Random Effects Specification Research Articles

A Bayesian Mixed Multinomial Logit Model for choice-sets and decision-makers’ heterogeneity

ABSTRACT We propose a Bayesian Mixed Multinomial Logit Model (MMLM) to deal with the critical issue of choice-set heterogeneity often present in policy evaluation studies enriched with microsimulated data. We also exploit the comparison of three clustering methods to capture decision-makers’ heterogeneity through a specific random effect. A case study, which aims to describe the determinants of labour choices of females in couples with microsimulated fiscal variables, is the test-bed for our methodological proposal. By virtue of this very flexible specification of the random components, the Bayesian MMLM proves to be more accurate, parsimonious and consistent in terms of point estimates with the research field than other models.

Characterizing the cognitive phenotype of Parkinson’s disease

AbstractBackgroundParkinson’s disease (PD) is a complex neurodegenerative disease in which combinations of motor and non‐motor symptoms result in a wide range of phenotypic presentations and disease progression. Precise characterization of these clinical endophenotypes can guide clinical practice. Such endophenotypes can also be powerful to identify genetic factors for complex diseases, like PD. Given the prevalence and impact of cognitive impairment in PD, we aimed to characterize cognitive endophenotypes in a cohort of PD patients with longitudinal assessments.MethodWe reviewed the medical records of 1028 individuals diagnosed with PD by a movement disorders specialist at our institution. We extracted scores from all available Mini‐Mental Status Examinations (MMSE). We fit proportional odds models with patient specific random effects to estimate odds ratios for higher versus lower cognition in association with age, sex and follow‐up time.ResultAmong the 237 patients with at least one MMSE, 108 had ≥2 time points and 118 were cognitively impaired (MMSE<27). The mean age was 71 years, 63 (27%) were female and the mean follow‐up time was 2.56 years. Overall, females with PD had higher MMSE scores than males with an odds ratio at age 70 of 2.43 (95% CI: 1.29, 4.97). However, MMSE scores decline with age at a faster rate in females than males (p=0.002) with ORs for a 1 year of 0.85 (0.79, 0.91) and 0.94 (95%CI: 0.91, 0.97), respectively. Within patients, declines in MMSE per year had an OR of 0.76 (0.60, 0.97) for females and 0.85 (0.75, 0.95) for males though this difference was not statistically significant. Corresponding expected MMSE trajectories are shown in the figure.ConclusionOur findings demonstrate sex differences in the cognitive phenotypes of PD. In particular, cognitive scores tend to be higher in females compared to males, but decline faster in females. These findings have clinical implications regarding counseling of patients with PD. We are currently expanding this approach to specific cognitive domains, other validated tests of cognition, and investigating the influence of genetic risk factors, such as APOE on cognitive decline in PD.

Mapping of anaemia prevalence among pregnant women in Kenya (2016\u20132019)

BackgroundReducing the burden of anaemia is a critical global health priority that could improve maternal outcomes amongst pregnant women and their neonates. As more counties in Kenya commit to universal health coverage, there is a growing need for optimal allocation of the limited resources to sustain the gains achieved with the devolution of healthcare services. This study aimed to describe the spatio-temporal patterns of maternal anaemia prevalence in Kenya from 2016 to 2019.MethodsQuarterly reported sub-county level maternal anaemia cases from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical negative binomial spatio-temporal conditional autoregressive (CAR) model was used to estimate maternal anaemia prevalence by sub-county and quarter. Spatial and temporal correlations were considered by assuming a conditional autoregressive and a first-order autoregressive process on sub-county and seasonal specific random effects, respectively.ResultsThe overall estimated number of pregnant women with anaemia increased by 90.1% (95% uncertainty interval [95% UI], 89.9–90.2) from 155,539 cases in 2016 to 295,642 cases 2019. Based on the WHO classification criteria, the proportion of sub-counties with normal prevalence decreased from 28.0% (95% UI, 25.4–30.7) in 2016 to 5.4% (95% UI, 4.1–6.7) in 2019, whereas moderate anaemia prevalence increased from 16.8% (95% UI, 14.7–19.1) in 2016 to 30.1% (95% UI, 27.5–32.8) in 2019 and severe anaemia prevalence increased from 7.0% (95% UI, 5.6–8.6) in 2016 to 16.6% (95% UI, 14.5–18.9) in 2019. Overall, 45.1% (95% UI: 45.0–45.2) of the estimated cases were in malaria-endemic sub-counties, with the coastal endemic zone having the highest proportion 72.8% (95% UI: 68.3–77.4) of sub-counties with severe prevalence.ConclusionAs the number of women of reproductive age continues to grow in Kenya, the use of routinely collected data for accurate mapping of poor maternal outcomes remains an integral component of a functional maternal health strategy. By unmasking the sub-county disparities often concealed by national and county estimates, our study findings reiterate the importance of maternal anaemia prevalence as a metric for estimating malaria burden and offers compelling policy implications for achieving national nutritional targets.

Spatial inequalities of COVID-19 mortality rate in relation to socioeconomic and environmental factors across England

In this study, we aimed to examine spatial inequalities of COVID-19 mortality rate in relation to spatial inequalities of socioeconomic and environmental factors across England. Specifically, we first explored spatial patterns of COVID-19 mortality rate in comparison to non-COVID-19 mortality rate. Subsequently, we established models to investigate contributions of socioeconomic and environmental factors to spatial variations of COVID-19 mortality rate across England (N = 317). Two newly developed specifications of spatial regression models were established successfully to estimate COVID-19 mortality rate (R2 = 0.49 and R2 = 0.793). The level of spatial inequalities of COVID-19 mortality is higher than that of non-COVID-19 mortality in England. Although global spatial association of COVID-19 mortality and non-COVID-19 mortality is positive, local spatial association of COVID-19 mortality and non-COVID-19 mortality is negative in some areas. Expectedly, hospital accessibility is negatively related to COVID-19 mortality rate. Percent of Asians, percent of Blacks, and unemployment rate are positively related to COVID-19 mortality rate. More importantly, relative humidity is negatively related to COVID-19 mortality rate. Moreover, among the spatial models estimated, the ‘random effects specification of eigenvector spatial filtering model’ outperforms the ‘matrix exponential spatial specification of spatial autoregressive model’.

Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England

To examine to what extent spatial inequalities in childhood obesity are attributable to spatial inequalities in socioeconomic characteristics across a country, we aimed to investigate the spatial associations of socioeconomic characteristics and childhood obesity. We first explored spatial patterns of childhood obesity prevalence, and subsequently investigated the spatial associations of socioeconomic factors and childhood obesity prevalence across England by selecting and estimating appropriate spatial regression models. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of socioeconomic factors and childhood obesity prevalence. As a result, among the two newly developed specifications of spatial regression models, the fast random effects specification of eigenvector spatial filtering (FRES-ESF) model appears to outperform the matrix exponential spatial specification of spatial autoregressive (MESS-SAR) model. Empirical results indicate that positive spatial dependence is found to exist in childhood obesity prevalence across England; and that socioeconomic factors are significantly associated with childhood obesity prevalence across England. In England, children living in areas with lower socioeconomic status are at higher risk of obesity. This study suggests effectively reducing spatial inequalities in socioeconomic status will plays a vital role in mitigating spatial inequalities in childhood obesity prevalence.

Mixed location scale hidden Markov model for the analysis of intensive longitudinal data

Hidden Markov models (HMM) presents an attractive analytical framework for capturing the state-switching process for auto-correlated data. These models have been extended to longitudinal data setting where simultaneous multiple processes are observed by including subject specific random effects. However, application of HMMs for intensive longitudinal data, where each subject gets measured intensively over relatively short period of time, has not been widely studied. In this paper, we extend the mixed hidden Markov model and allow subject heterogeneity with respect to the mean and within subject variance by including subject random effects in both perspectives. We focus on the application of this model to intensive longitudinal studies in psychological and behavioral research where individual’s latent states and state-switching process are of interest. Models are estimated using forward–backward algorithm via Bayesian sampling approach. Advantages over regular HMM and mixed HMM that only accounts for the subjects’ mean heterogeneity are illustrated through a series of simulation studies. Finally, models are applied to an adolescent mood study data set and results show that the proposed mixed location scale HMM achieves better model fit and more interpretative mood state identification in terms of state specific covariate effects compared to regular HMM and mixed HMM.

Analyzing weight evolution in mice infected by Trypanosoma cruzi

Concerning the specificities of a longitudinal study, the trajectories of a subject's mean responses not always present a linear behavior, which calls for tools that take into account the non-linearity of individual trajectories and that describe them towards associating possible random effects with each individual. Generalized additive mixed models (GAMMs) have come to solve this problem, since, in this class of models, it is possible to assign specific random effects to individuals, in addition to rewriting the linear term by summing unknown smooth functions, not parametrically specified, then using the P-splines smoothing technique. Thus, this article aims to introduce this methodology applied to a dataset referring to an experiment involving 57 Swiss mice infected by Trypanosoma cruzi, which had their weights monitored for 12 weeks. The analyses showed significant differences in the weight trajectory of the individuals by treatment group; besides, the assumptions required to validate the model were met. Therefore, it is possible to conclude that this methodology is satisfactory in modeling data of longitudinal sort, because, with this approach, in addition to the possibility of including fixed and random effects, these models allow adding complex correlation structures to residuals.

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya.

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012-2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

Understanding Unobserved Propensities of Suicide in the United States: A Hierarchial Model with Spatially Correlated Random Effects

This paper investigates the underlying causes of suicide. In contrast to previous literature, we use data from the United States at the county level. Our primary methodology is a two-level Bayesian hierarchical model with spatially correlated random effects. Our results show that the significant effects of observable factors on suicides found by earlier research may partially stem from excluding small area effects and time trends. Without controlling for these area and time effects, the true contribution of unobserved propensities and time trends can be hidden within observable factors. Most importantly, we find that a lot can be learned from unobserved yet persistent propensity toward suicide captured by the spatially correlated county specific random effects. We argue that resources should be allocated to counties with high suicide rates, but also counties with low raw suicide rates but high unobserved propensities of suicide.

A methodological comparison of mapping algorithms to obtain health utilities derived using cross-sectional and longitudinal data: Secondary analysis of NRG/RTOG 0415.

55 Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.

Impact of board characteristics on firm dividends: evidence from India

Purpose This study examined the impact of board size, independence and gender diversity on firm dividend payout. Furthermore, it examined whether the board characteristic–dividend payout relationship was moderated by free cash flows in the firm. Design/methodology/approach A total of 118 Indian firms representing multiple industries were examined for a period of four financial years from 2013 to 2016. The data are in panel form given the cross-sectional and time series nature of the study. Random effects specification was used for analysis Findings Results of the study indicated that the proportion of independent directors and proportion of female directors on the board have a negative and significant effect on dividend payout. In addition, the results showed a negative and significant moderating role of free cash flows, which implied that the magnitude of the impact of the proportion of independent directors and the proportion of female directors on the board on dividend payout is significantly greater in firms with high free cash flows. Overall, the results suggested that firms whose board characteristics signaled strong governance paid lower dividends. Originality/value This study contributes to a nuanced understanding of internal governance mechanisms by presenting evidence of the substitution hypothesis from an emerging economy, one in which firms operate within a unique regulatory framework of board composition.

Spatial Disease Mapping Using Directed Acyclic Graph Auto-Regressive (DAGAR) Models

Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Substantive simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Quality of life of chronic heart failure patients: a systematic review and meta-analysis.

Despite various individual studies on the quality of life (QOL) in patients with CHF, a comprehensive study has not yet been conducted; therefore, this study aims to assess the QOL of CHF patients. In the present systematic review and meta-analysis, PubMed, Scopus, and the Web of science databases were searched from January 1, 2000, to December 31, 2018, using QOL and heart failure as keywords. The searches, screenings, quality assessments, and data extractions were conducted separately by two researchers. A total of 70 studies including 25,180 participants entered the final stage. The mean QOL score was 44.1 (95% confidence interval (CI) 40.6, 47.5; I2 = 99.3%) using a specific random effects method in 40 studies carried out on 12,520 patients. Moreover, according to the geographical region, heart failure patients in the Americas had higher scores. In 14 studies, in which a general SF-36 survey was implemented, the average physical component score (PCS) and mental component score (MCS) were 33.3 (95% CI 31.9, 34.7; I2 = 88.0%) and 50.6 (95% CI 43.8, 57.4; I2 = 99.3%), respectively. The general and specific tools used in this study indicated moderate and poor QOL, respectively. Therefore, it is necessary to carry out periodic QOL measurements using appropriate tools as part of the general care of CHF patients.

Information Asymmetries, Financial Constraints and Institutional Investment: Evidence from the Real Estate Market

Employing an efficient Bayesian estimation procedure, Integrated Nested Laplace Approximation (INLA), we find evidence of Investor Size Premium. Controlling for investor skill, financing constraints, and prior market knowledge, as well as property and time-varying location specific factors and property random effects, we find that larger buyers tend to pay a significant price premium relative to smaller buyers, for the otherwise identical property. Debt plays a role for the length of the holding period. These results point to a significant role of intrinsic valuations and the role of bargaining intensity and can explain why real estate markets are highly segmented.

Initial conditions of dynamic panel data models: on within and between equations

Summary This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly defined, we show that the random effects model can be decomposed into a within equation and a between equation; hence, the random effects estimate is a pooling of the within and between estimates. Thus, our paper extends the pooling in the static panel data model (Maddala, 1971a) to the setting of dynamic panel data. This decomposition of a dynamic panel data model is revealing and valuable for estimation and the formulation of a Hausman test to test the possible correlation of individual effects with included regressors. Monte Carlo experiments are conducted to investigate the finite sample performance of estimators and the Hausman test. An empirical application of growth convergence in OECD countries is provided.

Regulations and location choices of top R&D investors worldwide

This paper explores how the interaction of different framework conditions affect the way in which top corporate R&D investors organise their cross-border operations worldwide. The analysis uses location-specific aspects, socio-economic factors and other controls common in the economic geography literature to investigate the distribution of a company's international subsidiaries. The location drivers are estimated using a multilevel mixed-effects logistic regression, controlling for both country characteristics and company specific random effects. Our results confirm that framework conditions, as product market regulation (PMR) and labour market legislation (EPL), affect the location strategies of top R&D investors. Adding to the literature we also found that: (i) PMR and EPL exert a mutually reinforcing negative effect on the location of subsidiaries, (ii) the effect of EPL is not significant for low levels of PMR, and (iii) barriers to trade and investment is the PMR component with the largest negative effect. Policy implications are drawn accordingly.

Pengaruh Manajemen Laba Terhadap Kinerja Keuangan Pada Perusahaan Yang Telah Menggunaan Sak Konvergensi Ifrs Mandatory (Studi Empiris Pada Perusahaan Manufakur Yang Terdaftar Di Bei Tahun 2013-2017)

This study aims to obtain empirical evidence about the effect of earnings management on financial performance. Earnings management is the act which is conducted by manager to maximize, minimize, or do income smoothing of company’s profit. Managers can affect their company’s market value by do earnings management, such as make their profit always increase every year to show their good job financial performance. Independent variable if this study is earnings management that measured by discretionary accruals and dependent variable is financial performance that measure by return on assets. This population of the study was all of the manufacturing companies on the Indonesia Stock Exchange for five years from 2013 to 2017, while the sample of the research was determined by purposive sampling method to obtain 51 sample companies. The study analyzed one independent variables using secondary data in the form of panel data with a cross-section in 51 companies and a time series of five years. The analysis was regression with a random effect specification. The analysis was regression with a random effect specification the results this study showed that earnings management affect return on assets significantlye.

The effect of post-retirement employment on health

Abstract This paper investigates the causal impact of re-entering the labour force from retirement on self-rated health and depressive symptoms paying careful attention to potential differences by gender. To address the potential endogeneity of work force re-entry, we use an instrumental variables approach in a conditional mixed process model combined with a correlated random effects specification estimated on data from 11 waves of the U.S. Health and Retirement Study. Results indicate a beneficial effect of post-retirement employment on self-assessed health and depressive symptoms for both women and men.

Multivariate functional clustering and its application to typhoon data

We propose a multivariate nonlinear mixed effects model for clustering multiple longitudinal data. Advantages of the nonlinear mixed effects model are that it is easy to handle unbalanced data which highly occur in the longitudinal study, and it can take into account associations among longitudinal variables at a given time point. The joint modeling for multivariate longitudinal data, however, requires a high computational cost because numerous parameters are included in the model. To overcome this issue, we perform a pairwise fitting procedure based on a pseudo-likelihood function. Unknown parameters included in each bivariate model are estimated by the maximum likelihood method along with the EM algorithm, and then the number of basis functions included in the model is selected by model selection criteria. After estimating the model, a non-hierarchical clustering algorithm by self-organizing maps is applied to the predicted coefficient vectors of individual specific random effect functions. We present the results of the application of the proposed method to the analysis of data of typhoons that occurred between 2000 and 2017 in Asia.

Framework to construct and interpret latent class trajectory modelling

ObjectivesLatent class trajectory modelling (LCTM) is a relatively new methodology in epidemiology to describe life-course exposures, which simplifies heterogeneous populations into homogeneous patterns or classes. However, for a given dataset,...