A note on comparing exposure data to a regulatory limit in the presence of unexposed and a limit of detection

被引:9
作者
Chu, HT [1 ]
Nie, L
机构
[1] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Epidemiol, Baltimore, MD 21205 USA
[2] Georgetown Univ, Dept Biostat & Biomath, Washington, DC 20057 USA
关键词
mixture models; detection limit; sample size; power; left censoring;
D O I
10.1002/bimj.200510174
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In some occupational health studies, observations occur in both exposed and unexposed individuals. If the levels of all exposed individuals have been detected, a two-part zero-inflated log-normal model is usually recommended, which assumes that the data has a probability mass at zero for unexposed individuals and a continuous response for values greater than zero for exposed individuals. However, many quantitative exposure measurements are subject to left censoring due to values falling below assay detection limits. A zero-inflated log-normal mixture model is suggested in this situation since unexposed zeros are not distinguishable from those exposed with values below detection limits. In the context of this mixture distribution, the information contributed by values falling below a fixed detection limit is used only to estimate the probability of unexposed. We consider sample size and statistical power calculation when comparing the median of exposed measurements to a regulatory limit. We calculate the required sample size for the data presented in a recent paper comparing the benzene TWA exposure data to a regulatory occupational exposure limit. A simulation study is conducted to investigate the performance of the proposed sample size calculation methods.
引用
收藏
页码:880 / 887
页数:8
相关论文
共 21 条
[1]  
Bohning D, 1998, BIOMETRICAL J, V40, P833, DOI 10.1002/(SICI)1521-4036(199811)40:7<833::AID-BIMJ833>3.0.CO
[2]  
2-O
[3]  
CHU H, 2005, APPL STAT, V54, P831
[4]   Assessing the effect of interventions in the context of mixture distributions with detection limits [J].
Chu, HT ;
Kensler, TW ;
Muñoz, A .
STATISTICS IN MEDICINE, 2005, 24 (13) :2053-2067
[5]   Sample size calculations for the mean in a two component nonstandard mixture distribution [J].
Chu, HT ;
Ren, SQ ;
Cole, SR .
BIOMETRICAL JOURNAL, 2004, 46 (05) :565-571
[6]   Hierarchical Bayesian analysis of correlated zero-inflated count data [J].
Dagne, GA .
BIOMETRICAL JOURNAL, 2004, 46 (06) :653-663
[7]  
Francis, 1987, APPL IND HYG, V2, P155, DOI 10.1080/08828032.1987.10390543
[8]   Zero-inflated Poisson and binomial regression with random effects: A case study [J].
Hall, DB .
BIOMETRICS, 2000, 56 (04) :1030-1039
[9]   Power and sample size requirements for two-part models [J].
Lachenbruch, PA .
STATISTICS IN MEDICINE, 2001, 20 (08) :1235-1238
[10]   Comparisons of two-part models with competitors [J].
Lachenbruch, PA .
STATISTICS IN MEDICINE, 2001, 20 (08) :1215-1234