A comparison of Markov and generalized linear models of hospital mortality

This paper reports a comparison of three modeling strategies for the analysis of hospital mortality in a sample of general medicine inpatients in a Department of Veterans Affairs medical center. Logistic regression, a Markov chain model, and longitudinal logistic regression were evaluated on predictive performance as measured by the c-index and on accuracy of expected numbers of deaths compared to observed. The logistic regression used patient information collected at admission; the Markov model was comprised of two absorbing states for discharge and death and three transient states reflecting increasing severity of illness as measured by laboratory data collected during the hospital stay; longitudinal regression employed Generalized Estimating Equations (GEE) to model covariance structure for the repeated binary outcome. Results showed that the logistic regression predicted hospital mortality as well as the alternative methods but was limited in scope of application. The Markov chain provides insights into how day to day changes of illness severity lead to discharge or death. The longitudinal logistic regression showed that increasing illness trajectory is associated with hospital mortality. The conclusion is reached that for standard applications in modeling hospital mortality, logistic regression is adequate, but for new challenges facing health services research today, alternative methods are equally predictive, practical, and can provide new insights. ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Generalized resistance resources of informal caregivers: A study of demands for care, competing demands, perception of burden and cognitive stress

This study examines the relationship among psychological resources (generalized resistance resources), care demands (demands for care, competing demands, perception of burden) and cognitive stress in a selected population of primary family caregivers. The study utilizes Antonovsky's Salutogenic Model of Health, specifically the concept of generalized resistance resources (GRRs), to analyze the relative effect of these resources on mediating cognitive stress, controlling for other care demands. The study is based on a sample of 784 eligible caregivers who (1) were relatives, (2) had the main responsibility for care, defined as a primary caregiver, and (3) provided a scaled stress score for the amount of overall care given to the care recipient (family member). The sample was drawn from the 1982 National Long-Term Care Survey (NLTCS) of individuals who assisted a given NLTCS sample person with ADL limitations.^ The study tests the following hypotheses: (a) There will be a negative relationship between generalized resistance resources (GRRs) and cognitive stress controlling for care demands (demands for care, competing demands, and perceptions of burden); (b) of the specific GRRs (material, cognitive, social, cultural-environmental) the social domain will represent the most significant factor predicting a decrease in cognitive stress; and (c) the social domain will be more significant for the female than the male primary family caregiver in decreasing cognitive stress.^ The study found that GRRs had a statistically significant mediating effect on cognitive stress, but the GRRs were a less significant predictor of stress than perception of burden and demands for care. Thus, although the analysis supported the underlying hypothesis, the specific hypothesis regarding GRRs' greater significance in buffering cognitive stress was not supported. Second, the results did not demonstrate the statistical significance or differences among the GRR domains. The hypothesis that the social GRR domain was most significant in mediating stress of family caregivers was not supported. Finally, the results confirmed that there are differences in the importance of social support help in mediating stress based on gender. It was found that gender and social support help were related to cognitive stress and gender had a statistically significant interaction effect with social support help. Implications for clinical practice, public health policy, and research are discussed. ^

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

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. ^

Association of part-time employment and sexual risk-taking behaviors among middle school students

Background. Previous research shows inconsistent results as to the association between part-time employment and sexual behavior among younger teens. Studies of older teens cannot be generalized to younger teens because of the wide differences in types of work performed, nature of work environments, and work intensity. Objective. Examine the relationship between part-time employment and sexual behavior in a cross-sectional sample of public middle school students in Houston, Texas. Methods . The study presents a secondary analysis of data from the It’s Your Game…Keep it Real baseline data collection (11/2004–1/2005). It’s Your Game… is an intervention program for middle school students designed to prevent Sexually Transmitted Infections. Statistical analysis. Univariate and multivariate logistic regression analyses were conducted to examine the association between part-time employment and vaginal intercourse: (a) ever had sex; and (b) current sexual activity. Results. Overall, 13.2% of students worked for pay; male students were 1.5 times as likely as females to be working. Of all the students, 11.0% had had sexual intercourse; students who worked were 3 times more likely to be sexually experienced than those who did not. Among students who were sexually experienced, 67.0% were currently sexually active. After adjusting for the other covariates, Hispanic students were almost 3.6 times more likely to report current sexual activity compared to students in other racial/ethnic groups. In univariate analysis, students who worked 1-5 hrs/week were more likely to be sexually experienced than those not currently employed, and the likelihood increased with number of hours worked. There is a similar pattern in the multivariate model, but the odds ratios are too close for the evidence to be more than suggestive. Of sexually experienced students, students working 1-5 hrs/week were 2.7 times more likely to report current sexual intercourse than those not working; those working >5 hrs/week were 4.7 times more likely. The multivariate model showed a similar increase in likelihood, and adjustment for covariates increased these associations: students who worked 1-5 hrs/week were 3.6 times more likely to report current sexual intercourse, and students who worked >5 hrs/week were 4.5 times more likely, than students not currently employed.^

Hispanic incorporation and access to health care in Houston and Harris County, Texas: Adopting an Hispanic perspective to the Aday and Andersen model

This study examines Hispanic levels of incorporation and access to health care. Applying the Aday and Andersen framework for the study of access, the study examined the relationship between two levels of Hispanic incorporation into U.S. society, i.e., mainstream versus ethnic, and potential and realized measures of access to health care. Data for the study were drawn from a 1992 telephone survey of 600 randomly selected Hispanics in Houston and Harris County.^ The hypotheses tested were: (1) Hispanics who are incorporated into mainstream society are more likely to have better potential and realized access to health care than those who are incorporated into ethnic-group enclaves regardless of their socioeconomic status (SES), health status and health needs, and (2) there is no interaction between the levels of incorporation (mainstream or ethnic) and SES, health status, and health needs in predicting potential and realized access.^ The data analysis supported Hypothesis One for the two measures of potential access. The results of bivariate and multiple logistic regression analyses indicated that for Hispanics in Houston and Harris County, being in the "mainstream" incorporation category increased their potential access to care, having "health insurance" and a "regular place of care". For the selected measure of realized access, having a "regular check-up", the analysis did not demonstrate statistically significant differences in having a regular check-up among Hispanics incorporated in the ethnic or mainstream incorporation categories.^ Hypothesis Two, that there is no interaction between the levels of incorporation and socioeconomic characteristics, health status, and health needs in predicting potential and realized access among Hispanics was supported by the data. The results of the logistic regression analysis showed that, after adjusting for socioeconomic status, health status, and health needs, the association between "level of incorporation" and the two measures of potential access ("health insurance" and having a "usual place of care") was not modified by the control variables nor by their interaction with level of incorporation. That is, the effect of incorporation on Hispanics' health insurance coverage, and having a usual place of care, was homogenous across Hispanics with different SES and health status.^ The main research implication of this dissertation is the employment of a theoretical framework for the assessment of cultural factors essential to research on migrating heterogeneous subpopulations. It also provided strategies to solve practical and methodological difficulties in the secondary analyses of data on these populations. ^

Logistic regression with Markov chains as covariates

The tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) is an obvious carcinogen for lung cancer. Since CBMN (Cytokinesis-blocked micronucleus) has been found to be extremely sensitive to NNK-induced genetic damage, it is a potential important factor to predict the lung cancer risk. However, the association between lung cancer and NNK-induced genetic damage measured by CBMN assay has not been rigorously examined. ^ This research develops a methodology to model the chromosomal changes under NNK-induced genetic damage in a logistic regression framework in order to predict the occurrence of lung cancer. Since these chromosomal changes were usually not observed very long due to laboratory cost and time, a resampling technique was applied to generate the Markov chain of the normal and the damaged cell for each individual. A joint likelihood between the resampled Markov chains and the logistic regression model including transition probabilities of this chain as covariates was established. The Maximum likelihood estimation was applied to carry on the statistical test for comparison. The ability of this approach to increase discriminating power to predict lung cancer was compared to a baseline "non-genetic" model. ^ Our method offered an option to understand the association between the dynamic cell information and lung cancer. Our study indicated the extent of DNA damage/non-damage using the CBMN assay provides critical information that impacts public health studies of lung cancer risk. This novel statistical method could simultaneously estimate the process of DNA damage/non-damage and its relationship with lung cancer for each individual.^

Prediction of sepsis in the burn intensive care unit patient

Sepsis is a significant cause for multiple organ failure and death in the burn patient, yet identification in this population is confounded by chronic hypermetabolism and impaired immune function. The purpose of this study was twofold: 1) determine the ability of the systemic inflammatory response syndrome (SIRS) and American Burn Association (ABA) criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit (ICU) was conducted for the period January 2005 to September 2010. Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data were collected for the 72 hours prior to each blood culture. The most significant predictors were evaluated using logistic regression, Generalized Estimating Equations (GEE) and ROC area under the curve (AUC) analyses to assess model predictive ability. Bootstrapping methods were employed to evaluate potential model over-fitting. Fifty-nine subjects were included, representing 177 culture periods. SIRS criteria were not found to be associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The variables identified for the model included: heart rate>130 beats/min, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature>36°C, use of vasoactive medications, and glucose>150 mg/d1. The model was significant in predicting "positive culture-sick" and sepsis state, with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. SIRS criteria are not appropriate for identifying sepsis in the burn population. The ABA criteria perform better, but only for the day prior to positive blood culture results. The time period useful to diagnose sepsis using clinical criteria may be limited to 24 hours. A combination of predictors is superior to individual variable trends, yet algorithms or computer support will be necessary for the clinician to find such models useful. ^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

The risk prediction for SPM and its impact on the survival in patients with head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth common malignancy in the world, with high rates of developing second primary malignancy (SPM) and moderately low survival rates. This disease has become an enormous challenge in the cancer research and treatments. For HNSCC patients, a highly significant cause of post-treatment mortality and morbidity is the development of SPM. Hence, assessment of predicting the risk for the development of SPM would be very helpful for patients, clinicians and policy makers to estimate the survival of patients with HNSCC. In this study, we built a prognostic model to predict the risk of developing SPM in patients with newly diagnosed HNSCC. The dataset used in this research was obtained from The University of Texas MD Anderson Cancer Center. For the first aim, we used stepwise logistic regression to identify the prognostic factors for the development of SPM. Our final model contained cancer site and overall cancer stage as our risk factors for SPM. The Hosmer-Lemeshow test (p-value= 0.15>0.05) showed the final prognostic model fit the data well. The area under the ROC curve was 0.72 that suggested the discrimination ability of our model was acceptable. The internal validation confirmed the prognostic model was a good fit and the final prognostic model would not over optimistically predict the risk of SPM. This model needs external validation by using large data sample size before it can be generalized to predict SPM risk for other HNSCC patients. For the second aim, we utilized a multistate survival analysis approach to estimate the probability of death for HNSCC patients taking into consideration of the possibility of SPM. Patients without SPM were associated with longer survival. These findings suggest that the development of SPM could be a predictor of survival rates among the patients with HNSCC.^

Neocortical hyperexcitability defect in a mutant mouse model of spike-wave epilepsy, {\it stargazer}

Single-locus mutations in mice can express epileptic phenotypes and provide critical insights into the naturally occurring defects that alter excitability and mediate synchronization in the central nervous system (CNS). One such recessive mutation (on chromosome (Chr) 15), stargazer(stg/stg) expresses frequent bilateral 6-7 cycles per second (c/sec) spike-wave seizures associated with behavioral arrest, and provides a valuable opportunity to examine the inherited lesion associated with spike-wave synchronization.^ The existence of distinct and heterogeneous defects mediating spike-wave discharge (SWD) generation has been demonstrated by the presence of multiple genetic loci expressing generalized spike-wave activity and the differential effects of pharmacological agents on SWDs in different spike-wave epilepsy models. Attempts at understanding the different basic mechanisms underlying spike-wave synchronization have focused on $\gamma$-aminobutyric acid (GABA) receptor-, low threshold T-type Ca$\sp{2+}$ channel-, and N-methyl-D-aspartate receptor (NMDA-R)-mediated transmission. It is believed that defects in these modes of transmission can mediate the conversion of normal oscillations in a trisynaptic circuit, which includes the neocortex, reticular nucleus and thalamus, into spike-wave activity. However, the underlying lesions involved in spike-wave synchronization have not been clearly identified.^ The purpose of this research project was to locate and characterize a distinct neuronal hyperexcitability defect favoring spike-wave synchronization in the stargazer brain. One experimental approach for anatomically locating areas of synchronization and hyperexcitability involved an attempt to map patterns of hypersynchronous activity with antibodies to activity-induced proteins.^ A second approach to characterizing the neuronal defect involved examining the neuronal responses in the mutant following application of pharmacological agents with well known sites of action.^ In order to test the hypothesis that an NMDA receptor mediated hyperexcitability defect exists in stargazer neocortex, extracellular field recordings were used to examine the effects of CPP and MK-801 on coronal neocortical brain slices of stargazer and wild type perfused with 0 Mg$\sp{2+}$ artificial cerebral spinal fluid (aCSF).^ To study how NMDA receptor antagonists might promote increased excitability in stargazer neocortex, two basic hypotheses were tested: (1) NMDA receptor antagonists directly activate deep layer principal pyramidal cells in the neocortex of stargazer, presumably by opening NMDA receptor channels altered by the stg mutation; and (2) NMDA receptor antagonists disinhibit the neocortical network by blocking recurrent excitatory synaptic inputs onto inhibitory interneurons in the deep layers of stargazer neocortex.^ In order to test whether CPP might disinhibit the 0 Mg$\sp{2+}$ bursting network in the mutant by acting on inhibitory interneurons, the inhibitory inputs were pharmacologically removed by application of GABA receptor antagonists to the cortical network, and the effects of CPP under 0 Mg$\sp{2+}$aCSF perfusion in layer V of stg/stg were then compared with those found in +/+ neocortex using in vitro extracellular field recordings. (Abstract shortened by UMI.) ^

Behavioral decision making and the levels of change: An application of the transtheoretical model to prenatal smoking in low -income women

Despite continued research and public health efforts to reduce smoking during pregnancy, prenatal cessation rates in the United States have decreased and the incidence of low birth weight has increased from 1985 to 1991. Lower socioeconomic status women who are at increased risk for poor pregnancy outcomes may be resistant to current intervention efforts during pregnancy. The purpose of this dissertation was to investigate the determinants of continued smoking and quitting among low-income pregnant women.^ Using data from cross-sectional surveys of 323 low-income pregnant smokers, the first study developed and tested measures of the pros and cons of smoking during pregnancy. The original decisional balance measure for smoking was compared with a new measure that added items thought to be more salient to the target population. Confirmatory factor analysis using structural equation modeling showed neither the original nor new measure fit the data adequately. Using behavioral science theory, content from interviews with the population, and statistical evidence, two 7-item scales representing the pros and cons were developed from a portion (n = 215) of the sample and successfully cross-validated on the remainder of the sample (n = 108). Logistic regression found only pros were significantly associated with continued smoking. In a discriminant function analysis, stage of change was significantly associated with pros and cons of smoking.^ The second study examined the structural relationships between psychosocial constructs representing some of the levels of and the pros and cons of smoking. The cross-sectional design mandates that statements made regarding prediction do not prove causation or directionality from the data or methods analysis. Structural equation modeling found the following: more stressors and family criticism were significantly more predictive of negative affect than social support; a bi-directional relationship was found between negative affect and current nicotine addiction; and negative affect, addiction, stressors, and family criticism were significant predictors of pros of smoking.^ The findings imply reversing the trend of decreasing smoking cessation during pregnancy may require supplementing current interventions for this population of pregnant smokers with programs addressing nicotine addiction, negative affect, and other psychosocial factors such as family functioning and stressors. ^