Longitudinal Mixed Membership trajectory models for disability survey data

Manrique-Vallier, Daniel

doi:10.1214/14-aoas769

Cited by 20 publications

(15 citation statements)

References 32 publications

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“…It must be modified for samples obtained through complex designs. For stratified simple random sample designs with just a few strata, one way of accounting for the design is to introduce the strata labels as covariate information through the population level common distribution of membership scores, similar to the method for incorporating cohort information in Manrique-Vallier (2010). Alternatively, when sample sizes are large enough within strata, GoM models can be estimated separately in each stratum.…”

Section: Discussionmentioning

confidence: 99%

Estimating Identification Disclosure Risk Using Mixed Membership Models

Manrique-Vallier

Reiter

2012

Journal of the American Statistical Association

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Statistical agencies and other organizations that disseminate data are obligated to protect data subjects’ confidentiality. For example, ill-intentioned individuals might link data subjects to records in other databases by matching on common characteristics (keys). Successful links are particularly problematic for data subjects with combinations of keys that are unique in the population. Hence, as part of their assessments of disclosure risks, many data stewards estimate the probabilities that sample uniques on sets of discrete keys are also population uniques on those keys. This is typically done using log-linear modeling on the keys. However, log-linear models can yield biased estimates of cell probabilities for sparse contingency tables with many zero counts, which often occurs in databases with many keys. This bias can result in unreliable estimates of probabilities of uniqueness and, hence, misrepresentations of disclosure risks. We propose an alternative to log-linear models for datasets with sparse keys based on a Bayesian version of grade of membership (GoM) models. We present a Bayesian GoM model for multinomial variables and offer an MCMC algorithm for fitting the model. We evaluate the approach by treating data from a recent US Census Bureau public use microdata sample as a population, taking simple random samples from that population, and benchmarking estimated probabilities of uniqueness against population values. Compared to log-linear models, GoM models provide more accurate estimates of the total number of uniques in the samples. Additionally, they offer record-level predictions of uniqueness that dominate those based on log-linear models.

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Section: Discussionmentioning

confidence: 99%

Estimating Identification Disclosure Risk Using Mixed Membership Models

Manrique-Vallier

Reiter

2012

Journal of the American Statistical Association

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“…This technique has been used to model trajectories toward physical disability [15] as well as pathways to dementia in the elderly [16, 17]. MMTMs combine features of longitudinal Multivariate Latent Trajectory Models [18] to identify distinct, canonical profiles, with features of cross-sectional Grade of Membership Models[19] to allow individuals to have weighted memberships in each profiles.…”

Section: Introductionmentioning

confidence: 99%

Mixed membership trajectory models of cognitive impairment in the multicenter AIDS cohort study

Molsberry

Lecci

Kingsley

et al. 2015

Aids

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Objective The longitudinal trajectories that individuals may take from a state of normal cognition to HIV-associated dementia are unknown. We applied a novel statistical methodology to identify trajectories to cognitive impairment, and factors that affected the “closeness” of an individual to one of the canonical trajectories. Design The Multicenter AIDS Cohort Study (MACS) is a four-site longitudinal study of the natural and treated history of HIV Disease among gay and bisexual men. Methods Using data from 3,892 men (both HIV-infected and uninfected) enrolled in the neuropsychology substudy of the MACS, a Mixed Membership Trajectory Model (MMTM) was applied to capture the pathways from normal cognitive function to mild impairment to severe impairment. MMTMs allow the data to identify canonical pathways and to model the effects of risk factors on an individual’s “closeness” to these trajectories. Results We identified three distinct trajectories to cognitive impairment – one “normal aging” (low probability of mild impairment until age 60), one “premature aging” (mild impairment starting at age 45–50), and one “unhealthy” (mild impairment in 20s and 30s) profile. Second, clinically defined AIDS and not simply HIV Disease, was associated with closeness to the premature aging trajectory. And, third, Hepatitis-C infection, Depression, Race, Recruitment Cohort and Confounding Conditions all affected individual’s closeness to these trajectories. Conclusions These results provide new insight into the natural history of cognitive dysfunction in HIV disease and provide evidence for a potential difference in the pathophysiology of the development of cognitive impairment based on trajectories to impairment.

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“…Mixed membership refers more generally to larger class of mixture models in which group assignment is not fixed but instead parameterized by a probability vector (Airoldi, Blei, Erosheva, & Fienberg, 2014). The concept of mixed membership has been applied in a number of statistical models across a wide variety of disciplines ranging from science publications (Erosheva, Fienberg, & Lafferty, 2004) to predicting trends in disability (Manrique-Vallier, 2014) to problem-solving strategies (Galyardt, 2012).…”

Section: Hnms For Mediationmentioning

confidence: 99%

Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation

Sweet

2018

Journal of Educational and Behavioral Statistics

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There are some educational interventions aimed at changing the ways in which individuals interact, and social networks are particularly useful for quantifying these changes. For many of these interventions, the ultimate goal is to change some outcome of interest such as teacher quality or student achievement, and social networks act as a natural mediator; the intervention changes the social networks of the teachers in schools, and teachers with certain types of social networks tend to use better teaching practices, for example. Due to lack of methodology, however, social networks have not been modeled as mediators. We present a new framework for modeling social networks as mediators in which a social network model is embedded into a mediation model and both models are estimated simultaneously. As a proof of concept, we introduce a new network model for mediation, applicable for interventions that affect subgroup structure. We provide a small simulation study to demonstrate the feasibility of this model and explore some potential operating characteristics. Finally, we apply our model to examine the effects of instructional coaches on teacher advice-seeking networks and subsequent changes in beliefs about mathematics.

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Longitudinal Mixed Membership trajectory models for disability survey data

Cited by 20 publications

References 32 publications

Estimating Identification Disclosure Risk Using Mixed Membership Models

Estimating Identification Disclosure Risk Using Mixed Membership Models

Mixed membership trajectory models of cognitive impairment in the multicenter AIDS cohort study

Modeling Social Networks as Mediators: A Mixed Membership Stochastic Blockmodel for Mediation

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