Generalizing Evidence From Randomized Clinical Trials to Target Populations: The ACTG 320 Trial

Abstract

Properly planned and conducted randomized clinical trials remain susceptible to a lack of external validity. The authors illustrate a model-based method to standardize observed trial results to a specified target population using a seminal human immunodeficiency virus (HIV) treatment trial, and they provide Monte Carlo simulation evidence supporting the method. The example trial enrolled 1,156 HIV-infected adult men and women in the United States in 1996, randomly assigned 577 to a highly active antiretroviral therapy and 579 to a largely ineffective combination therapy, and followed participants for 52 weeks. The target population was US people infected with HIV in 2006, as estimated by the Centers for Disease Control and Prevention. Results from the trial apply, albeit muted by 12%, to the target population, under the assumption that the authors have measured and correctly modeled the determinants of selection that reflect heterogeneity in the treatment effect. In simulations with a heterogeneous treatment effect, a conventional intent-to-treat estimate was biased with poor confidence limit coverage, but the proposed estimate was largely unbiased with appropriate confidence limit coverage. The proposed method standardizes observed trial results to a specified target population and thereby provides information regarding the generalizability of trial results.

A Flexible, Multifaceted Approach Is Needed in Health Technology Assessment of PET

“…A common limitation of RCTs is that they cannot provide information on all relevant patient populations, either because the target populations are too small or because the trials themselves use restrictive selection criteria. Novel methods and observational data can be leveraged to assess the representatives of RCT populations and to extrapolate trial results (28,29).…”

Generalizing Experimental Findings

“…? SjZ, appears in Hotz et al [5]; Cole and Stuart [14]; Tipton et al [15]; Hartman et al [9], and possibly other researchers confined to potential outcomes analysis. This assumption states that in every stratum Z ¼ z of the set Z, the potential outcome Y x is independent of the factors S that may produce cross-population differences.…”

A Linear “Microscope” for Interventions and Counterfactuals

“…In generalizing experimental findings from one population (or environment) to another, a common method of estimation invokes re-calibration or re-weighting [23][24][25]. The reasoning goes as follows: Suppose the disparity between the two populations can be attributed to a factor S such that the potential outcomes in the two population are characterized by E(Y x |S = 1) and E(Y x |S = 2), respectively.…”