Perturbed factor analysis: Accounting for group differences in exposure profiles

Roy, Arkaprava; Lavine, Isaac; Herring, Amy H.; Dunson, David B.

doi:10.1214/20-aoas1435

Cited by 10 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, insights obtained from PFA can be used in studying environmental justice research applications. Roy et al evaluated differences in exposure profiles across biological or social constructs of race/ethnicity [18]. Usual FA methods treat the different exposures as exchangeable a priori, while in the mixtures context there is typically information available on chemical class or related categories.…”

Section: Pattern Identificationmentioning

confidence: 99%

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Joubert

Kioumourtzoglou

Chamberlain

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

Humans are exposed to a diverse mixture of chemical and non-chemical exposures across their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure science and related technologies enable the investigation of the health impacts of mixtures. While existing statistical methods can address the most basic questions related to the association between environmental mixtures and health endpoints, there were gaps in our ability to learn from mixtures data in several common epidemiologic scenarios, including high correlation among health and exposure measures in space and/or time, the presence of missing observations, the violation of important modeling assumptions, and the presence of computational challenges incurred by current implementations. To address these and other challenges, NIEHS initiated the Powering Research through Innovative methods for Mixtures in Epidemiology (PRIME) program, to support work on the development and expansion of statistical methods for mixtures. Six independent projects supported by PRIME have been highly productive but their methods have not yet been described collectively in a way that would inform application. We review 37 new methods from PRIME projects and summarize the work across previously published research questions, to inform methods selection and increase awareness of these new methods. We highlight important statistical advancements considering data science strategies, exposure-response estimation, timing of exposures, epidemiological methods, the incorporation of toxicity/chemical information, spatiotemporal data, risk assessment, and model performance, efficiency, and interpretation. Importantly, we link to software to encourage application and testing on other datasets. This review can enable more informed analyses of environmental mixtures. We stress training for early career scientists as well as innovation in statistical methodology as an ongoing need. Ultimately, we direct efforts to the common goal of reducing harmful exposures to improve public health.

show abstract

Section: Pattern Identificationmentioning

confidence: 99%

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Joubert

Kioumourtzoglou

Chamberlain

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…An alternative (which we test on our real data) is to let

for all j and

. This introduces more free parameters than needed but has worked well in previous studies ( Roy et al , 2021 ).…”

Section: Methods For Estimation Of Causal Effectsmentioning

confidence: 85%

A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes

Samartsidis,

Seaman,

Harrison

et al. 2023

Biostatistics

View full text Add to dashboard Cite

Summary Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England’s Test and Trace programme for COVID-19.

show abstract

“… 39 , 40 PFA captures similarities and differences in exposures and has been used to express shared exposure profiles within groups and to evaluate differences in exposure across groups. 41 These methods lack some of PCP-LOD’s distinct features (i.e., LOD-specific penalty, nonnegativity constraint, and procedure to accommodate missing values), but they have certain characteristics (that PCP-LOD may lack), making them well suited for environmental mixtures research. It is useful to have a collection of tools to answer research questions concerning multipollutant exposures to allow for improved public health communication.…”

Section: Discussionmentioning

confidence: 99%

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

Gibson

Zhang

Yan

et al. 2022

Environ Health Perspect

View full text Add to dashboard Cite

Background: Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures. Objective: We adapted principal component pursuit (PCP)—a robust and well-established technique for dimensionality reduction in computer vision and signal processing—to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events. Methods: We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions and three noise structures. We applied PCP-LOD to evaluate its performance in comparison with principal component analysis (PCA). We next applied principal component pursuit with limit of detection (PCP-LOD) to an exposure mixture of 21 persistent organic pollutants (POPs) measured in 1,000 U.S. adults from the 2001–2002 National Health and Nutrition Examination Survey (NHANES). We applied singular value decomposition to the estimated low-rank matrix to characterize the patterns. Results: PCP-LOD recovered the true number of patterns through cross-validation for all simulations; based on an a priori specified criterion, PCA recovered the true number of patterns in 32% of simulations. PCP-LOD achieved lower relative predictive error than PCA for all simulated data sets with up to 50% of the data . When 75% of values were , PCP-LOD outperformed PCA only when noise was low. In the POP mixture, PCP-LOD identified a rank-three underlying structure and separated 6% of values as extreme events. One pattern represented comprehensive exposure to all POPs. The other patterns grouped chemicals based on known structure and toxicity. Discussion: PCP-LOD serves as a useful tool to express multidimensional exposures as consistent patterns that, if found to be related to adverse health, are amenable to targeted public health messaging. https://doi.org/10.1289/EHP10479

show abstract

Perturbed factor analysis: Accounting for group differences in exposure profiles

Cited by 10 publications

References 26 publications

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods

A Bayesian multivariate factor analysis model for causal inference using time-series observational data on mixed outcomes

Principal Component Pursuit for Pattern Identification in Environmental Mixtures

Contact Info

Product

Resources

About