Trajectory Modeling of Longitudinal Binary Data: Application of the EM Algorithm for Mixture Models

Chu, Man-Kee; Koval, John J.

doi:10.1080/03610918.2012.707455

Cited by 4 publications

(6 citation statements)

References 19 publications

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“…For non-continuous data, either longitudinal or multi-dimensional, the classification is often performed by mixture modeling. [15][16][17][18] In the case of trajectory classification, each individual trajectory is modeled by a mixture of a finite number of polynomials or spline functions, the mixing proportions varying from one individual to another. Some methods assume that there is no intra-group heterogeneity (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…For example: (i) the detection of prostate cancer recurrence relies on prostate specific antigen (PSA) levels which are longitudinally monitored after the first radiotherapy; 1 (ii) the detection of osteoporosis by regular measures of bone mineral density; (iii) the analysis of the long-term immune recovery of HIV-infected and treated patients through recurrent CD4 cell counts; 2 (iv) the identification of profiles of juvenile delinquency by the use of teacher reports of physical aggression by pupils aged [6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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An alternative classification to mixture modeling for longitudinal counts or binary measures

Subtil

Boussari

Bastard³

et al. 2016

Stat Methods Med Res

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Classifying patients according to longitudinal measures, or trajectory classification, has become frequent in clinical research. The k-means algorithm is increasingly used for this task in case of continuous variables with standard deviations that do not depend on the mean. One feature of count and binary data modeled by Poisson or logistic regression is that the variance depends on the mean; hence, the within-group variability changes from one group to another depending on the mean trajectory level. Mixture modeling could be used here for classification though its main purpose is to model the data. The results obtained may change according to the main objective. This article presents an extension of the k-means algorithm that takes into account the features of count and binary data by using the deviance as distance metric. This approach is justified by its analogy with the classification likelihood. Two applications are presented with binary and count data to show the differences between the classifications obtained with the usual Euclidean distance versus the deviance distance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An alternative classification to mixture modeling for longitudinal counts or binary measures

Subtil

Boussari

Bastard³

et al. 2016

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…In our analysis, we applied the EM algorithm for double penalized likelihood estimation as used by Lee et al 36 and Chu and Koval. 54 In the semiparametric multilevel regression models, estimation of parameters procedure is simplified and speeded up using the hybrid algorithms such as combining the EM algorithm with Newton-Raphson method. Alternatively, there are numerical integration approximations such as Gauss-Hermite quadrature for evaluating the integral over the random effects distribution for single-level zero-inflated models.…”

Section: Discussionmentioning

confidence: 99%

Semiparametric models for multilevel overdispersed count data with extra zeros

Mahmoodi

Moghimbeigi

Mohammad

et al. 2016

Stat Methods Med Res

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This study proposes semiparametric models for analysis of hierarchical count data containing excess zeros and overdispersion simultaneously. The methods discussed in this paper handle nonlinear covariate effects through flexible semiparametric multilevel regression techniques. This is performed by providing a comprehensive comparison of semiparametric multilevel zero-inflated negative binomial and semiparametric multilevel zero-inflated generalized Poisson models under the real and simulated data. An EM algorithm based on Newton-Raphson equations for maximum penalized likelihood estimation approach is developed. The performance of the proposed models is assessed by using a Monte Carlo simulation study. We also illustrated the methods by the analysis of decayed, missing, and filled teeth of children aged 5-14 years old.

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Section: Introductionmentioning

confidence: 99%

“…Since the fitting process involves maximizing a likelihood function, GBTM may converge slowly as the number of time points for each time series or the total number of time series grows, or it may even fail to converge when using higher orders of polynomial functions. [19][20][21] Recent advances in machine learning provide more statistical tools to understand and characterize time series. Methods such as recurrent neural networks (RNN) [22][23][24][25] and long-and short-term memory auto-encoders followed by K-means clustering 26 Without assuming any specific form of the exposure patterns, we explicitly define a list of features summarizing individual-level prescription data.…”

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confidence: 99%

Classification of drug use patterns

Righolt

Zhang

Mahmud

2020

Pharmacology Res & Perspec

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Characterizing long‐term prescription data is challenging due to the time‐varying nature of drug use. Conventional approaches summarize time‐varying data into categorical variables based on simple measures, such as cumulative dose, while ignoring patterns of use. The loss of information can lead to misclassification and biased estimates of the exposure‐outcome association. We introduce a classification method to characterize longitudinal prescription data with an unsupervised machine learning algorithm. We used administrative databases covering virtually all 1.3 million residents of Manitoba and explicitly designed features to describe the average dose, proportion of days covered (PDC), dose change, and dose variability, and clustered the resulting feature space using K‐means clustering. We applied this method to metformin use in diabetes patients. We identified 27,786 metformin users and showed that the feature distributions of their metformin use are stable for varying the lengths of follow‐up and that these distributions have clear interpretations. We found six distinct metformin user groups: patients with intermittent use, decreasing dose, increasing dose, high dose, and two medium dose groups (one with stable dose and one with highly variable use). Patients in the varying and decreasing dose groups had a higher chance of progression of diabetes than other patients. The method presented in this paper allows for characterization of drug use into distinct and clinically relevant groups in a way that cannot be obtained from merely classifying use by quantiles of overall use.

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Trajectory Modeling of Longitudinal Binary Data: Application of the EM Algorithm for Mixture Models

Cited by 4 publications

References 19 publications

An alternative classification to mixture modeling for longitudinal counts or binary measures

An alternative classification to mixture modeling for longitudinal counts or binary measures

Semiparametric models for multilevel overdispersed count data with extra zeros

Classification of drug use patterns

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