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					Adaptive Dose Ranging Studies

                Frank Bretz
               on behalf of the
PhRMA WG on “Adaptive Dose Ranging Studies”


           ROeS, September 12, 2007
                                                    Outline

• Background and motivation

• Adaptive Dose Ranging Studies PhRMA initiative: goals and
  scope

• Evaluating DF methods: simulation study

• Simulation results

• Conclusions and recommendations




                                                              2
                                                  Background

• Pharmaceutical industry pipeline problem: decreasing number
  of approved drugs, despite advances in basic science

• FDA’s Critical Path Initiative — “Innovation vs. Stagnation”
  White Paper

• Pharmaceutical industry (PhRMA) reaction: different working
  groups (WGs) addressing key drivers of poor performance

• Adaptive Dose Ranging Studies (ADRS) group formed to
  address problems with inefficient dose finding performance




                                                                 3
      Adaptive Dose Ranging Studies core WG members
• Alex Dmitrienko, Eli Lilly   • Qing Liu, J & J
• Amit Roy, BMS                • Rick Sax, AstraZeneca
• Brenda Gaydos, Eli Lilly     • Tom Parke, Tessella
• Frank Bretz, Novartis
• Frank Shen, BMS
• Greg Enas, Eli Lilly
• Jos´ Pinheiro, Novartis
     e
• Michael Krams, Pfizer




                                                         4
                           ADRS additional WG members
• Bj¨ rn Bornkamp, University of Dortmund
    o
• Beat Neuenschwander, Novartis
• Chyi-Hung Hsu, Pfizer
• Franz K¨ nig, Med. Univ. Vienna
         o




                                                        5
                               ADRS initiative – Motivation

• Poor understanding of dose response (DR) for both efficacy
  and safety is pervasive in drug development

• Indicated by both FDA and industry as one of root causes of
  late phase attrition and post-marketing problems with approved
  drugs

• Current dose finding designs and methods focus on selection
  of target dose (e.g., minimum effective dose) out of fixed,
  generally small number of dose levels, via pairwise hypothesis
  testing =⇒ inefficient




                                                                   6
                                     ADRS initiative – Goals

• Investigate and develop designs and methods for efficiently
  learning about safety and efficacy DR profile =⇒ benefit/risk
  profile

• More accurate and faster decision making on dose selection
  and improved labeling

• Evaluate statistical operational characteristics of alternative
  designs and methods to make recommendations on their use in
  practice

• Increase awareness about this class of designs, promoting their
  use, when advantageous


                                                                    7
                              ADRS – Definition and Scope

• Adaptive dose-ranging designs allowing dynamic allocation of
  patients and possibly variable number of dose levels based on
  accumulating information
• Intended to strike balance between need for additional DR
  information and increased costs and time-lines
• Emphasis on modeling/estimation (learning) as opposed to
  hypothesis testing (confirming)
• Investigate existing and new ADRS methods via simulation
• Evaluate potential benefits over traditional dose-ranging
  designs over variety of scenarios to make recommendations on
  practical usefulness of ADRS methods

                                                                  8
                        Dose Finding Methods – Fixed Doses
• Traditional ANOVA based on pairwise comparisons and multiplicity
  adjustment (Dunnett); common approach used in dose finding studies

• MCP-Mod combination of multiple comparison procedure (MCP) to
  identify presence of DR and modeling, to estimate target dose(s) and
  DR profile (Bretz, Pinheiro and Branson, 2005)

• MTT: novel method based on Multiple Trend Tests

• Bayesian Model Averaging: BMA

• Nonparametric local regression fitting: LOCFIT




                                                                         9
                             Dose Finding Methods – ADRS

• GADA: Dynamic dose allocation based on Bayesian normal
  dynamic linear model (Krams, Lees and Berry, 2005);
  allocation of patients to dose adaptively changed according to
  model-based optimization criteria (e.g., variance of target dose
  estimate)

• D-opt: novel adaptive dose allocation based on D-optimality
  criterion used with sigmoid-Emax model; model parameters
  re-estimated at interim analysis and corresponding D-optimal
  allocation determined for next interval




                                                                     10
                  Simulation study: design and assumptions
• Proof-of-concept + dose finding trial, motivated by neuropathic pain
  indication
• Key questions: whether there is evidence of dose response and, if so,
  which dose level to bring to confirmatory phase and how well dose
  response (DR) curve is estimated
• Primary endpoint: change from baseline in VAS at Week 6
• Dose design scenarios:
   – 5 equally spaced doses levels 0, 2, 4, 6, 8
   – 7 unequally spaced dose levels: 0, 2, 3, 4, 5, 6, 8
   – 9 equally spaced dose levels: 0, 1, . . . , 8
• Significance level: one-sided FWER α = 0.05
• Sample sizes: 150 and 250 patients (total)

                                                                          11
                                                                                                        Dose response profiles
                                                                                   0   2     4      6    8

                                                                Umbrella                   Emax                      Sigmoid Emax
                                                                                                                                            0.0
Expected change from baseline in VAS at Week 6




                                                                                                                                            -0.5



                                                                                                                                            -1.0



                                                                                                                                            -1.5


                                                                  Flat                     Linear                      Logistic
                                                 0.0



                                                 -0.5



                                                 -1.0



                                                 -1.5



                                                        0   2      4       6   8                             0   2        4         6   8


                                                                                           Dose



                                                                                                                                                   12
                                      Measuring performance

• Probability of identifying dose response: P r(DR)

• Probability of identifying clinical relevance and selecting a
  dose for confirmatory phase: P r(dose)

• Dose selection
   – Distribution of selected doses (rounded to nearest integer, if
     continuous estimate possible)




                                                                      13
                           Dose selection performance (cont.)
• Target dose interval – doses that produce effect within ±10%
  of target effect ∆
                     Target dose         Target interval
        Model     actual   rounded      actual      rounded
        Linear     6.30       6      (5.67, 6.93)    {6,7}
       Logistic    4.96       5      (4.65, 5.35)     {5}
      Umbrella     3.24       3      (2.76, 3.81)    {3,4}
        Emax       2.00       2      (1.44, 2.95)    {2,3}
      Sig-Emax     5.06       5      (4.68, 5.58)     {5}

• Probabilities of under-, over-, and correct interval estimation:
  P − = P (dtarg < dmin ), P + = P (dtarg > dmin ),
  P ◦ = 1 − (P − + P + )

                                                                     14
       Sample of Simulation Results

White Paper to appear (including FDA and EMEA discussants)
               in the J. Biopharm. Stat. (2007)
    Full report published at http://www.biopharmnet.com




                                                             15
                                                        Probability of identifying DR
                                      5 doses               7 doses                  9 doses
                                     60   70    80     90   100                            60   70     80      90   100

                   N = 250                  N = 250                        N = 250                   N = 250
                   logistic                 umbrella                        linear                    Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA

                   N = 150                  N = 150                        N = 150                   N = 150
                   logistic                 umbrella                        linear                    Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA


         60   70     80       90   100                        60      70     80      90   100

                                                            Pr(DR)




                                                                                                                          16
                 Probability dose selection – flat DR, N = 150
                                           0   1    2     3       4     5   6

                     N = 250                            N = 250                             N = 250
                     5 doses                            7 doses                             9 doses
LOCFIT
  BMA
   MTT
MCPMod
 GADA
  Dopt
ANOVA
                     N = 150                            N = 150                             N = 150
                     5 doses                            7 doses                             9 doses
LOCFIT
  BMA
   MTT
MCPMod
 GADA
  Dopt
ANOVA


         0   1   2     3       4   5   6                                        0   1   2     3       4   5   6
                                                   Pr(dose | flat DR)




                                                                                                                  17
                                                                 Probability dose selection
                                     5 doses               7 doses              9 doses
                                      60 70    80    90    100                             60   70   80    90   100

               N = 250                    N = 250                     N = 250                    N = 250
               logistic                   umbrella                     linear                     Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA

               N = 150                    N = 150                     N = 150                    N = 150
               logistic                   umbrella                     linear                     Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA


         60   70   80     90   100                              60   70   80    90   100

                                                          Pr(dose)




                                                                                                                      18
                                   Prob. of correct target dose interval
                                   5 doses               7 doses                  9 doses
                                  0    20    40     60                                   0   20      40     60

                  N = 250                N = 250                        N = 250                   N = 250
                  logistic               umbrella                        linear                    Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA

                  N = 150                N = 150                        N = 150                   N = 150
                  logistic               umbrella                        linear                    Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA


         0   20      40      60                            0       20      40     60

                                        Correct target interval probability (%)




                                                                                                                 19
                    Estimated dose distrib., Logistic model and N = 150
                                  2     4   6   8                      2     4   6   8                     2     4   6   8

                    9 doses           9 doses           9 doses            9 doses           9 doses           9 doses           9 doses
                    ANOVA               Dopt             GADA              MCPMod              MTT              BMA              LOCFIT
           50
           40
           30
           20
           10
            0
                    7 doses           7 doses           7 doses            7 doses           7 doses           7 doses           7 doses
                    ANOVA               Dopt             GADA              MCPMod              MTT              BMA              LOCFIT
                                                                                                                                                50
% Trials




                                                                                                                                                40
                                                                                                                                                30
                                                                                                                                                20
                                                                                                                                                10
                                                                                                                                                0
                    5 doses           5 doses           5 doses            5 doses           5 doses           5 doses           5 doses
                    ANOVA               Dopt             GADA              MCPMod              MTT              BMA              LOCFIT
           50
           40
           30
           20
           10
            0

                2     4   6   8                     2     4   6   8                      2     4   6   8                     2     4   6   8

                                                                      Dose selected




                                                                                                                                           20
                           Average prediction error per dose
                        5 doses            7 doses                9 doses
                        20 40 60 80                                         20 40 60 80

             N = 250         N = 250                    N = 250                 N = 250
             logistic        umbrella                    linear                  Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA

             N = 150         N = 150                    N = 150                 N = 150
             logistic        umbrella                    linear                  Emax

LOCFIT

  BMA

   MTT

MCPMod

 GADA

  Dopt

ANOVA


         20 40 60 80                             20 40 60 80

                           Relative abs(error) in dose estimate (%)




                                                                                          21
Sample predicted curves: Logistic, 9 doses and N = 150
                                LOCFIT


                   1                                                       Sample
                                                                           Median
                   0                                                       True

                   -1


                   -2


                   -3
                                MCPMod           MTT            BMA


                                                                                    1
    Predicted DR




                                                                                    0


                                                                                    -1


                                                                                    -2


                                                                                    -3
                                ANOVA            Dopt           GADA


                   1


                   0


                   -1


                   -2


                   -3

                        0   2     4      6   8          0   2    4     6      8


                                                 Dose




                                                                                         22
Sample predicted curves: Umbrella, 5 doses and N = 250
                                 LOCFIT


                                                                            Sample
                    0                                                       Median
                                                                            True

                    -1



                    -2



                                 MCPMod           MTT            BMA
     Predicted DR




                                                                                     0



                                                                                     -1



                                                                                     -2



                                 ANOVA            Dopt           GADA



                    0



                    -1



                    -2




                         0   2     4      6   8          0   2    4     6      8


                                                  Dose




                                                                                          23
                                                   Conclusions

• Detecting DR is considerably easier than estimating it
• Current sample sizes for DF studies, based on power to detect
  DR, are inappropriate for dose selection and DR estimation
• None of methods had good performance in estimating dose in
  the correct target interval: maximum observed percentage of
  correct interval selection – 60% =⇒ larger N needed
• Adaptive dose-ranging methods (i.e., ADRS) lead to gains in
  power to detect DR, precision to select target dose, and to
  estimate DR – greatest potential in the latter two
• GADA had best overall performance, especially on DR
  estimation

                                                                  24
                                         Conclusions (cont.)

• Model-based methods have superior performance compared to
  methods based on hypothesis testing

• Number of doses larger than 5 does not seem to produce
  significant gains (provided overall N is fixed) =⇒ trade-off
  between more detail about DR and less precision at each dose

• In practice, need to balance gains associated with adaptive
  dose ranging designs approach against greater methodological
  and operational complexity




                                                                 25
                                            Recommendations

• Adaptive, model-based dose-ranging designs should be used
  routinely in drug development, as they can lead to substantial
  gains in performance over traditional DF methods

• Sample size calculations for Phase II studies should take into
  account desired precision of estimated target dose and possibly
  also estimated DR (current methods are not appropriate)

• When resulting sample size is not feasible, should consider
  selecting two or three doses for confirmatory phase to increase
  likelihood of including “correct” dose – adaptive designs could
  be used in confirmatory phase for greater efficiency (e.g.,
  dropping less efficient doses earlier)


                                                                    26
                                    Recommendations (cont.)

• Proof-of-concept (PoC) and dose selection should be
  combined, when feasible, into one seamless trial

• Early stopping rules, for both efficacy and futility, should be
  used when feasible to allow greater efficiency in adaptive
  designs – Bayesian methods are particularly well-suited for
  this purpose

• Trial simulations should be used to determine appropriate
  sample sizes, as well as for estimating operational
  characteristics of designs/methods under consideration




                                                                   27
              Future investigations of the Working Group

• Joint modeling of efficacy and safety outcomes, with dose
  selection based on clinical utility indices

• Sample size calculations based on target dose estimate

• Longitud. data and biomarkers for (interim) decision making

• Integrated view of Phase II and III development program

• Impact of early stopping (futility and efficacy) on overall
  success probability




                                                                28
References

Bretz, F., Pinheiro, J. and Branson, M. (2005). Combining multiple
     comparisons and modeling techniques in dose-response
     studies, Biometrics 61(3): 738–748.

Krams, M., Lees, K. R. and Berry, D. A. (2005). The past is the
    future: Innovative designs in acute stroke therapy trials,
    Stroke 36(6): 1341–7.




                                                                     29

				
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