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. ^

Black/white disparities in infant mortality rates in the U.S.: A quality of care perspective

The infant mortality rate for non-Hispanic Black infants in the U.S. is 13.63 deaths per 1,000 live births while the IMR for non-Hispanic White persons in the U.S. is 5.76 deaths per 1,000 live births. Black women are 2 times as likely as White women to deliver preterm infants and Black women are 2 times as likely as White women to deliver low birth weight infants (weighing less than 2,500 grams at birth). Differential underlying risk factors among mothers of different racial/ethnic groups for delivering pre-term and low birth weight infants have been historically accepted as the cause of racial disparities in IMRs. However, differential underlying risk status may not be the only major causative factor. Differential or unequal access to and provision of care is widely speculated to be a leading contributing factor to the wide racial disparity in infant mortality.2 This paper conducts a systematic review of existing literature investigating racial disparities in obstetrical care provided by healthcare practitioners to evaluate whether inequities in healthcare services provided to pregnant mothers and their neonates exist. The search terms "racial disparities obstetrical care," "racial differences quality of prenatal care," and "infant mortality racial disparities" were entered into the EBSCO Medline, Ovid Medline, PubMed, and Academic Search Complete databases, and articles between years 1990–2011 were selected for abstract review. The only articles included were those that used statistical methods to assess whether racial inequalities were present in the obstetrical services provided to pregnant women. My literature search returned 5 articles. Four of the five studies yielded significant racial differences in obstetrical care. However, the one study that used a large, nationally representative valid sample did not represent significant differences. Thus, this review provides initial evidence for racial disparities in obstetrical care, but concludes that more studies are needed in this area. Not all of the studies reviewed were consistent in the use and measurement of services, and not all studies were significant. The policy and public health implications of possible racial disparities in obstetrical care include the need to develop standard of care protocols for ALL obstetrical patients across the United States to minimize and/or eliminate the inequities and differences in obstetrical services provided.^

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.^

Risk factors for metabolic syndrome among overweight and obese Hispanic children attending a pediatric clinic in New Braunfels, Texas

Childhood obesity is a persistent problem in the U.S., especially among Hispanics. Health complications like hypertension, type II diabetes, and metabolic syndrome (Met-S) are being seen at younger ages, and current screening procedures may be inadequate. This study sought to describe the risk factors for Met-S present in a sample of 106 overweight and obese Hispanic children, aged 5-14 years, participating in Nutrition and Exercise Start Today (NEST), a randomized weight management intervention trial at a rural health clinic in New Braunfels, Texas; and to determine associations between these factors and other clinical and socio-demographic characteristics linked to obesity. Baseline data was analyzed for the prevalence of large waist circumference (WC), elevated blood pressure (BP), high fasting serum glucose and serum triglycerides (TG), and low serum HDL cholesterol, in relationship with selected sample characteristics. Main findings included high baseline prevalence rates of large WC (77%), reduced HDL (57%), and elevated BP (30%). WC was significantly associated with BMI percentile and the serum liver function test alanine aminotransferase (ALT) by Fisher's exact test (p<0.001 and p=0.032, respectively), while there were significant relationships between HDL and both female gender and ALT. BMI percentile and ALT were associated with all sets of Met-S diagnostic criteria examined. BMI percentile also had a strong association (p=0.005) with total number of Met-S risk factors, while ALT had a weaker association (p=0.093). WC is a low-cost, simple measure whose use may improve clinic surveillance for childhood obesity and complications like Met-S. WC, BP, HDL and ALT may be used as part of targeted screening for obesity complications like Met-S, particularly in situations where resources are limited.^

Association between length of stay in the U.S. and diabetes risk factors among Chinese Americans in Houston, Texas in 2004-2005

Studies have suggested that acculturation is related to diabetes prevalence and risk factors among immigrant groups in the United States (U.S.), however scant data are available to investigate this relationship among Asian Americans and Asian American subgroups. The objective of this cross-sectional study was to examine the association between length of stay in the U.S. and type 2 diabetes prevalence and its risk factors among Chinese Americans in Houston, Texas. Data were obtained from the 2004-2005 Asian-American Health Needs Assessment in Houston, Texas (N=409 Chinese Americans) for secondary analysis in this study. Diabetes prevalence and risk factors (overweight/obesity and access to medical care) were based on self-report. Descriptive statistics summarized demographic characteristics, diabetes prevalence, and reasons for not seeing a doctor. Logistic regression, using an incremental modeling approach, was used to measure the association between length of stay and diabetes prevalence and related risk factors, while adjusting for the potential confounding factors of age, gender, education level, and income level. Although the prevalence of type 2 diabetes was highest among those living in the U.S. for more than 20 years, there was no significant association between length of stay in the U.S. and diabetes prevalence among these Chinese Americans after adjustment for confounding factors. No association was found between length of stay in the U.S. and overweight/obese status among this population either, after adjusting for confounding factors, too. On the other hand, a longer length of stay was significantly associated with increased health insurance coverage in both unadjusted and adjusted models. The findings of this study suggest that length of stay in the U.S. alone may not be an indicator for diabetes risk among Chinese Americans. Future research should consider alternative models to measure acculturation (e.g., models that reflect acculturation as a multi-dimensional, not uni-dimensional process), which may more accurately depict its effect on diabetes prevalence and related risk factors.^