Active-comparator design and new-user design in observational studies

Abstract

Over the past decade, an increasing number of observational studies have examined the effectiveness or safety of treatments for rheumatoid arthritis. Unlike randomized controlled trials (RCTs), however, observational studies of drug effects have methodological limitations such as confounding by indication. Active-comparator designs and new-user designs can help mitigate such biases in observational studies and improve the validity of their findings by making them more closely approximate RCTs. In an active-comparator study, the drug of interest is compared with another agent commonly used for the same indication, rather than with no treatment (a 'non-user' group). This principle helps to ensure that treatment groups have similar treatment indications, attenuating both measured and unmeasured differences in patient characteristics. The new-user study includes a cohort of patients from the time of treatment initiation, enabling assessment of patients' pretreatment characteristics and capture of all events occurring during follow-up. These two principles should be considered when designing or reviewing observational studies of drug effects.

FIGURE 1

Schematic illustration of how confounding by indication can cause a spurious association. Gastrointestinal bleeding risks raise the likelihood of COX-2 inhibitor prescription (short arrow). Such gastrointestinal risk factors, by definition, predispose patients to subsequent bleeding (long arrow). Spurious statistical association (broken line) arises between COX-2 inhibitor use and gastrointestinal bleeding the gastrointestinal bleeding risks (confounders; common causes of both COX-2 prescription and subsequent bleeding). Adjustment for gastrointestinal risks is necessary.

FIGURE 2

Greater difference in patient characteristics between the main treatment group vs non-users rather than active comparator. Compared to the non-users, the users of drug B prescribed for the same indication are more similar to the users of drug A in measured pretreatment characteristics, such as age and gender, and more importantly in unmeasured pretreatment characteristics, such as frailty, life style, disease activity. It is likely that non-users include subjects who have no indication for any treatment (e.g., very mild disease) or contraindication for all treatment (e.g., very severe coexisting conditions). Statistical analysis can only adjust for characteristics “visible” as variables (“above the surface”), and is efficient if the distributions of these characteristics are similar. Differences in unmeasured pretreatment characteristics (“below the surface”) cannot be addressed by statistical adjustment; therefore, such unmeasured differences need to be addressed by the study design.

FIGURE 3

Comparison of how observations are utilized in new user design and prevalent user design. Panel A: The colored line represents drug use and the gray line is for non-use. The red, yellow, and green color blocks represent different risk period for infection. The box is the study period captured in the database, and the shaded area is not. Panel B: Only those who start the drug during the study period are included (subjects 2, 3, and 6), thus, the reduced sample size. The follow up starts at the drug initiation for all remaining patients. The initial high riskhigh-risk period is well represented. Panel C All users can be included, thus, the sample size is six. However, the index date is the drug initiation date only for subjects 2, 3, and 6, and for others an arbitrary time point. Thus, the early follow up period is a mixture of the initial high riskhigh-risk period and later low risk period.

FIGURE 4

Schematic illustration of difference between confounders and mediators. Pretreatment disease activity is a potential confounder (common cause of both treatment choice and subsequent infection), thus, adjustment is necessary. Posttreatment disease activity is a result of the treatment choice (mediator). Conventional statistical adjustment is inappropriate for mediators, as such adjustment blocks the true causal relationship between the treatment and outcome of the interest, and will bias the result (overadjustment).