Analyzing Application Experimental Study Design and Intention to Treat

Experimental Study Design and Intention to Treat

Hepatitis C Virus (HCV) association with HIV Infection in Gay Men

The population for this research will comprise of persons enrolled in one out of five CPCRA (Community Programs for Clinical Research on AIDS) studies at sixteen CPCRA facilities across America. The five researches will take the form of four randomized clinical trials (RCTs) for antiretroviral (ARV) therapy strategies and one natural history analysis of ARV treatment-naive patients. During enrolment in the study, researchers will determine every participant's HCV serostatus. Past positive results for HCV antibody test will also be accepted. HCV serological examinations will be locally conducted at baseline in case of patients without recorded HCV antibody test results or those showing negative HCV antibody results from eleven years prior to randomization. The baseline characteristics outlined include ethnicity, gender, age, plasma HIV viral load, history of IDU (injection drug use), CD4+ count, ARV therapy status (experienced or naive), and history of sexual contact with persons of the same sex. Multivariate and univariate logistic regression will be conducted for identifying baseline covariates linked to HCV infection prevalence at baseline (Bradshaw, Matthews & Danta, 2013; Schmidt et al., 2014).

Specific Advantages of Randomization Study

In RCTs, interventions are studied through a comparison of a group of individuals receiving the intervention, along with their respective control arms/groups that don't. Control groups receive no or usual treatment; the measure of their outcome, or deviation of measure from baseline (i.e. starting point), is compared to intervention group outcome measure (Gupta, 2011). The practice of randomization ensures the absence of systematic differences in unknown and known factors capable of affecting outcome, among intervention groups. A blinding design guarantees the inability of preconceived investigator and subject views to systematically bias outcome assessment. Intention-to-treat (ITT) analysis has random allocation benefits, which might be lost if subjects were excluded from the analysis by means of, say, withdrawal or a need to make changes to intervention, owing to unforeseen circumstances.

Bias minimization is one aim of randomized studies. Allocation bias is said to occur when a difference arises between measured and true effect of treatment, due to control or intervention group participant selection methods. In RCT, after subjects are enrolled into a research, they are randomized to either of two groups: control or intervention. Randomization ensures characteristics, which may impact the link between outcome measures and the intervention, are almost equal across different study arms, thereby minimizing likely bias. Despite randomization of allocation, bias (known as performance bias) may occur. Such bias is said to occur when response of study subjects to a treatment is impacted by an awareness of which group they're assigned to, or when treatments are administered differently by health professionals between different arms of the treatment (Schmidt et al., 2014).

Effects of Intention to Treat on Design and Analysis

RCTs are usually linked to two significant complications, namely: missing outcomes and noncompliance. A potential way to resolve this issue is by using the statistical ITT concept, which includes every randomized patient in the group he/she was randomly assigned to, irrespective of: his/her meeting entry criteria; treatment they are actually administered, and deviation from protocol or subsequent treatment withdrawal (Gupta, 2011). That is, ITT analysis covers all randomized subjects in accordance with randomized assigning of treatment. It ignores protocol deviations, noncompliance, withdrawal, and other things occurring after randomization. In short, ITT analyses are "once randomized, always analyzed" analyses.