How to use prior knowledge and still give new data a chance?

Transcription

1 How to use prior knowledge and still give new data a chance? Kristina Weber1, Rob Hemmings2, Armin Koch now with Roche, 1 2 MHRA, London, UK

2 Part I Background Extrapolation and Bayesian methods: Extrapolation is rather common in medicine (patients underrepresented in trials, safety between indications, within drug classes, rare disease, from adults to children). Bayesian methods are proposed for situations with limited options to recruit patients into studies (rare disease, pediatric trials) or potentially limited need (extrapolation from adult to pediatric indications). Usually expert opinion is used to justify certain assumptions about priors that interlink some sort of pathophysiological or pharmacological plausibility with a response parameter

3 Bayesian extrapolation (and regulatory context) Traditions in drug regulation: Self standing data-based decision making Primary use of own data (class is of secondary interest) Pre-specified decision making process Thus: In case data are available, preference is given to data (and not to expert opinion) In case information is borrowed, then this should be primarily own information Conclusions should be non-trivial (e.g. the prior completely determines the evaluation of the new experiment)

4 Interest to incorporate data based prior knowledge Two approaches: Bayesians may use data-based priors. Frequentists could use Meta-Analysis (MA with two studies to combine prior knowledge and new data). There we found out: no method is sensitive to detect heterogeneity (which would challenge extrapolation) and no method has sufficient power while controlling T1E in the presence of heterogeneity (see Gonnermann et al. 2015). Research question: What about all the classical MA-problems if we do Bayesian MA and MA with one trial (and a Bayesian prior)?

5 Rare disease and paediatric extrapolation Rare disease: Expert opinion may be the only option to reduce the burden of evidence needed for proof of efficacy. Paediatric extrapolation: Available data have been sufficient for licensing a new drug PK/PD and mechanism of action are usually well understood PK/PD in paediatric patients available (or can be generated easily ) Why then clinical data in paediatric patients? Low belief that similar PK/PD leads to the same clinical efficacy No reliable PD endpoint Puzzling outcome in previous steps of the extrapolation exercise

6 Regulatory question Going for an extrapolation exercise assumes agreement that there is no need for formal (self-standing) proof of efficacy in the paediatric population. Instead, the following questions need to be addressed: Which paediatric experiment is needed to detect with good probability relevant deviations from adult expectations regarding the treatment effect? How to define and assess relevant deviations? To be presented here: 1. Play-games with differing amounts of information (e.g. a lot of information in adults and only a few children), 2. Discussing the EVR case-study in the light of this

7 Part II Extrapolation methods Often, one or more clinical trials have been conducted involving the control arm [ ]. In theory, bringing this existing information into the current trial holds the promise of more efficient trial design. (Viele et al., 2013) Methods that incorporate historical control information Possible benefits: smaller trials, unequal randomization Drawback: not well understood Are the proposed methods applicable in extrapolation exercises? Change setting to randomized trials Extrapolate between adjusted treatment effects of two populations

8 Paediatric extrapolation: investigated methods Bayesian (and frequentist) meta-analysis methods Adult data Paed. Data Heterogeneity Extrapolation Bayesian meta-analytic predictive method Adult data Prior Prior Paed. data Heterogeneity Extrapolation

9 General assumptions in both methods Controlled trials with two arms Dichotomous endpoint Analysis based on odds ratio Additive effect on log odds ratio scale

10 Bayesian (and frequentist) meta-analysis methods Joint analysis of adult and paediatric data Fixed-effect model Random-effects model Assumptions: Common true treatment effect Observed treatment effects differ due to sampling error (within-study variance) Assumptions: Normally distributed true treatment effects Observed treatment effects are a random sample (withinand between-study variance)

11 Bayesian meta-analytic predictive approach Analysis of paediatric data in light of adult data Fixed-effect model Random-effects model Assumptions: Fixed true treatment effects Sampling error Assumptions: Normally distributed true treatment effects Within- and between-study variance

12 Heterogeneity: Prior distributions Assumptions: * Possible ratio between observed ORs of two trials under the heterogeneity assumption and a true treatment effect of 0 (log OR ~ N(0, E(τ²))

13 Paediatric extrapolation example: adult data Immunosuppressive treatment in kidney-transplanted patients Endpoint: acute rejection, graft loss, death or loss to follow up Combination therapy with Everolimus vs. SOC Non-inferiority trial NI-margin: 1.54 on OR scale (10% difference, 0.43 on log OR scale) Very low heterogeneity

14 Paediatric extrapolation example: paediatric scenarios Paediatric investigation plan: 2*53 Endpoint: acute rejection, graft loss, death or loss to follow up Combination therapy with Everolimus vs. paediatric SOC Non-inferiority trial NI-margin: 1.54 on OR scale (10% difference, 0.43 on log OR scale)

15 Results for homogeneous scenario

16 Results for heterogeneous scenario

17 Summary I Extrapolation problems are also weighting problems Adult data overwhelm pediatric data in the MA approach in the heterogeneous scenario Different in MAP approach Pediatric data has a weight of ~70% under substantial heterogeneity and opposing effect estimate Estimates and conclusions are depending on assumptions about heterogeneity and not actually observed heterogeneity One pediatric study is not able to change these assumptions Models do not detect homogeneity or heterogeneity

18 Summary II Are we looking at the same thing? Methods answer different questions Frequentist and Bayesian MA methods do not make a difference between the populations MA predictive methods correct paediatric treatment effect for already known adult treatment effect MAP estimates are equal to the study-specific effect estimates of the paediatric study in the Bayesian MA models See also Schmidli et al,

19 Solution? Choose heterogeneity prior that leads to a pre-specified weight of the paediatric data Open Questions: Which distributional family has the best properties? How big should the impact of the paediatric data be? Simulation studies are needed

20 General Problem Is it really ok to give any weight to seemingly unfit adult data, If heterogeneity between adult and paed. data is observed?

21 Part III regulatory implications Brief history of paediatric development and extrapolation Motivation for development in paediatrics is not always high Extrapolation Two models Distinct from the rare disease problem Extrapolation concept and plan: quantifying what we already know. Experience tells us to expect naïve methodological approaches a pre-emptive strike on the risk of trivial conclusions avoid the tail wagging the dog Not Bayes bashing: no good methods are off limits Well-understood limitations for use of external data c/w indirect comparisons dynamic borrowing; seductive, does consistency of data suffice? Call for best practice to be developed and communicated

22 References Gonnermann et al (2015). No solution yet for combining two independent studies in the presence of heterogeneity. Statistics in Medicine, 34(16). doi: /sim.6473 Lorber et al. (2005). Everolimus versus mycophenolate mofetil in the prevention of rejection in de novo renal transplant recipients: A 3-year randomized, multicenter, phase III study. Transplantation 80.2, pp doi:0.1097/01.tp Shirkey et al. (1968). Editorial comment: Therapeutic orphans. The Journal of Pediatrics 72.1, doi: /s (68) Tedesco Silva et al. (2010). Everolimus plus reduced-exposure CsA versus mycophenolic acid plus standardexposure CsA in renal-transplant recipients. American Journal of Transplantation 10.6,pp doi: /j x. Vitko et al. (2005). Three-year efficacy and safety results from a study of everolimus versus mycophenolate mofetil in de novo renal transplant patients. American Journal of Transplantation 5.10, pp doi: /j x. Viele et al. (2014). Use of historical control data for assessingtreatment effects in clinical trials. Pharmaceutical Statistics 13, pp DOI: /pst.1589 Schmidli et al. (2014). Robust Meta-Analytic-Predictive Priors in Clinical Trials with Historical Control Information. Biometrics70, DOI: /biom Weber et al. (2018). How to use prior knowledge and still give new data a chance? In: Pharmaceutical Statistics 17, pp doi: /pst