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Implementation of Bayesian Logistic Regression for dose escalation

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					Implementation of Bayesian Logistic
Regression for dose escalation at Novartis
Oncology


  Glen Laird, Novartis Oncology
  Workshop in Phase I designs
  October 2, 2009
  With contributions from Beat Neuenschwander, Bill
  Mietlowski, Jyotirmoy Dey, and Stuart Bailey.
Outline of Presentation

  Background on Phase I needs

  CRM/MCRM background
         •        One Novartis experience

  FDA feedback on potential issues w/ CRM
         •        Example studies cited

  Novartis implementation of Bayesian Logistic Regression
         •        Statistical model
         •        Protocol planning and Study execution
         •        Decision making during dose-escalation teleconference

  Comparison of Bayesian Logistic and CRM methods

  Key messages
 2 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Flexible Phase I Oncology Designs

Requirements for dose-escalation
Challenges of Oncology Phase I Trials

 Accurately determine the Maximum Tolerated Dose (MTD)
 Untested drug in resistant patients
    • Unknown potential for toxicity – Avoid “overdosing” while trying to
      test a wide dose-range and learn about dose-toxicity relationship
    • Avoid sub-therapeutic doses while controlling “overdosing”
    • Identify active and acceptable doses for phase II/III

 Rare and very-ill patients
    • Use as few patients as possible – cohorts of 3-6
    • Inability to distinguish tox due to condition from tox due to drug
    • Ph 1 pts also hope for therapeutic benefit
    • Use all available information efficiently
4 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
CRM/MCRM background: implementation at
Novartis

  Many versions of CRM/MCRM exist. Novartis implementation used a
 power model
                   Prior probabilities of DLT at dose levels (“skeleton”) input
                   Learning model (posterior): P{DLT} = pi mean() , where pi are the initial
                   “skeleton” estimates of P{DLT}
                  Target DLT rate often 33% at Novartis
                   Prior uncertainty about  usually specified by lognormal distribution.
                  Starting at lowest dose
                  Not skipping doses
                  Enrolling in cohorts (often size 3-6)

  Emerging DLT data  updated estimate of exponent 
  Updated   Updated posterior probabilities of DLT
           •         > 1 decreases probabilities of DLT for all doses
           •         < 1 increases probabilities of DLT for all doses
 5 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
    Summary of MCRM: impact of exponent alpha
•
          1.0                                                                                                             A
                                                                                                                      A
          0.9                                                                                                   A
          0.8
          0.7                                                                                               A
          0.6
          0.5
          0.4                                                                                           A
          0.3
          0.2
                                                                                            A
          0.1
                            A         A        A        A        A        A        A
          0.0


                                      1                 2                 3                 4               5         6



                                   0.1
                               ALPHA                             0.2               0.3           0.4            0.5
                                   0.7                           1.0               2.0          AA
                                                                                               A 3.0            4.0


    6 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
  CRM/MCRM background
 CRM/MCRM uses 1-parameter model
    • depends on correct specification of skeleton (log posterior
      probability DLT proportional to log prior skeleton)
    • Serious mis-specification of skeleton can lead to excessive
      dosing
    • On-study recommendations may be impractical (or not
      followed by clinicians) even if final dose recommendation
      would be reasonable.

 CRM/MCRM ignores precision of updated estimate of
exponent 
   • Same updating if estimate of  came from cohorts of 1, 3, 6,
               or 12 patients




  7 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
One Novartis experience with MCRM
Motivating example (from Neuenschwander, et al, 2008)

 open-label, multicenter, non-comparative, dose-escalation
    cancer trial designed to characterize the safety, tolerability
    and PK profile of a drug and to determine its MTD.

 The pre-defined doses were 1, 2.5, 5, 10, 15, 20, 25, 30,
    40, 50, 75, 100, 150, 200 and 250 mg. Target P(DLT)=.3.

 The first cohort of patients was treated at 1mg. No DLTs
    were observed for the first four cohorts of patients.
     clinical team decided to skip 2 doses to 25mg (contradicting the
     planned MCRM in which doses were not supposed to be skipped)

 Both patients dosed at 25 mg experienced DLT
    • MCRM recommended further escalation, to the dismay of the team.

8 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Case study – CRM Results




 Recommendation:
    • from original pi: dose = 40 or 30 (not favored by team!)
    • from equidistant pi: dose = 25 (questionable)
    • Note: the pi are structural assumptions, should not be changed!

9 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
One Novartis experience with CRM- Case study
Results




10 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
    FDA concern about CRM methods
      Raji Sridhara and/or Sue-Jane Wang from FDA raise concerns
       about 3 trials using CRM methods.
1. Companion studies of 9-aminocamptothecin : 9 out of 17 patients
   experience DLTs and 12 out of 18 patients experience DLTs.
   (Piantadosi, et al, 1998)
2. Time to event (TITE) CRM model has 4 out of 8 patients at highest
   dose experience DLTs (Muler, et al, 2004)
3. Gleevec prostate trial has 8 out of 10 patients enrolled above MTD
   experience DLT (Matthew, et al).


Use of multi-parameter models more technically feasible for
   widespread use than in years past.
 Could more flexible Bayesian methods be developed that retain
   some of the improvements over 3+3 type methods?
    11 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Flexible Phase I Oncology Designs

Statistical Aspects
Combination of clinical and statistical expertise
Informed decisions: clinical, data, historical knowledge and statistics

      Historical
                                                                         Trial Data
        Data
                                                                       0/3,0/3,1/3,...
     (prior info)



                                      DLT rates                                         Dose           Decisions
                                  p1, p2,...,pMTD,...                                recommen-       Dose Escalation
                                    (uncertainty!)                                     dations          Decision



  Model based
    dose-DLT                                                                Clinical
   relationship                                                            Expertise


                                                         How certain are we that
(1) True DLT rate for recommended dose is in target interval (0.166,0.333), (2) not an overdose (>0.333) ?
13 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Recommendations rely on relevant inferential summaries

  Inference: for each dose we want to know
     • how likely is it that true DLT rate is in target interval?
     • how likely is it that true DLT rate is an overdose?
     •  Example (next slide)
  Dose recommendation: for next cohort, select dose that
    fulfills the following criteria

                                                                      Two criteria
      1    Dose that maximizes                                                       2 … with overdose control
        probability that true DLT rate                                                    e.g., less than 25%
            p is in target interval                                                   probability that true DLT rate
            e.g. (0.166, 0.333)                                                           p exceeds 0.333

 14 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Characterizing DLT rates requires us to go beyond point estimates

    • Example: 1 DLT in 6 patients. What do we really know about p?

                                                                          Uninformative prior: 0.25 (0.00,0.95)95%




15 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Characterizing DLT rates requires us to go beyond point estimates

    • Example: 1 DLT in 6 patients. What do we really know about p?

                                                                          Uninformative prior: 0.25 (0.00,0.95)95%
                                                                          Data: 1/6
                                                                          Summary for p: 0.17 (0.02,0.53)95%




16 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Characterizing DLT rates requires us to go beyond point estimates

    • Example: 1 DLT in 6 patients. What do we really know about p?

                                                                          Uninformative prior: 0.25 (0.00,0.95)95%
                                                                          Data: 1/6
                                                                          Summary for p: 0.17 (0.02,0.53)95%
                                                                          Additional information: there is a
                                                                                  • 35% probability for targeted
                                                                                    toxicity:
                                                                                    p in target interval in (0.166,0.333)




17 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Characterizing DLT rates requires us to go beyond point estimates

    • Example: 1 DLT in 6 patients. What do we really know about p?

                                                                          Uninformative prior: 0.25 (0.00,0.95)95%
                                                                          Data: 1/6
                                                                          Summary for p: 0.17 (0.02,0.53)95%
                                                                          Additional information: there is a
                                                                                  • 35% probability for targeted
                                                                                    toxicity:
                                                                                    p in target interval in (0.166,0.333)
                                                                                  • 16.8% probability for overdosing:
                                                                                    DLT rate p>1/3




18 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Quantifying the uncertainty in DLT rates
Characterizing DLT rates requires us to go beyond point estimates

    • Example: 1 DLT in 6 patients. What do we really know about p?

                                                                          Uninformative prior: 0.25 (0.00,0.95)95%
                                                                          Data: 1/6
                                                                          Summary for p: 0.17 (0.02,0.53)95%
                                                                          Additional information: there is a
                                                                             • 35% probability for targeted toxicity:
                                                                               p in target interval in (0.166,0.333)
                                                                             • 16.8% probability for overdosing:
                                                                               DLT rate p>1/3
                                                                             • 48.3% probability for underdosing:
                                                                               DLT rate p<1/6
                                            Conclusions
       - Considerable uncertainty due to sparse data
       - Therefore: good decisions require synergy of clinical and statistics expertise
19 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Methodology - Overview
Inference: model-based. Recommendations: target toxicity, overdose control

  Model: logistic regression
  Inference is Bayesian
  Priors
     • “uninformative”
     • priors based on historical data
     • mixture priors accounting for pre-clinical variability
  Dose recommendations: balancing target toxicity & safety
     • Target toxicity: recommend dose that is in target interval with high
       probability
     • Safety: dose must fulfill overdose criterion
     • Note: this approach is safer than recommending dose with an
       estimated DLT rate that is closest to target toxicity (e.g. 25%)
 20 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
 Models
 Reasonably flexible model is needed to ensure good performance

 Basic model: logistic regression
  • DLT rate p, dose = d: logit(p) = log(p/(1-p)) = log() + .log(d),  , > 0
  • reasonably flexible 2-parameter model


 Extensions of basic model
  • Covariates X (): logit(p) = log() + .log(d) + X
     - e.g. dose regimen or patient characteristics
     - e.g. levels of combination partner (Bailey, et al, 2009)
  • Combination setting: DLT rate for combination of two compounds
  • Ordinal Data: e.g. no DLT, low-grade toxicity, DLT
  • Joint Safety-Efficacy model




 21 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Protocol development

 Pre-define provisional dose escalation steps
          • Provisional doses decided on expected escalation scheme -
            typically indicate maximum one-step jump. Intermediate doses
            may be used on data-driven basis
 Minimum cohort-size – typically 3.
          • Allow enrollment of additional subjects for dropouts or cohort
            expansion
 Simulation tool exists to test operating characteristics
          • Performance of the design in terms of correct dose-determination,
            gain in efficiency under various assumed dose-toxicity
            relationships (truths)




22 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Protocol development

 Stopping rules (“rules for declaring the MTD”)
          • At least 6 evaluable patients at the MTD level with at least 21
            patients evaluated in total in the dose escalation phase

          or


          • At least 9 patients evaluated at a dose level with a high precision
            (model recommends the same dose as the highest dose that is
            not an overdose with 50% posterior probability in the target
            toxicity interval.)




23 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Priors
Typical priors represent different types of information

  Bivariate normal prior for (log(),log())  prior for DLT rates p1,p2,…

    Uninformative Prior
• wide 95%-intervals
• (default prior)




24 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Priors
Typical priors represent different types of information

  Bivariate normal prior for (log(),log())  prior for DLT rates p1,p2,…

    Uninformative Prior                                           Historical Prior
• wide 95%-intervals                                       • Data from historical trials
• (default prior)                                            (discounted due to
                                                             between-trial variation!)




25 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Priors
Typical priors represent different types of information

  Bivariate normal prior for (log(),log())  prior for DLT rates p1,p2,…

    Uninformative Prior                                           Historical Prior                           Mixture Prior
• wide 95%-intervals                                       • Data from historical trials             • Different prior information
• (default prior)                                            (discounted due to                        (pre-clinical variation)
                                                             between-trial variation!)               • different prior weights




26 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
   Output
   Interval Probabilities: underdosing, targeted toxicity, overdosing

   overdosing
    targeted
     toxicity
  underdosing


Top Panel
  probability of overdosing
  failed overdose criterion in red!
  Pr( true DLT rate p >0.333) > 25%
Middle Panel
  probability of targeted toxicity
Bottom Panel
  probability of underdosing
Recommended Dose
  15 (max target w/ overdose<25%)

   27 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
 Summary

          Model
           Prior
         Expertise

            Input                                              Inference                                Recommendations


1. Substantial uncertainty in MTD finding requires statistical component
2. Input: standard model (logistic regression) + prior
3. Inference: probabilistic quantification of DLT rates, a requirement that
   leads to informed recommendations/decisions
4. Dose Recommendations are based on the probability of targeted
   toxicity and overdosing.
 •      Overdose criterion is essential.
 28 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Comparison of Operating characteristics to
CRM/MCRM (Neuenschwander, et al, 2008)
 Simulations performed comparing
    • CRM (with 27% target rate);
    • MCRM;
    • Logistic Regression based on 27% target rate (LRmean).
    • Logistic Regression maximizing target toxicity (LRcat);
    • Logistic Regression maximizing target toxicity with 25% overdose
      control (LRcat25);
 Eight scenarios studies using 7 dose levels.
    • “true” MTD (27% DLT rate) varied from dose level 1,2,4,6, or 7
    • Flat and steep true curves studied
    • Same prior medians and vague priors used
    • Fixed sample size of 24 or 36 patients.
    • Older version of target toxicity used (20% - 35% DLT rate)

29 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
  Comparison of Operating characteristics to
  CRM/MCRM
 Performance
 • LRcat25 and MCRM have lower average number of DLTs than the
   more aggressive CRM, LRmean, and LRcat methods
 • LRcat25 selected correct dose with similar frequency as the more
   aggressive methods
   - It was slightly lower (approximately 6%-10% lower) for “flat” toxicity curves
     in which the true MTD was high (dose 6)
 • MCRM had worse targeting than other methods when the true dose
   was a high dose level.

 By being more aggressive only when the full posterior
 summary justifies it, LRcat25 appears to combine some of
 the added safety of the MCRM with the superior targeting
 of the CRM, LRmean, and LRcat methods.

 Thall and Lee (2003) also compared performance
  30 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Novartis experience case study revisited – BLR
Results




31 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Case study - comparison
                                                                                                     Priors

                                                                                                      Prior for BLR chosen
                                                                                                       to be similar to prior for
                                                                                                       CRM




                                                                                                     Posteriors

                                                                                                      CRM: “too” narrow
                                                                                                       intervals for doses
                                                                                                       where no data have
                                                                                                       been seen. Similar
                                                                                                       things happen for other
                                                                                                       1-parameter models
32 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Case study: Summary
 CRM not able to react to the toxicity data due to less
    flexibility in 1-parameter model
    • Lack of uncertainty at high (never tested) doses

 BLR does not suffer from the same issue and makes
    sensible on-study recommendations in this case
    • Parameterization allows uncertainty to remain at doses never tested
      and therefore model can adapt more easily

 BLR approach to estimating the MTD is more suitable in
    this case study than the CRM approach
    • Provide better estimation of the full dose response curve (still not the
      primary goal though!)


33 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
    FDA concern about CRM methods
•       Data from Mathew, et al study:

                cohort                        dose                          patients                     DLTs


                1                             30                            6                            0


                2                             45                            4                            3


                3                             35                            6                            5


                4                             30                            6                            3



    34 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Analysis of first cohort of Mathew et al data
alpha= 6.077

DoseLevel PtoxPrior Npat Ntox Ptox

1               20              0.07                     0                0          0.000

2               25              0.16                     0                0          0.000

3               30              0.30                     6                0          0.001

4               35              0.40                     0                0          0.004

5               40              0.46                     0                0          0.009

6               45              0.53                     0                0          0.021
35 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Re-examination of Mathew et al data using Novartis
methodology
 Re-examined Mathew et al data using Novartis method.
    • assume same prior medians as actual study design.
    • Match prior percentiles for 2.5%, median, and 97.5% percentiles as
      closely as possible to a bi-variate normal prior for (log(),log())




36 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
  Analysis of Mathew et al data using Novartis
  methodology, cont’d
dose 0-.16 .16-.33 .33-1                                                   mean sd                     2.5% 50% 97.5%

 20         .973                .025                .002                  .034 .045                    .001 .018 .164

 30         .831                .142                  .027                   .090 .088                  .004 .062 .337

 35         .687                .225                .088                  .136 .126                    .006 .097 .470

 40         .553                .268                .179                  .190 .170                    .009 .139 .640

 45         .450                .273                .278                  .246 .210                    .012 .185 .785

 Most excessive dose of 45 mg (narrowly) avoided despite
 mean being clearly below the targeted toxicity rate of 30%.

  37 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Implementation message taken from JSM
 Dan Sargent noted at JSM 2009 that the differences
    between Bayesian methodologies are not as important as
    the need to replace “3+3” methods with some form of
    Bayesian method.

 Good to continue to search for better dose escalation
    methods, but don’t let that stop the implementation of
    methods that are at least better than “3+3”




38 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
    Study conduct
    Patient enrollment / observation for each dose cohort

 To assure patient safety during the conduct of the study a
        close interaction within clinical team is required
          Clinician, statistician, clinical pharmacologist, etc
          Investigators

       Clinical trial leader provides regular updates on accrual:
          For each cohort enroll subjects per minimum cohort-size, typically 3
          May enroll additional subjects up to a pre-specified maximum

       In the case of unexpected or severe toxicity all investigators will
        be informed immediately
       The model will be updated in case the first 2 patients in a cohort
        experience DLT
    39 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Discussion at the dose escalation conference (DETC)


  Discussion with investigators during the DETC

     Investigators and sponsor review all available data (DLT, AE,
      labs, VS, ECG, PK, PD, efficacy) particularly from current
      cohort as well as previous cohorts

     Agree on total number of DLTs and evaluable subjects for
      current cohort

     Statistician informs participants of the highest dose level one
      may escalate to per statistical analysis and protocol
      restrictions


  40 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Dose escalation decision


   Participants decide if synthesis of relevant clinical
    data justifies a dose escalation and to which dose
    (highest supported by the Bayesian analysis and
    protocol or intermediate)


   Decisions are documented via minutes and
          communicated to all participants.




  41 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Combination of clinical and statistical expertise
Informed decisions: clinical, data, historical knowledge and statistics

      Historical                                                          Trial Data
        Data                                                             0/3@1 mg
     (prior info)

                                                                                        Dose
                                      DLT rates                                      recommen-         Decisions
                                  p1, p2,...,pMTD,...                                  dations       Dose Escalation
                                    (uncertainty!)                                                      Decision
Model based
  dose-DLT                                                                     Clinical
 relationship                                                                 Expertise

                                       Additional
                                    study data – AE,
                                    PK, BM, Imaging
                                       con-meds
42 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
Flexible Phase I Oncology Designs

Concluding remarks
  Key Messages
 Patient safety is the primary objective
 • Statistical approach quantifies knowledge about DLT data only
 • Statistical inference is used as one component of a decision-making
   framework
   - Provides upper bound for potential doses based on uncertainty statements
   - To reduce risk of overdose  obtain more information at lower doses

 Application of our approach can be protocol/drug specific
 • Maximum escalation steps, minimum and maximum cohort sizes,
   stopping rules are pre-specified

 Studies require active review of ongoing study data by Novartis
 and investigators

 Novartis method appears to have good targeting properties while
 preserving patient safety
  44 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |
 References
 Bailey, Neuenschwander, Laird, Branson (2009).
  A Bayesian case study in oncology phase I combination dose-finding using logistic regression
  with covariates. Journal of Biopharmaceutical Statistics, 19:369-484

 Mathew, Thall, Jones, Perez, Bucana, Troncoso, Kim, Fidler, and Logothetis (2004). Platelet-
  derived Growth Factor Receptor Inhibitor Imatinib Mesylate and Docetaxel: A Modular Phase I
  Trial in Androgen-Independent Prostate Cancer Journal of Clinical Oncology, 16, 3323-3329.

 Muler, McGinn, Normolle, Lawrence, Brosn, Hejna, and Zalupski (2004) Phase I Trial Using a
  Time-to-Event Continual Reassessment Strategy for Dose Escalation of Cisplatin Combined
  With Gemcitabine and Radiation Therapy in Pancreatic Cancer Journal of Clinical Onocology,
  22:238-243.

 Neuenschwander, Branson, Gsponer (2008)
  Critical aspects of the Bayesian approach to Phase I cancer trials. Statistics in Medicine,
  27:2420-2439.

 Piantadosi, Fisher, and Grossman (1998) Validation Of Doses Selected Using The Continual
  Reassessment Method (Crm) In Patients With Primary Cns Malignancies. ASCO meeting
  abstract. Abstract #819.

 Thall, Lee (2003) Practical model-based dose-finding in phase I clinical trials: methods based
  on toxicity. Int J Gynecol Cancer 13: 251-261


 45 | Novartis implementation of Bayesian Logistic Regression | Phase 1 Workshop | Oct 2 nd, 2009 |

				
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