A Bayesian approach to estimating the intraclass correlation coefficient

A Bayesian approach to estimating the intraclass correlation coefficient was used for this research project. The background of the intraclass correlation coefficient, a summary of its standard estimators, and a review of basic Bayesian terminology and methodology were presented. The conditional posterior density of the intraclass correlation coefficient was then derived and estimation procedures related to this derivation were shown in detail. Three examples of applications of the conditional posterior density to specific data sets were also included. Two sets of simulation experiments were performed to compare the mean and mode of the conditional posterior density of the intraclass correlation coefficient to more traditional estimators. Non-Bayesian methods of estimation used were: the methods of analysis of variance and maximum likelihood for balanced data; and the methods of MIVQUE (Minimum Variance Quadratic Unbiased Estimation) and maximum likelihood for unbalanced data. The overall conclusion of this research project was that Bayesian estimates of the intraclass correlation coefficient can be appropriate, useful and practical alternatives to traditional methods of estimation. ^

A study of nurses' judgement of and participation in decision-making for nursing home placement for the elderly

This study investigated the effects of patient variables (physical and cognitive disability, significant others' preference and social support) on nurses' nursing home placement decision-making and explored nurses' participation in the decision-making process.^ The study was conducted in a hospital in Texas. A sample of registered nurses on units that refer patients for nursing home placement were asked to review a series of vignettes describing elderly patients that differed in terms of the study variables and indicate the extent to which they agreed with nursing home placement on a five-point Likert scale. The vignettes were judged to have good content validity by a group of five colleagues (expert consultants) and test-retest reliability based on the Pearson correlation coefficient was satisfactory (average of.75) across all vignettes.^ The study tested the following hypotheses: Nurses have more of a propensity to recommend placement when (1) patients have severe physical disabilities; (2) patients have severe cognitive disabilities; (3) it is the significant others' preference; and (4) patients have no social support nor alternative services. Other hypotheses were that (5) a nurse's characteristics and extent of participation will not have a significant effect on their placement decision; and (6) a patient's social support is the most important, single factor, and the combination of factors of severe physical and cognitive disability, significant others' preference, and no social support nor alternative services will be the most important set of predictors of a nurse's placement decision.^ Analysis of Variance (ANOVA) was used to analyze the relationships implied in the hypothesis. A series of one-way ANOVA (bivariate analyses) of the main effects supported hypotheses one-five.^ Overall, the n-way ANOVA (multivariate analyses) of the main effects confirmed that social support was the most important single factor controlling for other variables. The 4-way interaction model confirmed that the most predictive combination of patient characteristics were severe physical and cognitive disability, no social support and the significant others did not desire placement. These analyses provided an understanding of the importance of the influence of specific patient variables on nurses' recommendations regarding placement. ^

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

The Spanish translation, cultural adaptation and validation of the Powe Fatalism Inventory

Background/significance. The scarcity of reliable and valid Spanish language instruments for health related research has hindered research with the Hispanic population. Research suggests that fatalistic attitudes are related to poor cancer screening behaviors and may be one reason for low participation of Mexican-Americans in cancer screening. This problem is of major concern because Mexican-Americans constitute the largest Hispanic subgroup in the U.S.^ Purpose. The purposes of this study were: (1) To translate the Powe Fatalism Inventory, (PFI) into Spanish, and culturally adapt the instrument to the Mexican-American culture as found along the U.S.-Mexico border and (2) To test the equivalence between the Spanish translated, culturally adapted version of the PFI and the English version of the PFI to include clarity, content validity, reading level and reliability.^ Design. Descriptive, cross-sectional.^ Methods. The Spanish language translation used a translation model which incorporates a cultural adaptation process. The SPFI was administered to 175 bilingual participants residing in a midsize, U.S-Mexico border city. Data analysis included estimation of Cronbach's alpha, factor analysis, paired samples t-test comparison and multiple regression analysis using SPSS software, as well as measurement of content validity and reading level of the SPFI. ^ Findings. A reliability estimate using Cronbach's alpha coefficient was 0.81 for the SPFI compared to 0.80 for the PFI in this study. Factor Analysis extracted four factors which explained 59% of the variance. Paired t-test comparison revealed no statistically significant differences between the SPFI and PFI total or individual item scores. Content Validity Index was determined to be 1.0. Reading Level was assessed to be less than a 6th grade reading level. The correlation coefficient between the SPFI and PFI was 0.95.^ Conclusions. This study provided strong psychometric evidence that the Spanish translated, culturally adapted SPFI is an equivalent tool to the English version of the PFI in measuring cancer fatalism. This indicates that the two forms of the instrument can be used interchangeably in a single study to accommodate reading and speaking abilities of respondents. ^

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. ^

Comparison of Bispectral Index (BIS) and Patient State Index (PSI) as objective measures of sedation and analgesia in critically ill/injured patients

Critically ill and injured patients require pain relief and sedation to reduce the body's stress response and to facilitate painful diagnostic and therapeutic procedures. Presently, the level of sedation and analgesia is guided by the use of clinical scores which can be unreliable. There is therefore, a need for an objective measure of sedation and analgesia. The Bispectral Index (BIS) and Patient State Index (PSI) were recently introduced into clinical practice as objective measures of the depth of analgesia and sedation. ^ Aim. To compare the different measures of sedation and analgesia (BIS and PSI) to the standard and commonly used modified Ramsay Score (MRS) and determine if the monitors can be used interchangeably. ^ Methods. MRS, BIS and PSI values were obtained in 50 postoperative cardiac surgery patients requiring analgesia and sedation from June to December 2004. The MRS, BIS and PSI values were assessed hourly for up to 6-h by a single observer. ^ The relationship between BIS and PSI values were explored using scatter plots and correlation between MRS, BIS and PSI was determined using Spearman's correlation coefficient. Intra-class correlation (ICC) was used to determine the inter-rater reliability of MRS, BIS and PSI. Kappa statistics was used to further evaluate the agreement between BIS and PSI at light, moderate and deep levels of sedation. ^ Results. There was a positive correlation between BIS and PSI values (Rho = 0.731, p<0.001). Intra-class correlation between BIS and PSI was 0.58, MRS and BIS 0.43 and MRS and PSI 0.27. Using Kappa statistics, agreement between MRS and BIS was 0.35 (95% CI: 0.27–0.43) and for MRS and PSI was 0.21 (95% CI: 0.15–0.28). The kappa statistic for BIS and PSI was 0.45 (95% CI: 0.37–0.52). Receiver operating characteristics (ROC) curves constructed to detect undersedation indicated an area under the curve (AUC) of 0.91 (95% CI = 0.87 to 0.94) for the BIS and 0.84 (95% CI = 0.79 to 0.88) for the PSI. For detection of oversedation, AUC for the BIS was 0.89 (95% CI = 0.84 to 0.92) and 0.80 (95% CI = 0.75 to 0.85) for the PSI. ^ Conclusions. There is a statistically significant positive correlation between the BIS and PSI but poor correlation and poor test agreement between the MRS and BIS as well as MRS and PSI. Both the BIS and PSI demonstrated a high level of prediction for undersedation and oversedation; however, the BIS and PSI can not be considered interchangeable monitors of sedation. ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^

AN ATTEMPT TO MEASURE FOOD SAFETY STATUS

The main objective of this study was to attempt to develop some indicators for measuring the food safety status of a country. A conceptual model was put forth by the investigator. The assumption was that food safety status was multifactorily influenced by medico-health levels, food-nutrition programs, and consumer protection activities. However, all these in turn depended upon socio-economic status of the country.^ Twenty-six indicators were reviewed and examined. Seventeen were first screened and three were finally selected, by the stepwise multiple regression analysis, to reflect the food safety status. Sixty-one countries/areas were included in this study.^ The three indicators were life expectancy at birth with multiple correlation coefficient (R2 = 34.62%), adult literacy rate (R2 = 29.66%), and child mortality rate for ages 1-4 (R2 = 9.99%). They showed a cumulative R2 of 57.79%. ^

The application of principal component analysis on human development indicators: Temporal approach from 1999 to 2010

Background and Objective. Ever since the human development index was published in 1990 by the United Nations Development Programme (UNDP), many researchers started searching and corporative studying for more effective methods to measure the human development. Published in 1999, Lai’s “Temporal analysis of human development indicators: principal component approach” provided a valuable statistical way on human developmental analysis. This study presented in the thesis is the extension of Lai’s 1999 research. ^ Methods. I used the weighted principal component method on the human development indicators to measure and analyze the progress of human development in about 180 countries around the world from the year 1999 to 2010. The association of the main principal component obtained from the study and the human development index reported by the UNDP was estimated by the Spearman’s rank correlation coefficient. The main principal component was then further applied to quantify the temporal changes of the human development of selected countries by the proposed Z-test. ^ Results. The weighted means of all three human development indicators, health, knowledge, and standard of living, were increased from 1999 to 2010. The weighted standard deviation for GDP per capita was also increased across years indicated the rising inequality of standard of living among countries. The ranking of low development countries by the main principal component (MPC) is very similar to that by the human development index (HDI). Considerable discrepancy between MPC and HDI ranking was found among high development countries with high GDP per capita shifted to higher ranks. The Spearman’s rank correlation coefficient between the main principal component and the human development index were all around 0.99. All the above results were very close to outcomes in Lai’s 1999 report. The Z test result on temporal analysis of main principal components from 1999 to 2010 on Qatar was statistically significant, but not on other selected countries, such as Brazil, Russia, India, China, and U.S.A.^ Conclusion. To synthesize the multi-dimensional measurement of human development into a single index, the weighted principal component method provides a good model by using the statistical tool on a comprehensive ranking and measurement. Since the weighted main principle component index is more objective because of using population of nations as weight, more effective when the analysis is across time and space, and more flexible when the countries reported to the system has been changed year after year. Thus, in conclusion, the index generated by using weighted main principle component has some advantage over the human development index created in UNDP reports.^

A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

A study of nutrition promotion in and consumer nutrition environments of hospitals

Dietary intake is a complex, health-related behavior, and although individual-level theoretical models explain some variation in dietary intake, comprehensive theoretical models such as the ecological framework describe the multiple levels which influence diet-related behaviors. Thus, the ecological framework is a preferred model for designing comprehensive nutrition interventions. While ecological-based nutrition interventions have been described, little work has focused on interventions in the hospital setting. Because hospitals are considered the hallmarks of health, it might seem that hospitals would regularly engage in worksite nutrition promotion; however, recent publications and other anecdotal evidence have indicated otherwise. The first paper of this dissertation systematically reviewed the scientific literature between 1996 and 2012 and identified 13 outcome evaluation trials for hospital-based worksite nutrition interventions. Of these 13 interventions, only one intervention targeted three of the four levels of the ecological framework and no intervention targeted all four levels. Only half of the interventions targeted the physical environment of hospitals, thus warranting more investigation into this specific level of the ecological framework in this setting. ^ A critical type of nutrition-related physical environments is the consumer nutrition environment. Although other tools measure the consumer nutrition environments of stores and restaurants, no tool specifically measured the consumer nutrition environments of hospitals until the CDC developed the Healthy Hospital Environment Scan for Cafeterias, Vending Machines, and Gift Shops (HHES-CVG). The HHES-CVG, a tool which measures the consumer nutrition environments of hospital cafeterias, vending machines, and gifts shops, was released in November 2011, and in the second paper of this dissertation, the reliability of this tool was investigated. Two trained raters visited 39 hospitals across Southern California between February and May 2012, and based on analyses of the raters' findings, the HHES-CVG exhibited strong reliability metrics (inter-observer agreement between 74 and 100%, and an intraclass correlation coefficient of 0.961 for the overall nutrition composite score). Because the HHES-CVG was found to be a reliable tool, the third paper of this dissertation presented HHES-CVG results from the 39 hospitals. Overall, hospitals only scored about one-fourth of the total possible points for the nutrition composite score, indicating that most facilities do not have acceptable consumer nutrition environments. Some of the best practices observed in cafeterias were significantly associated with having a large facility and with having a contracted foodservice operation, but overall nutrition composite score was not associated with any specific facility or operation type. ^ The dissertation concluded that much work is needed in order to improve the consumer nutrition environments of hospitals. Practitioners and healthcare administrators should consider starting with ecological-based interventions addressing all levels including the physical environment.^