How Should We Select and Define Trial Estimands How Should We Select and Define Trial Estimands? Examples Based on a Disease State July 29, 2019 Elena Polverejan, Ph.D. Janssen R&D

Acknowledgement Ideas discussed by ISCTM Estimands Working Group

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

Background

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.

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.

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

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)

Selecting and defining estimands for a clinical trial Points to consider

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

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

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

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

MDD Estimand Example

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

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(?)

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)

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

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.

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

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