Using real-world data for HTA thoughts from industry Sandro - - PDF document

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The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no [115303], resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. www.imi.europa.eu

Using real-world data for HTA – thoughts from industry

Sandro Gsteiger

MORSE – HTA group, F. Hoffmann-La Roche Ltd On behalf of GetReal Work Packages 1 & 4

Acknowledgments

MORSE – HTA group, F. Hoffmann-La Roche Ltd, in particular Aijing Shang, Maximo Carreras, Federico Felizzi, Yovanna Castro, Monica Daigl, Nicolas Staedler, Pierre Ducournau, Marlene Gyldmark IMI GetReal WP4, in particular Noemi Hummel, Eva-Maria Didden, Sven Trelle, Matthias Egger IMI GetReal WP1, in particular Keith Abrams, Reynaldo Martina, David Jenkins, Sylwia Bujkiewicz

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RWE – another “big data” movement in pharma

Broad interest to leverage RWE across all development stages

early development late development HTA

• Incidence / Prevalence estimates
• Define target product profile (TPP)
• Understand drug use/treatment patterns
• Background mortality
• Long-term effects
• Heterogeneous patient

populations

Benchmarks & extrapolation (time and “space”)

Bridge knowledge gaps in NMA with RWD

• Bridge gaps
• Use all evidence

E D A B C E D A B C E B RCTs RWD RCTs + RWD ?

• Data availability

– Most uncertainty around new compound – RWD on new compound will not be available at time of decision making

• How to weight RWD?

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Weighting: compare discounting of historical data – how much can we learn from RCTs on a new trial?

y1 θ1

…

y2 θ2 yH θH

µ

y* θ*

historical trials new trial

Exchangeable information from RCTs

Ref. Appl. Total n Eff. n % “use” Neuenschwander et al (2010) Transplantation 930 90 10% Neuenschwander et al (2010) Ulcerative collitis 363 22 6% Gsteiger et al (2013) MS 1936 45 2% Gsteiger et al (2013) MS 412 63 15% Baeten el al (2013) AS 533 43 8%

Massive discounting needed with historical controls (exchangeable!). Expect even more discounting from (non-exchangeable) RWD?

?

NMA with combinded RCT + RWD: sensitivity analysis giving different weights α to RWD

Source: K. Abrams. MORSE Academy 2015.

Power prior model: P(θ|RCT, RWD) ∝ L(RCT|θ) ⋅ L(RWD|θ)α ⋅ P(θ) Use grid of (fixed) values α

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Extrapolation

• Benchmarks from registries
• Blending of short-term RCT

with long-term RWD

• “Anchoring” predictions

with registry estimate [Cf Abrams et al. WP1] Particularly interesting in adaptive pathways context

• Extrapolation

(short/mid-term)

• Monitoring
• Validation

Are these methods acceptable for decision makers?

“This new method is a black-box to me. I would not accept it.” “I do not understand this. I could end up being so confused that I would not be willing to take any decision at all!”

[Statements from regulators at IMI GetReal WP1 workshop]

… unless you can explain the method really well. … unless you can “fully” establish the properties of the method. … unless the method is well accepted in the literature. “Take NMA as an example: initially a lot of skepticism, but now a standard (in some countries)!”

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Some final remarks

• RWD valuable, but not the only source to inform effectiveness
• Not forget about the local level

– RWD: inevitably a “local reality” … – Initiatives: EAMS (UK), AIFA mandated registries (IT)

• Predictions: adopt mindset including validation
• Populations: we should systematically share individual patient

baseline characteristics from our trials!

Theory RWD Disease progression models Exposure-response models

References

Dominique Baeten et al., “Anti-Interleukin-17A Monoclonal Antibody Secukinumab in Treatment of Ankylosing Spondylitis: A Randomised, Double-Blind, Placebo-Controlled Trial,” The Lancet 382, no. 9906 (November 29, 2013): 1705–13, doi:10.1016/S0140-6736(13)61134-4.

• D. Jenkins et al, Including Real World Evidence (RWE) in network meta-analysis; ISCB 36th Annual Conference,

Utrecht 23-27 August, 2015

• R. Martina et al, The inclusion of real world evidence (RWE) in clinical development planning; ISCB 36th Annual

Conference, Utrecht 23-27 August, 2015

• B. Neuenschwander et al., “Summarizing Historical Information on Controls in Clinical Trials,” Clin Trials 7, no. 1

(February 2010): 5–18, doi:10.1177/1740774509356002.

• S. Gsteiger et al., “Using Historical Control Information for the Design and Analysis of Clinical Trials with

Overdispersed Count Data,” Stat Med 32 (May 31, 2013): 3609–22, doi:10.1002/sim.5851. Susanne Schmitz, Roisin Adams, and Cathal Walsh, “Incorporating Data from Various Trial Designs into a Mixed Treatment Comparison Model,” Statistics in Medicine 32, no. 17 (2013): 2935–49, doi:10.1002/sim.5764. Kert Viele et al., “Use of Historical Control Data for Assessing Treatment Effects in Clinical Trials,” Pharmaceutical Statistics 13, no. 1 (January 1, 2014): 41–54, doi:10.1002/pst.1589.