Dataset Information

LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS.

ABSTRACT: Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience symptoms improvement early on during antidepressant treatment, which is considered to be an indication of a placebo rather than a true pharmacological response. We want to relate the likelihood of belonging to this subgroup of early responders to baseline electroencephalography (EEG) measurement that takes the form of a matrix. The proposed method is built upon a low rank Candecomp/Parafac (CP) decomposition of the target coefficient matrix through low-dimensional latent variables, which effectively reduces the model dimensionality. We adopt a Bayesian hierarchical modeling approach to estimate the latent variables, which allows a flexible way to incorporate prior knowledge about covariate effect heterogeneity and offers a data-driven method of regularization. Simulation studies suggest that the proposed method is robust against potentially misspecified rank in the CP decomposition. With the motivating example we show how the proposed method can be applied to extract valuable information from baseline EEG measurements that explains the likelihood of belonging to the early responder subgroup, helping to identify placebo responders and suggesting new targets for the study of placebo response.

SUBMITTER: Jiang B

PROVIDER: S-EPMC5687521 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS.

Jiang Bei B Petkova Eva E Tarpey Thaddeus T Ogden R Todd RT

The annals of applied statistics 20170901 3

Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience sympto ...[more]

PMID: 29152032

Similar Datasets

Project description:ObjectiveIn the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.DesignThe present findings rest on a large nationally-representative sample from a cross-sectional study.Setting and participantsThe study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017-2018.Primary and secondary outcome measuresThe study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.ResultsThe present study demonstrates that older adults in the country can be segmented into six patterns: 'relatively healthy', 'hypertension', 'gastrointestinal disorders-hypertension-musculoskeletal disorders', 'musculoskeletal disorders-hypertension-asthma', 'metabolic disorders' and 'complex cardiometabolic disorders'. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.ConclusionsThe identified classes 'hypertension', 'metabolic disorders' and 'complex cardiometabolic disorders' reflect three stages of cardiometabolic morbidity with hypertension as the first and 'complex cardiometabolic disorders' as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.

Dataset Information

LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS.

Publications

LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets