Search Results

View Selected Citations Select and View All Search Citations

Source: Statistics in Medicine

Resulting in 5 citations.

1.	Heggeseth, Brianna Jewell, Nicholas P.
1.	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.
2.	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
3.	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
4.	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.

5.	Zhang, Wenling Cotton, Cecilia A.
5.	Causal Inference for Recurrent Events via Aggregated Marginal Odds Ratio Zhang, W, Cotton, CA. Causal inference for recurrent events via aggregated marginal odds ratio. Statistics in Medicine. 2023; 1- 28. Also: doi: 10.1002/sim.9802 Cohort(s): NLSY97 Publisher: Wiley Online Keyword(s): Depression (see also CESD); Health Care; Statistical Analysis; Statistics Permission to reprint the abstract has not been received from the publisher. Researchers often work with treatments and outcomes that vary over time. For example, psychologists are interested in the curative effect of cognitive behavior therapies on patients' recurrent depression symptoms. While there are various causal effect measures designed for one-time treatment, the causal effect measures for time-varying treatment and recurrent events are relatively under-developed. In this article, a new causal measure is proposed to quantify the causal effect of time-varying treatments on recurrent events. We suggest estimators with robust standard errors that are based on various weight models for both conventional causal measures and the proposed measure in different time settings. We outline the approaches and describe how using some stabilized inverse probability weight models are more advantageous than others. We demonstrate that the proposed causal estimand can be consistently estimated for study periods of moderate length, and the estimation results are compared under different treatment settings with various weight models. We also find that the proposed method is suitable for both absorbing and nonabsorbing treatments. The methods are applied to the 1997 National Longitudinal Study of Youth as an illustrative example.
	Bibliography Citation Zhang, Wenling and Cecilia A. Cotton. "Causal Inference for Recurrent Events via Aggregated Marginal Odds Ratio." Zhang, W, Cotton, CA. Causal inference for recurrent events via aggregated marginal odds ratio. Statistics in Medicine. 2023; 1- 28.

Admin