FACTORS INFLUENCING CHOICE OF CONTRACEPTIVE METHOD AMONG MARRIED FECUND WOMEN WHO INTEND NO ADDITIONAL BIRTHS: HEALTH BELIEF MODEL AND ECONOMIC PERSPECTIVES

This research examined to what extent Health Belief Model (HBM) and socioeconomic variables were useful in explaining the choice whether or not more effective contraceptive methods were used among married fecund women intending no additional births. The source of the data was the 1976 National Survey of Family Growth conducted under the auspices of the National Center for Health Statistics. Using the HBM as a framework for multivariate analyses limited support was found (using available measures) that the HBM components of motivation and perceived efficacy influence the likelihood of more effective contraceptive method use. Support was also found that modifying variables suggested by the HBM can influence the effects of HBM components on the likelihood of more effective method use. Socioeconomic variables were found, using all cases and some subgroups, to have a significant additional influence on the likelihood of use of more effective methods. Limited support was found for the concept that the greater the opportunity costs of an unwanted birth the greater the likelihood of use of more effective contraceptive methods. This research supports the use of HBM and socioeconomic variables to explain the likelihood of a protective health behavior, use of more effective contraception if no additional births are intended.^

Survey analysis on dental infection control & occupational safety from India---1999 & 2010

The objective was to study knowledge, attitudes, practice (KAP) and needs regarding infection control measures using two cross-sectional surveys from 1999 and 2010 conducted in India. Both data collection instruments had only about 35 comparable variables in common. In 1999, there were 456 respondents (dentists) who completed a self-administered survey instrument compared to 272 respondents in 2010. Both the 1999 and 2010 samples were mutually independent with no overlap, had regional differences, and therefore, were not completely comparable for changes in KAP over time. While almost all respondents from both surveys felt that education in dental safety was needed and wanted mandatory dental safety curriculum in dental schools, severe inadequacies in dental safety knowledge, protection against immunizable diseases, and practice of universal precaution were noted. Data from the study demonstrated that there is a substantial opportunity to improve the knowledge, attitude and practice of dental infection control and occupational safety in India. Few respondents (27%) reported that the infectious disease status of a patient is always known and a significant number reported that they had the right to refuse care for patients of known infectious disease status. This indicates that Stigma in treating HIV/AIDS patients remains a concern, which in turn suggests that a stronger focus on educating dentists about dental safety and on stigma and infectious disease is needed. Information obtained from this study could be utilized for developing policies oriented towards increasing dental safety educational efforts, in both dental schools as curriculum, and for practicing dentists through professional updates or continuing dental education.^

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.^

Risk factors of pneumonia in traumatic spinal-cord-injured patients

Pneumonia is a well-documented and common respiratory infection in patients with acute traumatic spinal cord injuries, and may recur during the course of acute care. Using data from the North American Clinical Trials Network (NACTN) for Spinal Cord Injury, the incidence, timing, and recurrence of pneumonia were analyzed. The two main objectives were (1) to investigate the time and potential risk factors for the first occurrence of pneumonia using the Cox Proportional Hazards model, and (2) to investigate pneumonia recurrence and its risk factors using a Counting Process model that is a generalization of the Cox Proportional Hazards model. The results from survival analysis suggested that surgery, intubation, American Spinal Injury Association (ASIA) grade, direct admission to a NACTN site and age (older than 65 or not) were significant risks for first event of pneumonia and multiple events of pneumonia. The significance of this research is that it has the potential to identify patients at the time of admission who are at high risk for the incidence and recurrence of pneumonia. Knowledge and the time of occurrence of pneumonias are important factors for the development of prevention strategies and may also provide some insights into the selection of emerging therapies that compromise the immune system. ^

Identification of micro-RNAs targeting genes of PI3K/AKT pathway in ovarian cancer

Ovarian cancer is the leading cause of cancer-related death for females due to lack of specific early detection method. It is of great interest to find molecular-based biomarkers which are sensitive and specific to ovarian cancer for early diagnosis, prognosis and therapeutics. miRNAs have been proposed to be potential biomarkers that could be used in cancer prevention and therapeutics. The current study analyzed the miRNA and mRNA expression data extracted from the Cancer Genome Atlas (TCGA) database. Using simple linear regression and multiple regression models, we found 71 miRNA-mRNA pairs which were negatively associated between 56 miRNAs and 24 genes of PI3K/AKT pathway. Among these miRNA and mRNA target pairs, 9 of them were in agreement with the predictions from the most commonly used target prediction programs including miRGen, miRDB, miRTarbase and miR2Disease. These shared miRNA-mRNA pairs were considered to be the most potential genes that were involved in ovarian cancer. Furthermore, 4 of the 9 target genes encode cell cycle or apoptosis related proteins including Cyclin D1, p21, FOXO1 and Bcl2, suggesting that their regulator miRNAs including miR-16, miR-96 and miR-21 most likely played important roles in promoting tumor growth through dysregulated cell cycle or apoptosis. miR-96 was also found to directly target IRS-1. In addition, the results showed that miR-17 and miR-9 may be involved in ovarian cancer through targeting JAK1. This study might provide evidence for using miRNA or miRNA profile as biomarker.^

Global practice experience: A useful lens for understanding patient and staff perceptions of successful integration of health information technology in primary care

Making healthcare comprehensive and more efficient remains a complex challenge. Health Information Technology (HIT) is recognized as an important component of this transformation but few studies describe HIT adoption and it's effect on the bedside experience by physicians, staff and patients. This study applied descriptive statistics and correlation analysis to data from the Patient-Centered Medical Home National Demonstration Project (NDP) of the American Academy of Family Physicians. Thirty-six clinics were followed for 26 months by clinician/staff questionnaires and patient surveys. This study characterizes those clinics as well as staff and patient perspectives on HIT usefulness, the doctor-patient relationship, electronic medical record (EMR) implementation, and computer connections in the practice throughout the study. The Global Practice Experience factor, a composite score related to key components of primary care, was then correlated to clinician and patient perspectives. This study found wide adoption of HIT among NDP practices. Patient perspectives on HIT helpfulness on the doctor-patient showed a suggestive trend that approached statistical significance (p = 0.172). Clinicians and staff noted successful integration of EMR into clinic workflow and their perception of helpfulness to the doctor-patient relationship show a suggestive increase also approaching statistical significance (p=0.06). GPE was correlated with clinician/staff assessment of a helpful doctor-patient relationship midway through the study (R 0.460, p = 0.021) with the remaining time points nearing statistical significance. GPE was also correlated to both patient perspectives of EMR helpfulness in the doctor-patient relationship (R 0.601, p = 0.001) and computer connections (R 0.618, p = 0.0001) at the start of the study. ^

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^

Evaluation of CHIP perinatal program outcomes in Harris County, Texas

This study compared initial year trends in prenatal care and birth outcomes of women enrolled in the Texas Children's Health Insurance Program (CHIP) Perinatal program to trends in Medicaid program women. The study utilized claims data from Community Health Choice (CHC), a health plan in Harris County, Texas that provides coverage to both populations. Quarterly data was analyzed and compared for the first two years of the CHIP Perinatal program (2007-2008) to determine if outcome trends for the CHIP program improved over the outcome trends seen with those enrolled in Medicaid. Study findings indicate an increase in the quarterly prenatal care utilization for the CHIP Perinatal population from 2007 to 2008 and the associated birth weights of babies delivered also had marginal improvements during the same timeframe. Enrollees in Medicaid continued to have overall better outcomes than those enrolled within the CHIP Perinatal program. However, the study showed that the rate of improvement in both prenatal care utilization and birth outcomes were greater for the CHIP Perinatal enrollees than those enrolled in Medicaid. ^ The majority of these improvements were significant when comparing each coverage program and from year to year. Lastly, the study showed that there was a correlation between prenatal care utilization and birth outcomes. However, further analysis of the data could not conclusively indicate that access to prenatal care services provided by the CHIP Perinatal program contributed to the increases observed in utilization and birth outcomes for the study's sample population.^

A hybrid method in combining treatment effects from matched and unmatched studies

In the biomedical studies, the general data structures have been the matched (paired) and unmatched designs. Recently, many researchers are interested in Meta-Analysis to obtain a better understanding from several clinical data of a medical treatment. The hybrid design, which is combined two data structures, may create the fundamental question for statistical methods and the challenges for statistical inferences. The applied methods are depending on the underlying distribution. If the outcomes are normally distributed, we would use the classic paired and two independent sample T-tests on the matched and unmatched cases. If not, we can apply Wilcoxon signed rank and rank sum test on each case. ^ To assess an overall treatment effect on a hybrid design, we can apply the inverse variance weight method used in Meta-Analysis. On the nonparametric case, we can use a test statistic which is combined on two Wilcoxon test statistics. However, these two test statistics are not in same scale. We propose the Hybrid Test Statistic based on the Hodges-Lehmann estimates of the treatment effects, which are medians in the same scale.^ To compare the proposed method, we use the classic meta-analysis T-test statistic on the combined the estimates of the treatment effects from two T-test statistics. Theoretically, the efficiency of two unbiased estimators of a parameter is the ratio of their variances. With the concept of Asymptotic Relative Efficiency (ARE) developed by Pitman, we show ARE of the hybrid test statistic relative to classic meta-analysis T-test statistic using the Hodges-Lemann estimators associated with two test statistics.^ From several simulation studies, we calculate the empirical type I error rate and power of the test statistics. The proposed statistic would provide effective tool to evaluate and understand the treatment effect in various public health studies as well as clinical trials.^

A comparison of adherence with minocycline treatment in Parkinson's disease and rheumatoid arthritis clinical trials

Background: Little is known about the effects on patient adherence when the same study drug is administered in the same dose in two populations with two different diseases in two different clinical trials. The Minocycline in Rheumatoid Arthritis (MIRA) trial and the NIH Exploratory Trials in Parkinson's disease (NET-PD) Futility Study I provide a unique opportunity to do the above and to compare methods measuring adherence. This study may increase understanding of the influence of disease and adverse events on patient adherence and will provide insights to investigators selecting adherence assessment methods in clinical trials of minocycline and other drugs in future.^ Methods: Minocycline adherence by pill count and the effect of adverse events was compared in the MIRA and NET-PD FS1 trials using multivariable linear regression. Within the MIRA trial, agreement between assay and pill count was compared. The association of adverse events with assay adherence was examined using multivariable logistic regression.^ Results: Adherence derived from pill count in the MIRA and NET-PD FS1 trials did not differ significantly. Adverse events potentially related to minocycline did not appear useful to predict minocycline adherence. In the MIRA trial, adherence measured by pill count appears higher than adherence measured by assay. Agreement between pill count and assay was poor (kappa statistic = 0.25).^ Limitations: Trial and disease are completely confounded and hence the independent effect of disease on adherence to minocycline treatment cannot be studied.^ Conclusion: Simple pill count may be preferred over assay in the minocycline clinical trials to measure adherence. Assays may be less sensitive in a clinical setting where appointments are not scheduled in relation to medication administration time, given assays depend on many pharmacokinetic and instrument-related factors. However, pill count can be manipulated by the patient. Another study suggested that self-report method is more sensitive than pill count method in differentiating adherence from non-adherence. An effect of medication-related adverse events on adherence could not be detected.^

External validation study of a clinical prediction rule for ambulation outcomes after traumatic spinal cord injury

The main objective of this study was to determine the external validity of a clinical prediction rule developed by the European Multicenter Study on Human Spinal Cord Injury (EM-SCI) to predict the ambulation outcomes 12 months after traumatic spinal cord injury. Data from the North American Clinical Trials Network (NACTN) data registry with approximately 500 SCI cases were used for this validity study. The predictive accuracy of the EM-SCI prognostic model was evaluated using calibration and discrimination based on 231 NACTN cases. The area under the receiver-operating-characteristics curve (ROC) curve was 0.927 (95% CI 0.894 – 0.959) for the EM-SCI model when applied to NACTN population. This is lower than the AUC of 0.956 (95% CI 0.936 – 0.976) reported for the EM-SCI population, but suggests that the EM-SCI clinical prediction rule distinguished well between those patients in the NACTN population who were able to achieve independent ambulation and those who did not achieve independent ambulation. The calibration curve suggests that higher the prediction score is, the better the probability of walking with the best prediction for AIS D patients. In conclusion, the EM-SCI clinical prediction rule was determined to be generalizable to the adult NACTN SCI population.^

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.^

Optimal, minimax, and Bayesian two-stage designs in two-dose phase II clinical trials

Background: For most cytotoxic and biologic anti-cancer agents, the response rate of the drug is commonly assumed to be non-decreasing with an increasing dose. However, an increasing dose does not always result in an appreciable increase in the response rate. This may especially be true at high doses for a biologic agent. Therefore, in a phase II trial the investigators may be interested in testing the anti-tumor activity of a drug at more than one (often two) doses, instead of only at the maximum tolerated dose (MTD). This way, when the lower dose appears equally effective, this dose can be recommended for further confirmatory testing in a phase III trial under potential long-term toxicity and cost considerations. A common approach to designing such a phase II trial has been to use an independent (e.g., Simon's two-stage) design at each dose ignoring the prior knowledge about the ordering of the response probabilities at the different doses. However, failure to account for this ordering constraint in estimating the response probabilities may result in an inefficient design. In this dissertation, we developed extensions of Simon's optimal and minimax two-stage designs, including both frequentist and Bayesian methods, for two doses that assume ordered response rates between doses. ^ Methods: Optimal and minimax two-stage designs are proposed for phase II clinical trials in settings where the true response rates at two dose levels are ordered. We borrow strength between doses using isotonic regression and control the joint and/or marginal error probabilities. Bayesian two-stage designs are also proposed under a stochastic ordering constraint. ^ Results: Compared to Simon's designs, when controlling the power and type I error at the same levels, the proposed frequentist and Bayesian designs reduce the maximum and expected sample sizes. Most of the proposed designs also increase the probability of early termination when the true response rates are poor. ^ Conclusion: Proposed frequentist and Bayesian designs are superior to Simon's designs in terms of operating characteristics (expected sample size and probability of early termination, when the response rates are poor) Thus, the proposed designs lead to more cost-efficient and ethical trials, and may consequently improve and expedite the drug discovery process. The proposed designs may be extended to designs of multiple group trials and drug combination trials.^

Study on the effect of metformin in patients of metastatic renal cell cancer with diabetes receiving tyrosine kinase inhibitor therapy

Objective: The primary objective of our study was to study the effect of metformin in patients of metastatic renal cell cancer (mRCC) and diabetes who are on treatment with frontline therapy of tyrosine kinase inhibitors. The effect of therapy was described in terms of overall survival and progression free survival. Comparisons were made between group of patients receiving metformin versus group of patients receiving insulin in diabetic patients of metastatic renal cancer on frontline therapy. Exploratory analyses were also done comparing non-diabetic patients of metastatic renal cell cancer receiving frontline therapy compared to diabetic patients of metastatic renal cell cancer receiving metformin therapy. ^ Methods: The study design is a retrospective case series to elaborate the response rate of frontline therapy in combination with metformin for mRCC patients with type 2 diabetes mellitus. The cohort was selected from a database, which was generated for assessing the effect of tyrosine kinase inhibitor therapy associated hypertension in metastatic renal cell cancer at MD Anderson Cancer Center. Patients who had been started on frontline therapy for metastatic renal cell carcinoma from all ethnic and racial backgrounds were selected for the study. The exclusion criteria would be of patients who took frontline therapy for less than 3 months or were lost to follow-up. Our exposure variable was treatment with metformin, which comprised of patients who took metformin for the treatment of type 2 diabetes at any time of diagnosis of metastatic renal cell carcinoma. The outcomes assessed were last available follow-up or date of death for the overall survival and date of progression of disease from their radiological reports for time to progression. The response rates were compared by covariates that are known to be strongly associated with renal cell cancer. ^ Results: For our primary analyses between the insulin and metformin group, there were 82 patients, out of which 50 took insulin therapy and 32 took metformin therapy for type 2 diabetes. For our exploratory analysis, we compared 32 diabetic patients on metformin to 146 non-diabetic patients, not on metformin. Baseline characteristics were compared among the population. The time from the start of treatment until the date of progression of renal cell cancer and date of death or last follow-up were estimated for survival analysis. ^ In our primary analyses, there was a significant difference in the time to progression of patients receiving metformin therapy vs insulin therapy, which was also seen in our exploratory analyses. The median time to progression in primary analyses was 1259 days (95% CI: 659-1832 days) in patients on metformin therapy compared to 540 days (95% CI: 350-894) in patients who were receiving insulin therapy (p=0.024). The median time to progression in exploratory analyses was 1259 days (95% CI: 659-1832 days) in patients on metformin therapy compared to 279 days (95% CI: 202-372 days) in non-diabetic group (p-value <0.0001). ^ The median overall survival was 1004 days in metformin group (95% CI: 761-1212 days) compared to 816 days (95%CI: 558-1405 days) in insulin group (p-value<0.91). For the exploratory analyses, the median overall survival was 1004 days in metformin group (95% CI: 761-1212 days) compared to 766 days (95%CI: 649-965 days) in the non-diabetic group (p-value<0.78). Metformin was observed to increase the progression free survival in both the primary and exploratory analyses (HR=0.52 in metformin Vs insulin group and HR=0.36 in metformin Vs non-diabetic group, respectively). ^ Conclusion: In laboratory studies and a few clinical studies metformin has been proven to have dual benefits in patients suffering from cancer and type 2-diabetes via its action on the mammalian target of Rapamycin pathway and effect in decreasing blood sugar by increasing the sensitivity of the insulin receptors to insulin. Several studies in breast cancer patients have documented a beneficial effect (quantified by pathological remission of cancer) of metformin use in patients taking treatment for breast cancer therapy. Combination of metformin therapy in patients taking frontline therapy for renal cell cancer may provide a significant benefit in prolonging the overall survival in patients with metastatic renal cell cancer and diabetes. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^