1. How Should We Select and Define Trial Estimands
How Should We Select and Define Trial Estimands? Examples Based on a Disease State July 29, Elena Polverejan, Ph.D. Janssen R&D

2. Acknowledgement
Ideas discussed by ISCTM Estimands Working Group

3. Outline
Background
Selecting and defining estimands for a clinical trial – Points to consider in implementation
Estimand example – Major Depressive Disorder (MDD)
Summary

4. Background

5. Example Clinical Trial – Before Estimands
MDD monotherapy, placebo-controlled trial
Primary endpoint: change from baseline to week X in a depression score
Full Analysis Set: all randomized and dosed (called ITT)
Subjects are discontinued from the double-blind (DB) phase if they:
Discontinue the study treatment
Have severe non-compliance with the study drug
Start protocol prohibited medication
Analysis based on MMRM using DB phase measurements, so on- treatment measurements for compliant subjects who take allowed medication only.

6. Example Clinical Trial – Points to Be Aware Of
Analysis set called ITT; however, observations are not collected for some subjects up to the end of DB phase
Quote from the ICH E9(R1) Addendum
Point 2:
Implicit assumption of the MMRM analysis using only measurements prior to treatment discontinuation: If the subjects who discontinued study treatment would have continued the treatment as planned, they would have similar efficacy as the subjects who remained on treatment.

7. ICH E9(R1) - Trial Planning Framework
Objective(s)
Estimands
Design
Estimators/Analyses
(Both Main + Sensitivity)
Estimands for different scientific questions of interest; one selected as primary
For each
estimand

8. Estimand
An estimand defines the target of estimation (i.e. “what is to be estimated”) to address a trial scientific question of interest
Defined by the following attributes/components:
Population
Variable (endpoint)
Intercurrent events and their corresponding strategies
Summary measure
Treatment component (TBD)

9. Selecting and defining estimands for a clinical trial Points to consider

10. Stakeholders for a Clinical Trial
Regulatory Agency 1
Sponsor
(e.g. Ph 2)
Payers
Prescribers
Patients
Regulatory Agency 2
Multiplicity Issue?

11. Dealing with Multiple Stakeholders for a Clinical Trial
Regulatory Agency 1
Regulatory Agency 2
Estimand 1
Estimand 4
Estimand 2
Estimand 2
Estimand 3
Estimand 3
Risk of multiplicity might remain

12. Trial Objective General or very specific?
A trial objective could be general and could encompass different scientific questions of interest, one to be chosen as primary.
For each stakeholder, useful to:
define the scientific question of interest for each estimand
think how that estimand is useful for the targeted stakeholder

13. Intercurrent Events – Examples for MDD
Events that occur after treatment initiation and either preclude observation of the variable or affect its interpretation.
Related to study treatment:
Treatment discontinuation (DC)
Treatment non-compliance (intermittent or partial treatment adherence)
Dose adjustment: allowed or not by the protocol
Related to concomitant meds:
Rescue therapy
Initiation or adjustment of concomitant medication related to other symptoms related to depression (e.g. for insomnia)
Initiation of concomitant medications not allowed by the protocol
Adjunctive trials (add-on + background):
Discontinuation of add-on medication only
Discontinuation of background medication only
Discontinuation of both add-on + background
Death
Special category: Study DC, intermediate events leading to inter. missing

14. MDD Estimand Example

15. MDD Estimand Example: Adjunctive, Placebo Controlled Short Term Phase 3 Trial
Trial Objective: The drug has superior benefit in treating MDD vs placebo, when administered with the protocol allowed background medication
Potential Stakeholders: Regulatory, Payers, Prescribers
Estimand Scientific Question of Interest: What is the effect of assigning subjects for the pre-specified duration to the experimental add-on drug administered together ONLY with the protocol allowed background medication?
Estimand Utility: An estimand with broadly use of the observed data, “eliminating” the effects of any background medications not allowed by the protocol

16. Estimand Components
Population: Subjects with MDD acute episodes, as defined by the inclusion-exclusion criteria of the study
Variable: Change from baseline to Week X (e.g. Week 6) in a depression measure (e.g. MADRS)
Intercurrent events and their corresponding strategies: see final slide of estimand components
Summary measure: Difference in treatment means for the variable
Treatment component(?)

17. Treatment Component - Example
Add-on Treatment: Initial dose: xx mg orally once a day, increased after x weeks if sufficient clinical improvement is not observed; Maintenance dose: xx to yy mg per day.
Background Treatment: drugs(s) and allowed dosage
Additional Specifications:
Certain pre-specified rescue therapies are allowed, as well as concomitant medications related to other depression symptoms (e.g. insomnia).
After discontinuation of add-on treatment due to lack of efficacy (LoE), subject will not remain on the background treatment (so both add-on and background will be discontinued)
After discontinuation of add-on treatment due to other reasons than LoE, subject could remain on the background treatment.
After discontinuation of background treatment only, subject will not remain on the add-on treatment (so both add-on and background will be discontinued)

18. Estimand Strategies for Intercurrent Events
Name of Strategy for Addressing Intercurrent Events and Its Description
Discontinuation (DC) of the Add-on Treatment, except DC due to LoE
Treatment Policy strategy: All observed values of the variable are used, regardless of whether or not the subject had experienced the intercurrent event.
Add-on or Background Treatment Non-compliance
Treatment Policy strategy
Protocol allowed dose adjustment
Initiation of rescue therapy
Initiation or adjustment of concomitant medication related to other symptoms related to depression
DC of both Add-on and Background Treatment (including DC of the Add-on Treatment due to LoE or DC of background only)
Hypothetical “as reference group” strategy: What would happen if after this event subjects are considered to take background medication only?
Initiation of concomitant medication not allowed by the protocol
Hypothetical “as reference group” strategy

19. Data Handling and Assumptions
Include data after all intercurrent events handled by the Treatment Policy strategy (including DC of the Add-on Treatment, except DC due to Lack of Efficacy)
Data missing:
Due to Intermediate events such as missed visits, missed data collections.
After intercurrent events handled by the hypothetical strategy:
DC of both Add-on and Background Treatment
Initiation of concomitant medication not allowed by the protocol
After study DC
Missing Data assumptions:
For 1: Similar to own treatment group.
For 2 and 3: Similar to reference group (e.g. placebo add-on + background group), as if, after discontinuation the subject had always been member of the reference group.

20. Trial Design and Estimators
Design: parallel, double-blind, 1:1 randomization into:
add-on + background vs
placebo + background
Main estimator:
Impute intermediate missing based on MCMC
Impute monotone missing based on the Copy Reference method
Analysis based on ANCOVA using treatment and certain baseline variables
Combine results based on Rubin’s rules
Several other estimands could also be of interest for same trial:
Supplementary, supporting the same endpoint
For other secondary endpoints

21. Summary and Next Steps
Implementation of the estimand framework complex:
Multiple stakeholders
Trial objective vs estimand scientific question of interest
Multiple intercurrent events
handled by different strategies, reflected in different estimand components
Multidisciplinary collaboration essential:
“the construction of the estimand(s) in any given clinical trial is a multi-disciplinary undertaking including clinicians, statisticians and other disciplines involved in clinical trial design and conduct”.
Statisticians:
Understand the types of estimators available for various strategies for intercurrent events and impact on sample size, power and other trial characteristics
Next steps: Broaden the experience of defining estimands and estimators for different TAs