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Statistical Issues and Challenges in Combination Vaccines

Statistical Issues and Challenges in Combination Vaccines. Ivan S. F. Chan, Ph.D. Merck Research Laboratories Sang Ahnn, Ph.D. CBER, FDA 2005 FDA/Industry Statistics Workshop Washington D.C. September 14-16, 2005 Note: Sang Ahnn's opinions are his own and do not

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Statistical Issues and Challenges in Combination Vaccines

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  1. Statistical Issues and Challenges in Combination Vaccines Ivan S. F. Chan, Ph.D.Merck Research Laboratories Sang Ahnn, Ph.D. CBER, FDA 2005 FDA/Industry Statistics Workshop Washington D.C. September 14-16, 2005 Note: Sang Ahnn's opinions are his own and do not necessarily reflect FDA policy

  2. Outline of Presentation • Introduction of vaccine development • Combination vaccines • Statistical considerations for combination vaccine studies • Special challenges • A recent example • Summary

  3. Vaccination Motor-vehicle safety Safer workplaces Control of infectious diseases Decline in deaths from coronary heart disease and stroke Safer and healthier foods Healthier mothers and babies Family planning Fluoridation of drinking water Recognition of tobacco use as a health hazard The Ten Greatest Public Health Achievements of the 20th Century MMWR (1999);48:1141

  4. Vaccines Are Different From Drugs • Typically for prophylaxis, not treatment • Focus on vaccines for children traditionally, with recent shift to adult vaccines • Public health issues • Risk benefits at individual vs population level • Indirect vaccine effect (herd immunity) • Highly complex immunologic milieu • Array of humoral and cellular immune responses • Large, complex molecules of biological origin • Unique manufacturing and control issues

  5. Clinical Development of Vaccines • Safety • Assess local (injection-site) and systemic adverse experiences • Need a large database, particularly because of giving vaccines to healthy subjects • Efficacy • Require a high level of evidence and precision • Success typically requires showing efficacy greater than a non-zero (e.g. 25% - 50%) lower bound

  6. Clinical Development of Vaccines • Immunogenicity • Important in understanding the biology • Antibody responses – priming, first defense • T-cell responses – prevent virus reactivation, kill infected cells • Correlates of protection (“surrogate” endpoints) and immune markers used for • bridging studies (e.g., new vs. old formulations) • assessing consistency of vaccine manufacturing process • assessing combination vaccines or concomitant use of vaccines

  7. Combination Vaccines • Offer multiple vaccines (or vaccines with multiple sero-type antigens) at one administration • Reduce the number of injections given to children (fear of injection “needle”) • Reduce infant crying time and cost associated with parental perceptions of pain and emotional distress • Reduce the frequency of clinic visits • Increase vaccination coverage and public health benefits

  8. Examples of Combination Vaccine • Diphtheria/Tetanus/Pertussis (DTaP) • Hepatitis A/Hepatitis B • Hepatitis B/Haemophilus Influenza b • Measles/Mumps/Rubella (MMR) • Measles/Mumps/Rubella/Varicella (MMRV)

  9. An Example of Combination Vaccine:MMRV (ProQuad™) • Combines two well-established vaccines • M-M-R™ II for measles, mumps, and rubella (licensed in 1978) • VARIVAX™ for chickenpox (licensed in 1995) • Clinical development of ProQuad starts in 1984 • Includes over 5800 subjects • Provides similar immunogenicity and safety profiles compared with component vaccines • Licensed on Sept 6, 2005

  10. Statistical Considerations for Combination Vaccine Studies • Noninferiority design • Analysis methods • Consistency lots study

  11. Study Design of Combination Vaccines • Aim is to show non-inferiority of combination vaccine compared to separately administered components • Immune responses are the key measures • Percent of subjects achieving a response (definition depends on specific vaccines) • Geometric mean titer (or concentration) • Evaluate potential interactions on immune responses and safety among components

  12. Typical Non-inferiority Analysis Setupin Combination Vaccine Trials • Hypothesis of response rate for each component: H0: PT - PC -  versus H1: PT - PC> -  where >0 is a prespecified non-inferiority margin • Hypothesis of geometric mean titer for each component: H0: GMTT/GMTC R versus H1: GMTT/GMTC> R where 0<R<1 is a prespecified non-inferiority margin • Rejection of H0 implies that the combination vaccine is not inferior to the control • Success requires demonstration of non-inferiority for all components regarding both endpoints

  13. Selection of Non-inferiority Margin • Choice of margin depends on • Clinically meaningful difference • Regulatory standard • Statistical power/sample size considerations • For response rate,  may be a step-function • 5 pct pts for responses ≥95% • 10 pct pts for responses 90 to 95% • 15 pct pts for responses 80 to 90% • For geometric mean titers, typical choices are 1.5-fold and 2-fold differences

  14. Example of Non-inferiority Margins: MMRV Vaccine • Both antibody response rates and GMTs are co-primary endpoints • For measles, mumps, rubella, expected response rates are >95% and the noninferiority margins are 5 pct points • For varicella, the expected response rate is ~90% and the noninferiority margin is 10 pct points • For antibody titers, the margin is 1.5 fold-difference for the GMTs for each component

  15. Analysis Approachesto Combination Vaccine Trials • Noninferiority analysis for response rates: • Asymptotic (e.g., Miettinen & Nurminen 1985) • Exact (e.g., Chan and Zhang 1999, Chan 2002, 2003) • Confidence interval (CI) for  • Test-based methods provide consistency with p-value • CI lower bound is > (-) if and only if the non-inferiority is demonstrated • Regression (ANCOVA) for GMT analysis

  16. Power and Sample Size Determination • Asymptotic (Farrington and Manning 1990) or exact (Chan 2002) methods • Power is sensitive to the true responses • best case scenario is PT = PC (or GMTT = GMTC ) • useful to assess power assuming PT (or GMTT) slightly less than PC (or GMTC) • Need to evaluate overall power for demonstrating success for all components

  17. Consistency Lots Study • For licensure of a new vaccine, consistency of the manufacturing process must be supported by clinical trials using at least 3 lots of the vaccine • Objective of study is to show equivalence of clinical response among consistency lots • Evaluate both immune responses and safety • For combination vaccine, an active control may be included to demonstrate “assay sensitivity” • First demonstrate lot consistency • Then combine data from consistency lots to show noninferiority to active control

  18. Analysis of Consistency Lots Study • Hypothesis of interest: H0: |Pi – Pj| ≥  for at least one pair (i, j) vs. H1: |Pi – Pj| <  for every pair • Same as testing 3 pairs of noninferiority hypothesesH0: |P1 – P2| ≥   H0A: P1 – P2≥  or H0B: P2 – P1≥  • Test each hypothesis at one-sided 5% level • i.e., requiring 90% CI completely within [-, ] • Control overall type I error rate at 5%

  19. Example:Consistency Lots Study for MMRV • Study enrolled ~4000 subjects • 3 lots of MMRV and an active control (MMR+V) • 1st step: showed consistency of 3 MMRV lots • Antibody response rates and GMTs for measles, mumps, rubella, and varicella • 24 pairwise comparisons • 2nd step: showed the combined responses of MMRV is noninferior to those of MMR + V • 8 comparisons

  20. Some Challenges of Developing Combination Vaccines • Statistical challenge with multiple endpoints • Clinical challenge with potential interaction among components on immunogenicity and safety • Formulation issues • Compatibility of components/Stability?

  21. Multiplicity Challenge with Combination Vaccines • Major impact on power and sample size because of the need to show success on all components • Impact even more in consistency lots study • Accounting for correlation among components only increases power slightly • May consider multivariate analysis (e.g. T2test)?

  22. Multiplicity Hit on Power and Sample SizeCombination Vaccine Studies

  23. Challenges of Potential Interaction with Combination Vaccines • Potential interactions among components are difficult to predict • Immune responses may be lower • Safety concern may arise • A new dose-response study may be needed to re-establish the optimal dosing

  24. Examples of Interactions:MMRV Vaccine • Interaction observed in early clinical studies • Suboptimal varicella responses • A new dose-response study (N>1500) established optimal potency range for varicella • Acceptable varicella responses • But more fever (≥102 F) and measles-like rash • A large database (N>5800) confirms that MMRV is well tolerated • Both fever and measles-like rash were transient and resolved with no long-term complications

  25. Formulation Issues with ProQuadTM • M-M-R™ II is refrigerated product • VARIVAXTM is frozen product • European market needs refrigerated combination vaccine – how to develop? • First, develop frozen ProQuad to gain regulatory approval • Then, introduce refrigerated ProQuad via manufacturing supplement (a 4-6 months regulatory review time)

  26. Summary • Combination vaccines provide substantial public health benefits • Immune marker and selection of endpoints and margins are important to the development of combination vaccines • Special considerations for studies of consistency lots • Potential interactions among components and multiplicity of endpoints pose special challenges

  27. References • Blackwelder WC. Similarity/equivalence trials for combination vaccines.Combined Vaccines and Simultaneous Administration (ed by Williams JC,Goldenthal KL, Burns DL, Lewis BP), Ann New York AcadSciences 1995;754:321-328. • Chan ISF and Zhang Z. Test-based exact confidence intervals for the difference of two binomial proportions. Biometrics 1999; 55: 1202-1209. • Chan ISF. Power and sample size determination for non-inferiority trials using an exact method. Journal of Biopharmaceutical Statistics 2002; 12: 457-469. • Chan ISF. Proving non-inferiority or equivalence of two treatments with dichotomous endpoints using exact methods. Statistical Methods in Medical Research 2003; 12 (1): 37-58. • Chan ISF, Wang WWB, Heyse J. Vaccine clinical trials. Encyclopedia of Biopharmaceutical Statistics, 2nd Edition, 2003, 1005-1022. • Farrington CP and Manning G. Test statistics and sample size formulae for comparative binomial trials with null hypothesis of non-zero risk difference or non-unity relative risk. Statistics in Medicine 1990; 9, 1447-1454. • FDA: Guidance for industry for the evaluation of combination vaccines for preventable diseases: production, testing and clinical studies. 1997. http://www.FDA.GOV/CBER/gdlns/combvacc.pdf. • Kuter B, Hartzel J, Schodel F. The Challenges of Developing a combination Measles, Mumps, Rubella & Varicella Vaccine (ProQuad™), ESPID symposium, Valencia, Spain, May 2005. • Miettinen O and Nurminen M. Comparative analysis of two rates. Statistics in Medicine 1985; 4, 213-226.

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