%0 Conference Paper
%B Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
%D 2020
%T A model for assessing the association in the repeated measures of depression among the elderly
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Bae, S.
%A Singh, K.P.
%K Bivariate binary outcomes
%K Conditional model
%K joint model
%K marginal model
%K regressive model
%K Transition probability
%X The dependence in the outcome variables is a major issue of concern in modeling the correlated data stemmed from the repeated observations. The marginal models such as GEE and the conditional models based on Markov chain have been employed for longitudinal data in the past. However, it has been evident that without addressing the underlying association parameters, the analysis of repeated outcome variables remains far from being resolved. In this paper, a method has been demonstrated to model such data using the underlying dependence in the outcome variables as well as dependence between outcome and explanatory variables. An extension of the regressive model is shown in this paper and a comparison is demonstrated between the existing model (reduced model) and the proposed model (extended model). The models are illustrated for depression among the elderly population in the USA using the Health and Retirement Study data from 1992 to 1998. © International Congress on Modelling and Simulation, MODSIM 2013.All right reserved.
%B Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013
%C Adelaide, Australia
%G eng
%U https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080923221&partnerID=40&md5=3ec1edb7d724b41427a9f89bc8043c6c
%0 Journal Article
%J Biometrical Journal
%D 2020
%T Regressive models for risk prediction of repeated multinomial outcomes: An illustration using Health and Retirement Study data
%A Rafiqul I Chowdhury
%A M. Ataharul Islam
%K multinomial outcomes
%K regressive model
%K repeated measures
%K risk prediction
%K sequence of events
%X Abstract Life expectancy is increasing in many countries and this may lead to a higher frequency of adverse health outcomes. Therefore, there is a growing demand for predicting the risk of a sequence of events based on specified factors from repeated outcomes. We proposed regressive models and a framework to predict the joint probabilities of a sequence of events for multinomial outcomes from longitudinal studies. The Markov chain is used to link marginal and sequence of conditional probabilities to predict the joint probability. Marginal and sequence of conditional probabilities are estimated using marginal and regressive models. An application is shown using the Health and Retirement Study data. The bias of parameter estimates for all models from all bootstrap simulation is less than 1% in most of the cases. The estimated mean squared error is also very low. Results from the simulation study show negligible bias and the usefulness of the proposed model. The proposed model and framework would be useful to solve real-life problems from various fields and big data analysis.
%B Biometrical Journal
%V n/a
%G eng
%U https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201800101
%R 10.1002/bimj.201800101
%0 Journal Article
%J Journal of Applied Statistics
%D 2017
%T Analyzing dependence in incidence of diabetes and heart problem using generalized bivariate geometric models with covariates
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Khalaf S. Sultan
%K Diabetes
%K Heart disease
%X For analyzing incidence data on diabetes and health problems, the bivariate geometric probability distribution is a natural choice but remained unexplored largely due to lack of models linking covariates with the probabilities of bivariate incidence of correlated outcomes. In this paper, bivariate geometric models are proposed for two correlated incidence outcomes. The extended generalized linear models are developed to take into account covariate dependence of the bivariate probabilities of correlated incidence outcomes for diabetes and heart diseases for the elderly population. The estimation and test procedures are illustrated using the Health and Retirement Study data. Two models are shown in this paper, one based on conditional-marginal approach and the other one based on the joint probability distribution with an association parameter. The joint model with association parameter appears to be a very good choice for analyzing the covariate dependence of the joint incidence of diabetes and heart diseases. Bootstrapping is performed to measure the accuracy of estimates and the results indicate very small bias. © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
%B Journal of Applied Statistics
%V 44
%P 2890-2907
%8 Feb-12-2017
%G eng
%U https://www.tandfonline.com/doi/full/10.1080/02664763.2016.1266467
%N 16
%! Journal of Applied Statistics
%R 10.1080/02664763.2016.1266467
%0 Journal Article
%J Statistical Papers
%D 2017
%T A conditional count model for repeated count data and its application to GEE approach
%A Dey, Rajib
%A M. Ataharul Islam
%K Survey Methodology
%X In this article, a conditional model is proposed for modeling longitudinal count data. The joint density is disintegrated into the marginal and conditional densities according to the multiplication rule. It allows both positive and negative correlation among variables, which most multivariate count models do not possess. To show the efficiency of the proposed model for count data, we have applied to the generalized estimating equations and the inverse Fisher information matrix is compared with the covariance matrix from estimating equations. A simulation experiment is displayed and an application of the model to divorce data is presented. In addition, a comparison of conditional model and bivariate Poisson model proposed by Kocherlakota and Kocherlakota has shown using simulated data.
%B Statistical Papers
%V 58
%P 485-504
%8 Jan-06-2017
%G eng
%U https://link.springer.com/article/10.1007%2Fs00362-015-0708-9
%N 2
%! Stat Papers
%R 10.1007/s00362-015-0708-9
%0 Journal Article
%J PLoS One
%D 2017
%T A generalized right truncated bivariate Poisson regression model with applications to health data.
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%K Poisson regression model
%K Survey Methodology
%X A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.
%B PLoS One
%V 12
%P e0178153
%8 2017
%G eng
%N 6
%1 http://www.ncbi.nlm.nih.gov/pubmed/28586344?dopt=Abstract
%R 10.1371/journal.pone.0178153
%0 Journal Article
%J Advances and Applications in Statistics
%D 2014
%T ASSESSING THE ASSOCIATION IN REPEATED MEASURES OF DEPRESSION
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Bae, Sejong
%A Singh, Karan
%X The dependence in the outcome variables is a major issue of concern in modeling the correlated data stemmed from the repeated observations. The marginal models such as GEE and the conditional models based on Markov chain have been employed for longitudinal data in the past. However, it has been evident that without addressing the underlying association parameters, the analysis of repeated outcome variables remains far from being resolved. In this paper, a method has been demonstrated to model such data using the underlying dependence in the outcome variables as well as dependence between outcome and explanatory variables. An extension of the regressive model is shown in this paper and a comparison is demonstrated between the existing model (reduced model) and the proposed model (extended model). The models are illustrated for depression by an example.
%B Advances and Applications in Statistics
%V Volume 42
%P 83-93
%8 10
%G eng
%U https://www.researchgate.net/publication/269093572_ASSESSING_THE_ASSOCIATION_IN_REPEATED_MEASURES_OF_DEPRESSION
%0 Journal Article
%J Bulletin of the Malaysian Mathematical Sciences Society
%D 2014
%T Dependence in binary outcomes: A quadratic exponential model approach
%A Maboobeh Zangeneh Sirdari
%A M. Ataharul Islam
%A Norhashidah Awang
%K Methodology
%K Other
%X Repeated measurements data appear in many applications of study subjects such as correlated binary data. Most of studies often focus on the dependence of marginal response probabilities. There is a lack of study based on joint probability distributions that yield estimation and test procedure using conditional probabilities, marginal means and correlated binary data. In this paper, the quadratic exponential form model has been extended for a Markov chain framework. This study extends the quadratic exponential model for displaying the estimation procedure for the nature and extent of dependence among the binary outcomes. In addition, a test procedure is extended to test for the goodness of fit of the model as well as for testing the order of the underlying Markov chain. The proposed model and the test procedures have been examined thoroughly with an application to elderly population data from the Health and Retirement Study (HRS) data.
%B Bulletin of the Malaysian Mathematical Sciences Society
%I 37
%V 37
%P 129-137
%G eng
%U http://www.scopus.com/inward/record.url?eid=2-s2.0-84890077284andpartnerID=40andmd5=b5707512075fa6411d8415bdec6bf441
%N 1
%4 And dependence in outcomes/Conditional model/Marginal model/Markov model/Quadratic exponential form/Repeated observations
%$ 999999
%0 Journal Article
%J Pakistan Journal of Statistics
%D 2014
%T Prediction of Disease Status: Transition Model Approach for Repeated Measures
%A Rafiqul I Chowdhury
%A M. Ataharul Islam
%K Conditional model
%K Goodness of fit
%K Markov Model
%K Prediction of Disease
%K Transitions
%X This paper develops models for prediction of disease status from longitudinal data. The estimation of area under curve (AUC) is illustrated on the basis of estimates of sensitivity and specificity for repeated binary outcomes of disease status. There are several research papers in this field on cross-sectional data but only a few dealt with the repeated observations. This paper shows the procedures to deal with repeated observations employing Markov models. These procedures employ covariate dependent Markov models for estimating sensitivity and specificity, which in turn, produce the estimates for area under curve. The tests for equality of areas under curve for two models are also suggested. An application is illustrated for depression data from the Health and Retirement Survey, USA. The results indicate that the transition model approach can reveal the matching of disease status very efficiently; an estimate of more than 0.96 was obtained for the AUC for a transition model based prediction of disease from the depression data.
%B Pakistan Journal of Statistics
%V 30
%P 181-196
%G eng
%U https://www.researchgate.net/publication/260715364_Prediction_of_disease_status_Transition_model_approach_for_repeated_measures
%N 2
%0 Journal Article
%J Journal of Applied Statistics
%D 2013
%T A generalized bivariate Bernoulli model with covariate dependence
%A M. Ataharul Islam
%A Abdulhamid A. Alzaid
%A Rafiqul I Chowdhury
%A Khalaf S. Sultan
%K Bernoulli Model
%K Covariate Dependence
%K Statistics
%X Dependence in outcome variables may pose formidable difficulty in analyzing data in longitudinal studies. In the past, most of the studies made attempts to address this problem using the marginal models. However, using the marginal models alone, it is difficult to specify the measures of dependence in outcomes due to association between outcomes as well as between outcomes and explanatory variables. In this paper, a generalized approach is demonstrated using both the conditional and marginal models. This model uses link functions to test for dependence in outcome variables. The estimation and test procedures are illustrated with an application to the mobility index data from the Health and Retirement Survey and also simulations are performed for correlated binary data generated from the bivariate Bernoulli distributions. The results indicate the usefulness of the proposed method.
%B Journal of Applied Statistics
%V 40
%P 1064-1075
%G eng
%U https://doi.org/10.1080/02664763.2013.780156
%R 10.1080/02664763.2013.780156
%0 Journal Article
%J World Applied Sciences Journal
%D 2013
%T A multistate transition model for analyzing diseases in elderly population
%A Noor, Norlida Mohd
%A M. Ataharul Islam
%A Zalila Ali
%K Demographics
%K Health Conditions and Status
%K Methodology
%K Other
%K Risk Taking
%X In this study, a competing risk model is proposed to analyze the transitions to diseases among elderly people employing the proportional hazards model. This study provides important findings regarding the disease pattern among the elderly people and the factors associated with such transitions. The Health and Retirement Study data are considered for the period of 1992-2000. The major diseases or complications considered in this study are stroke, lung diseases, diabetes mellitus, blood pressure and arthritis. The transitions to different diseases or complications are explained by selected covariates such as gender, race, marital status, smoking, drinking, physical exercise and BMI. The results indicate that gender, race, smoking and BMI are significantly associated with transitions to different diseases or complications. The results are displayed for transitions to each disease or complication separately for competing risk framework as well as for the combined transition to any of the selected diseases or complications. This paper reveals some important health issues related to the transitions to the selected diseases or complications among the elderly people and the factors associated with such transitions are identified.
%B World Applied Sciences Journal
%I 21
%V 21
%P 1700-1707
%G eng
%U http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.388.6381&rep=rep1&type=pdf
%N 12
%4 Competing risk/Elderly population/Hazards model/Multistate model/Transition to diseases/GENDER/Smoking
%$ 69178
%R 10.5829/idosi.wasj.2013.21.12.2570
%0 Report
%D 2013
%T A Multistate Transition Model for Analyzing Longitudinal Depression Data
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Huda, Shahariar
%X In longitudinal data analysis, there are many practical situations where we need to deal with transitions to a number of states and which are repeated over time generating a large number of trajectories from beginning to end of the study. This problem becomes increasingly difficult to model if the number of follow-ups is increased for a set of longitudinal data. A covariate-dependent Markov transition model is proposed using the logistic link function for polytomous outcome data. A generalized and more flexible approach of constructing the likelihood function for the first or higher order is demonstrated in this paper to deal with the branching of a number of transition types starting from no depression at the beginning of the study. The proposed method can be employed to resolve a longstanding problem in dealing with modeling for transitions, reverse transitions and repeated transitions by reducing the number of trajectories to a large extent resulting in estimating relatively few parameters. The problem of depression in elderly, in terms of short and longterm health and economic consequence, needs to be assessed more critically. This study uses the longitudinal data from the six waves of the Health and Retirement Survey to examine the transition to depression, reverse transition from depression to no depression and also repeated transition from no depression to depression after experiencing a reverse transition during a study period. The results indicate that age is negatively associated with reverse and repeated transitions, gender is negatively associated with transition and reverse transition indicating that females are more likely to experience both. The proposed method clearly provides a wider range of useful information in revealing the dynamics of the depression pattern among elderly.
%I BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY
%G eng
%U http://www.kurims.kyoto-u.ac.jp/EMIS/journals/BMMSS/pdf/v36n3/v36n3p8.pdf
%0 Journal Article
%J Bulletin of the Malaysian Mathematical Sciences Society
%D 2012
%T A bivariate binary model for testing dependence in outcomes
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Briollais, L
%K bivariate Bernoulli
%K Conditional model
%K correlated outcomes
%K joint model
%K marginal model
%K test for dependence
%X The problem of dependence in the outcome variables has become an increasingly important issue of concern during the past two decades attributable mainly to the increase in the demand for techniques in analyzing repeated measures data. In the past, most of the longitudinal models developed are based on marginal approaches and relatively few are based on conditional models. The joint models are examined mainly to focus on the characterization problems but not much has been employed to focus the covariate dependent models with dependence in the outcomes. This paper develops a new simple procedure to take account of the bivariate binary model with covariate dependence. The model is based on the integration of conditional and marginal models. Test procedures are suggested for testing the dependence in binary outcomes. Simulations are employed to demonstrate the utility of the proposed test procedures in different dependence settings. Finally, an application to the depression data has been shown. All the results confirm that the proposed model for testing the dependence in outcomes can be applied very successfully for a wide variety of situations.
%B Bulletin of the Malaysian Mathematical Sciences Society
%V 35
%P 845-858
%G eng
%U http://emis.impa.br/EMIS/journals/BMMSS/pdf/v35n4/v35n4p2.pdf
%N 4
%0 Journal Article
%J Pakistan Journal of Statistics and Operation Research
%D 2012
%T A Markov Model for Analyzing Polytomous Outcome Data
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Singh, K. P.
%K Covariate Dependence
%K Emotional Health
%K Higher Order
%K Logistic Regression
%K Markov Models
%K Multiple States
%X This paper highlights the estimation and test procedures for multi-state Markov models with covariate dependences in higher orders. Logistic link functions are used to analyze the transition probabilities of three or more states of a Markov model emerging from a longitudinal study. For illustration purpose the models are used for analysis of panel data on Health and Retirement Study conducted in USA during 1992-2002. The applications use self reported data on perceived emotional health at each round of the nationwide survey conducted among the elderly people. Useful and detailed results on the change in the perceived emotional health status among the elderly people are obtained.
%B Pakistan Journal of Statistics and Operation Research
%V 8
%P 593-603
%G eng
%N 3
%R 10.18187/pjsor.v8i3.530
%0 Journal Article
%J Applied Mathematics
%D 2012
%T Parameter Estimation in Logistic Regression for Transition, Reverse Transition and Repeated Transition from Repeated Outcomes
%A Rafiqul I Chowdhury
%A M. Ataharul Islam
%A Huda, S.
%A Briollais, L.
%K Computer Program
%K Markov Model
%K Repeated Transition
%K Reverse Transition
%K Transition
%X Covariate dependent Markov models dealing with estimation of transition probabilities for higher orders appear to be restricted because of over-parameterization. An improvement of the previous methods for handling runs of events by expressing the conditional probabilities in terms of the transition probabilities generated from Markovian assumptions was proposed using Chapman-Kolmogorov equations. Parameter estimation of that model needs extensive pre-processing and computations to prepare data before using available statistical softwares. A computer program developed using SAS/IML to estimate parameters of the model are demonstrated, with application to Health and Retirement Survey (HRS) data from USA.
%B Applied Mathematics
%P 1739-1749
%8 11/2012
%G eng
%0 Journal Article
%J Journal of Statistical Research
%D 2012
%T Tests for Dependence in Binary Repeated Measures Data
%A M. Ataharul Islam
%E Rafiqul I Chowdhury
%E Alzaid, A.
%K Dependence Tests
%K Statistics
%X If we observe repeated binary outcomes over time then there may be dependence in outcomes and a test for dependence may be sought for such data. However, tests for dependence in models for repeated measures remain a challenge where covariates are associated with previous outcomes and both covariates and previous outcomes are included simultaneously in a model. This paper displays the nature of such problems (i.e. dependence among outcomes may depend on the association between covariates and previous outcomes) inherent in models for repeated binary outcomes that can distort the estimates and may produce misleading results. In the context of application of regressive models, this paper discusses conditions for which the regressive models can be safely employed. All these are shown on the basis of simple relationships between the conditional, marginal and joint probability mass functions for the bivariate binary outcomes which can be extended to the multivariate data stemmed from repeated measures. Some test procedures are suggested and applications are demonstrated using both simulations and real life data. Both the applications clearly indicate the utility of the proposed tests.
%B Journal of Statistical Research
%V 46
%P 203-217
%8 2012
%G eng
%N 2
%& 203
%0 Journal Article
%J Statistical Methodology
%D 2010
%T Prediction of disease status: A regressive model approach for repeated measures
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%K Health Conditions and Status
%K Methodology
%K Other
%K Risk Taking
%X In this paper, regressive models are proposed for modeling a sequence of transitions in longitudinal data. These models are employed to predict the future status of the outcome variable of the individuals on the basis of their underlying background characteristics or risk factors. The estimation of parameters and also estimates of conditional and unconditional probabilities are shown for repeated measures. The goodness of fit tests are extended in this paper on the basis of the deviance and the Hosmer-Lemeshow procedures and generalized to repeated measures. In addition, to measure the suitability of the proposed models for predicting the disease status, we have extended the ROC curve approach to repeated measures. The procedure is shown for the conditional models for any order as well as for the unconditional model, to predict the outcome at the end of the study. The test procedures are also suggested. For testing the differences between areas under the ROC curves in subsequent follow-ups, two different test procedures are employed, one of which is based on permutation test. In this paper, an unconditional model is proposed on the basis of conditional models for the disease progression of depression among the elderly population in the USA on the basis of the Health and Retirement Survey data collected longitudinally. The illustration shows that the disease progression observed conditionally can be employed to predict the outcome and the role of selected variables and the previous outcomes can be utilized for predictive purposes. The results show that the percentage of correct predictions of a disease is quite high and the measures of sensitivity and specificity are also reasonably impressive. The extended measures of area under the ROC curve show that the models provide a reasonably good fit in terms of predicting the disease status during a long period of time. This procedure will have extensive applications in the field of longitudinal data analysis where the objective is to obtain estimates of unconditional probabilities on the basis of series of conditional transitional models.
%B Statistical Methodology
%I 7
%V 7
%P 520-540
%G eng
%N 5
%L newpubs20100729_Islam.pdf
%4 methodology/risk Factors/disease/Hosmer-Lemeshow
%$ 23000
%R https://doi.org/10.1016/j.stamet.2010.03.001
%0 Journal Article
%J Statistical Methodology
%D 2009
%T Estimation and Tests for a Longitudinal Regression Model Based on the Markov Chain
%A M. Ataharul Islam
%A Khalaf S. Sultan
%A Rafiqul I Chowdhury
%K Methodology
%X In this paper, the dependence of transition probabilities on covariates and a test procedure for covariate dependent Markov models are examined. The nonparametric test for the role of waiting time proposed by Jones and Crowley M. Jones, J. Crowley, Nonparametric tests of the Markov model for survival data Biometrika 79 (3) (1992) 513522 has been extended here to transitions and reverse transitions. The limitation of the Jones and Crowley method is that it does not take account of other covariates that might have association with the probabilities of transition. A simple test procedure is proposed that can be employed for testing: (i) the significance of association between covariates and transition probabilities, and (ii) the impact of waiting time on the transition probabilities. The procedure is illustrated using panel data on hospitalization of the elderly population in the USA from the Health and Retirement Survey (HRS).
%B Statistical Methodology
%I 6
%V 6
%P 478-489
%G eng
%N 5
%L newpubs20100129_Islam.pdf
%4 Models, Statistical
%$ 21540
%R https://doi.org/10.1016/j.stamet.2009.04.003
%0 Book
%D 2009
%T Markov Models with Covariate Dependence for Repeated Measures
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Huda, Shahariar
%K Methodology
%X During the recent past, there has been a great deal of interst in solving problems of repeated measures data employing the Markov chain models. Most of the researchers and users of such techniques are only transition probabilities of various orders to show relationships among various states. However, in the recent past, there are attempts to include covariates in order to analyse the transition probabilities. Due to lack of a book on this topic, it is difficult for the researchers, students, and other users to have a thorough understanding in applying the methods based on sound knowledge. In addition, there is a lack of suitable software to handle repeated measures for Markov model applications. The main purpose of the book is to provide a theoretical base to the readers who will be willing to use these techniques for real life situations as well as for those who intend to continue advanced research in this field. This book provides a comprehensive discussion and theoretical details of the techniques in this field along with their estimation and test procedures, application of the techniques to real life problems, and the computer programs for using the techniques.
%I Nova Science Publishers
%C New York
%G eng
%4 Statistics and Numerical Data/Models, Statistical
%$ 20230
%0 Book Section
%B Progress in Applied Mathematical Modeling
%D 2008
%T First and Higher Order Transition Models with Covariate Dependence
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%E F. Yang
%K Demographics
%K Methodology
%X The covariate dependent Markov models can be employed in various fields of research for analyzing time series or repeated measures data. This paper highlights the covariate dependent Markov models for the first and higher orders. The first order covariate dependent Markov model developed by Muenz and Rubinstein (1985) is reviewed and then second and higher order models for binary sequence are developed along with their estimation and test procedures based on Islam and Chowdhury (2006). The models for more than two outcomes are also shown. A general procedure based on the Chapman-Kolmogorov equations is proposed here in order to take account of the transitions at unequal intervals. A simple test procedure is suggested here to determine the order of the underlying Markov models. The proposed methods are illustrated with the Health and Retirement Survey data from the USA on the mobility difficulty of the elderly population. The results indicate the utility of the transitional models for first or higher orders of underlying transitions with binary or multiple outcomes.
%B Progress in Applied Mathematical Modeling
%I Nova Science Publishers
%C USA
%P 153-96
%G eng
%U https://www.researchgate.net/publication/258212212_First_and_Higher_Order_Transition_Models_with_Covariate_Dependence_Chapter_4_ed_F_Yang_Nova_Science_Publishers_Hauppage_NY_pp_153-196_2008
%4 Mobility/Models, Statistical/Elderly
%$ 18790
%! First and Higher Order Transition Models with Covariate Dependence
%0 Report
%D 2007
%T A Multistage Model for Analyzing Repeated Observations on Depression in Elderly
%A M. Ataharul Islam
%A Rafiqul I Chowdhury
%A Huda, Shahariar
%K Demographics
%K Health Conditions and Status
%X The problem of depression in elderly, in terms of short and long-term health and economic consequence, needs to be assessed more critically. This study uses the longitudinal data from the six waves of the Health and Retirement Survey to examine the transition to depression, reverse transition from depression to no depression and also repeated transition from no depression to depression after experiencing a reverse transition during a study period. Covariate-dependent Markov models are proposed using the logistic link function based on the Chapman-Kolmogorov equation. A simplified and more flexible approach of constructing the likelihood function is demonstrated in this paper to deal with the branching of a number of transition types starting from a group of subjects with no depression at the beginning of the study.
%G eng
%L newpubs20080528_Ataharul
%4 Elderly/Depression
%$ 19060