Prognostic enrichment versus predictive enrichment

Prognostic enrichment and predictive enrichment are both strategies used in clinical trials to select patients for inclusion. Both strategies aim to improve trial efficiency but address different aspects of clinical trial design.

Prognostic enrichment
Selects patients who are more likely to experience a disease-related event or condition. This strategy can help reduce the sample size required for event-driven trials.

Predictive enrichment
Selects patients who are more likely to benefit from a treatment or intervention based on a physiological or biological mechanism.

From FDA's guidance "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":

FDA guidance includes some detail examples of using prognostic enrichment strategies or predictive enrichment strategies. 

The differences between prognostic enrichment and predictive enrichment can be summarized as following:

	Prognostic Enrichment	Predictive Enrichment
Definition	Selecting patients based on their likelihood of experiencing a specific outcome (e.g., disease progression or event) regardless of treatment.	Selecting patients based on their likelihood of responding to a specific treatment due to a biomarker or characteristic.
Goal	To increase the event rate or outcome frequency in the trial population, improving statistical power.	To identify patients who are more likely to benefit from the investigational treatment.
Focus	Focuses on the natural history of the disease or risk of an outcome.	Focuses on the interaction between the treatment and a specific patient characteristic (e.g., biomarker).
Patient Selection	Patients are selected based on prognostic factors (e.g., disease severity, biomarkers, or risk scores).	Patients are selected based on predictive factors (e.g., presence of a biomarker or genetic mutation).
Outcome	Increases the proportion of patients who experience the outcome of interest.	Increases the likelihood of observing a treatment effect in the selected population.
Example	Enrolling patients with advanced-stage cancer to ensure a higher rate of disease progression. Enrolling severe patients who may be more likely to develop clinical worsening events	Enrolling only patients with a specific genetic mutation (genetic biomarker) that is targeted by the therapy. Enrolling only patients in a specific etiology sub-group who are expected to be more responsive to the investigational treatment
Impact on Trial	Reduces sample size and trial duration by enriching for patients with a higher event rate.	Improves treatment effect size by focusing on patients who are more likely to respond.
Statistical Benefit	Increases statistical power by reducing variability in the control group.	Increases effect size by reducing heterogeneity in treatment response.
Risk	May exclude patients who could still benefit from the treatment.	May limit generalizability of trial results to a broader population.
Common Use Cases	Trials where the primary endpoint is time-to-event (e.g., survival, disease progression).	Trials where the treatment mechanism is targeted (e.g., precision medicine, biomarker-driven therapies).

Further Reading: 

• FDA's guidance for industry (2019) "Enrichment Strategies for Clinical Trials to Support Determination of Effectiveness of Human Drugs and Biological Products":
• Bob Temple (2013) Enrichment Strategies for Clinical Trials
• Stanski and Hong (2020) Prognostic and predictive enrichment in sepsis. Nature Reviews Nephrology
• Moscicki and Tandon (2017) Drug-Development Challenges for Small Biopharmaceutical Companies. NEJM