Characterizing Components in a Mixture Model for Birthweight Distribution

Deng, Wei; Charnigo, Richard; Dai, Hongying; Kirby, Russell S.

doi:10.4172/2155-6180.1000118

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Of note, the more common finite mixture of (univariate) normals can be seen as a special case of the finite mixture of regressions when no covariates other than an intercept term are considered . In perinatal epidemiology, covariate‐ and noncovariate‐based mixture models have been regularly used to model the distinctive left skewed distribution of birthweight …”

Section: Review Of Univariate Finite Mixture Of Regressionsmentioning

confidence: 99%

“…15 In perinatal epidemiology, covariate-and noncovariate-based mixture models have been regularly used to model the distinctive left skewed distribution of birthweight. [16][17][18][19][20][21] The univariate mixture of regression model (1) naturally arises from the presence of omitted or unmeasured binary or categorical predictors. To illustrate this and following Samoilenko et al, 22 suppose that birthweight (Y) is a function of fetal sex (S) and a given genotype (G, with 0/1 coding) such as…”

Section: Review Of Univariate Finite Mixture Of Regressionsmentioning

confidence: 99%

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A Bayesian finite mixture of bivariate regression model for causal mediation analyses

Lefebvre

Samoilenko

Boucoiran

et al. 2018

Statistics in Medicine

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Building on the work of Schwartz et al, Joint Bayesian analysis of birthweight and censored gestational age using finite mixture models in Statistics in Medicine, we propose a Bayesian finite mixture of bivariate regression model for causal mediation analyses. Using an identifiability condition within each component of the mixture, we express the natural direct and indirect effects of the exposure on the outcome as functions of the component-specific regression coefficients. On the basis of simulated data, we examine the behavior of the model for estimating these effects in situations where the associations between exposure, mediator, and outcome are confounded or not. Additionally, we demonstrate that this mixture model can be used to account for heterogeneity arising through unmeasured binary or categorical mediator-outcome confounders. Considering gestational age as a potential mediator, we then illustrate our mediation mixture model to estimate the natural direct and indirect effects of exposure to inhaled corticosteroids during pregnancy on birthweight using a cohort of asthmatic women from the province of Quebec (Canada).

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Section: Review Of Univariate Finite Mixture Of Regressionsmentioning

confidence: 99%

Section: Review Of Univariate Finite Mixture Of Regressionsmentioning

confidence: 99%

A Bayesian finite mixture of bivariate regression model for causal mediation analyses

Lefebvre

Samoilenko

Boucoiran

et al. 2018

Statistics in Medicine

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“…Mixture modeling has been applied to interesting problems in disciplines, as varied as epidemiology [14,15], astronomy [16,17], biochemistry [18,19], and genetics [20,21].…”

Section: Background On Mixture Modelingmentioning

confidence: 99%

Contaminated Chi-Square Modeling and Large-Scale ANOVA Testing

Charnigo¹

2013

J Biomet Biostat

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We propose a convenient moment-based procedure for testing the omnibus null hypothesis of no contamination of a central chi-square distribution by a non-central chi-square distribution. In sharp contrast with likelihood ratio tests for mixture models, there is no need for re-sampling or random field theory to obtain critical values. Rather, critical values are available from an asymptotic normal distribution, and there is excellent agreement between nominal and actual significance levels. This procedure may be used to model numerous chi-square statistics, obtained via monotonic transformations of F statistics, from large-scale ANOVA testing, such as that encountered in microarray data analysis. In that context, modeling chi-square statistics instead of p-values may improve detection of differential gene expression, as we demonstrate through simulation studies, while also reducing false declarations of the same, as we illustrate in a case study on aging and cognition. Our procedure may also be incorporated into a gene filtration process, which may reduce type II errors on genewise null hypotheses by justifying lighter controls for Type I errors. Let X i denote the rescaled test statistic (K-1) F i. With large (g 1 +g 2 +… +g K-K), X i is distributed approximately χ 2 K-1 (0) under the genewise null hypothesis, and approximately χ 2 K-1 (μ), under the genewise alternative hypothesis, for some μ. We explain this approximation in the Appendix. If g 1 , g 2 , …, g K are not large enough to warrant this approximation, then a more sophisticated approach may be employed to transform F statistics into chi-square statistics; one such approach is described in and used for our case study. Letting λ denote the proportion of genes for which mean expression Journa l o f B io m etrics & B io s ta tistics

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Using a Mixture-of-Bivariate-Regressions Model to Explore Heterogeneity of Effects of the Use of Inhaled Corticosteroids on Gestational Age and Birth Weight Among Pregnant Women With Asthma

Samoilenko

Blais

Boucoiran

et al. 2018

American Journal of Epidemiology

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Asthma is a heterogeneous disease, and responses to asthma medications vary noticeably among patients. A substantively oriented objective of this study was to explore the potentially heterogeneous effects of exposure to maternal inhaled corticosteroids (ICS) on gestational age (GA) at delivery and birth weight (BW) using a cohort of 6,197 pregnancies among women with asthma (Quebec, Canada, 1998-2008). A methodologically oriented objective was to comprehensively describe the application of a Bayesian 2-component mixture-of-bivariate-regressions model to address this issue and estimate the effects of ICS on GA and BW jointly. Based on the proposed model, no association between ICS and GA/BW was found for a large proportion of asthmatic pregnancies. However, a positive association between ICS exposure and GA/BW was revealed in a small subset of pregnancies comprising mainly preterm and low-birth-weight infants. A novel application of this model was also subsequently performed using BW z score instead of BW as the outcome variable. In conclusion, the studied mixture-of-bivariate-regressions model was useful for detecting heterogeneity in the effect of ICS on GA and BW in our population of women with asthma. These analyses pave the way for analogous uses of this model for general assessment of exposure effect heterogeneity for these perinatal outcomes.

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Characterizing Components in a Mixture Model for Birthweight Distribution

Cited by 3 publications

References 19 publications

A Bayesian finite mixture of bivariate regression model for causal mediation analyses

A Bayesian finite mixture of bivariate regression model for causal mediation analyses

Contaminated Chi-Square Modeling and Large-Scale ANOVA Testing

Using a Mixture-of-Bivariate-Regressions Model to Explore Heterogeneity of Effects of the Use of Inhaled Corticosteroids on Gestational Age and Birth Weight Among Pregnant Women With Asthma

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