Nonadherenceadjusted

Nonadherence-adjusted refers to analytical approaches in clinical and epidemiological research that attempt to account for patient nonadherence to prescribed therapies when estimating treatment effects. The goal is to approximate how outcomes would look under real-world adherence patterns, rather than assuming perfect or uniform adherence.

In practice, nonadherence-adjusted analyses use data on actual medication-taking behavior, which may come from pharmacy refills, electronic monitoring, patient diaries, or self-report, and incorporate it into statistical models. Methods include time-varying adherence modeling, per-protocol and as-treated analyses, and causal inference techniques such as inverse probability weighting or marginal structural models that handle time-dependent confounding and changing exposure. Pharmacometric or pharmacokinetic models may be used to link adherence patterns to drug exposure and response, enabling more nuanced effect estimates.

Challenges of nonadherence adjustment include measurement error in adherence data, misclassification of exposure, and the complexity of modeling dynamic, patient-specific patterns. Adherence is often correlated with other factors that influence outcomes, raising concerns about residual confounding and selection bias. Data requirements are substantial, and results can be sensitive to assumptions about how nonadherence affects exposure and effect.

Nonadherence-adjusted analyses are most relevant in observational studies of real-world effectiveness, where adherence substantially influences treatment outcomes. They complement traditional approaches such as intention-to-treat and as-treated analyses by explicitly addressing how nonadherence shapes estimates of benefit and risk.