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Futility stopping

Futility stopping. Carl-Fredrik Burman, PhD Statistical Science Director AstraZeneca R&D. Stakeholder perspectives. The patient A pharmaceutical company The public (MRC, NIHR). The fundamental design requirement: Ethics. ”My old mother – principle” The trial is ethical if (and only if)

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Futility stopping

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  1. Futility stopping Carl-Fredrik Burman, PhD Statistical Science Director AstraZeneca R&D RSS / MRC / NIHR HTA Futility Meeting

  2. Stakeholder perspectives • The patient • A pharmaceutical company • The public (MRC, NIHR) RSS / MRC / NIHR HTA Futility Meeting

  3. The fundamental design requirement:Ethics ”My old mother – principle” The trial is ethical if (and only if) I would recommend my mother to take part in the trial, given that she would be eligible. RSS / MRC / NIHR HTA Futility Meeting

  4. Interim stopping • Stop the trial as soon as I would not include my mother, e.g. if • One (publicly available) treatment is clearly better • A “new” treatment fails to show sufficient effect, when it has known safety disadvantages • No ethical obligation to stop • If two treatments with similar safety have no clear difference in effect RSS / MRC / NIHR HTA Futility Meeting

  5. (Genuine) informed consent • The patient should get • Full information regarding the trial treatments (and procedures), including previous data, potential risks, etc. • Help to understand the information and • Apply it to his/her specific situation (health status, preferences) • When would a fully informed, fully competent patient give consent? • If and only if it is better (not worse) for him/her to take part in the trial, as compared to receiving standard therapy. • Cf. “my old mother” principle RSS / MRC / NIHR HTA Futility Meeting

  6. Easy-going clinical equipose is not enough • Clinical equipose • If there is uncertainty about which treatment is better • (Alternatively, compelling evidence of one treatment being better) • (Alternatively, medical experts disagree) • It’s far too easy to say that we are uncertain • I expect my doctor to say what he believes is best RSS / MRC / NIHR HTA Futility Meeting

  7. Our old Mother Earth • Scientific equipose • Not every expert agree on CO2-induced global warming • Do you suggest a randomised N-of-1 trial? • Of course not — choose the treatment we believe is best RSS / MRC / NIHR HTA Futility Meeting

  8. What is ”best” for the patient?May depend on e.g. • Effect (best guess + uncertainty) • Safety • Better care in the trial? • Economic compensation (but beware of exploitation) • Altruism Likely effect will differ between individuals (covariates) Preferences are different Decision theory may help decide (at least in theory …) RSS / MRC / NIHR HTA Futility Meeting

  9. Decision analysis (DA) Patient perspective • Utility function U(effect, safety, QoL, cost, …) • Model for effect, safety, etc., based on best information (data, expert knowledge, …). Often Bayesian prior. • Choose decision (volunteer to participate in trial, or not) to maximise expected utility The DA approach can also be used by a trial sponsor RSS / MRC / NIHR HTA Futility Meeting

  10. A pharmaceutical company perspective(simplified) • A new drug will be licensed if and only if the (next) phase III trial has a statistically significant effect (p<5%) • If licensed, the company will make a profit of V (unit: £) • The trial cost is k·N, where N is the sample size • The assumed (believed) treatment effect is d. • Maximise V · Power(N) – k · N Of course, this model is wrong (as all models are). Should e.g. have V=V(T)=V(T(N)), where T is time. RSS / MRC / NIHR HTA Futility Meeting

  11. Optimal sample size Gain Net gain = Gain–Cost Cost Nopt = 1010 RSS / MRC / NIHR HTA Futility Meeting

  12. The interim decision(continue vs. stop for futility) • Value V if significant • Conditional power CP if trial is continued • C additional trial cost if continued (compared to if stopped) • Continue iff V · CP > C, that is, iff CP > C / V RSS / MRC / NIHR HTA Futility Meeting

  13. DA vs. ”least clinically relevant” effect • DA approach: • Maximise expected utility based on ”best guess” effect (or prior) • Traditional approach: • 90% power at ”least clinically relevant” effect • What is the least clinically relevant effect? • If no adverse effects, no cost • And the outcome is death • One single saved life is clinically relevant … at least to the one saved • What is a relevant effect depends on safety, cost etc. RSS / MRC / NIHR HTA Futility Meeting

  14. Conditional power at interim • Final estimate d is N(D, 1/N). • Stage i has sample size Ni and estimate di. Then d = (N1·d1+N2·d2)/N • Statistical significance if d > C / N (where C=1.96 say) • CP = P(d > C / N ) = F( D·N2+ d1·N1/N2-C(N/N2) ) • But which D to use when calculating CP? • Original alternative DAlternative ? • Interim estimate d1 ? • Linear combination of d1 and DAlternative ? • Bayesian posterior based on interim data ? RSS / MRC / NIHR HTA Futility Meeting

  15. Stop, continue, or something else? • Run a new trial? • Sample size reestimation, based on interim estimates • Flexible design methodology (Bauer & Köhne –94) • Predefined weights for the different stages (generally, weight not proportional to information) • May change the sample size for stage 2 after viewing interim results • Discussion on CP • Somewhat controversial • May be better than design with only futility stopping • Group-sequential designs should often be preferred RSS / MRC / NIHR HTA Futility Meeting

  16. Publicly funded trial:Treatments with similar safety • Assume • Whole patient population will receive one of these treatments • Efficacy is the only unknown • Same safety, cost, etc. • The closer the interim effect is to zero, the more value in continuing • Thus, no reason to stop for futility RSS / MRC / NIHR HTA Futility Meeting

  17. Example 1: Value of information • Compare 2 treatments with probabilities pA, pB for death. • Assume total future population size is T (10,000 say) • If we knew that D>0, we would choose treatment A • T·D lives would be spared as compared to using B • Similarily, choose B if D<0 • Net value T·Abs(D) or T·Abs(D)/2 if compared to using random treatment RSS / MRC / NIHR HTA Futility Meeting

  18. Example cont’d Maximal value of information • Before trial, D=p2-p1 has approximately normal prior with mean=0, SD=t (say 10%) • What would the value be if we could learn the exact value of D ? • Take the Bayesian expectation of the value T·Abs(D)/2, Eprior [T·Abs(D)/2] = T· t / (2) • With T=10,000 and t=10%, about 400 lives would be spared RSS / MRC / NIHR HTA Futility Meeting

  19. RSS / MRC / NIHR HTA Futility Meeting

  20. Publicly funded trial:Intervention vs. no treatment (placebo) • Assume • Intervention is associated with some cost, safety risks • Not clear whether intervention has a positive effect • If effect, then the size of the effect will determine the size of the patient population which will get a positive net benefit • First objective: is there any effect? • Reasonable to stop for futility if interim estimate is low • Expected value by continuing study is then small RSS / MRC / NIHR HTA Futility Meeting

  21. Information leakage • In regulatory setting, large discussion on who should see interim data • Does the DMC have to be independent from the sponsor • What are the risks of potential information leakage? • Problems may be over-emphasised? • The ethical aspect RSS / MRC / NIHR HTA Futility Meeting

  22. Summary • Futility stopping may be an ethical requirement • Industry funded trials: Tradeoff cost and expected value • Publicly funded trials (examples) • Don’t stop for futility if two active treatments differ only in effect • May stop for futility if “active” treatment unlikely to have sufficient effect (tradeoff cost and value) • (If basic science objective …) RSS / MRC / NIHR HTA Futility Meeting

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