HCV and HIV in Gay Men: Experimental Study Design Guide

Study Population and Baseline Characteristics

The population for this research comprises persons enrolled in one of five Community Programs for Clinical Research on AIDS (CPCRA) studies at sixteen CPCRA facilities across the United States. The five studies 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 enrollment, researchers determine each participant's HCV serostatus. Past positive results for an HCV antibody test are also accepted.

HCV serological examinations are conducted locally at baseline for patients without a recorded HCV antibody test result, or for those showing a negative HCV antibody result from more than eleven years prior to randomization. The baseline characteristics recorded include ethnicity, gender, age, plasma HIV viral load, history of injection drug use (IDU), CD4+ count, ARV therapy status (experienced or naive), and history of sexual contact with persons of the same sex. Multivariate and univariate logistic regression are conducted to identify baseline covariates associated with HCV infection prevalence at baseline (Bradshaw, Matthews, & Danta, 2013; Schmidt et al., 2014).

Advantages of Randomization in Clinical Trials

In randomized controlled trials (RCTs), interventions are studied by comparing a group of individuals receiving the intervention with their respective control arms or groups that do not. Control groups receive no treatment or usual treatment; the measure of their outcome — or deviation of that measure from baseline — is compared to the intervention group's outcome measure (Gupta, 2011). Randomization ensures the absence of systematic differences in both known and unknown factors capable of affecting outcomes among intervention groups.

A blinding design guarantees that preconceived views held by investigators and subjects cannot systematically bias outcome assessment. Intention-to-treat (ITT) analysis preserves the benefits of random allocation, which might otherwise be lost if subjects were excluded from analysis due to withdrawal or a need to change the intervention owing to unforeseen circumstances.

Bias Minimization Through Randomization

Bias minimization is a central aim of randomized studies. Allocation bias is said to occur when a difference arises between the measured and true effect of a treatment, due to the methods used to select participants for control or intervention groups. In an RCT, after subjects are enrolled, they are randomized to either a control or an intervention group. Randomization ensures that characteristics which may affect the relationship between outcome measures and the intervention are approximately equal across all study arms, thereby minimizing likely bias.

Despite the use of randomization, another form of bias — known as performance bias — may still occur. Performance bias arises when a study subject's response to a treatment is influenced by their awareness of which group they have been assigned to, or when health professionals administer treatments differently across different arms of the trial (Schmidt et al., 2014).

Intention-to-Treat: Definition and Application

RCTs are commonly associated with two significant complications: missing outcomes and noncompliance. One approach to resolving these issues is the intention-to-treat (ITT) analytical framework, which includes every randomized patient in the group to which they were randomly assigned, irrespective of whether they met entry criteria, what treatment they actually received, or whether they deviated from protocol or subsequently withdrew from treatment (Gupta, 2011). In other words, ITT analysis covers all randomized subjects according to their originally assigned treatment group. It does not account for protocol deviations, noncompliance, withdrawal, or other events occurring after randomization. In short, ITT analyses operate on the principle of "once randomized, always analyzed."