A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers

Zhang, Yukun; Carroll, Raymond J.; Keadle, Sarah Kozey; Sampson, Joshua N.; Matthews, Charles E.

doi:10.1002/sim.7401

Cited by 12 publications

(11 citation statements)

References 22 publications

(39 reference statements)

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“…Within the joint modelling framework, our proposed model provides additional knowledge by extending the number of outcomes that can be analysed simultaneously, including multiple longitudinal outcomes and multiple count outcomes, focussing on drug safety assessment. We acknowledge that other researchers have previously proposed similar kind of modelling but applied in the efficacy context [28][29][30][31][32]. Buu et al considered a joint model of count and binary outcome [30].…”

Section: Discussionmentioning

confidence: 99%

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Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Patson

Mukaka

D’Alessandro

et al. 2021

Preprint

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Background In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy. Methods We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether–lumefantrine (AL), amodiaquine–artesunate (ASAQ) and dihydroartemisinin–piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment.Results Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; p<0.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: -0.5045, 0.4469; p=0.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: -0.0889, 0.3194; p=0.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; p=0.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; p=0.004) for Poisson model.Conclusion We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes. Trial registration ClinicalTrials.gov: NCT00852423.

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Section: Discussionmentioning

confidence: 99%

“…Buu et al considered a joint model of count and binary outcome [30]. Li et al considered jointly modelling proportion, count and continuous longitudinal outcomes [31]. Yang et al introduced a joint model for mixed Poisson outcome and continuous outcome [32].…”

Section: Discussionmentioning

confidence: 99%

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Patson

Mukaka

D’Alessandro

et al. 2021

Preprint

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“…In addition, other problems such as heterogeneity and nonlinearity in the data also need to be addressed. The heterogeneity problem has been studied by many researchers, for example, Li et al., 23 Madansingh et al. 24 and Xu et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluating upper limb function after stroke using the free-living accelerometer data

Tang

Halloran

Shi

et al. 2020

Stat Methods Med Res

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Accelerometer devices are becoming efficient tools in clinical studies for automatically measuring the activities of daily living. Such data provides a time series describing activity level at every second and displays a subject’s activity pattern throughout a day. However, the analysis of such data is very challenging due to the large number of observations produced each second and the variability among subjects. The purpose of this study is to develop efficient statistical analysis techniques for predicting the recovery level of the upper limb function after stroke based on the free-living accelerometer data. We propose to use a Gaussian Mixture Model (GMM)-based method for clustering and extracting new features to capture the information contained in the raw data. A nonlinear mixed effects model with Gaussian Process prior for the random effects is developed as the predictive model for evaluating the recovery level of the upper limb function. Results of applying to the accelerometer data for patients after stroke are presented.

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Section: Introductionmentioning

confidence: 99%

“…To model the multivariate outcomes, derived from performing these simple exercises, conditional to the available covariates, the regression approach is the natural starting point of any analysis. 8 Moreover, the data are often collected as repeated measurements over time, ie, have a (multivariate) longitudinal structure. Components that need to be described by a model for longitudinal data include the dependence of the variables on covariates, serial dependence, and heterogeneity in the patients over time.…”

Section: Introductionmentioning

confidence: 99%

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population

Punzo

Ingrassia

Maruotti

2018

Statistics in Medicine

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A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data.

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A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers

Cited by 12 publications

References 22 publications

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: Application to artemisinin-based treatment during pregnancy clinical trial

Evaluating upper limb function after stroke using the free-living accelerometer data

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population

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