Use of light intensity, temperature, and humidity to verify exposure location

Research studies on the association between exposures to air contaminants and disease frequently use worn dosimeters to measure the concentration of the contaminant of interest. But investigation of exposure determinants requires additional knowledge beyond concentration, i.e., knowledge about personal activity such as whether the exposure occurred in a building or outdoors. Current studies frequently depend upon manual activity logging to record location. This study's purpose was to evaluate the use of a worn data logger recording three environmental parameters—temperature, humidity, and light intensity—as well as time of day, to determine indoor or outdoor location, with an ultimate aim of eliminating the need to manually log location or at least providing a method to verify such logs. For this study, data collection was limited to a single geographical area (Houston, Texas metropolitan area) during a single season (winter) using a HOBO H8 four-channel data logger. Data for development of a Location Model were collected using the logger for deliberate sampling of programmed activities in outdoor, building, and vehicle locations at various times of day. The Model was developed by analyzing the distributions of environmental parameters by location and time to establish a prioritized set of cut points for assessing locations. The final Model consisted of four "processors" that varied these priorities and cut points. Data to evaluate the Model were collected by wearing the logger during "typical days" while maintaining a location log. The Model was tested by feeding the typical day data into each processor and generating assessed locations for each record. These assessed locations were then compared with true locations recorded in the manual log to determine accurate versus erroneous assessments. The utility of each processor was evaluated by calculating overall error rates across all times of day, and calculating individual error rates by time of day. Unfortunately, the error rates were large, such that there would be no benefit in using the Model. Another analysis in which assessed locations were classified as either indoor (including both building and vehicle) or outdoor yielded slightly lower error rates that still precluded any benefit of the Model's use.^

HIV-associated malignancies among children attending the Baylor Clinic in Kampala, Uganda

Introduction: HIV-associated malignancies such as Kaposi’s sarcoma and Non-Hodgkin’s lymphoma occur in children and usually lead to significant morbidity and mortality. No studies have been done to establish prevalence and outcome of these malignancies in children in a hospital setting in Uganda. ^ Research question: What proportion of children attending the Baylor-Uganda COE present with HIV-associated malignancies and what are the characteristics and outcome of these malignancies? The objective was to determine the prevalence, associated factors and outcome of HIV-associated malignancies among children attending the Baylor-Uganda Clinic in Kampala, Uganda. Study Design: This was a retrospective case series involving records review of patients who presented to the Baylor-Clinic between January 2004 and December 2008. Study Setting: The Baylor-Uganda Clinic, where I worked as a physician before coming to Houston, is a well funded, well staffed; Pediatric HIV clinic located in Mulago Hospital, Kampala, Uganda and is affiliated to Makerere University Medical School. Study Participants: Medical charts of patients aged 6 weeks to 18 years who enrolled for care at the clinic during the years 2004 to 2008 were retrieved for data abstraction. Selection Criteria: Study participants had to be patients of Baylor-Uganda seen during the study period; they had to be aged 6 weeks to 18 years; and had to be HIV positive. Patients with incomplete data or whose malignancies were not confirmed by histology were excluded. Study Variables: Data on patient’s age, sex, diagnosis, type of malignancy, anatomic location of the malignancy; pathology report, baseline laboratory results and outcome of treatment, were abstracted. Data Analysis: Cross tabulation to determine associations between variables using Pearson’s chi square at 95% level of significance was done. Proportions of malignancies among different groups were determined. In addition, Kaplan Meier survival analysis and comparison of survival distributions using the log-rank test was done. Change in CD4 percentages from baseline was assessed with the Wilcoxon signed rank test. Results: The proportion of children with malignancies during the study period was found to be 1.65%. Only 2 malignancies: Kaposi’s sarcoma and Non-Hodgkin’s lymphoma were found. 90% of the malignancies were Kaposi’s sarcoma. Lymph node involvement in children with Kaposi’s sarcoma was common, but the worst prognosis was seen with visceral involvement. Deaths during follow-up were seen in the first few weeks to months. Upon starting treatment the CD4 cell percentage increased significantly from a baseline median of 6% to 14% at 6 months and 15.8% at 12 months of follow-up.^

Total cholesterol and anthropometric measurements distributions in a cross-sectional study of school children

Cross-sectional age and sex specific distributions of serum total cholesterol were described for 1091 children age 6-18 years, in The Woodlands, Texas. Associations of serum total cholesterol with five anthropometric measurements (weight, height, body mass index, arm circumference, and triceps skinfold thickness) were examined by correlation and regression analyses. Examination of serum total cholesterol distributions showed lower levels in boys than in girls for most of the age groups studied. Mean levels of total cholesterol peaked at age 9 for boys and 8 for girls. Serum total cholesterol leveled off until age 14 for boys and 11 for girls, and then dropped through age 18 for both boys and girls. These results support the hypothesis that serum total cholesterol concentration drops at pre-adolescence.^ Age adjusted correlations were observed between serum total cholesterol and triceps skinfold thickness for both boys and girls. This association was stronger in boys. Triceps skinfold thickness and arm circumference were consistently the strongest correlates for serum total cholesterol in boys. Weight and arm circumference were consistently the strongest correlates for serum total cholesterol in girls. ^

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. ^

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.^