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Thinking outside the curve, part I: modeling birthweight distribution.

Charnigo R, Chesnut LW, Lobianco T, Kirby RS - BMC Pregnancy Childbirth (2010)

Bottom Line: We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals.The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed.Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics and Biostatistics University of Kentucky Lexington, KY 40506-0027, USA. RJCharn2@aol.com

ABSTRACT

Background: Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the first of a two-part series that introduces such a framework.

Methods: We propose describing a birthweight distribution via a normal mixture model in which the number of components is determined from the data using a model selection criterion rather than fixed a priori.

Results: We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals. As an illustration, we find that a 4-component normal mixture model reasonably describes the birthweight distribution for a population of white singleton infants born to heavily smoking mothers. We also compare this 4-component normal mixture model to two competitors from the existing literature: a contaminated normal model and a 2-component normal mixture model. In a second illustration, we discover that a 6-component normal mixture model may be more appropriate than a 4-component normal mixture model for a general population of black singletons.

Conclusions: The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed. Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.

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Four- and Six-Component Mixture Models for Birthweight Distribution. (a) A 4-component normal mixture model for birthweight distribution, with parameters estimated by combining the results for 25 samples of size 50000 from the population of black singletons in general, is shown. (b) A 6-component normal mixture model, with parameters estimated in the same manner, is depicted.
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Figure 4: Four- and Six-Component Mixture Models for Birthweight Distribution. (a) A 4-component normal mixture model for birthweight distribution, with parameters estimated by combining the results for 25 samples of size 50000 from the population of black singletons in general, is shown. (b) A 6-component normal mixture model, with parameters estimated in the same manner, is depicted.

Mentions: We also drew 25 samples of size 50,000 from the 1,749,827 black singletons who were born (or experienced fetal death) from 2000 to 2002, regardless of maternal smoking status. Table 6 records the frequencies with which the FLIC selected the 2- through 7-component models as well as the overall estimates of component proportions, means, and standard deviations for each of these models. The 6-component model was overwhelmingly preferred by the FLIC. Figure 4 juxtaposes the fitted 4-component and 6-component models implied by the overall estimates. The four components in the 4-component model are loosely analogous to the second through fifth components in the 6-component model, so that the main rationale for adding two more components appears to be providing a more elaborate description of the far left and right tails of the birthweight distribution.


Thinking outside the curve, part I: modeling birthweight distribution.

Charnigo R, Chesnut LW, Lobianco T, Kirby RS - BMC Pregnancy Childbirth (2010)

Four- and Six-Component Mixture Models for Birthweight Distribution. (a) A 4-component normal mixture model for birthweight distribution, with parameters estimated by combining the results for 25 samples of size 50000 from the population of black singletons in general, is shown. (b) A 6-component normal mixture model, with parameters estimated in the same manner, is depicted.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2927479&req=5

Figure 4: Four- and Six-Component Mixture Models for Birthweight Distribution. (a) A 4-component normal mixture model for birthweight distribution, with parameters estimated by combining the results for 25 samples of size 50000 from the population of black singletons in general, is shown. (b) A 6-component normal mixture model, with parameters estimated in the same manner, is depicted.
Mentions: We also drew 25 samples of size 50,000 from the 1,749,827 black singletons who were born (or experienced fetal death) from 2000 to 2002, regardless of maternal smoking status. Table 6 records the frequencies with which the FLIC selected the 2- through 7-component models as well as the overall estimates of component proportions, means, and standard deviations for each of these models. The 6-component model was overwhelmingly preferred by the FLIC. Figure 4 juxtaposes the fitted 4-component and 6-component models implied by the overall estimates. The four components in the 4-component model are loosely analogous to the second through fifth components in the 6-component model, so that the main rationale for adding two more components appears to be providing a more elaborate description of the far left and right tails of the birthweight distribution.

Bottom Line: We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals.The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed.Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics and Biostatistics University of Kentucky Lexington, KY 40506-0027, USA. RJCharn2@aol.com

ABSTRACT

Background: Greater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the first of a two-part series that introduces such a framework.

Methods: We propose describing a birthweight distribution via a normal mixture model in which the number of components is determined from the data using a model selection criterion rather than fixed a priori.

Results: We address a number of methodological issues, including how the number of components selected depends on the sample size, how the choice of model selection criterion influences the results, and how estimates of mixture model parameters based on multiple samples from the same population can be combined to produce confidence intervals. As an illustration, we find that a 4-component normal mixture model reasonably describes the birthweight distribution for a population of white singleton infants born to heavily smoking mothers. We also compare this 4-component normal mixture model to two competitors from the existing literature: a contaminated normal model and a 2-component normal mixture model. In a second illustration, we discover that a 6-component normal mixture model may be more appropriate than a 4-component normal mixture model for a general population of black singletons.

Conclusions: The framework developed in this paper avoids assuming the existence of an interval of birthweights over which there are no compromised pregnancies and does not constrain birthweights within compromised pregnancies to be normally distributed. Thus, the present framework can reveal heterogeneity in birthweight that is undetectable via a contaminated normal model or a 2-component normal mixture model.

Show MeSH