Application of the general linear model to assess the effect of missing data on bone marrow mononuclear cell therapy with infusion timing and follow-up MRI

With most clinical trials, missing data presents a statistical problem in evaluating a treatment's efficacy. There are many methods commonly used to assess missing data; however, these methods leave room for bias to enter the study. This thesis was a secondary analysis on data taken from TIME, a phase 2 randomized clinical trial conducted to evaluate the safety and effect of the administration timing of bone marrow mononuclear cells (BMMNC) for subjects with acute myocardial infarction (AMI).^ We evaluated the effect of missing data by comparing the variance inflation factor (VIF) of the effect of therapy between all subjects and only subjects with complete data. Through the general linear model, an unbiased solution was made for the VIF of the treatment's efficacy using the weighted least squares method to incorporate missing data. Two groups were identified from the TIME data: 1) all subjects and 2) subjects with complete data (baseline and follow-up measurements). After the general solution was found for the VIF, it was migrated Excel 2010 to evaluate data from TIME. The resulting numerical value from the two groups was compared to assess the effect of missing data.^ The VIF values from the TIME study were considerably less in the group with missing data. By design, we varied the correlation factor in order to evaluate the VIFs of both groups. As the correlation factor increased, the VIF values increased at a faster rate in the group with only complete data. Furthermore, while varying the correlation factor, the number of subjects with missing data was also varied to see how missing data affects the VIF. When subjects with only baseline data was increased, we saw a significant rate increase in VIF values in the group with only complete data while the group with missing data saw a steady and consistent increase in the VIF. The same was seen when we varied the group with follow-up only data. This essentially showed that the VIFs steadily increased when missing data is not ignored. When missing data is ignored as with our comparison group, the VIF values sharply increase as correlation increases.^

A space-time analysis of reported shigellosis and salmonellosis cases in Texas from 2000 to 2004

Background. The purpose of this study was to describe the risk factors and demographics of persons with salmonellosis and shigellosis and to investigate both seasonal and spatial variations in the occurrence of these infections in Texas from 2000 to 2004, utilizing time series analyses and the geographic information system digital mapping methods. ^ Methods. Spatial Analysis: MapInfo software was used to map the distribution of age-adjusted rates of reported shigellosis and salmonellosis in Texas from 2000–2004 by zip codes. Census data on above or below poverty level, household income, highest level of educational attainment, race, ethnicity, and urban/rural community status was obtained from the 2000 Decennial Census for each zip code. The zip codes with the upper 10% and lower 10% were compared using t-tests and logistic regression to determine whether there were any potential risk factors. ^ Temporal analysis. Seasonal patterns in the prevalence of infections in Texas from 2000 to 2003 were determined by performing time-series analysis on the numbers of cases of salmonellosis and shigellosis. A linear regression was also performed to assess for trends in the incidence of each disease, along with auto-correlation and multi-component cosinor analysis. ^ Results. Spatial analysis: Analysis by general linear model showed a significant association between infection rates and age, with young children aged less than 5 and those aged 5–9 years having increased risk of infection for both disease conditions. The data demonstrated that those populations with high percentages of people who attained a higher than high school education were less likely to be represented in zip codes with high rates of shigellosis. However, for salmonellosis, logistic regression models indicated that when compared to populations with high percentages of non-high school graduates, having a high school diploma or equivalent increased the odds of having a high rate of infection. ^ Temporal analysis. For shigellosis, multi-component cosinor analyses were used to determine the approximated cosine curve which represented a statistically significant representation of the time series data for all age groups by sex. The shigellosis results show 2 peaks, with a major peak occurring in June and a secondary peak appearing around October. Salmonellosis results showed a single peak and trough in all age groups with the peak occurring in August and the trough occurring in February. ^ Conclusion. The results from this study can be used by public health agencies to determine the timing of public health awareness programs and interventions in order to prevent salmonellosis and shigellosis from occurring. Because young children depend on adults for their meals, it is important to increase the awareness of day-care workers and new parents about modes of transmission and hygienic methods of food preparation and storage. ^

Statistical issues on studies of mixed longitudinal design

Mixed longitudinal designs are important study designs for many areas of medical research. Mixed longitudinal studies have several advantages over cross-sectional or pure longitudinal studies, including shorter study completion time and ability to separate time and age effects, thus are an attractive choice. Statistical methodology used in general longitudinal studies has been rapidly developing within the last few decades. Common approaches for statistical modeling in studies with mixed longitudinal designs have been the linear mixed-effects model incorporating an age or time effect. The general linear mixed-effects model is considered an appropriate choice to analyze repeated measurements data in longitudinal studies. However, common use of linear mixed-effects model on mixed longitudinal studies often incorporates age as the only random-effect but fails to take into consideration the cohort effect in conducting statistical inferences on age-related trajectories of outcome measurements. We believe special attention should be paid to cohort effects when analyzing data in mixed longitudinal designs with multiple overlapping cohorts. Thus, this has become an important statistical issue to address. ^ This research aims to address statistical issues related to mixed longitudinal studies. The proposed study examined the existing statistical analysis methods for the mixed longitudinal designs and developed an alternative analytic method to incorporate effects from multiple overlapping cohorts as well as from different aged subjects. The proposed study used simulation to evaluate the performance of the proposed analytic method by comparing it with the commonly-used model. Finally, the study applied the proposed analytic method to the data collected by an existing study Project HeartBeat!, which had been evaluated using traditional analytic techniques. Project HeartBeat! is a longitudinal study of cardiovascular disease (CVD) risk factors in childhood and adolescence using a mixed longitudinal design. The proposed model was used to evaluate four blood lipids adjusting for age, gender, race/ethnicity, and endocrine hormones. The result of this dissertation suggest the proposed analytic model could be a more flexible and reliable choice than the traditional model in terms of fitting data to provide more accurate estimates in mixed longitudinal studies. Conceptually, the proposed model described in this study has useful features, including consideration of effects from multiple overlapping cohorts, and is an attractive approach for analyzing data in mixed longitudinal design studies.^

HIV-related malignancies: A community-based study using a linkage of cancer registry and HIV registry data

Background. Increased incidence of cancer is documented in immunosuppressed transplant patients. Likewise, as survival increases for persons infected with the Human Immunodeficiency Virus (HIV), we expect their incidence of cancer to increase. The objective of this study was to examine the current gender specific spectrum of cancer in an HIV infected cohort (especially malignancies not currently associated with Acquired Immunodeficiency Syndrome (AIDS)) in relation to the general population.^ Methods. Cancer incidence data was collected for residents of Harris County, Texas who were diagnosed with a malignancy between 1975 and 1994. This data was linked to HIV/AIDS registry data to identify malignancies in an HIV infected cohort of 14,986 persons. A standardized incidence ratio (SIR) analysis was used to compare incidence of cancer in this cohort to that in the general population. Risk factors such as mode of HIV infection, age, race and gender, were evaluated for contribution to the development of cancer within the HIV cohort, using Cox regression techniques.^ Findings. Of those in the HIV infected cohort, 2289 persons (15%) were identified as having one or more malignancies. The linkage identified 29.5% of these malignancies (males 28.7% females 60.9%). HIV infected men and women had incidences of cancer that were 16.7 (16.1, 17.3) and 2.9 (2.3, 3.7) times that expected for the general population of Harris County, Texas, adjusting for age. Significant SIR's were observed for the AIDS-defining malignancies of Kaposi's sarcoma, non-Hodgkin's lymphoma, primary lymphoma of the brain and cancer of the cervix. Additionally, significant SIR's for non-melanotic skin cancer in males, 6.9 (4.8, 9.5) and colon cancer in females, 4.0 (1.1, 10.2) were detected. Among the HIV infected cohort, race/ethnicity of White (relative risk 2.4 with 95% confidence intervals 2.0, 2.8) or Spanish Surname, 2.2 (1.9, 2.7) and an infection route of male to male sex, with, 3.0 (1.9, 4.9) or without, 3.4 (2.1, 5.5) intravenous drug use, increased the risk of having a diagnosis of an incident cancer.^ Interpretation. There appears to be an increased risk of developing cancer if infected with the HIV. In addition to the malignancies routinely associated with HIV infection, there appears to be an increased risk of being diagnosed with non-melanotic skin cancer in males and colon cancer in females. ^

Effect of maternal weight status on the relationship between prenatal smoking and infant birth weight

The purpose of this study was to assess the effect of maternal pre-pregnancy weight status on the relationship between prenatal smoking and infant birth weight (IBW). Prenatal cigarette smoking and maternal weight exert opposing effects on IBW; smoking decreases birth weight while maternal pre-pregnancy weight is positively correlated with birth weight. As such, mutual effect modification may be sufficiently significant to alter the independent effects of these two birth weight correlates. Finding of such an effect has implications of prenatal smoking cessation education. Perception of risk is an important determinant of smoking cessation, and reduced or low birth weight (LBW) as a smoking-associated risk predominates prenatal smoking counseling and education. In a population such as the US, where obesity is becoming epidemic, particularly among minority and low-income groups, perception of risk may be lowered should increased maternal size attenuate the effect of smoking. Previous studies have not found a significant interaction effect of prenatal smoking and maternal pre-pregnancy weight on IBW; however, use of self-reported smoking status may have biased findings. Reliability of self-reported smoking status reported in the literature is variable, with deception rates ranging from a low of 5% to as high as 16%. This study, using data from a prenatal smoking cessation project, in which smoking status was validated by saliva cotinine, was an opportunity to assess effect modification of smoking and maternal weight using biochemically determined smoking status in lieu of self report. Stratified by saliva cotinine, 151 women from a prenatal smoking cessation cohort, who were 18 years and older and had full-term, singleton births, were included in this study. The effect of smoking in terms of mean birth weight across three levels of maternal pre-pregnancy weight was assessed by general linear modeling procedures, adjusting for other known correlates of IBW. Effect modification was marginally significant, p = .104, but only with control for differential effects among racial/ethnic groups. A smaller than planned sample of nonsmokers, or women who quit smoking during the pregnancy, prohibited rejection of the null hypothesis of no difference in the effect of smoking across levels of pre-pregnancy weight. ^

Obesity during pregnancy on vagal tone, hemoglobin A1c, oxygenation, and perinatal outcomes

Purpose: To examine the effect of obesity and gestational weight gain on heart rate variability (HRV), oxygenation (HbO 2 and SpO2), hemoglobin A1c (HbA1c) and the frequency of pregnancy complications in obese (O) and non-obese (NO) women.^ Design: The study was an observational comparison study with a repeated measures design. ^ Setting: The setting was a low risk prenatal, university clinic located in a large southeastern metropolitan city. ^ Sample: The sample consisted of a volunteer group of 41 pregnant women who were observed at the three time points of 20, 28, and 36 weeks gestation. ^ Analysis: Analysis included general linear modeling with repeated measures to test for group differences with changes over time on vagal response, HbA1c, and oxygenation. Odds ratios were computed to compare the frequency of birth outcomes. ^ Findings: The interaction effect of time between O and NO women on HbO2 was significant. The mean HP, RSA, and HbO2 changed significantly over time within the NO women. The mean HbA 1c increased significantly over time within the O women. Women with excess gestational weight gain had significantly lower heart period than women with weight gain within the IOM recommendations. Obese women were more likely to have Group B streptococcal infections, gestational hypertension, give birth by cesarean or instrument assistance, and have at least one postnatal event. ^ Conclusions: Monitoring HRV, oxygenation, and HbA1c using minimally invasive measures may permit early identification of alterations in autonomic response. Implementation of interventions to promote vagal tone may help to reduce risks for adverse perinatal outcomes related to obesity. Future studies should examine the effect of obesity on the vagal response and perinatal outcomes. ^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

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. ^

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. ^