scholarly journals Shared parameter and copula models for analysis of semicontinuous longitudinal data with nonrandom dropout and informative censoring

2021 ◽  
pp. 096228022110605
Author(s):  
Miran A. Jaffa ◽  
Mulugeta Gebregziabher ◽  
Ayad A. Jaffa
Keyword(s):  
Likelihood Function ◽  
Random Effect ◽  
Joint Modeling ◽  
Right Censoring ◽  
Simulation Studies ◽  
Copula Models ◽  
Extensive Simulation ◽  
Clayton Copula ◽  
Semicontinuous Data ◽  
Informative Right Censoring

Analysis of longitudinal semicontinuous data characterized by subjects’ attrition triggered by nonrandom dropout is complex and requires accounting for the within-subject correlation, and modeling of the dropout process. While methods that address the within-subject correlation and missing data are available, approaches that incorporate the nonrandom dropout, also referred to informative right censoring, in the modeling step are scarce due to the computational intensity and possible intractable integration needed for its implementation. Appreciating the complexity of this problem and the need for a new methodology that is feasible for implementation, we propose to extend a framework of likelihood-based marginalized two-part models to account for informative right censoring. The censoring process is modeled using two approaches: (1) Poisson censoring for the count of visits before dropout and (2) survival time to dropout. Novel consideration was given to the proposed joint modeling approaches for the semicontinuous and censoring components of the likelihood function which included (1) shared parameter, and (2) Clayton copula. The cross-part and within-part correlations were accounted for through a complex random effect structure that models correlated random intercepts and slopes. Feasibility of implementation, and accuracy of these approaches were investigated using extensive simulation studies and clinical application.

scholarly journals Using Cure Models to Estimate the Serial Interval of Tuberculosis With Limited Follow-up

2020 ◽  
Vol 189 (11) ◽  
pp. 1421-1426
Author(s):  
Yicheng Ma ◽  
Helen E Jenkins ◽  
Paola Sebastiani ◽  
Jerrold J Ellner ◽  
Edward C Jones-López ◽  
...  
Keyword(s):  
Likelihood Function ◽  
The United States ◽  
Simulation Studies ◽  
Serial Interval ◽  
Slow Progression ◽  
Cure Models ◽  
Study Population ◽  
Credible Information ◽  
Active Disease

Abstract Serial interval (SI), defined as the time between symptom onset in an infector and infectee pair, is commonly used to understand infectious diseases transmission. Slow progression to active disease, as well as the small percentage of individuals who will eventually develop active disease, complicate the estimation of the SI for tuberculosis (TB). In this paper, we showed via simulation studies that when there is credible information on the percentage of those who will develop TB disease following infection, a cure model, first introduced by Boag in 1949, should be used to estimate the SI for TB. This model includes a parameter in the likelihood function to account for the study population being composed of those who will have the event of interest and those who will never have the event. We estimated the SI for TB to be approximately 0.5 years for the United States and Canada (January 2002 to December 2006) and approximately 2.0 years for Brazil (March 2008 to June 2012), which might imply a higher occurrence of reinfection TB in a developing country like Brazil.

A threshold linear mixed model for identification of treatment-sensitive subsets in a clinical trial based on longitudinal outcomes and a continuous covariate

2020 ◽  
Vol 29 (10) ◽  
pp. 2919-2931
Author(s):  
Xinyi Ge ◽  
Yingwei Peng ◽  
Dongsheng Tu
Keyword(s):  
Clinical Trial ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Function ◽  
Specific Treatment ◽  
Likelihood Method ◽  
Simulation Studies ◽  
Longitudinal Outcomes ◽  
Continuous Covariate

Identification of a subset of patients who may be sensitive to a specific treatment is an important problem in clinical trials. In this paper, we consider the case where the treatment effect is measured by longitudinal outcomes, such as quality of life scores assessed over the duration of a clinical trial, and the subset is determined by a continuous baseline covariate, such as age and expression level of a biomarker. A threshold linear mixed model is introduced, and a smoothing maximum likelihood method is proposed to obtain the estimation of the parameters in the model. Broyden-Fletcher-Goldfarb-Shanno algorithm is employed to maximize the proposed smoothing likelihood function. The proposed procedure is evaluated through simulation studies and application to the analysis of data from a randomized clinical trial on patients with advanced colorectal cancer.

On the effect of measurement errors in simultaneous monitoring of mean vector and covariance matrix of multivariate processes

2016 ◽  
Vol 40 (1) ◽  
pp. 318-330 ◽  
Author(s):  
Amirhossein Amiri ◽  
Reza Ghashghaei ◽  
Mohammad Reza Maleki
Keyword(s):  
Measurement Errors ◽  
Control Charts ◽  
Moving Average ◽  
Real Data ◽  
Simulation Studies ◽  
Process Mean ◽  
Extensive Simulation ◽  
Monitoring Procedure ◽  
Mean Vector ◽  
Weighted Moving Average

In this paper, we investigate the misleading effect of measurement errors on simultaneous monitoring of the multivariate process mean and variability. For this purpose, we incorporate the measurement errors into a hybrid method based on the generalized likelihood ratio (GLR) and exponentially weighted moving average (EWMA) control charts. After that, we propose four remedial methods to decrease the effects of measurement errors on the performance of the monitoring procedure. The performance of the monitoring procedure as well as the proposed remedial methods is investigated through extensive simulation studies and a real data example.

Lightweight Design in Architecture – Forming of Stainless Steel Composites for Modern Facades

2015 ◽  
Vol 639 ◽  
pp. 77-82
Author(s):  
Marc Tulke ◽  
Jennifer Watzke ◽  
Alexander Brosius ◽  
Michael Schomäcker
Keyword(s):  
Stainless Steel ◽  
Composite Material ◽  
Lightweight Design ◽  
Mechanical Stretch ◽  
Simulation Studies ◽  
Stretch Forming ◽  
Forming Process ◽  
Extensive Simulation ◽  
Selection Of

This paper shows the characterisation of a new composite material for architectural applications. The stainless steel and polyethylene laminate offers new possibilities in forming optically pleasing facade shapes. A selection of possible structures is presented as a result of extensive simulation studies. The presented structures are generated with a new pneumo-mechanical stretch forming process.

Prediction of Finite Population Proportion When Responses Are Misclassified

2020 ◽  
Author(s):  
Sumanta Adhya ◽  
Surupa Roy ◽  
Tathagata Banerjee
Keyword(s):  
Empirical Study ◽  
Finite Population ◽  
Asymptotic Properties ◽  
Auxiliary Variable ◽  
Binary Response ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Computationally Efficient ◽  
Extensive Simulation ◽  
Population Proportion

Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

scholarly journals Estimation of parameters in stochastic differential equations with two random effects

2016 ◽  
Vol 5 (2) ◽  
pp. 97
Author(s):  
Mohammed Alsukaini ◽  
Walaa Alkreemawi ◽  
Xiang-Jun Wang
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Random Effects ◽  
Posterior Distribution ◽  
Diffusion Coefficients ◽  
Likelihood Function ◽  
Random Effect ◽  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
Consistency And Asymptotic Normality

<p>In this paper we investigate consistency and asymptotic normality of the posterior distribution of the parameters in the stochastic differential equations (SDE’s) with diffusion coefficients depending nonlinearly on a random variables  and  (the random effects).The distributions of the random effects  and  depends on unknown parameters which are to be estimated from the continuous observations of the independent processes . We propose the Gaussian distribution for the random effect  and the exponential distribution for the random effect    , we obtained an explicit formula for the likelihood function and find the estimators of the unknown parameters in the random effects.</p>

A marginalized two-part model for longitudinal semicontinuous data

2015 ◽  
Vol 26 (4) ◽  
pp. 1949-1968 ◽  
Author(s):  
Valerie A Smith ◽  
Brian Neelon ◽  
John S Preisser ◽  
Matthew L Maciejewski
Keyword(s):  
Health Care ◽  
Health Services ◽  
Random Effect ◽  
Continuous Distribution ◽  
Health Care Expenditures ◽  
Health Services Use ◽  
Services Research ◽  
Population Mean ◽  
Semicontinuous Data ◽  
Part Model

In health services research, it is common to encounter semicontinuous data, characterized by a point mass at zero followed by a right-skewed continuous distribution with positive support. Examples include health expenditures, in which the zeros represent a subpopulation of patients who do not use health services, while the continuous distribution describes the level of expenditures among health services users. Longitudinal semicontinuous data are typically analyzed using two-part random-effect mixtures with one component that models the probability of health services use, and a second component that models the distribution of log-scale positive expenditures among users. However, because the second part conditions on a non-zero response, obtaining interpretable effects of covariates on the combined population of health services users and non-users is not straightforward, even though this is often of greatest interest to investigators. Here, we propose a marginalized two-part model for longitudinal data that allows investigators to obtain the effect of covariates on the overall population mean. The model additionally provides estimates of the overall population mean on the original, untransformed scale, and many covariates take a dual population average and subject-specific interpretation. Using a Bayesian estimation approach, this model maintains the flexibility to include complex random-effect structures and easily estimate functions of the overall mean. We illustrate this approach by evaluating the effect of a copayment increase on health care expenditures in the Veterans Affairs health care system over a four-year period.

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