Document Detail


Statistical methods for estimating doubling time in in vitro cell growth.
MedLine Citation:
PMID:  9156345     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Doubling time has been widely used to represent the growth pattern of cells. A traditional method for finding the doubling time is to apply gray-scaled cells, where the logarithmic transformed scale is used. As an alternative statistical method, the log-linear model was recently proposed, for which actual cell numbers are used instead of the transformed gray-scaled cells. In this paper, I extend the log-linear model and propose the extended log-linear model. This model is designed for extra-Poisson variation, where the log-linear model produces the less appropriate estimate of the doubling time. Moreover, I compare statistical properties of the gray-scaled method, the log-linear model, and the extended log-linear model. For this purpose, I perform a Monte Carlo simulation study with three data-generating models: the additive error model, the multiplicative error model, and the overdispersed Poisson model. From the simulation study, I found that the gray-scaled method highly depends on the normality assumption of the gray-scaled cells; hence, this method is appropriate when the error model is multiplicative with the log-normally distributed errors. However, it is less efficient for other types of error distributions, especially when the error model is additive or the errors follow the Poisson distribution. The estimated standard error for the doubling time is not accurate in this case. The log-linear model was found to be efficient when the errors follow the Poisson distribution or nearly Poisson distribution. The efficiency of the log-linear model was decreased accordingly as the overdispersion increased, compared to the extended log-linear model. When the error model is additive or multiplicative with Gamma-distributed errors, the log-linear model is more efficient than the gray-scaled method. The extended log-linear model performs well overall for all three data-generating models. The loss of efficiency of the extended log-linear model is observed only when the error model is multiplicative with log-normally distributed errors, where the gray-scaled method is appropriate. However, the extended log-linear model is more efficient than log-linear model in this case.
Authors:
D K Kim
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  In vitro cellular & developmental biology. Animal     Volume:  33     ISSN:  1071-2690     ISO Abbreviation:  In Vitro Cell. Dev. Biol. Anim.     Publication Date:  1997 Apr 
Date Detail:
Created Date:  1997-07-07     Completed Date:  1997-07-07     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9418515     Medline TA:  In Vitro Cell Dev Biol Anim     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  289-93     Citation Subset:  IM    
Affiliation:
Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea.
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MeSH Terms
Descriptor/Qualifier:
Cell Division*
Linear Models*
Monte Carlo Method

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