Identifying Subgroups in Product Labeling: 2 Recent Case Studies. Martin King AbbVie
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1 Identifying Subgroups in Product Labeling: 2 Recent Case Studies Martin King AbbVie
2 Disclosures The presentation was sponsored by AbbVie. AbbVie contributed to the design, research, and interpretation of data, writing, reviewing, and approving the presentation Martin King is an employee of AbbVie, Inc. Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
3 Introduction/Overview In the HCV therapeutic area, combination treatment with directacting antivirals (DAAs) has led to dramatic improvements Combination DAA regimens, compared to previous standard of care: Better tolerability (1-2% discontinuations vs. 10%+) Better efficacy (cure rates of ~95% vs. ~75%) Shorter treatment durations (8-24 weeks vs weeks) In 2 recent approvals of DAA combination regimens, labeling identified subgroups for whom shorter treatment may be appropriate We describe these cases in relation to the EMA draft subgroup guidance Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
4 Hepatitis C Virus (HCV) Basics Primary endpoint: Sustained virologic response (SVR) SVR12: 12 weeks after the end of treatment, patient has undetectable viral load (HCV RNA) Reasons for non-response: Virologic Viral breakthrough while on treatment Relapse after the end of treatment Non-virologic Discontinue treatment prematurely Lost to follow-up before post-treatment Week 12 Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
5 Types of non-response (non-svr) illustrated 10,000,000 1,000, ,000 10,000 1, Breakthrough 10,000,000 1,000, ,000 10,000 1, Relapse Treatment Post Treatment Post 10,000,000 10,000,000 1,000,000 1,000, ,000 10,000 1,000 Premature Discontinuation 100,000 10,000 1,000 Lost to follow-up Treatment Post Treatment Post Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
6 Case 1
7 Case 1: Ledipasvir/sofosbuvir in treatment-naïve patients without cirrhosis Response LDV-SOF for 8 weeks (N=215) LDV-SOF + Ribavirin for 8 weeks (N=216) LDV-SOF for 12 weeks (N=216) SVR (94%) 201 (93%) 206/216 (95%) Reasons for failure Breakthrough Relapse 11 (5%) 9 (4%) 3 (1%) Non-virologic 2 (1%) 6 (3%) 7 (3%) No difference observed on primary endpoint (SVR12) Difference observed among reasons for failure significantly higher relapse rate with 8-week treatment ION-3: Kowdley, et al. NEJM 2014;370: Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
8 Case 1: Which EMA Guidance Scenario are we in (1)? SAP-defined secondary analysis: non-inferiority of the 8-week arm vs. the 12-week arm based on the primary endpoint of SVR12 and a margin of -12% With a 1% difference in SVR12, this endpoint is easily achieved, suggesting we may be in Scenario 1: EMA Draft Subgroup Guidance ( accessed April 2015 Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
9 Case 1: Which EMA Guidance Scenario are we in (2)? When response rates are >90%, a NI margin of 12% may be considered too large SVR12 is the primary endpoint but does not optimally address the effect of different treatment durations Among all the ways to fail, only relapse is attributable to treatment duration A statistically significant difference in relapse could suggest we are in Scenario 2: EMA Draft Subgroup Guidance ( accessed April 2015 Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
10 Case 1: FDA analysis of relapse rates by subgroup 14 subgroups defined in SAP Evidence for interaction generally not observed FDA Harvoni Statistical Review ( Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
11 Case 1: Reviewer Comments and Conclusions Statistical review Clinical review FDA Harvoni Statistical Review ( and Medical Review ( Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
12 Case 1: Product labeling U.S. Package Insert Harvoni US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
13 Case 1: FDA analysis of relapse rates by baseline viral load FDA Harvoni Statistical Review ( Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
14 Case 1: Model-based approach 8-week arm 12-week arm 500,000 1M 3M 6M 10M 15M 500,000 1M 3M 6M 10M 15M 500,000 1M 3M 6M 10M 15M Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
15 Case 1: Risk of misclassification Draft guidance notes the potential for misclassification: A recent analysis of a similar patient population compared HCV RNA values ~3 weeks apart 17% of patients had discordant values values on opposite sides of a threshold of 6 million IU/mL Such patients could be at risk for receiving the wrong duration EMA Draft Subgroup Guidance ( accessed April 2015, Brown, et al. EASL 2015 #LP39 Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
16 Case 2
17 Case 2: Ombitasvir/paritaprevir/ritonavir + dasabuvir and ribavirin in treatment-naïve or -experienced patients with cirrhosis Response 12 weeks (N=208) 24 weeks (N=172) SVR (91.8%) 166 (96.5%) Reasons for failure Breakthrough 1 (0.5%) 3 (2%) Relapse 12 (6%) 1 (1%) Non-virologic 4 (2%) 2 (1%) 4-5% difference in SVR12 rate and in relapse rate TURQUOISE-II: Poordad, et al. NEJM 2014;370: , Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
18 Case 2: Scenario 1 vs. Scenario 2? Response 12 weeks (N=208) 24 weeks (N=172) SVR (91.8%) 166 (96.5%) EMA Draft Subgroup Guidance ( accessed April 2015, Poordad, et al. NEJM 2014;370: Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
19 Case 2: SVR12 by randomization strata Randomization stratified by HCV genotype (GT): GT1a vs. GT1b (GT1b easier to cure) Prior treatment/response (naïve, relapser, partial responder, null responder) Likelihood of cure, easiest to hardest: naïve/relapser > partial > null Randomization Stratum 12 weeks 24 weeks Genotype 1b Naïve 22/22 (100%) 18/18 (100%) Relapser 14/14 (100%) 10/10 (100%) Partial Responder 6/7 (86%) 3/3 (100%) Null Responder 25/25 (100%) 20/20 (100%) Genotype 1a Naïve 59/64 (92%) 53/56 (95%) Relapser 14/15 (93%) 13/13 (100%) Partial Responder 11/11 (100%) 10/10 (100%) Null Responder 40/50 (80%) 39/42 (93%) Poordad, et al. NEJM 2014;370: , Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
20 Case 2: SVR12 by randomization strata Randomization stratified by HCV genotype (GT): GT1a vs. GT1b (GT1b easier to cure) Prior treatment/response (naïve, relapser, partial responder, null responder) Likelihood of cure, easiest to hardest: naïve/relapser > partial > null Randomization Stratum 12 weeks 24 weeks Genotype 1b Naïve 22/22 (100%) 18/18 (100%) Relapser 14/14 (100%) 10/10 (100%) Partial Responder 6/7 (86%) 3/3 (100%) Null Responder 25/25 (100%) 20/20 (100%) Genotype 1a Naïve 59/64 (92%) 53/56 (95%) Relapser 14/15 (93%) 13/13 (100%) Partial Responder 11/11 (100%) 10/10 (100%) Null Responder 40/50 (80%) 39/42 (93%) 1 patient difference 2.5% difference 1 patient difference 13% difference Poordad, et al. NEJM 2014;370: , Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
21 Case 2: How should we use the strata in subgroup analyses? How should subgroups be formed for assessing heterogeneity? All 8 strata? GT1b vs. GT1a? GT1a Null responders vs. all others? Randomization Stratum 12 weeks 24 weeks Genotype 1b Naïve 22/22 (100%) 18/18 (100%) Relapser 14/14 (100%) 10/10 (100%) Partial Responder 6/7 (86%) 3/3 (100%) Null Responder 25/25 (100%) 20/20 (100%) Genotype 1a Naïve 59/64 (92%) 53/56 (95%) Relapser 14/15 (93%) 13/13 (100%) Partial Responder 11/11 (100%) 10/10 (100%) Null Responder 40/50 (80%) 39/42 (93%) Poordad, et al. NEJM 2014;370: , Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
22 Case 2: Assessing heterogeneity Subgroup definition 12 weeks 24 weeks P-value (Zelen s test) By Genotype GT1b GT1a 67/68 (99%) 124/140 (89%) 51/51 (100%) 115/121 (95%) 1.00 By genotype and null response GT1a null responders All others 40/50 (80%) 151/158 (96%) 39/42 (93%) 127/130 (98%) 0.65 Within GT1a Null responders All others 40/50 (80%) 84/90 (93%) 39/42 (93%) 76/79 (96%) 0.64 All 8 strata Poordad, et al. NEJM 2014;370: , Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
23 Case 2: FDA analysis of heterogeneity For GT1a vs. GT1b: For subgroups within GT1a: FDA Viekira Pak Statistical Review ( Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
24 Case 2: FDA Cross-discipline review FDA Viekira Pak Cross Discipline Review ( Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
25 Case 2: Product labeling U.S. Package Insert Viekira Pak US Package Insert Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
26 Reflections Several factors may affect decisions for differential labeling by subgroups Evidence of heterogeneity Biological plausibility Pre-specification Risk of misclassification The relative importance of these factors may vary from case to case Duke-Industry Statistics Symposium 2015 Subgroups in pivotal trials October
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