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Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N= 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials. [ABSTRACT FROM AUTHOR]

Reverse-phase protein arrays (RPPAs) are widely used in biological and biomedical fields of study. One of the most popular analytic methods in RPPA data analysis is the SuperCurve method, which requires estimation of the background fluorescence level. This estimation is usually not accurate and has sample bias and spatial bias. Here, we propose a taking-the-difference method to overcome this problem. Briefly, for each two consecutive RPPA cycles, we subtract the later cycle from the earlier cycle, transforming the m-cycle data into m−1 cycle of data. This removes most of the background fluorescence noise. We then use the m−1 cycle of data to fit a new model accordingly derived from the SuperCurve model. To evaluate our proposed method, we compare the accuracy and precision between our proposed model and the original SuperCurve model by testing them on both real and simulated datasets. For both situations, our modified model shows improved results. The modified SuperCurve method is easy to perform and the taking-the-difference idea is recommended for application to all current methods of RPPA data analysis. [ABSTRACT FROM AUTHOR]

Following latent class analysis (LCA) approach we examined patterns of HIV risk using two related domains of behavior: drug use, and sexual activity among 523 injection drug users (IDUs) recruited into the 2009 National HIV behavioral surveillance system. Using posterior probability of endorsing six drug and sexual items, we identified three distinct classes representing underlying HIV risk. Forty percent of our participants were at highest risk, 25 % at medium risk, and 35 % at lowest risk for HIV infection. Compared to the Lowest-risk class members, the Highest-risk class members had riskier drug and sexual behaviors and had higher prevalence of HIV cases (6 vs. 4 %). This analysis underscores the merit of LCA to empirically identify risk patterns using multiple indicators and our results show HIV risk varies among IDUs as their drug and sexual behaviors. Tailored and targeted prevention and treatment interventions for the dual risk pattern are required rather than for drug or sexual risk in silos. [ABSTRACT FROM AUTHOR]

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantification method that has been extensively used in biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle method and linear and nonlinear model-fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence can hardly be accurate and therefore can distort results. We propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtract the fluorescence in the former cycle from that in the latter cycle, transforming the n cycle raw data into n−1 cycle data. Then, linear regression is applied to the natural logarithm of the transformed data. Finally, PCR amplification efficiencies and the initial DNA molecular numbers are calculated for each reaction. This taking-difference method avoids the error in subtracting an unknown background, and thus it is more accurate and reliable. This method is easy to perform, and this strategy can be extended to all current methods for PCR data analysis. [ABSTRACT FROM AUTHOR]

Recent studies have reported a clustered pattern of high-risk drug using and sexual behaviors among younger injection drug users (IDUs), however, no studies have looked at this clustering pattern in relatively older IDUs. This analysis examines the interplay and overlap of drug and sexual HIV risk among a sample of middle-aged, long-term IDUs in Houston, Texas. Our study includes 452 eligible IDUs, recruited into the 2009 National HIV Behavioral Surveillance project. Four separate multiple logistic regression models were built to examine the odds of reporting a given risk behavior. We constructed the most parsimonious multiple logistic regression model using a manual backward stepwise process. Participants were mostly male, older (mean age: 49.5±6.63), and nonHispanic Black. Prevalence of receptive needle sharing as well as having multiple sex partners and having unprotected sex with a partner in exchange for money, drugs, or other things at last sex were high. Unsafe injecting practices were associated with high-risk sexual behaviors. IDUs, who used a needle after someone else had injected with it had higher odds of having more than three sex partners (odds ratio (OR) = 2.10, 95% confidence interval (CI): 1.40–3.12) in last year and who shared drug preparation equipment had higher odds of having unprotected sex with an exchange partner (OR = 3.89, 95% CI: 1.66–9.09) at last sex. Additionally, homelessness was associated with unsafe injecting practices but not with high-risk sexual behaviors. Our results show that a majority of the sample IDUs are practicing sexual as well as drug-using HIV risk behaviors. The observed clustering pattern of drug and sexual risk behavior among this middle-aged population is alarming and deserve attention of HIV policy-makers and planners. [ABSTRACT FROM PUBLISHER]

Abstract: In this article, we extended the empirical distribution function based test statistic of Skaug and Tjostheim [1993. Nonparametric test of serial independence based on the empirical distribution function. Biometrika 80, 591–602] in the time series setting to for spatial lattice data and derived the asymptotic distribution of the proposed test statistic under the null hypothesis of spatial independence. The size and power of the proposed test statistic under conditional autoregressive model (CAR) were simulated. We applied , Moran''s I and Geary''s c to the transformed and well-studied sudden infant death syndrome data from North Carolina and found that produced a much smaller -value in testing spatial independence. [Copyright &y& Elsevier]

The Center for Disease Control (CDC) in the United States collects and maintains records of communicable (so‐called notifiable) infectious diseases that cause significant morbidity and mortality and impact the national economy. This investigation focused on seasonal patterns in the primarily childhood and young adult infectious diseases of meningococcal meningitis, mumps, pertussis, typhoid fever, streptococcal toxic shock syndrome (1990 to 2003 CDC database), and varicella (1993 to 2003 CDC database). Linear regression was performed to ascertain the trend in the incidence of each disease, and multi‐component cosinor analysis was applied to determine and describe periodicities. Significant decreasing trends in incidence were detected in meningococcal meningitis, mumps, typhoid fever, and streptococcal toxic shock syndrome, and increasing trends were found in pertussis and varicella. Significant annual patterns were documented in meningococcal meningitis (January peak), mumps (April peak), pertussis (August peak), varicella (April peak), typhoid fever (August peak), and in the hospital‐acquired streptococcal toxic shock syndrome (February peak). Such seasonal patterns and long‐term trends in infectious diseases are of practical public health significance in indicating which can benefit from timely prevention interventions. [ABSTRACT FROM AUTHOR]

A new theoretical relation that does not require the constant age-specific mortality ratio assumption is established between the standardized mortality ratio (SMR) and the life expectancy. A set of regression equations is developed from the theoretical relation to derive estimates of the future expectation of life from estimates of the SMR. Curves are presented showing the changes in life expectancy that are associated with a given SMR for individuals aged 25, 45, and 65 years. These results will provide practical applications in estimating remaining life expectancy in epidemiologic studies in which the SMR is the summary statistic. An application is shown for studies in occupational health to develop and illustrate the method. Am J Epidemiol1996; 143: 832–41. [ABSTRACT FROM AUTHOR]