Molecular cross talk between misfolded proteins in animal models of Alzheimer's and prion diseases.

The central event in protein misfolding disorders (PMDs) is the accumulation of a misfolded form of a naturally expressed protein. Despite the diversity of clinical symptoms associated with different PMDs, many similarities in their mechanism suggest that distinct pathologies may cross talk at the molecular level. The main goal of this study was to analyze the interaction of the protein misfolding processes implicated in Alzheimer's and prion diseases. For this purpose, we inoculated prions in an Alzheimer's transgenic mouse model that develop typical amyloid plaques and followed the progression of pathological changes over time. Our findings show a dramatic acceleration and exacerbation of both pathologies. The onset of prion disease symptoms in transgenic mice appeared significantly faster with a concomitant increase on the level of misfolded prion protein in the brain. A striking increase in amyloid plaque deposition was observed in prion-infected mice compared with their noninoculated counterparts. Histological and biochemical studies showed the association of the two misfolded proteins in the brain and in vitro experiments showed that protein misfolding can be enhanced by a cross-seeding mechanism. These results suggest a profound interaction between Alzheimer's and prion pathologies, indicating that one protein misfolding process may be an important risk factor for the development of a second one. Our findings may have important implications to understand the origin and progression of PMDs.

Physician coding for mental illness reimbursement and the need for mental health parity

Many patients with anxiety and depression initially seek treatment from their primary care physicians. Changes in insurance coverage and current mental parity laws, make reimbursement for services a problem. This has led to a coding dilemma for physicians seeking payment for their services. This study seeks to determine first the frequency at which primary care physicians use alternative coding, and secondly, if physicians would change their coding practices, provided reimbursement was assured through changes in mental parity laws. A mail survey was sent to 260 randomly selected primary care physicians, who are family practice, internal medicine, and general practice physicians, and members of the Harris County Medical Society. The survey evaluated the physicians' demographics, the number of patients with psychiatric disorders seen by primary care physicians, the frequency with which physicians used alternative coding, and if mental parity laws changed, the rate at which physicians would use a psychiatric illness diagnosis as the primary diagnostic code. The overall response rate was 23%. Only 47 of the 59 physicians, who responded, qualified for the study and of those 45% used a psychiatric disorder to diagnose patients with a primary psychiatric disorder, 47% used a somatic/symptom disorder, and 8% used a medical diagnosis. From the physicians who would not use a psychiatric diagnosis as a primary ICD-9 code, 88% were afraid of not being reimbursed and 12% were worried about stigma or jeopardizing insurability. If payment were assured using a psychiatric diagnostic code, 81% physicians would use a psychiatric diagnosis as the primary diagnostic code. However, 19% would use an alternative diagnostic code in fear of stigmatizing and/or jeopardizing patients' insurability. Although the sample size of the study design was adequate, our survey did not have an ideal response rate, and no significant correlation was observed. However, it is evident that reimbursement for mental illness continues to be a problem for primary care physicians. The reformation of mental parity laws is necessary to ensure that patients receive mental health services and that primary care physicians are reimbursed. Despite the possibility of improved mental parity legislation, some physicians are still hesitant to assign patients with a mental illness diagnosis, due to the associated stigma, which still plays a role in today's society. ^

AN ASSESSMENT OF THE CURRENT STATUS OF MENTAL ILLNESS IN BENDEL STATE OF NIGERIA: IMPLICATIONS FOR PUBLIC MENTAL HEALTH POLICY AND EDUCATION

This descriptive study assesses the current status of mental illness in Bendel State of Nigeria to determine its implications for mental health policy and education. It is a study of the demographic characteristics of psychiatric patients in the only two modern western psychiatric facilities in Bendel State, the various treatment modalities utilized for mental illness, and the people's choice of therapeutic measures for mental illness in Bendel State.^ This study investigated ten aspects of mental illness in Bendel State (1) An increase of the prevalence of mental illness (psychiatric disorder) in Bendel State. (2) Unaided, unguided, and uncared for mentally ill people roaming about Bendel State. (3) Pluralistic Treatment Modalities for mentally ill patients in Bendel State. (4) Traditional Healers treating more mentally ill patients than the modern western psychiatric hospitals. (5) Inadequate modern western psychiatric facilities in Bendel State. (6) Controversy between Traditional Health and modern western trained doctors over the issue of possible cooperation between traditional and modern western medicine. (7) Evidence of mental illness in all ethnic groups in Bendel State. (8) More scientifically based and better organized modern western psychiatric hospitals than the traditional healing centers. (9) Traditional healers' level of approach with patients, and accessibility to patients' families compared with the modern western trained doctors. (10) An urgent need for an official action to institute a comprehensive mental health policy that will provide an optimum care for the mentally ill in Bendel State, and in Nigeria in general.^ Of the eight popular treatment modalities generally used in Bendel State for mental illness, 54% of the non-patient population sampled preferred the use of traditional healing, 26.5% preferred the use of modern western treatment, and 19.5% preferred religious healers.^ The investigator concluded at this time not to recommend the integration of Traditional Healing and modern western medicine in Nigeria. Rather, improvement of the existing modern western psychiatric facilities and a proposal to establish facilities to enable traditional healing and modern western medicine to exist side by side were highly recommended. ^

EFFECTS OF INFORMATION DISPLAY ON THE CONSTRUCTION OF CLINICIAN MENTAL MODELS

Objective: To determine how a clinician’s background knowledge, their tasks, and displays of information interact to affect the clinician’s mental model. Design: Repeated Measure Nested Experimental Design Population, Sample, Setting: Populations were gastrointestinal/internal medicine physicians and nurses within the greater Houston area. A purposeful sample of 24 physicians and 24 nurses were studied in 2003. Methods: Subjects were randomized to two different displays of two different mock medical records; one that contained highlighted patient information and one that contained non-highlighted patient information. They were asked to read and summarize their understanding of the patients aloud. Propositional analysis was used to understand their comprehension of the patients. Findings: Different mental models were found between physicians and nurses given the same display of information. The information they shared was very minor compared to the variance in their mental models. There was additionally more variance within the nursing mental models than the physician mental models given different displays of the same information. Statistically, there was no interaction effect between the display of information and clinician type. Only clinician type could account for the differences in the clinician comprehension and thus their mental models of the cases. Conclusion: The factors that may explain the variance within and between the clinician models are clinician type, and only in the nursing group, the use of highlighting.

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Improving the accuracy of radiation pneumonitis dose response models

The prognosis for lung cancer patients remains poor. Five year survival rates have been reported to be 15%. Studies have shown that dose escalation to the tumor can lead to better local control and subsequently better overall survival. However, dose to lung tumor is limited by normal tissue toxicity. The most prevalent thoracic toxicity is radiation pneumonitis. In order to determine a safe dose that can be delivered to the healthy lung, researchers have turned to mathematical models predicting the rate of radiation pneumonitis. However, these models rely on simple metrics based on the dose-volume histogram and are not yet accurate enough to be used for dose escalation trials. The purpose of this work was to improve the fit of predictive risk models for radiation pneumonitis and to show the dosimetric benefit of using the models to guide patient treatment planning. The study was divided into 3 specific aims. The first two specifics aims were focused on improving the fit of the predictive model. In Specific Aim 1 we incorporated information about the spatial location of the lung dose distribution into a predictive model. In Specific Aim 2 we incorporated ventilation-based functional information into a predictive pneumonitis model. In the third specific aim a proof of principle virtual simulation was performed where a model-determined limit was used to scale the prescription dose. The data showed that for our patient cohort, the fit of the model to the data was not improved by incorporating spatial information. Although we were not able to achieve a significant improvement in model fit using pre-treatment ventilation, we show some promising results indicating that ventilation imaging can provide useful information about lung function in lung cancer patients. The virtual simulation trial demonstrated that using a personalized lung dose limit derived from a predictive model will result in a different prescription than what was achieved with the clinically used plan; thus demonstrating the utility of a normal tissue toxicity model in personalizing the prescription dose.

Design and evaluation of the parent education component of a comprehensive violence prevention project in middle schools

Low parental monitoring is related to youth risk behaviors such as delinquency and aggression. The purpose of this dissertation was to describe the development and evaluation of a parent education intervention to increase parental monitoring in Hispanic parents of middle school children.^ The first study described the process of intervention mapping as used to develop Padres Trabajando por la Paz, a newsletter intervention for parents. Using theory, empirical literature, and information from the target population, performance objectives and determinants for monitoring were defined. Learning objectives were specified and a staged social-cognitive approach was used to develop methods and strategies delivered through newsletters.^ The second study examined the outcomes of a randomized trial of the newsletter intervention. Outcome measures consisted of a general measure of monitoring, parent and child reports of monitoring behaviors targeted by the intervention, and psychosocial determinants of monitoring (self-efficacy, norms, outcome expectancies, knowledge, and beliefs). Seventy-seven parents completed the randomized trial, half of which received four newsletters over an eight-week period. Results revealed a significant interaction effect for baseline and treatment for parent's reports of norms for monitoring (p =.009). Parents in the experimental condition who scored low at baseline reported increased norms for monitoring at follow-up. A significant interaction effect for child reports of parental monitoring behaviors (p =.04) reflected an small increase across baseline levels in the experimental condition and decreases for the control condition at higher baseline scores. Both groups of parents reported increased levels of monitoring at follow-up. No other outcome measures varied significantly by condition.^ The third study examined the relationship between the psychosocial determinants of parental monitoring and parental monitoring behaviors in the study population. Weak evidence for a relationship between outcome expectancies and parental monitoring behaviors suggests further research in the area utilizing stronger empirical models such as longitudinal design and structural equation modeling.^ The low-cost, minimal newsletter intervention showed promise for changing norms among Hispanic parents for parental monitoring. In light of the importance of parental monitoring as a protective factor for youth health risk behaviors, more research needs to be done to develop and evaluate interventions to increase parental monitoring. ^

Interrelationships between the structural and functional aspects of social relationships, comorbidity and health related quality of life in breast cancer survivors

It is widely acknowledged in theoretical and empirical literature that social relationships, comprising of structural measures (social networks) and functional measures (perceived social support) have an undeniable effect on health outcomes. However, the actual mechanism of this effect has yet to be clearly understood or explicated. In addition, comorbidity is found to adversely affect social relationships and health related quality of life (a valued outcome measure in cancer patients and survivors). ^ This cross sectional study uses selected baseline data (N=3088) from the Women's Healthy Eating and Living (WHEL) study. Lisrel 8.72 was used for the latent variable structural equation modeling. Due to the ordinal nature of the data, Weighted Least Squares (WLS) method of estimation using Asymptotic Distribution Free covariance matrices was chosen for this analysis. The primary exogenous predictor variables are Social Networks and Comorbidity; Perceived Social Support is the endogenous predictor variable. Three dimensions of HRQoL, physical, mental and satisfaction with current quality of life were the outcome variables. ^ This study hypothesizes and tests the mechanism and pathways between comorbidity, social relationships and HRQoL using latent variable structural equation modeling. After testing the measurement models of social networks and perceived social support, a structural model hypothesizing associations between the latent exogenous and endogenous variables was tested. The results of the study after listwise deletion (N=2131) mostly confirmed the hypothesized relationships (TLI, CFI >0.95, RMSEA = 0.05, p=0.15). Comorbidity was adversely associated with all three HRQoL outcomes. Strong ties were negatively associated with perceived social support; social network had a strong positive association with perceived social support, which served as a mediator between social networks and HRQoL. Mental health quality of life was the most adversely affected by the predictor variables. ^ This study is a preliminary look at the integration of structural and functional measures of social relationships, comorbidity and three HRQoL indicators using LVSEM. Developing stronger social networks and forming supportive relationships is beneficial for health outcomes such as HRQoL of cancer survivors. Thus, the medical community treating cancer survivors as well as the survivor's social networks need to be informed and cognizant of these possible relationships. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

The association between mental health and hypertension in the 2005 Department of Defense Population Survey

Major objectives within Healthy People 2010 include improving hypertension and mental health management of the American population. Both mental health issues and hypertension exist in the military which may decrease the health status of military personnel and diminish the ability to complete assigned missions. Some cases may be incompatible with military service even with optimum treatment. In the interest of maintaining a fit fighting force, the Department of Defense regularly conducts a survey of health related behaviors among active duty military personnel. The 2005 DoD Survey was conducted to obtain information regarding health and behavioral readiness among active duty military personnel to assess progress toward selected Healthy People 2010 objectives. ^ This study is a cross-sectional prevalence design looking at the association of hypertension treatment with mental health issues (either treatment or perceived need for treatment) within the military population sampled in the 2005 DoD Survey. There were 16,946 military personnel in the final cross-sectional sample representing 1.3 million active duty service members. The question is whether there is a significant association between the self-reported occurrence of hypertension and the self-reported occurrence of mental health issues in the 2005 DoD Survey. In addition to these variables, this survey examined the contribution of various sociodemographic, occupational, and behavioral covariates. An analysis of the demographic composition of the study variables was followed by logistic analysis, comparing outcome variables with each of the independent variables. Following univariate regression analysis, multivariate regression was performed with adjustment (for those variables with an unadjusted alpha level less than or equal to 0.25). ^ All the mental health related indicators were associated with hypertension treatment. The same relationship was maintained after multivariate adjustment. The covariates remaining as significant (p < 0.05) in the final model included gender, age, race/ethnicity and obesity. There is a need to recognize and treat co-morbid medical diagnoses among mental health patients and to improve quality of life outcomes, whether in the military population or the general population. Optimum health of the individual can be facilitated through discovery of treatable cases, to minimize disruptions of military missions, and even allow for continued military service. ^

The association between mental health and cigarette smoking in active duty military members

Previous research has shown an association between mental health status and cigarette smoking. This study examined four specific mental health predictors and the outcome variable any smoking, defined as smoking one or more cigarettes in the past 30 days. The population included active duty military members serving in the United States Army, Air Force, Navy and Marine Corps. The data was collected during the 2005 Department of Defense Survey of Health Related Behaviors Among Active Duty Military Personnel, a component of the Defense Lifestyle Assessment Program. The sample size included 13,603 subjects. This cross sectional prevalence study consisted of descriptive statistics, univariate analysis, and multivariate logistic regression analysis of the four mental health predictors and the any smoking outcome variable. Multivariate adjustment showed an association between the four mental health predictors and any smoking. This association is consistent with previous literature and can help guide public health officials in the development of smoking prevention and cessation programs.^

The feasibility of self-report advance directives among elderly patients with cognitive impairment

The central objective of this dissertation was to determine the feasibility of self-completed advance directives (AD) in older persons suffering from mild and moderate stages of dementia. This was accomplished by identifying differences in ability to complete AD among elderly subjects with increasing degrees of dementia and cognitive incompetence. Secondary objectives were to describe and compare advance directives completed by elders and identified proxy decision makers. Secondary objectives were accomplished by measuring the agreement between advance directives completed by proxy and elder, and comparing that agreement across groups defined by the elder's cognitive status. This cross-sectional study employed a structured interview to elicit AD, followed by a similar interview with a proxy decision maker identified by the elder. A stratified sampling scheme recruited elders with normal cognition, mild, and moderate forms of dementia using the Mini Mental-State Exam (MMSE). The Hopkins Competency Assessment Test (HCAT) was used for evaluation of competency to make medical decisions. Analysis was conducted on "between group" (non-demented $\leftrightarrow$ mild dementia $\leftrightarrow$ moderate dementia, and competent $\leftrightarrow$ incompetent) and "within group" (elder $\leftrightarrow$ family member) variation.^ The 118 elderly subjects interviewed were generally male, Caucasian, and of low socioeconomic status. Mean age was 77. Overall, elders preferred a "trial of therapy" regarding AD rather than to "always receive the therapy". No intervention was refused outright more often than it was accepted. A test-retest of elders' AD revealed stable responses. Eleven logic checks measured appropriateness of AD responses independent of preference. No difference was found in logic error rates between elders grouped by MMSE or HCAT. Agreement between proxy and elder responses showed significant dissimilarity, indicating that proxies were not making the same medical decisions as the elders.^ Conclusions based on these data are: (1) Self reporting AD is feasible among elders showing signs of cognitive impairment and they should be given all opportunities to complete advance directives, (2) variation in preferences for advance directives in cognitively impaired elders should not be assumed to be the effects of their impairment alone, (3) proxies do not appear to forego life-prolonging interventions in the face of increasing impairment in their ward, however, their advance directives choices are frequently not those of the elder they represent. ^

Understanding mental illness in Nigeria: Bringing culture and traditional medicine into mental health policy

One of the major challenges in treating mental illness in Nigeria is that the health care facilities and mental health care professionals are not enough in number or well equipped to handle the burden of mental illness. There are several barriers to treatment for individual Nigerians which include the following: such as the lack of understanding of the root causes of mental illness, lack of financial support to get mental treatment, lack of social support (family, friends, neighbors), the fear of stigmatization concerning being labeled as mentally ill or being in association with the mentally ill, and the consultation of traditional native healers who may be unknowingly prolonging illness, rather than addressing and treating them due to lack of formal education and standardization of their treatments. Another barrier is the non-health nature of the mental health services in Nigeria. Traditional healers are essentially the mental health system. The elderly, women, and children are the most vulnerable groups in times of strife and hardships. Their mental well-being must be taken into account as well as their special needs in times of personal or societal crisis. ^ Nigerian mental health policy is geared toward forming a mental health system, but in actuality only a mental illness care system is the observed result of the policy. The government of Nigeria has drafted a mental health policy, yet its actual implementation into the Nigerian health infrastructure and society waits to be materialized. The limited health legislation or policy implementations tend to favor those who have access to these urban areas and the facilities' health services. Nigerians living in rural areas are at a disadvantage; many of them may not even be aware of services available to help them understand and treat mental illness. Perhaps, government driven health interventions geared toward mental illness in rural areas would reach an underserved Nigerians and Africans in general. Issues with political instability and limited infrastructure often hinder crucial financial resources and legislation from reaching the people that are truly in need of governmental leadership in regards to mental health policy.^ Traditional healers are a severely untapped resource in the treatment of mental illness within the Nigerian population. They are abundant within Nigerian communities and are meeting a real need for the mentally ill. However, much can be done to remove the barriers that prevent the integration of traditional healers within the mental health system and improve the quality of care they administer within the population. Mental illness is almost exclusively coped with through traditional medicine practices. Mobilization and education from each strata of Nigerian society and government as well as input from the medical community can improve how traditional medicine is utilized as a treatment for clinical illness and help alleviate the heavy burden of mental illness in Nigeria. Currently, there is no existing policy making structure for a working mental health system in Nigeria, and traditional healers are not taken into account in any formulation of mental health policy. Advocacy for mental illness is severely inadequate due to fear of stigmatization, with no formally recognized national of regional mental health association.^

Slope estimation in a parametric measurement error model: A simulation study

In regression analysis, covariate measurement error occurs in many applications. The error-prone covariates are often referred to as latent variables. In this proposed study, we extended the study of Chan et al. (2008) on recovering latent slope in a simple regression model to that in a multiple regression model. We presented an approach that applied the Monte Carlo method in the Bayesian framework to the parametric regression model with the measurement error in an explanatory variable. The proposed estimator applied the conditional expectation of latent slope given the observed outcome and surrogate variables in the multiple regression models. A simulation study was presented showing that the method produces estimator that is efficient in the multiple regression model, especially when the measurement error variance of surrogate variable is large.^