Acknowledgement: PhRMA Adaptive Designs Working Group Co-Chairs: Michael Krams Brenda Gaydos Authors: Keaven Anderson Suman Bhattacharya Alun Bedding Don Berry Frank Bretz Christy Chuang-Stein Vlad Dragalin Paul Gallo Brenda Gaydos Michael Krams Qing Liu Jeff Maca Inna Perevozskaya Jose Pinheiro Judith Quinlan Members: Carl-Fredrik Burman David DeBrota Jonathan Denne Greg Enas Richard Entsuah Andy Grieve David Henry Tony Ho Telba Irony Larry Lesko Gary Littman Cyrus Mehta Allan Pallay Michael Poole Rick Sax Jerry Schindler Michael D Smith Marc Walton Sue-Jane Wang Gernot Wassmer Pauline Williams
Upcoming DIJ Publications by PhRMA working group on adaptive designs 1. P. Gallo, M. Krams Introduction 2. V. Dragalin Adaptive Designs: Terminology and Classification 3. J. Quinlan, M. Krams Implementing Adaptive Designs: Logistical and Operational Considerations 4. P. Gallo. Confidentiality and trial integrity issues for adaptive designs 5. B.Gaydos, M. Krams, I. Perevozskaya, F.Bretz; Q. Liu, P. Gallo, D. Berry; C. Chuang-Stein, J. Pinheiro, A. Bedding. Adaptive Dose Response Studies 6. J. Maca, S. Bhattacharya, V. Dragalin, P. Gallo, M. Krams, Adaptive Seamless Phase II / III Designs – Background, Operational Aspects, and Examples 7. C. Chuang-Stein, K.Anderson, P. Gallo, S. Collins. Sample Size Re-estimation: A Review and Recommendations
10/27/2006Philadelphia ASA Chapter Meeting4 Dose-Response Paper Overview 1. Motivation: Challenges in evaluation of dose- response 2. Summary of key recommendations from PhRMA dose-response workstream 3. Overview of traditional dose-response designs 4. Overview of adaptive dose-response methods in early exploratory studies 5. Adaptive Frequentist approaches for late stage exploratory development 6. Developing a Bayesian adaptive dose design 7. Monitoring issues and processes in adaptive dose-response trials 8. Rolling dose studies
10/27/2006Philadelphia ASA Chapter Meeting5 1. Motivation: Challenges in Evaluation of Dose-Response Insufficient exploration of the dose response is often a key shortcoming of clinical drug development. Initial proof-of-concept (PoC) studies often rely on testing just one dose level (e.g. the maximum tolerated dose) Additional exploration of dose-response typically done later (Phase IIb trials) Adaptive designs offer efficient ways to learn about the dose response and guide decision making (dose selection/program termination) It is both feasible and advantageous to design a PoC study as an adaptive dose response trial. Continuation of a dose response trial into a confirmatory stage in a seamless design is a further opportunity to increase information on the right dose(s) Adaptive dose-response trial may offer deduction in the total clinical development timeline
10/27/2006Philadelphia ASA Chapter Meeting6 1. Motivation (cont.) Efficient learning about the dose response earlier in development could reduce overall costs and provide better information on dose in the filing package This review primarily focuses on phase Ib and II study designs Applicable to endpoints that support filing or are predictive of the filing endpoint (e.g. biomarkers).
10/27/2006Philadelphia ASA Chapter Meeting7 2. Key recommendations of the PhRMA Adaptive Dose-Response workstream Consider adaptive dose response designs in exploratory development. Consider adaptive dose response designs to establish proof-of-concept Whenever possible use an approach that incorporates a model for the dose response. Consider seamless approaches to improve the efficiency of learning Define the dose assignment mechanism prospectively and fully evaluate its operational characteristics through simulation prior to initiating the study.
10/27/2006Philadelphia ASA Chapter Meeting8 Key recommendations of the PhRMA Adaptive Dose-Response workstream (cont.) Stop the trial at the earliest time point when there is enough information to make the decision. A committee must monitor the study on an ongoing basis to verify that the performance of the design is as expected Engage the committee early in scenario simulations prior to protocol approval. Leverage the information from disease state and exposure-response models to design studies.
10/27/2006Philadelphia ASA Chapter Meeting9 3. Traditional methods to explore dose- response Fixed-dose parallel-group designs Target: average population response at a dose shape of the population dose response curve Downside: potential to allocate a fair number of patients to several non-informative doses sample size considerations often limit the number of doses feasible to explore Fixed dose cross-over design, Forced titration design, Optional titration design Primarily aimed at learning about individual dose- response For both efficacy and safety
10/27/2006Philadelphia ASA Chapter Meeting10 4. Adaptive dose-response methods for early exploratory studies Review traditional phase I designs with respect to estimation of the Maximum Tolerated Dose (MTD) Discuss novel adaptive design approaches aimed at improving the relatively poor performance of traditional designs Majority of these methods originated from oncology There is methodological overlap with other dose response methods (late stage) applicability can be generalized from MTD determination to learning about the dose response profile for any defined response (e.g. tolerability, safety or efficacy measure) More work generally needs to be done to extend applicability beyond the area of cancer research
10/27/2006Philadelphia ASA Chapter Meeting11 Specifics of oncology Phase I trials Typically very small, uncontrolled sequential studies in patients Binary outcome: toxicity response Designed to determine the maximum tolerated dose (MTD) of the experimental drug Design challenges are driven by severe side effects of cytotoxic drugs, limited number of patients available Certain degree of side effects is acceptable, but every effort should be made to minimize exposure to highly toxic doses Balance between individual and collective ethics: maximum information from the minimal number of patients Be open-minded: the designs presented here originated in Phase I oncology But can be potentially be useful for other early development trials (e.g. efficacy assessment; dose-ranging, POC)
10/27/2006Philadelphia ASA Chapter Meeting12 Statistical modeling of efficient learning about MTD Schacter et al. (1997): “a well-designed phase I study will identify a dose at which patients can be safely treated and one which can benefit the patient”. Assumption: monotone relationship between the dosage and response Two different philosophies in MTD definition: 1.Risk of toxicity is a sample statistic, identified by the doses studied e.g., 3+3 design: MTD is highest dose studied with < 1/3 toxicities 2.Risk of toxicity is a parameter of a dose-response model e.g., dose associated with 30% incidence of toxic response Two different approaches in designing phase I clinical trials
10/27/2006Philadelphia ASA Chapter Meeting13 Summary of available methods for phase I clinical trials (Rosenberger and Haines, 2002) 1. Conventional (standard) method (Simon et al., 1997; Korn et al., 1994) 2.MTD as a quantile vs. conventional method a) Random walk rules (RWR) Durham and Flournoy (1994) b) Continual reassessment method (CRM) O’Quigley, Pepe, Fisher (1990) c) Escalation with overdose control (EWOC) Babb, Rohatko, Zacks (1998) d) Decision-theoretic approaches Whitehead and Brunier (1995) e) Bayesian sequential optimal design Haines, Perevozskaya, Rosenberger (2003) Bayesian Methods
10/27/2006Philadelphia ASA Chapter Meeting14 Learning about MTD: current and novel designs summary Key feature: Prior response data used for sequential allocation of dose/treatment to subsequent (group of) subjects Up-and-down type designs: utilize only last response in decision rule Conventional 3+3 designs for cancer (traditional) Random-walk-rule designs Bayesian type designs: all previous responses from the current study are utilized Continual Reassessment Method Other Bayesian approaches Common goal: limit allocation to extreme doses of little interest
10/27/2006Philadelphia ASA Chapter Meeting15 1. Current practice in MTD estimation: ‘conventional’ 3+3 design for cancer Designed under philosophy that MTD is identifiable from the data Patients treated in groups of 3 Designed to screen doses quickly; no estimation involved Probability of stopping at incorrect dose level is higher than generally believed (Reiner, Paoletti, O’Quigley; 1999) First 3 patients treated at initial dose If no toxicities, moves to next higher dose If ≥2 toxicities, moves to next lower dose If 1 toxicity, stays at the current dose If 1 toxicity out of 6 treated, moves to next higher dose If ≥2 toxicities out of 6 treated, moves to next lower dose
10/27/2006Philadelphia ASA Chapter Meeting design for cancer Pros (+) & Cons (-) + has been around for a long time, properties well documented - estimates MTD at ~ 20% toxicity level + Weili He, et al., show how to estimate MTD at intended 30% level - derived estimates conditional on doses yielded by design - not well suited to yield any efficacy info (not suitable for estimating any rate of response other than 30%; - yields little info above MTD
10/27/2006Philadelphia ASA Chapter Meeting17 2. Random Walk Rules (RWR) or “biased coin” designs Nonparametric model-based approach: MTD is a quantile of a certain dose-response distribution but there is no underlying parametric family. Biased-coin design generalizes the up-and-down approach of the conventional method: can target any response rate of interest (not only 30%). Similarity: patients are treated sequentially with the next higher, same, or next lower doses Difference: rule for dose escalation (probability of next dose assignment depends on previous response)
10/27/2006Philadelphia ASA Chapter Meeting18 2. Random Walk Rules (cont.) Patient j-1 assigned to dose di Toxic response Patient j assigned to dose di-1 Non-toxic response Flip a biased coin Pr (heads)=b<1/2 HEADS: next assignment di+1 TAILS: next assignment at di
10/27/2006Philadelphia ASA Chapter Meeting19 Random Walk Rule Example An example of particular case of RWR with P=1 Developed in MRL in 1980’s for efficacy dose-ranging studies called “up-and-down design” then Applied to simulated dose-ranging study in dental pain (full description in back-ups) Demonstrates ~50% reduction in sample size without big loss in precision of estimates of dose-response compared to parallel group design
10/27/2006Philadelphia ASA Chapter Meeting20 Pros (+) & Cons (-) of Random Walk Rules + Simple and intuitive to explain; easy to implement + flexible enough to target any level of response + assigned doses cluster around quantile of interest ( MTD) - Consequently, some patients will be assigned above the MTD (concern for oncology only) + minimizes observations at doses too small or too large, in comparison to randomized design - derived estimates conditional on doses yielded by design + derived info useful to design definitive studies + simulations indicate estimated response proportion at each dose is unbiased + Have workable finite distribution theory + Reliable MTD estimates can be obtained using isotonic regression - May not converge to MTD as fast as some Bayesian methods (wider spread of doses) + But, for practical considerations (safety), slow dose escalation is desirable
10/27/2006Philadelphia ASA Chapter Meeting21 3. Designs based on Bayesian methods Continual Reassessment Method (CRM) Escalation With Overdose Control (EWOC) Decision Theoretic Approaches Bayesian Optimal Sequential Design
01/25/2006Innovative Clinical Drug Development Conference 22 Continual Reassessment Method (CRM) Most known Bayesian method for Phase I trials Underlying dose-response relationship is described by a 1- parameter function For a predefined set of doses to be studied and a binary response, estimates dose level (MTD) that yields a particular proportion (P) of responses CRM uses Bayes theorem with accruing data to update the distribution of MTD based on previous responses After each patient’s response, posterior distribution of model parameter is updated; predicted probabilities of a toxic response at each dose level are updated The dose level for next patient is selected as the one with predicted probability closest to the target level of response Procedure stops after N patients enrolled Final estimate of MTD: dose with posterior probability closest to P after N patients The method is designed to converge to MTD
10/27/2006Philadelphia ASA Chapter Meeting23 Continual Reassessment Method (cont.) Choose initial estimate of response distribution & choose initial dose Obtain next Patient’s Observation Update Dose Response Model & estimate Prob. each dose Max N Reached? Next Pt. Dose = Dose w/ Prob. (Resp.) Closest to Target level no Stop. EDxx = Dose w/ Prob. (Resp.) Closest to Target level yes
10/27/2006Philadelphia ASA Chapter Meeting24 CRM Design example (1) Post-anesthetic care patients received a single IV dose of 0.25, 0.50, 0.75, or 1.00 μg/kg nalmefene. Response was Reversal of Analgesia (ROA) = increase in pain score of two or more integers above baseline on NRS after nalmefene Patients entered sequentially, starting with the lowest dose The maximum tolerated dose = dose, among the four studied, with a final mean posterior probability of ROA closest to 0.20 (i.e., a 20% chance of causing reversal) Modified continual reassessment method (iterative Bayesian proc) selected the dose for each successive pt. as that having a mean posterior probability of ROA closest to the preselected target 1-parameter logistic function for probability of ROA used to fit the data at each stage Dougherty,et al. ANESTHESIOLOGY (2000)
10/27/2006Philadelphia ASA Chapter Meeting25 CRM example (1) results * including the 1 st patient treated (MTD), i.e., estimated mean posterior probability closest to 0.20 target ^ extrapolated Dose (ug/kg) # pts.# w/ ROA% w/ ROA mean post. prob. ROA median post. prob. ROA *0 0% (MTD) 18317% % ^0.80^
10/27/2006Philadelphia ASA Chapter Meeting26 CRM example (1) results Posterior ROA Probability (with 95% probability intervals)
10/27/2006Philadelphia ASA Chapter Meeting27 Escalation with overdose control (EWOC) Assumes more flexible model for the dose-response curve in terms of two parameters: MTD probability of response at dose D1 Similar to CRM in a way the distribution it updates posterior distribution of MTD based on this two-parameter model Introduces overdose control: predicted probability of next assignment exceeding MTD is controlled (Bayesian feasible design) Assigns doses similarly to CRM, except for overdose control; this distinction is particularly important in oncology EWOC is optimal in the class of the feasible designs
10/27/2006Philadelphia ASA Chapter Meeting28 Decision-theoretic approaches Wide class of methods characterized by application of Bayesian Decision Theory to address various design goals: shorter trials, reducing number of patients, maximizing information, reducing cost etc. Similar to CRM: parametric model-based approach where the posterior distribution of model parameters is updated after addition of each new patient Uses gain functions depending on the desired goal Constructs a design maximizing the gain function using the set of “action” (pre-selected doses).
10/27/2006Philadelphia ASA Chapter Meeting29 Decision-theoretic approaches (cont.) Whitehead and Brunier (1995): Loss function minimizes asymptotic variance of MTD Two-parameter model with for dose response with prior distributions on the parameters Posterior distribution estimates of the 2 parameters used to derive next dose, i.e., that estimated to have desired response level Most versatile: CRM and Bayesian D-optimal designs can be written as special cases Can be extended for simultaneous assessments of efficacy & toxicity Patterson et al (1999) and (Whitehead et al (2001) extend this methodology in looking at pharmacokinetic data with two gain functions.
10/27/2006Philadelphia ASA Chapter Meeting30 Bayesian D-Optimal Sequential design The methodology is similar to decision-theoretic approach, i.e. principally concerned with efficiency of estimation A two parameter model is used with logistic link function defining the dose response curve Based on formal theory of optimal design (Atkinson and Donev, 1992) Optimality criterion chosen to minimizes variance of posterior distribution of model parameters Similar to EWOC, a constraint is added to address the ethical dilemma of avoiding extremely toxic doses
10/27/2006Philadelphia ASA Chapter Meeting31 Bayesian D-Optimal Sequential design (cont.) General methodology developed for the case when the dose space is unknown (continuous dose space) Case when doses are fixed in advance is particularly important in practice (discrete dose space) Sequential procedure developed consisting of: “Pilot” design stage for seeding ( small group of subjects; dose assignments based on prior information only) Subsequent assignments for each patient chosen in accordance with D-optimality criterion to maximize information from the design Posterior updated after each response and affects future dose assignments
10/27/2006Philadelphia ASA Chapter Meeting32 Simultaneous assessment of efficacy and toxicity Penalized D-optimal designs (V. Dragalin and V. Fedorov, 2005) Non-Bayesian Accomplish “learning” by sequential updating of likelihood function afetr each patient’s response D-optimality criterion (maximizing Fisher’s information) is “ driving” the design Optimization subject to constraint (reflecting ethical concerns, cost, sample size etc.) Flexibility of constraints and bivariate model allow to address a number of questions involving efficacy and safety dose-response curves simultaneously
10/27/2006Philadelphia ASA Chapter Meeting33 5. Adaptive Frequentist approaches for late stage exploratory development These designs more typically applicable to phase II studies Strongly control type I error rate 2 sources of multiplicity in adaptive dose- response trials: Multiple comparisons of various doses vs. control Multiple interim looks at the data
10/27/2006Philadelphia ASA Chapter Meeting34 5. Adaptive Frequentist approaches for late stage exploratory development (cont.) Classical group-sequential design (Jennison & Turnbull, 2000) Planned SS or information may be reduced if trial (or arm) stopped early At each interim looks test statistics compared to pre- determined boundaries Multi-arm trials: Stallard & Todd, 2003 Adaptive design ( Jennison & Turnbull, 2005; Bauer & Brannath, 2004) More flexible in adaptation->more suitable for multi- stage framework Allowed adaptations may include: sample size, modifying patient population, adapting doses
10/27/2006Philadelphia ASA Chapter Meeting35 5. Adaptive Frequentist approaches for late stage exploratory development (cont.) Further methods Use standard single-stage multiplicity adjustment; some doses may be dropped (Bretz et. al, 2006) Combining phase II/III using a surrogate (Liu & Pledger, 2005; Todd & Stallard, 2005)
10/27/2006Philadelphia ASA Chapter Meeting36 6. Developing a Bayesian adaptive dose design Key feature of all Bayesian methods: updating information as it accrues posterior updates) Calculating predictive probabilities of future results Assessing increment in information about dose-response curve depending on next dose assignment Type I error is not the focus, but can be studied via simulations Downside: computational complexity
10/27/2006Philadelphia ASA Chapter Meeting37 6. Developing a Bayesian adaptive dose design (cont.) Modeling is critical In general, no restriction on the model other than it must have parameters Prior distribution is put on model parameters can be “non-informative” or incorporate objective historical information appropriately simulations used to evaluate robustness w/respect to choice of prior As the data accrues, distribution of unknown parameters is updated (posterior)
10/27/2006Philadelphia ASA Chapter Meeting38 6. Developing a Bayesian adaptive dose design (cont.) Bayesian approaches are standard in Phase I cancer trials The methods reviewed earlier were presented in somewhat restrictive context: binary response strong safety concerns (upper-end dose restriction) monotonic dose-response curve specific parametric family for model More general Bayesian designs are gaining popularity in phase II dose-ranging studies
10/27/2006Philadelphia ASA Chapter Meeting39 6. Developing a Bayesian adaptive dose design (cont.) Example: ASTIN trial (Berry et. al 2001) Flexible model not restricting shape of D-R curve; non-monotone allowed Two-stages: dose ranging (15 doses & pbo) and confirmatory Incorporated futility analyses Long-term endpoints were handled via longitudinal model predicting patient’s long term endpoint using patient’s intermediate endpoint measurements Key advantages of BD vs. fixed (in general): finds “the right dose” more efficiently More doses can be considered If futility analysis is used -> may save resources
10/27/2006Philadelphia ASA Chapter Meeting40 7. Monitoring issues and processes in adaptive dose-response trials All adaptive trials raise issues/concerns about credibility of the trial conclusions It is beneficial to have a separate body without other direct trial responsibilities to review interim results & recommend adaptations Other precautions need to be taken limiting disclosure of specific numerical information and/or statistical methodology These recommendations are especially important in trials with potential regulatory submission (even if it is not confirmatory)
10/27/2006Philadelphia ASA Chapter Meeting41 8. Rolling Dose Studies Broad class of design and methods that allow flexible, dynamic allocation of patients to dose level as the trial progress Not a distinct set of methods Rely more on modeling and estimation rather than hypothesis testing Examples include Bayesian, D-optimality, and many more Comprehensive simulation project by PhRMA RDS working group is under way Developing different RDS methods Evaluating and comparing to traditional fixed dose finding approaches
10/27/2006Philadelphia ASA Chapter Meeting42 9. Conclusions Recommend routine assessment of appropriateness of AD in CDPs Opportunity to efficiently gain more information about D-R early in the development (POC) for maximum benefit More streamlined Phase III trial plan Reduction in timelines & cost More information at the time of filing AD are not necessarily always better than traditional fixed dose There are many choices of ADs Extensive planning, simulations, etc. Added operational and scientific complexity should be justified Planning is extremely important Limited examples of AD are available in the literature
10/27/2006Philadelphia ASA Chapter Meeting43 References Dragalin V. Adaptive designs: terminology and classification. Drug Inf J (submitted) Rosenberger WF, Haines LM. Competing designs for phase I clinical trials: a review. Stat Med. 2002;21: Durham SD, Flournoy N. Random walks for quantile estimation. In Gupta SS, Berger JO, ed. Statistical Decision Theory and Related Topics. New York: Springer;1994:467–476. O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics 1990;46:33– 48 Dougherty TB, Porche VH, Thall PF. Maximum tolerated dose of Nalmefene in patients receiving epidural fentanyl and dilute bupivacaine for postoperative analgesia. Anesthesiology 2000;92(4): Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation with overdose control. Stat Med. 1998;17: Whitehead J, Brunier H. Bayesian decision procedures for dose determining experiments. Stat Med. 1995;14: Patterson S, Jones B. Bioequivalence and Statistics in Clinical Pharmacology London: Chapman & Hall;2005. Whitehead J, Zhou Y, Stevens J, Blakey G. An evaluation of a Bayesian method of dose escalation based on bivariate binary responses. J Biopharm Stat. 2004;14(4):
10/27/2006Philadelphia ASA Chapter Meeting44 References (cont.) Haines LM, Perevozskaya I, Rosenberger WF. Bayesian optimal designs for phase I clinical trials. Biometrics 2003;59: Dragalin V, Fedorov V. Adaptive designs for dose-finding based on efficacy-toxicity response. Journal of Statistical Planning and Inference 2005;136: Jennison C, Turnbull BW. Group Sequential Methods with Applications to Clinical Trials. London: Chapman and Hall;2000. Stallard N, Todd S. Sequential designs for phase III clinical trials incorporating treatment selection. Stat Med. 2003;22: Jennison C, Turnbull BW. Meta-analyses and adaptive group sequential designs in the clinical development process. J Biopharm Stat. 2005;15: Bauer P, Brannath W. The advantages and disadvantages of adaptive designs for clinical trials. Drug Discovery Today 2004;9(8): Bretz F, Schmidli H, König F, Racine A, Maurer W. Confirmatory seamless phase II/III clinical trials with hypothesis selection at interim: General concepts. Biom J (in press). Liu Q, Pledger WG. Phase 2 and 3 combination designs to accelerate drug development. J Am Stat Assoc. 2005;100: Todd S, Stallard N. A new clinical trial design combining phases II and III: sequential designs with treatment selection and a change of endpoint. Drug Inf J. 2005;39: Berry DA, Müller P, Grieve AP, Smith M, Parke T, Blazek R, Mitchard N, Krams M. Adaptive Bayesian Designs for Dose-Ranging Drug Trials. In Gatsonis C, Carlin B, Carriquiry A ed. Case Studies in Bayesian Statistics V New York: Springer- Verlag;2001.
10/27/2006Philadelphia ASA Chapter Meeting47 Back-up set 1: Up&Down Design Definition Yields distribution of doses clustered around dose with 50% responders (ED50) 1 st subject receives dose chosen based on prior information Subsequent subjects receive next lower dose if previous subject responded, next higher dose if no response Data Summaries: proportion of responders at each dose continuous data via summary statistics by dose Inference based on conditional distribution of response given the doses yielded by the dosing scheme 5 MRL examples from 1980’s
10/27/2006Philadelphia ASA Chapter Meeting48 Up & Down Design Simulated from Past Trial Results Single-dose dental pain study (total 399 patients) 51 placebo patients 75 Dose 1 patients 76 Dose 2 patients 74 Dose 3 patients 76 Dose 4 patients 47 ibuprofen patients Primary endpoint is Total Pain Relief (AUC) during 0-8 hours post dose (TOPAR8) Up&Down design in sequential groups of 12 patients sampled from study results sorted by AN within treatment.
10/27/2006Philadelphia ASA Chapter Meeting49 Simulated Up&Down Design from completed Dental Pain Study Sequential groups of 12 patients (3 placebo, 6 test drug, 3 ibuprofen) First group receives Dose 2 Subsequent group receives next higher dose if previous group is non-response, next lower dose if response Response (both conditions satisfied): Mean test drug – mean placebo ≥ 15 units TOPAR8 Mean test drug – mean ibuprofen > 0 Algorithm continues until all ibuprofen data exhausted originally planned precision for ibuprofen vs placebo (16 groups = 191 total patients)
10/27/2006Philadelphia ASA Chapter Meeting50 Dental Pain Study Complete Results
10/27/2006Philadelphia ASA Chapter Meeting51 Simulated Up&Down Results from Dental Pain Study data (1 st 8 Groups in sequence) Test drug Dose mean of 3 placebo mean of 6 Test drug mean of 3 ibuprofen Resp/ Non-Resp Dose NR Dose R Dose R Dose NR Dose R Dose NR Dose NR Dose R
10/27/2006Philadelphia ASA Chapter Meeting52 Simulated Up&Down Results from Dental Pain Study data (last 8 Groups in sequence) Test drug dose mean of 3 placebo mean of 6 Test drug mean of 3 ibuprofen Resp/ Non-Resp Dose NR Dose NR Dose R Dose R Dose R Dose NR Dose R Dose NR
10/27/2006Philadelphia ASA Chapter Meeting55 Conclusions from Simulated Up&Down Design in Dental Pain Up&Down design is viable for dose-ranging in Dental Pain Yields similar dose-response information as parallel group design Can use substantially fewer patients than parallel group design Logistics of implementation more complicated than usual parallel group design Can be accomplished in single center or small number of centers
10/27/2006Philadelphia ASA Chapter Meeting56 Back-up slide set 2: D-optimal design implemented in a user-friendly software: iDose (Interactive Doser) Web-based application is available to any workstation equipped with a web browser (Rosenberger et al., Drug Information Journal, 2004) Nothing to install/maintain on the client side Integration with other software for patient information is easy Service-oriented architecture of web-based application Addition of high-value services is easy to deploy, update, and maintain Service can be offered by external providers Clinician access control must be implemented Low security requirements: no actual patient information transferred
10/27/2006Philadelphia ASA Chapter Meeting57 iDose Software (cont.) iDose supports long transactions Dose and toxicity of each patent reported over time Server keeps track of clients state while waiting for patients response Statistical part is implemented in Mathworks Matlab product Matlab Server product allows Matlab to run on a server as an external process accessed through Common Gateway Interface Intermediate stages are preserved as a file Clinicians use their keyword to retrieve the state where they left Any existing access control systems may be layered for additional security All parameters entered are checked for validity Dynamic, context-sensitive help provided for each parameter entered
10/27/2006Philadelphia ASA Chapter Meeting58 Simulated Bayesian D-optimal design for ED50 (iDose website) Osteoarthritis efficacy % good/excellent assumed underlying distribution Dose: %G/E: Prior estimates: ED25 between 15 and 30 mg ED50 between 30 and 60 mg 6 patients in Stage 1 for seeding purposes Optimal Design: 3 pts at 15 mg, 2 at 60 mg, 1 at 90 mg 24 subsequent patients entered sequentially at doses yielding minimum variance of ED50 estimate Responses / non-response assigned to approximate targeted %G/E distribution above
10/27/2006Philadelphia ASA Chapter Meeting60 Simulated Bayesian Optimal Design for ED50 – Summary Osteoarthritis efficacy % good/excellent assumed underlying distribution Dose: assumed %G/E: #Responses: #pts.: observed %: Bayesian estimated ED50 = 48.7mg using only 30 patients!!! However, little info about other doses due to nature of D- optimal design for ED50
10/27/2006Philadelphia ASA Chapter Meeting61 Graphic Summary of Results from iDose software