Implementation of Estimand framework in Oncology Clinical Trials Kalyanee V Appanna, Bharani Dharan, Yuanbo Song, Ekkehard Glimm Novartis Pharmaceuticals 29 July 2019

Outline Introduction to Estimand ICH E9 addendum Impact on our work Case Studies Randomized phase III study in patients with lung neuroendocrine tumors Randomized phase II study in patients with NSCLC in the neoadjuvant setting

D. Population-level summary Estimand – definition A. Population Patients targeted by the scientific question B. Variable Measure(s) required to address the scientific question (to be obtained for each patient) An estimand defines the target of estimation for a particular trial objective (i.e. “ what is to be estimated”)         C. Intercurrent event The specification of how to account for intercurrent events to reflect the scientific question of interest D. Population-level summary Provide, as required, a basis for a comparison between treatment conditions

Intercurrent Event Definition Events that occur after randomization/ treatment initiation and either preclude observation of the variable or affect its interpretation.” withdrawal from follow-up death [when not a major trial outcome] discontinuation of trial treatment treatment switching [i.e. to other trial treatment] use of an alternative treatment [e.g. rescue] Estimand strategy need to take into account how intercurrent events will be handled. What is the issue with all these intercurrent events in addressing the scientific questions of interest? In the example of ACZ855, it is possible that more placebo patients drop out due to lack of efficacy? | Biostatistics and PMX | Business Use Only

Estimand Strategy and Handling of Intercurrent Events Treatment-policy strategy The occurrence of the intercurrent event is irrelevant, i.e., data collected for the variable of interest are used regardless of whether or not the intercurrent event occurs Composite strategy The occurrence of the intercurrent event is taken to be a component of the variable, i.e., the intercurrent event is integrated with one or more other measures of clinical outcome as the variable of interest Hypothetical strategy A hypothetical scenario is envisaged in which the intercurrent event would not occur By definition, such value cannot be observed but will need to be implicitly or explicitly predicted/imputed Estimands in Canakinumab Neoadjuvant Study / Yuanbo Song / Business Use Only

Estimand Strategy and Handling of Intercurrent Events Principal stratum strategy Principal stratification is defined by a patient’s potential intercurrent events on either or both treatments (e.g., treatment discontinuation due to intolerability to treatment drug) The target population is then considered to be the stratum in which an intercurrent event would not occur in either group (e.g., e.g., patients who can tolerate and remain on either treatment) While on treatment strategy Response to treatment prior to the occurrence of the intercurrent event if of interest. For example, the last measurement (or all measurements) before death (or other intercurrent event). Estimands in Canakinumab Neoadjuvant Study / Yuanbo Song / Business Use Only

ICH E9 addendum Working group was tasked to create the addendum Members include representatives from the regulatory bodies and industry associations across the ICH regions Other participants from EMA, MHRA, EFPIA, MHLW/PMDA, JPMA, FDA, PhRMA, Health Canada

ICH E9 addendum Draft E9 addendum has been released Significant impact on our work which requires a change of mindset It proposes a framework for treatment effects to be more precisely specified, facilitating discussion between sponsor and regulator Regulatory agencies are adopting the estimand framework Increasing number of HA requests Impact on design and conduct of new trials

Impact on statistician’s role Allows for early discussions with regulators and other key stakeholders to harmonize trial objectives Need to engage clinical teams in estimand discussions Estimand choice impacts trial design and conduct e.g. data collection after treatment discontinuation, new endpoints New designs, endpoints and statistical methodologies may be needed to address the estimands of interest Protocol and SAP should outline how estimands are defined for primary and secondary endpoints

Case Study 1: Phase III Study in Lung Neuroendocrine Tumors Drug A + BSC n=150 Placebo + BSC n=75 Stable, advanced, well differentiated typical and atypical NET of lung origin (N=225) Study population: Adult patients with histologically confirmed typical and atypical NET of lung origin with measurable disease at baseline. Primary endpoint: Progression Free Survival (PFS) as per investigator assessment ‘ITT approach’: Not censoring for new antineoplastic therapy. Protocol was written in the traditional style, not according to estimand framework BSC: best supportive care; excludes use of SSA R: Randomization Double-blind design

Regulator Feedback (March 2017) Agreed that the handling of progression assignment independent of new anti-neoplastic therapy was in line with EMA guideline In particular, this reduced potential informative censoring The estimand that is intended to be addressed by the primary analysis should be explicitly defined and discussed Analyses targeting an alternate estimand and analyses targeting the same estimand (e.g. different assumptions on missing data) should be differentiated Business Use Only

What is the estimand of interest? Need to think about intercurrent (I/C) events Initiation of new antineoplastic therapy (ANP), ... Which estimand is implied by not censoring for ANP? Which estimand would be implied if we did censor? Estimand needs to be defined precisely upfront and should not be implied through choices made for the analysis, e.g. censor vs. don’t censor ANP EOS Patient 1 Patient 2 Patient 3 Patient 4 Randomisation Primary endpoint TIMELINE Study discontinuation ? ANP ANP

Primary Estimand Scientific question of interest: What is the effect of Drug A+ BSC versus Placebo + BSC on PFS in patients with advanced NET regardless of adherence to treatment and regardless of intake of new antineoplastic therapies? The primary efficacy variable of the study is progression-free survival (PFS), defined as the time from the date of randomization to the date of the first documented progression or death due to any cause. Primary estimand is handled by treatment-policy approach: the effect of treatment in advanced well-differentiated neuroendocrine tumors of lung origin to Drug A plus BSC or placebo plus BSC regardless of adherence to treatment and initiation of new antineoplastic therapies. Summary measure: Hazard ratio for PFS from the stratified Cox model

Sensitivity analysis for primary estimand Sensitivity analyses target the same estimand as the primary analysis, however under different PFS evaluation criteria or analytical methods. Sensitivity analysis 1 The distribution of PFS will be compared between the treatment groups using an un-stratified log-rank test. The hazard ratio along with the associated 95% confidence interval resulting from an un-stratified Cox model will also be presented. Sensitivity analysis 2 The distribution of PFS will be compared between the treatment groups where all PFS events are included regardless of any missing tumor assessments before progression. Otherwise the same analysis conventions as per the primary analysis will be used.

Supplementary analysis The supplementary analyses target different estimands from the primary estimand. This supplementary analysis targets an estimand which has the same attributes as the primary estimand except for the intervention effect. In this case, interest lies in the effect had no patient initiated new cancer therapy. I/C events: PFS events that occur after the start date of a new anti- neoplastic therapy will be censored at the last adequate assessment prior to initiation of the new cancer therapy. Statistical measure: The same analysis conventions as per the primary estimand will be used.

Case Study 2: Phase II Study in NSCLC neo-adjuvant setting R 1:1 Arm A: Drug A n=50 Arm B: Drug A + Drug B Adult with histologically confirmed stage IB-IIIA NSCLC (N=100) Scientific question of interest: What is the treatment activity in subjects who had surgical resection or discontinued prior to surgical resection due to treatment related reasons (e.g. AE)? Study population: Adult subjects with histologically confirmed stage IB-IIIA NSCLC planned for surgery in approximately 4-6 weeks. Primary endpoint: Major Pathological Response (MPR) based on central review R: Randomization Open-label design This is a proof of concept study, with the objective of understanding the effect of arm A and arm B in comparison to historical controls. In this study, the treatment is administered for two cycles (approx. 6 weeks). Following the administration of treatment, surgery will be performed approx. 4 to 6 weeks after start of treatment. The surgically resected sample will be analyzed for percentage of viable tumor cells. The primary aim of the study is to explore the preliminary efficacy of study treatment based on major pathological response, which is a binary variable defined as whether the resected tumor having ≤10% residual viable tumor cells.

Primary Estimand The primary efficacy variable is major pathological response (MPR), a binary variable defined as whether having ≤10% residual viable tumor. MPR will be assessed based on central review. Primary estimand assesses the MPR rate of evaluable subjects with histologically confirmed stage IB-IIIA NSCLC in each treatment arm. (Principal Stratum Strategy) Target population is in which the following intercurrent events do not occur Withdrawal of consent prior to surgery Lost to follow-up prior to surgery Start of antineoplastic therapy prior to surgery Statistical measure: MPR rate with exact binomial confidence interval for each treatment arm.

Supplementary analyses The supplementary analyses target different estimands from the primary estimand. Supplementary analysis 1 This analysis targets an estimand which has the same attributes as the primary estimand except for the target population and intervention effect. Population: all randomized subjects are of interest I/C events: subjects who had withdrawal of consent, lost to follow-up or had systemic antineoplastic therapy prior to surgery will be treated as MPR non- responder. Statistical measure: same as in the primary efficacy analysis. Supplementary analysis 2 I/C events: subjects who had withdrawal of consent, lost to follow-up prior to surgery will be treated as MPR non-responder and systemic antineoplastic therapy prior to surgery will be ignored.

Summary Primary analyses should be explicitly stated in estimand framework based on the main scientific question of interest Estimand framework facilitates better understanding of handling of population, intercurrent events and statistical measure to assess treatment effect Supportive analyses should be differentiated between alternate estimands and same estimands based on different assumptions Business Use Only

Neoadjuvant Therapy in NSCLC Neoadjuvant therapy refers to treatment given as a first step to shrink a tumor before the main treatment, which is usually surgery (NCI Dictionary). Examples of neoadjuvant therapy include chemotherapy, radiation therapy, hormone therapy and immunotherapy. It is a type of induction therapy. Numerous trials have demonstrated a substantial survival advantage, supporting the use of neoadjuvant therapy in multiple types of cancer for patients with resectable tumors Pathological complete response (PCR) and major pathological response (MPR) are commonly used surrogate endpoints in neoadjuvant settings Pathological response is associated with significantly improved disease recurrence and survival Footer

Sensitivity and Supplementary Analysis Sensitivity Analysis is a series of analyses targeting the same estimand, with differing assumptions to explore the robustness of inference from the main estimator to deviations from its underlying modeling assumptions and limitations in the data. Missing data require particular attention in a sensitivity analysis because the assumptions underlying any methods may be hard to justify fully and may be impossible to test. Supplementary analysis targets different estimands or different estimators to the same estimand and plays a secondary role for interpretation of trial results with additional insights. For example, investigating assumptions of normality associated with ANOVA would result in sensitivity analysis; analyzing the dataset instead based on comparison of median would be considered a supplementary analysis. Estimands in Canakinumab Neoadjuvant Study / Yuanbo Song / Business Use Only