Progressive censoring is essential for researchers in industry as a mean to remove subjects before the final termination point in order to save time and reduce cost. Recently, kernel density estimation has been intensively investigated due to its asymptotic properties and applications. In this paper, we investigate the asymptotic properties of the kernel density estimators based on progressive type-II censoring and their application to hazard function estimation. A bias-adjusted kernel density estimator is also proposed. Our simulation indicates that the kernel density estimates under progressive type-II censoring is competitive compared with kernel density estimates under simple random sampling, depending on the censoring schemes. An example regarding failure times of aircraft windshields is used to illustrate the proposed methods.

Almost one-third of U.S. adults (29%) have a tattoo, and almost half (47%) of millennials reported having a tattoo. With more people getting tattoos, there is an increased risk for infectious diseases, skin infections, and allergic reactions. Tattoo artists can influence these health risks with their standards of practice, tattoo inks, and sterilization techniques. Although tattoos are becoming mainstream, it was unclear if tattoo artists would be a hard-to-reach population. Using social media sites represent a promising method for recruiting tattoo artists for online survey research studies. To evaluate various online platforms and traditional methods for recruiting tattoo artists into a descriptive, online survey study. From September 2015 to February 2016, tattoo artists who primarily tattooed in the United States were recruited using both online and traditional methods. Recruitment occurred via Facebook advertisements, Instagram, Twitter, website, online advertisement, emails, and postcards mailed to tattoo shops. Recruitment methods resulted in 2332 respondents, of which 1845 answered question one, “Are you a tattoo artist?” Only 1571 were tattoo artists. Facebook advertisements recruited the most study participants. Facebook accounted for 1228 (78.17%) respondents who were tattoo artists. This number surpassed the next leading category of HTTP referer unknown, which had 268 (17.06%). The [removed for blinded manuscript] website recruited 45 (2.86%) tattoo artists while other online sources contributed to 28 (1.78%). Twitter and email had the lowest response rate with only 0.06% (n=1) each. Social media sites enhanced survey participation, making it easier to reach tattoo artists nationwide. Of the recruitment methods used, Facebook advertisements were the most effective option, both for cost and recruitment rates. This study’s findings extend previous research studies that demonstrated the timeliness, ease, and effectiveness of using Facebook advertisements for recruitment.

In the literature, the properties and the application of mode estimation is considered under simple random sampling and ranked set sampling (RSS). We investigate some of the asymptotic properties of kernel density-based mode estimation using stratified simple random sampling (SSRS) and stratified ranked set sampling designs (SRSS). We demonstrate that kernel density-based mode estimation using SRSS and SSRS is consistent, asymptotically normally distributed and using SRSS has smaller variance than that under SSRS. Improved performance of the mode estimation using SRSS compared to SSRS is supported through a simulation study. We will illustrate the method by using biomarker data collected in China Health and Nutrition Survey data.

Background: Almost one-third of US adults (29%) have a tattoo, and almost half (47%) of millennials reported having a tattoo.With more people getting tattoos, there is an increased risk of infectious diseases, skin infections, and allergic reactions.Tattoo artists can influence these health risks with their standards of practice, tattoo inks, and sterilization techniques.Although tattoos are becoming mainstream, it was unclear if tattoo artists would be a hard-to-reach population.Using social media sites represents a promising method for recruiting tattoo artists for Web-based survey studies. Objective:The aim of this study was to evaluate various Web-based platforms and traditional methods for recruiting tattoo artists into a descriptive Web-based survey study.Methods: Recruitment occurred via Facebook ads , Instagram, Twitter, website, Web-based advertisement, emails, and postcards mailed to tattoo shops.Results: Recruitment methods resulted in 2332 respondents, of which 1845 answered question 1, "Are you a tattoo artist?"Only 1571 were tattoo artists.Facebook ads recruited the most study participants.Facebook accounted for 1228 (1228/1571, 78.17%) respondents who were tattoo artists.This number surpassed the next leading category of HTTP Referer unknown, which had 268 (268/1571, 17.06%).The Tattoo Survey 2015 website recruited 45 (45/1571, 2.86%) tattoo artists, whereas other Web-based sources contributed to the recruitment of 28 (28/1571, 1.78%) tattoo artists.Twitter and email had the lowest response rate with only 0.06% (1/1571) each.Conclusions: Social media sites enhanced survey participation, making it easier to reach tattoo artists nationwide.Of the recruitment methods used, Facebook ads were the most effective option, both for cost and recruitment rates.This study's findings extend those of the previous research studies that demonstrated the timeliness, ease, and effectiveness of using Facebook ads for recruitment.

Two quasi-likelihood ratio tests are proposed for the homoscedasticity assumption in the linear regression models. They require few assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests.

Wastewater workers are exposed to different occupational hazards such as chemicals, gases, viruses, and bacteria. Personal protective equipment (PPE) is a significant factor that can reduce or decrease the probability of an accident from hazardous exposures to chemicals and microbial contaminants. The purpose of this study was to examine wastewater worker’s beliefs and practices on wearing PPE through the integration of the Health Belief Model (HBM), identify the impact that management has on wastewater workers wearing PPE, and determine the predictors of PPE compliance among workers in the wastewater industry. Data was collected from 272 wastewater workers located at 33 wastewater facilities across the southeast region of the United States. Descriptive statistical analysis was conducted to present frequency distributions of participants’ knowledge and compliance with wearing PPE. Univariate and multiple linear regression models were applied to determine the association of predictors of interest with PPE compliance. Wastewater workers were knowledgeable of occupational exposures and PPE requirements at their facility. Positive predictors of PPE compliance were perceived susceptibility and perceived severity of contracting an occupational illness (p < 0.05). A negative association was identified between managers setting the example of wearing PPE sometimes and PPE compliance (p < 0.05). Utilizing perceived susceptibility and severity for safety programs and interventions may improve PPE compliance among wastewater workers.

Aim: To examine the effects of an upper-extremity, community-based, and power-training intervention.Methods: Twelve participants with cerebral palsy (CP) [8 males, 4 females; mean age 14 years 6 months (SD 5 years 4 months), range 7-24] were randomly assigned to a rest-training (RT; n = 6) or training-rest (n = 6) group in this randomized, cross-over design. Training took place in participants' home or school, three times per week for 6 weeks. We examined changes in upper extremity average power output (Pavg) in watts (W) and changes in function via the Pediatric Outcomes Data Collection Instrument (PODCI).Results: Each participant completed at least 15 of the 18 total training sessions (91.2% adherence). Pavg increased 92.2% on average among participants (p < .05). There was a significant three-way interaction among treatment, sequence, and period with the data stratified by (Bimanual Fine Motor Function [BFMF]) level on the pain subscale of the PODCI (p = 0.0118). All participants decreased pain after training with the exception of individuals with lower functioning (BFMF II-V) in the RT group.Conclusion: A community-based upper extremity power-training intervention was feasible and effective at improving power among young people with CP and has the potential to improve pain.

Survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Accelerated failure time (AFT) models provide a linear relationship between the log of the failure time and covariates that affect the expected time to failure by contracting or expanding the time scale. The AFT model has intensive application in the field of social, medical, behavioral, and public health sciences. In this article we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) or an Extreme Ranked Set Sampling (ERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This article demonstrates that these approaches provide a more powerful testing procedure, as well as a more efficient estimate of hazard ratio, than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural Health Study data are used to illustrate the methods developed in this article.

The mode is a measure of the central tendency as well as the most probable value. Additionally, the mode is not influenced by the tail of the distribution. In the literature the properties and the application of mode estimation is only considered under simple random sampling (SRS). However, ranked set sampling (RSS) is a structural sampling method which improves the efficiency of parameter estimation in many circumstances and typically leads to a reduction in sample size. In this paper we investigate some of the asymptotic properties of kernel density based mode estimation using RSS. We demonstrate that kernel density based mode estimation using RSS is consistent and asymptotically normal with smaller variance than that under SRS. Improved performance of the mode estimation using RSS compared to SRS is supported through a simulation study. An illustration of the computational aspect using a Duchenne muscular dystrophy data set is provided.

BACKGROUND:Premature infants may require packed red blood cell transfusions, but current guidelines lack empirical evidence and vary among institutions and prescribers.OBJECTIVE:To compare the physiological changes in cardiovascular hemodynamics and oxygen delivery between premature infants with anemia who receive packed red blood cell transfusions and premature infants without anemia.METHODS:The study was a prospective observational cohort investigation of 75 premature infants. Comparisons among the data were made before, during, and after transfusion in infants with anemia and over time in infants in the control group. In infants with anemia, feedings were withheld 12 hours before and after transfusions.RESULTS:Electrical cardiometry and near-infrared spectroscopy measurements in premature infants with anemia revealed changes in hemodynamic parameters not detected by standard bedside monitoring. Statistically significant changes were seen before and after transfusions in cardiac output, fractional tissue oxygen extraction, heart rate variability, heart rate complexity, and splanchnic regional tissue oxygen saturation.CONCLUSION:Bedside monitoring of cardiovascular hemodynamics and oxygen delivery during packed red blood cell transfusion may inform individualized care for the premature infant with anemia and could be useful for the development of evidence-based practice guidelines.

Abstract In general, survival data are time-to-event data, such as time to death, time to appearance of a tumor, or time to recurrence of a disease. Models for survival data have frequently been based on the proportional hazards model, proposed by Cox. The Cox model has intensive application in the field of social, medical, behavioral and public health sciences. In this paper we propose a more efficient sampling method of recruiting subjects for survival analysis. We propose using a Moving Extreme Ranked Set Sampling (MERSS) scheme with ranking based on an easy-to-evaluate baseline auxiliary variable known to be associated with survival time. This paper demonstrates that this approach provides a more powerful testing procedure as well as a more efficient estimate of hazard ratio than that based on simple random sampling (SRS). Theoretical derivation and simulation studies are provided. The Iowa 65+ Rural study data are used to illustrate the methods developed in this paper.

The purpose of the current work is to introduce stratified bivariate ranked set sampling (SBVRSS) and investigate its performance for estimating the population mean using both naive and ratio methods. The properties of the proposed estimator are derived along with the optimal allocation with respect to stratification. We conduct a simulation study to demonstrate the relative efficiency of SBVRSS as compared to stratified bivariate simple random sampling (SBVSRS) for ratio estimation. Data that consist of weights and bilirubin levels in the blood of 120 babies are used to illustrate the procedure on a real data set. Based on our simulation, SBVRSS for ratio estimation is more efficient than using SBVSRS in all cases.

Two types of stratified regression estimators for the population mean, the separate and the combined estimators, are investigated using stratified random sampling scheme (SSRS) and stratified ranked set sampling (SRSS). We derived mean and variance of the proposed estimators. In addition, we compared the performance of the regression estimators using SRSS with respect to SSRS by simulation. Our derivations and simulations revealed that our proposed estimators are unbiased and using SRSS is more efficient than using SSRS. The procedure are illustrated by using the bilirubin levels in babies in a neonatal intensive care unit data.

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

Missing observations often occur in cross-classified data collected during observational, clinical, and public health studies. Inappropriate treatment of missing data can reduce statistical power and give biased results. This work extends the Baker, Rosenberger and Dersimonian modeling approach to compute maximum likelihood estimates for cell counts in three-way tables with missing data, and studies the association between two dichotomous variables while controlling for a third variable in \( 2\times 2 \times K \) tables. This approach is applied to the Behavioral Risk Factor Surveillance System data. Simulation studies are used to investigate the efficiency of estimation of the common odds ratio.

The overall purpose of this study was to explore predictors of forced sex among a sample of middle school students. Youth Risk Behavior Surveys were distributed to middle school youth in southeast Florida. Data were analyzed using descriptive statistics, Chi-Square Automatic Interaction Detector (CHAID), and logistic regression. In the final CHAID model, the segment most at risk was comprised of youth who had been harassed for being gay, lesbian, or bisexual and youth who had experienced dating violence. Past exposure with violence yielded the highest association with forced sex. Moreover, having multiple sexual partners, use of prescription drugs, and experiencing harassment for being gay, lesbian, or bisexual are predictors of forced sex. This study has implications for school-based prevention of forced sex through the identification of risk and protective factors that can be targeted with evidence-based interventions.