Literature DB >> 24812444

Estimating Gompertz Growth Curves from Marine Mammal Strandings in the Presence of Missing Data.

Mary Shotwell1, Wayne McFee2, Elizabeth H Slate1.   

Abstract

Stranded bottlenose dolphins (Tursiops truncatus) off the coast of South Carolina (SC) provide data essential for population health assessment. Of the 598 bottlenose dolphin strandings in SC from 1993 to 2007, 91 were of sufficient body condition to obtain organ weights. Of these 91 animals, only 52 were brought back to the laboratory for total body weight measurements. Because it is more feasible to transport smaller animals to the laboratory setting for necropsy procedures, a selection bias is present in that data for larger animals are often missing. Regression and propensity score multiple imputation methods are utilized to account for missing data needed to compute growth. Fitted Gompertz growth curves for SC animals with and without adjustment for missing data are compared to those found from the northwestern Gulf of Mexico. South Carolina animals display a trend in lower asymptotic mean total body weights and faster growth rates compared to the Gulf of Mexico population. The differences generally increased in magnitude after imputation methods. South Carolina females were originally estimated to reach larger maximum sizes than Gulf of Mexico females, but after imputation this relationship reversed. The findings suggest selection bias should be accounted for in sampling stranded dolphins.

Entities:  

Keywords:  Tursiops truncatus; bottlenose dolphin; missing data; multiple imputation; stranding

Year:  2010        PMID: 24812444      PMCID: PMC4011081     

Source DB:  PubMed          Journal:  Int J Ecol Econ Stat        ISSN: 0973-1385


  2 in total

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Authors:  Anastasios A Tsiatis; Marie Davidian
Journal:  Stat Sci       Date:  2007       Impact factor: 2.901

2.  Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group.

Authors:  R B D'Agostino
Journal:  Stat Med       Date:  1998-10-15       Impact factor: 2.373

  2 in total
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1.  A Bayesian mixture model for missing data in marine mammal growth analysis.

Authors:  Mary E Shotwell; Wayne E McFee; Elizabeth H Slate
Journal:  Environ Ecol Stat       Date:  2016-10-04       Impact factor: 1.119

  1 in total

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