Improving The Inference Of Some Experiemtal Studies By Using Ranked Auxiliary Covariates

In many studies, the measurement of sampling units according to the response variable is costly or time consuming, however, it is possible to rank sampling units according to baseline auxiliary covariates, which are available, easily obtainable, and cost efficient. In these cases, when estimating the population mean, Ranked Set Sampling (RSS) can be a more efficient sampling method than the Simple Random Sampling (SRS) method. In this dissertation, we propose a modified approach of the RSS method to allocate units into an experimental study, aimed to compare two or more groups. Ranked auxiliary covariates, which are typically correlated with the variable of interest, are involved in sampling design; these covariates are available and affordable. Computer simulation is used to estimate the empirical nominal values and the empirical power values for the modified RSS, by using the regression approach in analysis of covariance (ANCOVA) models, and compared to the SRS. 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 conducted by the Department of Psychology, in collaboration with the College of Public Health, Department of Biostatistics, at Georgia Southern University. 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. Using the RSS method resulted in significant cost reduction associated with smaller sample size without losing the significant precision of the analysis

Using Ranked Auxiliary Covariate as a More Efficient Sampling Design for ANCOVA Model: Analysis of a Psychological Intervention to Buttress Resilience

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

Improving Some Clinical Studies Inference by Using Ranked Auxiliary Covariate

The main objective in a randomized clinical trial or studies such as in cancer, AIDS, etc. is to compare the outcome of interest between two or more groups. Clinical trials are considered the gold standard of biomedical research and of its strengths are the ability to measure changes and/or evaluate of treatments over time with maximizing power of statistics and validity. Clinical trials are expensive, and the cost of clinical trials on developing new drugs, medical treatments and devices, public health investigators are increasing with each phase and continue to escalate, especially in phase III. The idea proposed in this project is to use auxiliary covariates by adopting Ranked Set Sampling (RSS) technique to select the subjects for each treatment-arms, to utilize inexpensive auxiliary covariates information into a randomized clinical trials. Our goal is to provide a more precise estimator of the population mean (µ) of the outcome of interest (Y) to recover the difficult to obtain information, without making any additional assumptions other than those already necessary for (RSS) and the ordinary least square estimators from a regression model to hold

Stari instrumenti, instrumenti preteče trube

Diplomski rad donosi povijesni razvoj trube i njenih preteča u kulturološkom kontekstu. Od Rimskog Carstva, preko srednjeg vijeka, renesanse, baroka kao zlatnog doba trube, klasike i novih načina proizvodnje trube, do modernog vremena i tehničkog napretka instrumenta

Improving Some Clinical Trials Interference by Using Ranked Auxiliary Covariate

The main objective in a randomized clinical trial of studies such as in cancer, AIDS, etc. is to compare the outcome of interest between two or more groups. Clinical trials are considered the “gold standard” of biomedical research and of its strengths are the ability to measure changes and/or evaluate of treatments over time with maximizing power of statistics and validity. Clinical trials are expensive, and the cost of clinical trials on medical treatments and devices, public health investigators are increasing with each phase and continue to escalate, especially in phase III. The idea proposed in this project is to use auxiliary covariates by adopting Ranked Set Sampling (RSS) technique to select the subjects for each treatment-arms, to utilize inexpensive auxiliary covariates information into a randomized clinical trials. The goal is to provide a more precise estimator of the population mean of the outcome of interest to recover the difficult to obtain information, without making any additional assumptions other than those already necessary for (RSS) and the ordinary least square estimators from a regression model to hold

Association between C-Reactive Protein and Depression: Modulated by Gender and Mediated by Body Weight

Literature on the relationship between depression and C-reactive protein (CRP), a biomarker of systematic inflammation, remains inconsistent. Insufficient adjustment for confounders and effect modifiers might be one explanation. We used the data of 6396 men and 6610 women aged 18 or older, who completed a depression screening and had blood collected as a part of the National Health and Nutrition Examination Survey, 2005–2010. Depression was measured using the 9-item depression scale of the Patient Health Questionnaire (PHQ-9). The odds ratios (ORs) of depression were 1.00 (reference), 1.89 (95% CI=0.77–4.67) and 3.41(1.25–9.25) respectively for men with low, intermediate and upper quartile of CRP. Adjustment for covariates, mainly body mass index, diminished the association among women, from 1.65(1.00–2.74) to 1.08(0.57–2.03) for intermediate, from 2.44 (1.43–4.16) to 1.05 (0.56–1.98) for upper quartile of CRP. Adjustment for the history of major medical illnesses changed ORs neither among men nor among women. The study concluded that CRP remained significantly associated with depression in a dose–response fashion among men but women after being adjusted for body weight. Abnormal body weight, both under and overweight, explained a substantial part of the relationship between CRP and depression among women