Search Results

Source: Statistics in Medicine
Resulting in 4 citations.
1. Heggeseth, Brianna
Jewell, Nicholas P.
The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling
Statistics in Medicine 32,16 (20 July 2013): 2790-2803.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.5729/abstract
Cohort(s): NLSY79
Publisher: Wiley Online
Keyword(s): Body Mass Index (BMI); Modeling; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence—that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.
Bibliography Citation
Heggeseth, Brianna and Nicholas P. Jewell. "The Impact of Covariance Misspecification in Multivariate Gaussian Mixtures on Estimation and Inference: An Application to Longitudinal Modeling." Statistics in Medicine 32,16 (20 July 2013): 2790-2803.
2. Li, Kai
Poirier, Dale J.
The Roles of Birth Inputs and Outputs in Predicting Health, Behavior, and Test Scores in Early Childhood
Statistics in Medicine 22 (2003): 3489-3514.
Also: http://finance.sauder.ubc.ca/~kaili/child_SM.pdf
Cohort(s): Children of the NLSY79, NLSY79
Publisher: Wiley Online
Keyword(s): Alcohol Use; Bayesian; Behavior Problems Index (BPI); Birthweight; Cigarette Use (see Smoking); Endogeneity; Home Observation for Measurement of Environment (HOME); Peabody Individual Achievement Test (PIAT- Math); Peabody Individual Achievement Test (PIAT- Reading); Pre-natal Care/Exposure; Racial Differences; Simultaneity

Permission to reprint the abstract has not been received from the publisher.

The goal of this study is to address directly the predictive value of birth inputs and outputs, particularly birth weight, for measures of early childhood development in a simultaneous equations modeling framework. Strikingly, birth outputs have virtually no structural/casual effects on early childhood developmental outcomes, and only maternal smoking and drinking during pregnancy have some effects on child height. Not surprisingly, family child-rearing environment has sizeable negative and positive effects on a behavioral problems index and a math/reading test score, respectively, and a mildly surprising negative effect on child height. Despite little evidence of a structural/causal effect of birth weight on early childhood developmental outcomes, our results demonstrate that birth weight nonetheless has strong predictive effects on early childhood outcomes. Furthermore, these effects are largely invariant to whether family child-rearing environment is taken into account. Family child-rearing environment has both structural and predictive effects on early childhood outcomes, but they are largely orthogonal and in addition to the effects of birth weight. Copyright: 2003 John Wiley & Sons, Ltd.
Bibliography Citation
Li, Kai and Dale J. Poirier. "The Roles of Birth Inputs and Outputs in Predicting Health, Behavior, and Test Scores in Early Childhood." Statistics in Medicine 22 (2003): 3489-3514.
3. Muthen, Bengt O.
Asparouhov, Tihomir
Growth Mixture Modeling with Non-normal Distributions
Statistics in Medicine 34,6 (March 2015): 1041-1058.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.6388/abstract
Cohort(s): NLSY97
Publisher: Wiley Online
Keyword(s): Modeling, MIxture Models/Finite Mixture Models; Socioeconomic Background; Statistical Analysis

Permission to reprint the abstract has not been received from the publisher.

A limiting feature of previous work on growth mixture modeling is the assumption of normally distributed variables within each latent class. With strongly non-normal outcomes, this means that several latent classes are required to capture the observed variable distributions. Being able to relax the assumption of within-class normality has the advantage that a non-normal observed distribution does not necessitate using more than one class to fit the distribution. It is valuable to add parameters representing the skewness and the thickness of the tails. A new growth mixture model of this kind is proposed drawing on recent work in a series of papers using the skew-t distribution. The new method is illustrated using the longitudinal development of body mass index in two data sets. The first data set is from the National Longitudinal Survey of Youth covering ages 12-23 years. Here, the development is related to an antecedent measuring socioeconomic background. The second data set is from the Framingham Heart Study covering ages 25-65 years. Here, the development is related to the concurrent event of treatment for hypertension using a joint growth mixture-survival model.
Bibliography Citation
Muthen, Bengt O. and Tihomir Asparouhov. "Growth Mixture Modeling with Non-normal Distributions." Statistics in Medicine 34,6 (March 2015): 1041-1058.
4. Zhang, Qiang
Ip, Edward Hak-Sing
Generalized Linear Model for Partially Ordered Data
Statistics in Medicine 31,1 (13 January 2012): 56-68.
Also: http://onlinelibrary.wiley.com/doi/10.1002/sim.4318/abstract
Cohort(s): NLSY97
Publisher: Wiley Online
Keyword(s): Cigarette Use (see Smoking); Modeling; Smoking (see Cigarette Use)

Permission to reprint the abstract has not been received from the publisher.

Within the rich literature on generalized linear models, substantial efforts have been devoted to models for categorical responses that are either completely ordered or completely unordered. Few studies have focused on the analysis of partially ordered outcomes, which arise in practically every area of study, including medicine, the social sciences, and education. To fill this gap, we propose a new class of generalized linear models—the partitioned conditional model—that includes models for both ordinal and unordered categorical data as special cases. We discuss the specification of the partitioned conditional model and its estimation. We use an application of the method to a sample of the National Longitudinal Study of Youth to illustrate how the new method is able to extract from partially ordered data useful information about smoking youths that is not possible using traditional methods. © 2011 John Wiley & Sons, Ltd.
Bibliography Citation
Zhang, Qiang and Edward Hak-Sing Ip. "Generalized Linear Model for Partially Ordered Data." Statistics in Medicine 31,1 (13 January 2012): 56-68.