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

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)]

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.

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

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

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.

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

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

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

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.

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)]

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.

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