%0 Journal Article %J Biometrics %D 2014 %T Calibration using Constrained Smoothing with Application to Mass Spectrometry Data %A Feng, X. %A Sedransk, N. %A Xia, J-Q %B Biometrics %V 70 %P 398-408 %G eng %U http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291541-0420 %& 398 %R 10.1111/biom.12135 %0 Journal Article %J PLoS1 %D 2011 %T Variance Component Analysis of a Multi-Site Study of Multiple Reaction Monitoring Measurements of Peptides and Proteins in Human Plasma %A Xia, J. %A Sedransk, N. %A Feng, X. %K analysis of Variance %K blood plasma %K experimental design %K Instrument calibration %K linear regression analysis %K peptides %K plasma proteins %K proteomic databases %X

In the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.

%B PLoS1 %V 6 %P e14590 %G eng %R 10.1371/journal.pone.0014590 %0 Book Section %B Case Studies in Environmental Statistics %D 1998 %T Categorical Exposure-Response Regression Analysis of Toxicology Experiments %A Xie, Minge %A Simpson, Douglas %E Nychka, Douglas %E Piegorsch, Walter W. %E Lawrence H. Cox %X

In the mid-1980s, an accident at the Union Carbide pesticides plant in Bhopal, India released the toxic gas methylisocyanate (MIC) in that densely populated region, killing more than 4000 people and injuring 500,000 others. Even today, many people in Bhopal are affected by illnesses related to that earlier exposure. This notorious industrial disaster not only forced scientists to pay greater attention to identifying and handling of hazardous chemicals but also prompted greater awareness of those common industrial products that contain hazard pollutants.

%B Case Studies in Environmental Statistics %S Lecture Notes in Statistics %I Springer US %V 132 %P 121-141 %@ 978-0-387-98478-0 %G eng %U http://dx.doi.org/10.1007/978-1-4612-2226-2_7 %R 10.1007/978-1-4612-2226-2_7 %0 Book Section %B Modelling Longitudinal and Spatially Correlated Data %D 1997 %T Scaled Link Functions for Heterogeneous Ordinal Response Data* %A Xie, Minge %A Simpson, Douglas G %A Carroll, Raymond J. %E Gregoire, Timothy G. %E Brillinger, David R. %E Diggle, PeterJ. %E Russek-Cohen, Estelle %E Warren, William G. %E Wolfinger, Russell D. %K Aggregated observations %K Generalized likelihood inference %K Marginal modeling approach %K Ordinal regression %X

This paper describes a class ordinal regression models in which the link function has scale parameters that may be estimated along with the regression parameters. One motivation is to provide a plausible model for group level categorical responses. In this case a natural class of scaled link functions is obtained by treating the group level responses as threshold averages of possible correlated latent individual level variables. We find scaled link functions also arise naturally in other circumstances. Our methodology is illustrated through environmental risk assessment data where (correlated) individual level responses and group level responses are mixed.

%B Modelling Longitudinal and Spatially Correlated Data %S Lecture Notes in Statistics %I Springer New York %V 122 %P 23-36 %@ 978-0-387-98216-8 %G eng %U http://dx.doi.org/10.1007/978-1-4612-0699-6_3 %R 10.1007/978-1-4612-0699-6_3 %0 Journal Article %J Journal of Agricultural Biological and Environmental Statistics %D 1996 %T Interval Censoring And Marginal Analysis In Ordinal Regression %A Simpson, Douglas G %A Carroll, Raymond %A Xie, Minge %K categorical data %K categorical response %K environmental statistics %X

This paper develops methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in toxicological risk assessment. Responses are scored on an ordinal severity scale, but not all responses can be scored completely. For instance, in a mortality study, information on nonfatal but adverse outcomes may be missing. In order to address possible within–study correlations we develop a generalized estimating approach to the problem, with appropriate adjustments to uncertainty statements. We develop expressions relating parameters of the implied marginal model to the parameters of a conditional model with random effects, and, in a special case, we note an interesting equivalence between conditional and marginal modeling of ordinal responses. We illustrate the methodology in an analysis of a toxicological data-base.

%B Journal of Agricultural Biological and Environmental Statistics %V 4 %G eng %R 10.2307/1400524