응용통계연구 (The Korean Journal of Applied Statistics)

  • 제21권6호
  • /
  • Pages.933-945
  • /
  • 2008
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

미완결 발병연령에 근거한 연관성 추세 검정법의 비교

Comparison of Trend Tests for Genetic Association on Censored Ages of Onset

  • 윤혜경 (가톨릭대학교 의학통계학과) ;
  • 송혜향 (가톨릭대학교 의학통계학과)
  • 발행 : 2008.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

발병연령과 이에 관련되었다고 의심되는 좌위와의 연관성이 실제로 존재하는 경우에 유전자형 정보에 따라 발병연령(age of onset) 분포의 추세가 뚜렷하게 나타난다. 그러나 연관성 검정에서 주로 채택하고 있는 발병연령의 상한연령(cutoff age)을 제한하는 표본추출은 유전자형에 따른 여러 군의 미완결 자료의 분포가 다름을 초래하게 되며, 이러한 미완결 분포 차이는 발병연령의 추세 검정에 있어 효율성을 낮추는 원인이 된다. 일반적으로 두 군의 경우에 그 대책으로써 윌콕슨(Wilcoxon) 통계량보다는 미완결 자료의 분포가 다름에 영향을 덜 받는다고 알려진 로그순위(log-rank) 통계량을 사용한다. 본 논문에서는 로그순위 통계량 적용을 유전자형에 따른 여러 군의 경우로 확장하여 Jones와 Browley (1989)에 언급된 일반화 로그순위 추세 검정통계량(generalized log-rank statistic for trend)을 제안하며, 연관성 연구에서 이 검정통계량과 여러 다른 추세 검정통계량의 효율성을 모의실험으로 알아본다.

The genetic association test on age of onset trait aims to detect the putative gene by means of linear rank tests for a significant trend of onset distributions with genotypes. However, due to the selective sampling of recruiting subjects with ages less than a pre-specified limit, the genotype groups are subject to substantially different censored distributions and thus this is one reason for the low efficiencies in the linear rank tests. In testing the equality of two survival distributions, log-rank statistic is preferred to the Wilcoxon statistic, when censored observations are nonignorable. Therefore, for more then two groups, we propose a generalized log-rank test for trend as a genetic association test. Monte Carlo studies are conducted to investigate the performances of the test statistics examined in this paper.

키워드

참고문헌

  1. 서울대병원 신경정신과 우종인 교수팀 (2002). 서울시 관악구 치매관리사업 보고서 http://www.siverweb.or.kr /html/m1 03.asp
  2. Agresti, A. (2002). Categorical Data Analysis, 2nd Edition, John Wiley & Sons, New York
  3. Armitage, P. (1955). Tests for linear trends in proportions and frequencies, Biometrics, 11, 375-386 https://doi.org/10.2307/3001775
  4. Cantor, A. B. (2003). SAS Survival Analysis Techniques for Medical Research, SAS Institute Inc
  5. Cochran, W. G. (1954). Some methods for strengthening the common $x^{2}$ tests, Biometrics, 10, 417-451 https://doi.org/10.2307/3001616
  6. Gehan, E. A. (1965). A generalized Wilcoxon test for comparing arbitrarily singly-censored samples, Biometrika, 52, 203-223 https://doi.org/10.1093/biomet/52.1-2.203
  7. Haseman, J. K. and Elston, R. C. (1972). The investigation of linkage between a quantitative trait and a marker locus, Behavior Genetics, 2, 3-19 https://doi.org/10.1007/BF01066731
  8. Jennrich, R. I. (1983). A note on the behaviour of the log rank permutation test under unequal censoring, Biometrika, 70, 133-137 https://doi.org/10.1093/biomet/70.1.133
  9. Jonckheere, A. R. (1954). A distribution-free k-sample test against ordered alternatives, Biometrika, 41, 133-145 https://doi.org/10.1093/biomet/41.1-2.133
  10. Jones, M. P. and Crowley, J. (1989). A general class of nonparametric tests for survival analysis, Biometrics, 45, 157-170 https://doi.org/10.2307/2532042
  11. Kendall, M. G. (1948). Rank Correlation Methods, Griffn, London
  12. Lagakos, S. W. (1979). General right censoring and its impact on the analysis of survival data, Biometrics, 35, 139-156 https://doi.org/10.2307/2529941
  13. Le, C. T., Grambsch, P. M. and Louis, T. A. (1994). Association between survival time and ordinal covariates, Biometrics, 50, 213-219 https://doi.org/10.2307/2533211
  14. Lehmann, E. L. (1975). Nonparametrics: Statistical Methods Based on Ranks, Mcgraw-Hill, San Francisco
  15. Newswire (2007). 10월 10일 기사, 효자한의원, 고혈압. 당뇨 동시치료 결과발표, http://www.newswire.co.kr/ ?job=news&n0=287746
  16. Patel, K. M. and Hoel, D. G. (1973). A generalized Jonckheere k-sample test against ordered alternatives when observations are subject to arbitrary right censorship, Communications in Statistics - Simulation and Computation, 2, 373-380 https://doi.org/10.1080/03610917308548261
  17. Prentice, R. L. and Marek, P. (1979). A qualitative discrepancy between censored data rank tests, Biometrics, 35, 861-868 https://doi.org/10.2307/2530120
  18. Samani, N. J., Erdmann, J., Hall, A. S., Hengstenberg, C., Mangino, M., Mayer, B., Dixon, R. J., Meitinger, T., Braund, P., Wichmann, E., Barrett, J. H., Konig, I. R., Stevens, S. E., Szymczak, S., Tregouet, D. A., Iles, M. M., Pahlke, F., Pollard, H., Lieb, W., Cambien, F., Fischer, M., Ouwehand, W., Blankenberg, S., Balmforth, A. J., Baessler, A., Ball, S. G., Strom, T. M., Braenne, I., Gieger, C., Deloukas, P., Tobin, M. D., Ziegler, A., Thompson, J. R. and Schunkert, H. (2007). Genome-wide association analysis of coronary artery disease, The New England Journal of Medicine, 357, 443-453 https://doi.org/10.1056/NEJMoa072366
  19. Tarone, R. E. (1975). Tests for trend in life table analysis, Biometrika, 62, 679-690 https://doi.org/10.1093/biomet/62.3.679
  20. Terpstra, T. J. (1952). The asymptotic normality and consistency of Kendall's test against trend, when ties are present in one ranking, Indagationes Mathematicae, 14, 327-333
  21. Zhu, X., Olson, J. M., Schnell, A. H., Elston, R. C. (1997). Model-free age-of-onset methods applied to the linkage of bipolar disorder, Genetic Epidemiology, 14, 711-716 https://doi.org/10.1002/(SICI)1098-2272(1997)14:6<711::AID-GEPI27>3.0.CO;2-S