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A conditional predictive p-value to compare a multinomial with an overdispersed multinomial in the analysis of T-cell populations.


ABSTRACT: Immunological experiments that record primary molecular sequences of T-cell receptors produce moderate to high-dimensional categorical data, some of which may be subject to extra-multinomial variation caused by technical constraints of cell-based assays. Motivated by such experiments in melanoma research, we develop a statistical procedure for testing the equality of two discrete populations, where one population delivers multinomial data and the other is subject to a specific form of overdispersion. The procedure computes a conditional-predictive p-value by splitting the data set into two, obtaining a predictive distribution for one piece given the other, and using the observed predictive ordinate to generate a p-value. The procedure has a simple interpretation, requires fewer modeling assumptions than would be required of a fully Bayesian analysis, and has reasonable operating characteristics as evidenced empirically and by asymptotic analysis.

SUBMITTER: Pei Q 

PROVIDER: S-EPMC3862212 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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A conditional predictive p-value to compare a multinomial with an overdispersed multinomial in the analysis of T-cell populations.

Pei Qinglin Q   Zuleger Cindy L CL   Macklin Michael D MD   Albertini Mark R MR   Newton Michael A MA  

Biostatistics (Oxford, England) 20131004 1


Immunological experiments that record primary molecular sequences of T-cell receptors produce moderate to high-dimensional categorical data, some of which may be subject to extra-multinomial variation caused by technical constraints of cell-based assays. Motivated by such experiments in melanoma research, we develop a statistical procedure for testing the equality of two discrete populations, where one population delivers multinomial data and the other is subject to a specific form of overdisper  ...[more]

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