1. Jared Christensen and Steve Gilbert Pfizer, Inc July 29th, 2019
Lessons Learned from Implementing ICH E9(R1) in Phase 2 Trials Across Multiple Therapeutic Areas
Jared Christensen and Steve Gilbert
Pfizer, Inc
July 29th, 2019

2. ICH E9(R1) Vision of Estimands
“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. It should be the subject of discussion in a sponsor’s interactions with regulators about the objectives and designs for prospective clinical trials. The construction of an estimand should be consequent to the trial objectives and should inform choices relating to data collection and analytic approaches. Avoiding or over-simplifying this process risks misalignment between trial objectives, trial design, data collection and statistical analysis. An iterative process may be required.” [ICH E9 (R1)]
“… an estimand defines in detail what needs to be estimated to address a specific scientific question of interest.” [ICH E9 (R1)]

3. Our vision of Estimands
Require cross-functional thinking and input
Be understandable to all team members
Clearly translate from a trial objective
Lead to an appropriate estimator
Succinctly summarizes the trial results
Statisticians enabling study teams to prospectively address important scientific questions and ensure data and methods are available to answer them
Estimator. Point out that you need data and statistical thinking to make the estimand work.

4. Feedback Attributes Broadly representative of Pfizer experience
Gathered from multiple disease areas
Focused on phase 2 studies with lessons from phase 3 folded in
Includes general regulatory feedback
Focused on first team use of estimands
Mainly gathered from statisticians
Convenience sample for data collection

5. Estimands in a Team Setting
Teams not yet thinking in estimand framework
Many teams expected statistician to draft all estimand language in protocol and handle regulatory feedback
New protocol and SAP templates created
Oncology had slightly more non-statistical team members input
Possible advantage of thinking of subjects included in risk sets for time-to-event analyses?
Lesson 1: Estimands largely viewed as a statistical endeavor and not yet viewed as key to understanding the treatment effect
Templates show what is needed but teams don’t fully grasp them yet. Stats line has driven language in templates too.
Oncology could be overinterpretation of limited data

6. The language of Estimands
To be useful, estimand vocabulary must be fixed and clearly defined
Effectiveness vs efficacy
De facto vs de jure
Treatment policy, composite, hypothetical, principal stratum and while on treatment strategies
Intercurrent events vs missing data
Lesson 2: The language of estimands is confusing to teams. Statisticians need to communicate clearly and need adequate time to explain concepts to teams.

7. Regulatory feedback and Phase 2 Estimands
Regulatory input on phase 2 estimands is variable
Phase 2 studies should enable calculation of a treatment policy estimand
Not necessarily the primary analysis
Regulators may ask for treatment policy estimands in phase 2
Teams can pushback in phase 2 more easily than in phase 3
Lesson 3: Estimands are more negotiable in phase 2 and regulatory feedback in phase 2 may guide preparation for phase 3 estimands

8. Withdrawal and Data Strategy
Estimands may need new withdrawal and data collection strategies
Withdrawal handling, minimal loss to follow-up and data collection after discontinuation
Teams may not connect data collection strategy to estimands
Teams need to be clear on data requirements for different strategies
Statisticians, clinical and data management must collaborate
Lesson 4: Required data collection needs proactive thinking for appropriate phase 2 and phase 3 estimands
Clinical needed for off treatment options

9. changes in data collection
Additional data are required for treatment policy estimands
New strategies have been used for:
Intercurrent events (i.e. rescue medication usage)
Reason for treatment discontinuation
Data modules at discontinuation of treatment
Retrieved data collection (even if limited)
Lesson 5: Estimates of treatment policy estimands from phase 2 are helpful for decisions to proceed to phase 3 and may also be informed by retrieved data experience

10. Regulatory Interactions and Acceleration
Early dialogue is required to agree on estimand approaches for accelerated programs and in Phase 3
Protocol feedback on estimands from regulatory bodies has been limited
More extensive estimand feedback seen when SAPs are reviewed
Lesson 6: In order to accelerate, feedback on estimands should be requested early. Regulatory feedback on estimands in an SAP may be late relative to defining data collection, CRFs, etc.

11. Intercurrent Events & Missing Data
“Intercurrent Events: Events that occur after treatment initiation and either preclude observation of the variable or affect its interpretation.
Missing Data: Data that would be meaningful for the analysis of a given estimand but were not collected. They should be distinguished from data that do not exist or data that are not considered meaningful because of an intercurrent event.” [ICH E9(R1)]

12. Intercurrent Events & Missing Data
Protocol objectives should include a description of intercurrent events and a summary of how they will be handled
Proper data collection required to classify intercurrent events and missing data handling (discontinuation for AE vs lack of efficacy)
Missing data often tied to intercurrent events
Lesson 7: Teams need help understanding the intercurrent events and missing data and their effects on estimation. Clearer descriptions of missing data with intercurrent events are needed.

13. Conclusions Estimands are still early in the adoption phase
Estimand thinking hasn’t permeated team approaches yet
Phase 2 estimands should prepare for phase 3 requirements
Proactive withdrawal and data strategies are required to drive estimands
Statisticians need to improve communication to enable estimand implementation