucleic acid DNA Genetic Predisposition to Disease genetic risk Genome-Wide Association Study Humans Models, Genetic Polymorphism Polymorphism, Single Nucleotide Proportional Hazards Models Random Allocation Research Design Sample Size Single-nucleotide polymorphism SNP association study Software Survival Rate |
| Title | Power and Sample Size Calculations for SNP Association Studies With Censored Time-to-Event Outcomes |
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