7 resultados para test case optimization

em DigitalCommons@The Texas Medical Center


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Introduction. Several studies have reported a positive association of body mass index (BMI) with multiple myeloma; however, the period of adulthood where BMI is most important remains unclear. In addition, it is well known that body fat is associated with both sex-steroid hormone storage and with increasing insulin levels; therefore, it was hypothesized that the association between obesity and multiple myeloma may be attributed to increased aromatization of androgen in adipose tissue. Objective. The overall objective of this case-control study was to determine whether multiple myeloma cases had higher BMI and greater adult weight gain relative to healthy controls. In addition, we tested the hypothesis that hormone replacement therapy use among women will further increase the association between BMI and risk of multiple myeloma. This study used data from a pilot case-control study at M.D. Anderson Cancer Center (MDACC), entitled Etiology of multiple myeloma, directed by Dr. Sara Strom and Dr. Sergio Giralt. Methods. The pilot study recruited a total of 122 cases of histopathologically confirmed multiple myeloma from MDACC. Controls (n=183) were selected from a database of random digit dialing controls accrued in the Department of Epidemiology at MDACC and were frequency matched to the cases on age (±5 years), gender, and race/ethnicity. Demographic and risk factor information were obtained from all participants who completed a self-administered questionnaire. Items included in the questionnaire include demographic information, height and weight at age 25, 40 and current/diagnosis, medical history, family history of cancer, smoking and alcohol use. Statistical analysis. Initial descriptive analysis included Student's t-test and Pearson's chi-squared tests. Odds ratios and 95% confidence intervals were calculated to quantify the association between the variables of interest and multiple myeloma. A multivariable model will be developed using unconditional logistic regression. Results. MM cases were 1.79 times (95% CI=0.99-3.32) more likely to have been overweight or obese (BMI > 25 kg/m2) at age 25 relative to healthy controls after controlling for age, gender, race/ethnicty, education and family history of cancer. Being overweight or obese at age 40 was not significantly associated with mutliple myeloma risk (OR=1.42, 95% CI=0.86-2.34) nor was being overweight or obses at diagnosis (OR=1.43, 95% CI=0.78, 2.63). We observed a statistically significant 2-fold increased odds of multiple myeloma in individuals who gained more than 4.7 kg during between 25 and 40 years (OR=1.97, 95% CI=1.15-3.39). When assessing HRT as a modifier of the BMI and multiple myeloma association among women (N=123), no association between obesity and MM status was observed among women who have never used HRT (OR=0.60, 95% CI=0.23-1.61; n=73). Yet among women who have ever used HRT (n=50), being overweight or obese was associated with an increase in MM risk (OR=2. 93, 95% CI=0.81-10.6) after adjusting for age; however, the association was not statistically significant. Significance. This study provides further evidence that increased BMI increases the risk of multiple myeloma. Furthermore, among women, HRT use may modify risk of disease. ^

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Acute kidney Injury (AKI) in hospitalized pediatric patients can be a significant event that can result in increased patient morbidity and mortality. The incidence of medication associated AKI is increasing in the pediatric population. Currently, there are no data to quantify the risks of developing AKI for various potentially nephrotoxic medications. The primary objective of this study was to determine the odds of nephrotoxic medication exposure in hospitalized pediatric patients with AKI as defined by the pediatric modified pRIFLE criteria. A retrospective case-control study was performed with patients that developed AKI, as defined by the pediatric pRIFLE criteria, as cases, and patients without AKI as controls that were matched by age category, gender, and disease state. Patients between 1 day and 18 years of age, admitted to a non-intensive care unit at Texas Children's Hospital for at least 3 days, and had at least 2 serum creatinine values drawn were included. Patient data was analyzed with Student's t test, Mann-Whitney U test, Chi square analysis, ANOVA, and conditional logistic regression. ^ Out of 1,660 patients identified for inclusion, 561 (33.8%) patients had AKI, and 357 cases were matched with 357 controls to become pairs. Of the cases, 441 were category 'R', 117 category 'I', 3 patients were category 'F', and no patient died. Cases with AKI were significantly younger than controls (p < 0.05). Significantly longer hospital length of stays, increased hospital costs, and exposure to more nephrotoxic medications for a longer period of time were characteristics of patients with AKI compared to patient without AKI. Patients with AKI had greater odds of exposure to one or more nephrotoxic medication than patients without AKI (OR 1.3, 95% CI 1.1–1.4, p < 0.05). Percent changes in estimated creatinine clearance (eCCl) from baseline were greatest with increased number of nephrotoxic medication exposures. ^ Exposure to potentially nephrotoxic medications may place pediatric patients at greater risk of acute kidney injury. Multiple nephrotoxic medication exposure may confer a greater risk of development of acute kidney injury, and result in increased hospital costs and patient morbidity. Due to the high percentage of patients that were exposed to potentially nephrotoxic medications, monitoring and medication selection strategies may need to be altered to prevent or minimize risk.^

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Although the processes involved in rational patient targeting may be obvious for certain services, for others, both the appropriate sub-populations to receive services and the procedures to be used for their identification may be unclear. This project was designed to address several research questions which arise in the attempt to deliver appropriate services to specific populations. The related difficulties are particularly evident for those interventions about which findings regarding effectiveness are conflicting. When an intervention clearly is not beneficial (or is dangerous) to a large, diverse population, consensus regarding withholding the intervention from dissemination can easily be reached. When findings are ambiguous, however, conclusions may be impossible.^ When characteristics of patients likely to benefit from an intervention are not obvious, and when the intervention is not significantly invasive or dangerous, the strategy proposed herein may be used to identify specific characteristics of sub-populations which may benefit from the intervention. The identification of these populations may be used both in further informing decisions regarding distribution of the intervention and for purposes of planning implementation of the intervention by identifying specific target populations for service delivery.^ This project explores a method for identifying such sub-populations through the use of related datasets generated from clinical trials conducted to test the effectiveness of an intervention. The method is specified in detail and tested using the example intervention of case management for outpatient treatment of populations with chronic mental illness. These analyses were applied in order to identify any characteristics which distinguish specific sub-populations who are more likely to benefit from case management service, despite conflicting findings regarding its effectiveness for the aggregate population, as reported in the body of related research. However, in addition to a limited set of characteristics associated with benefit, the findings generated, a larger set of characteristics of patients likely to experience greater improvement without intervention. ^

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The existence of an association between leukemia and electromagnetic fields (EMF) is still controversial. The results of epidemiologic studies of leukemia in occupational groups with exposure to EMF are inconsistent. Weak associations have been seen in a few studies. EMF assessment is lacking in precision. Reported dose-response relationships have been based on qualitative levels of exposure to EMF without regard to duration of employment or EMF intensity on the jobs. Furthermore, potential confounding factors in the associations were not often well controlled. The current study is an analysis of the data collected from an incident case-control study. The primary objective was to test the hypothesis that occupational exposure to EMF is associated with leukemia, including total leukemia (TL), myelogenous leukemia (MYELOG) and acute non-lymphoid leukemia (ANLL). Potential confounding factors: occupational exposure to benzene, age, smoking, alcohol consumption, and previous medical radiation exposures were controlled in multivariate logistic regression models. Dose-response relationships were estimated by cumulative occupational exposure to EMF, taking into account duration of employment and EMF intensity on the jobs. In order to overcome weaknesses of most previous studies, special efforts were made to improve the precision of EMF assessment. Two definitions of EMF were used and result discrepancies using the two definitions were observed. These difference raised a question as to whether the workers at jobs with low EMF exposure should be considered as non-exposed in future studies. In addition, the current study suggested use of lifetime cumulative EMF exposure estimates to determine dose-response relationship. The analyses of the current study suggest an association between ANLL and employment at selected jobs with high EMF exposure. The existence of an association between three types of leukemia and broader categories of occupational EMF exposure, is still undetermined. If an association does exist between occupational EMF exposure and leukemia, the results of the current study suggest that EMF might only be a potential factor in the promotion of leukemia, but not its initiation. ^

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Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^

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Results from epidemiologic studies suggest that persons working in occupations with presumed electric and magnetic field (EMF) exposures are at increased risk of brain cancer. This study utilized data from a completed, population-based, interview case-control study of central nervous system (CNS) tumors and employment in the petrochemical industry to test the hypothesis that employment in EMF-related occupations increases CNS tumor risk. A total of 375 male residents of the Texas-Louisiana Gulf Coast Area, age 20 to 79, with primary neuroglial CNS tumors diagnosed during the period 1980-84 were identified. A population-based comparison group of 450 age, race and geographically matched males was selected. Occupational histories and potential risk factor data were collected via personal interviews with study subjects or their next-of-kin.^ Adjusted odds ratios were less than 1.0 for persons ever employed in an electrical occupation (OR = 0.65; 95% CI = 0.40-1.09) or whose usual occupation was electrical (OR = 0.76; 95% CI = 0.33-1.73). Relative risk estimates did not increase significantly as time since first employment or duration of employment increased. Examination of CNS tumor risk by high (OR = 0.80), medium (OR = 0.88) and low (OR = 0.45) exposure categories for persons whose usual occupation was electrical did not indicate a dose-response pattern. In addition, the mean age of exposed cases was not significantly younger than that for unexposed cases. Analysis of risk by probability of exposure to EMFs showed non-significant elevations in the adjusted odds ratio for definite exposed workers defined by their usual occupation (OR = 1.78; 95% CI = 0.70-4.51) and ever/never employed status (OR = 1.54; 95% CI = 0.17-4.91).^ These findings suggest that employment in occupations with presumed EMF exposures does not increase CNS tumor risk as was suggested by previous investigations. The results of this study also do not support the EMF-tumor promotion hypothesis. ^

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Objective: The objective of this study is to investigate the association between processed and unprocessed red meat consumption and prostate cancer (PCa) stage in a homogenous Mexican-American population. Methods: This population-based case-control study had a total of 582 participants (287 cases with histologically confirmed adenocarcinoma of the prostate gland and 295 age and ethnicity-matched controls) that were all residing in the Southeast region of Texas from 1998 to 2006. All questionnaire information was collected using a validated data collection instrument. Statistical Analysis: Descriptive analyses included Student's t-test and Pearson's Chi-square tests. Odds ratios and 95% confidence intervals were calculated to quantify the association between nutritional factors and PCa stage. A multivariable model was used for unconditional logistic regression. Results: After adjusting for relevant covariates, those who consume high amounts of processed red meat have a non-significant increased odds of being diagnosed with localized PCa (OR = 1.60 95% CI: 0.85 - 3.03) and total PCa (OR = 1.43 95% CI: 0.81 - 2.52) but not for advanced PCa (OR = 0.91 95% CI: 1.37 - 2.23). Interestingly, high consumption of carbohydrates shows a significant reduction in the odds of being diagnosed with total PCa and advanced PCa (OR = 0.43 95% CI: 0.24 - 0.77; OR = 0.27 95% CI: 0.10 - 0.71, respectively). However, consuming high amounts of energy from protein and fat was shown to increase the odds of being diagnosed with advanced PCa (OR = 4.62 95% CI: 1.69 - 12.59; OR = 2.61 95% CI: 1.04 - 6.58, respectively). Conclusion: Mexican-Americans who consume high amounts of energy from protein and fat had increased odds of being diagnosed with advanced PCa, while high amounts of carbohydrates reduced the odds of being diagnosed with total and advanced PCa.^