Disclaimer This presentation expresses my personal views on this topic and must not be interpreted as the regulatory views or the policy of the FDA

Size: px
Start display at page:

Download "Disclaimer This presentation expresses my personal views on this topic and must not be interpreted as the regulatory views or the policy of the FDA"

Transcription

1 On multiplicity problems related to multiple endpoints of controlled clinical trials Mohammad F. Huque, Ph.D. Div of Biometrics IV, Office of Biostatistics OTS, CDER/FDA JSM, Vancouver, August 2010 Disclaimer This presentation expresses my personal views on this topic and must not be interpreted as the regulatory views or the policy of the FDA 2

2 Clinical trials poses diverse multiplicity problems For example: investigate treatment effects for more than one endpoint measured at different time points evaluate treatments at several dose levels compare treatment to control for non-inferiority and superiority on multiple endpoints and doses. perform subgroup analysis carry out analysis by baseline and demographic factors assess regional differences select models conduct interim analysis make design modifications, etc. These and other multiple testing activities pose multiplicity problems of different complexity 3 Evolution of creative statistical methods Clinical trial multiple testing problems can be framed into testing of hierarchical families of hypotheses (F 1 F 2 F n ) A number of new methods and concepts since the two articles: 1. Westfall and Krishen (JSPI, 2001): Optimally weighted, fixed sequnence, and gatekeeping multiple testing procedures 2. Dmitrienko, Offen and Westfall (Stat in Med, 2003): Gatekeeping strategies that do not require all primary effects to be significant 4

3 Last 3 years new useful statistical methods on Recycling of alpha from one family to the next (on using Bonferroni and truncated Holm s method) Graphical approaches Hybrid methods (e.g., combining the Bonferroni and Holm s critical values) and the concept of separability Computation of adjusted p-values for any complex hierarchical testing method, e.g., gatekeeping testing schemes Lower limit for 1-sided confidence intervals for step-up and stepdown procedures Adaptive alpha allocation approach (the 4A method) Partitioning principle based testing strategies Methods for subgroup analysis Consistency ensured (adaptive) methods Others (e.g., related to interim analyses and adaptive designs) 5 Some key statistical principles/ concepts underlying new methods Union-Intersection (UI) and Intersection-Union (IU) testing principles Closed testing principle Partitioning principle Gatekeeping principles Graphical concept of transporting alpha from one hypothesis to others (has led to improvements in the fallback methods) Separability concept for recycling of unused alpha from one family to the next Adaptive alpha allocation concept 6

4 Rest of the presentation on: multiple endpoints of confirmatory trials Outline Distinction between primary and secondary endpoints Concept of clinical win for efficacy When is it necessary to adjust for multiplicity and when is it not? Types of FWER control for treatment benefit claims Methods for primary endpoints The issue of Type I error adjustments for secondary endpoints Co-primary and composite endpoints issues Concluding remarks 7 Distinction between primary and secondary endpoints Primary endpoints: These are critical endpoints such that unless there is clinically meaningful and statistically significant evidence of efficacy in one or more of these endpoints for the study treatment, there is no justification for a claim. These endpoints can either form a single family or multiple hierarchical families depending for example on their relative importance and power considerations, and the win criteria 8

5 Distinction between primary and secondary endpoints (cont d) Primary endpoints: A primary endpoint is a primary endpoint, it can not be called a secondary or key secondary E.g., If mortality is a primary endpoint for an oncology indication, It can not be a key secondary or secondary because of power considerations. In this case, it can take the position of the second primary endpoint in the hierarchy, the first primary endpoint will then be the one that will have greater likelihood of success No efficacy claim: if no statistically significant and clinically meaningful evidence of treatment benefit on one or more primary endpoints 9 Secondary endpoints Not sufficient to support efficacy in the absence of an effect on one or more primary endpoints. However, the secondary endpoints can provide additional claims and other important clinical information 10

6 Efficacy win criteria Simply triaging endpoints to primary and secondary is not sufficient. The trial should specify a win scenario for the set of primary endpoints that determines whether or not the trial has met its efficacy objectives. Examples of efficacy win criteria: 1) All specified primary endpoints needs to show clinically meaningful and statistically significant treatment efficacy 2) At least one of the specified primary endpoints need to show clinically meaningful and statistically significant treatments efficacy 3) A pre-specified subset of primary endpoints need to show clinically meaningful and statistically significant treatment efficacy. (More examples in Chapter 1 of the book: Multiple testing problems in pharmaceutical statistics; Edts., Dmitrienko, Tamhane and Bretz, 2010, CRC Press), 11 When multiplicity adjustments are not necessary 1. When the trial specifies a single primary or single composite endpoint for a claim of treatment efficacy 2. All specified primary endpoints need to show clinically relevant treatment benefits. o No type I error rate inflation concern, but concern about the type II error rate. 3. Primary endpoints are hierarchically ordered and are tested in a fixed-sequence. o If the earlier endpoints in the sequence are under powered, the procedure is likely to stop early and miss the opportunity to evaluate treatment effects for latter potentially useful endpoints. 12

7 Multiple analyses for the ITT data set (for the same endpoint and the method) Irregularities are common in the intention-to-treat (ITT) data set because of: Some patients may drop early Some may fail protocol criteria Some may not take medications as prescribed Some may take concomitant medications Usual Dilemma: How to deal with these irregularities? As the true endpoint measurements for these cases are unknown, there is a usual concern about bias in the result. Therefore, multiple analyses are done for same endpoint on varying the assumptions about these unknown measurements As the purpose of these analyses is to investigate the extent of bias, there is no multiplicity adjustment. 13 Analyses of the same endpoint data by alternative methods Analysis of the same endpoint by alternative methods, in addition to the analysis by the pre-specified method, e.g., analysis of the same time-to-event endpoint by log-rank test and by the generalized Wilcoxon test analysis of variance on excluding/including certain design factors. analysis by the parametric and non-parametric methods Technically, one can adjust for these multiple analyses if they were pre-specified. However, this is rarely done, as the purpose of these analyses is usually to demonstrate that the results found are robust and hold regardless of different methods applied 14

8 Other situations Correction for bias: imbalance in certain key risk factors (pre-specification needed) Performing a less conservative after a conservative analysis (e.g., ITT analysis ) is significant: for better estimate of the size of the treatment effect and the statistical strength Descriptive analyses: E.g., for interpreting the result of an analysis of a primary or a secondary endpoint. E.g., After the result for a continuous endpoint is significant showing the results by response categories E.g., Forest plot for a visual demonstration of consistency of results by baseline risk factor or by center and region (caution: some results may go in wrong direction by chance) 15 When is it necessary to adjust for multiplicity? When the type I error rate inflates as a result of multiple ways to achieve a successful outcome Example: CHF trial with 2 PEs (death, hospitalizations) Success criterion: superiority of the treatment to control for at least one of the two endpoints; Each endpoint tested at the 0.05 level FWER can be as high as ; an unaccepted trial alpha level for making regulatory decisions. 16

9 The issue of FWER control for the primary and secondary endpoint families Should there be separate FWER control for the primary endpoint families and separate FWER control for the secondary endpoint families? E.g., Allocate alpha = 0.05 for the primary endpoint families Allocate separate alpha = 0.05 for the secondary endpoint families. But test secondary endpoints only after statistically significant and clinically meaningful evidence of treatment benefit by one or more primary endpoints 17 Benefits? Reduction in type II error for secondary endpoint analyses after the drug can be approved based on the results of one or more primary endpoints No influence of the secondary endpoints on the results of the primary endpoints 18

10 FWER control concepts: weak or strong? Concepts easy for statisticians with some training in multiplicity Difficult for statisticians without training in multiplicity Concepts very difficult for clinicians 19 Weak and strong FWER control approaches differ in critical respects Strong FWER control: Assure that conclusion of success on an endpoint can be interpreted as a conclusion that alpha for that endpoint is < 0.05, regardless of the size of the treatment effects in other endpoints. Statistically: the probability of at least one type I error < 0.05 across null hypotheses configurations (complete and partial ones) Weak FWER control: Control of alpha at level 0.05 for the conclusion that some endpoints (either individually or collectively) have treatment effects. Null hypothesis: no effect in any endpoint No intention to identify or to claim as to which endpoints have treatments or which win scenario makes it. 20

11 Regulatory applications Require strong FWER control for the primary as well as secondary families Except perhaps in rare situations for serious diseases, when weak FWER control may be OK E.g., treatment of stroke trials; Tilley et al., 1996) 21 Which analysis methods for primary endpoint families? Methods should be valid for independent as well as for correlated endpoints and for any joint distribution of test statistics or p-values Examples: Bonferroni Holm s PAAS (for positively correlated endpoints) Sequential testing method Bonferroni based gatekeeping procedures (Dmitrienko et al. and others) (Sequentially rejective) graphical approach (Bretz et al., 2009) Other methods (e.g., truncated Holm s, fallback, etc.) Note: Hochberg procedure generally not recommended: Known to fail FWER control in the strong sense for some correlation structures 22

12 About the Bonferroni method Not all that conservative When the number of endpoints m = 2 to 5, and correlation = 0.3 or less Alpha adjustments (i.e., 0.05/m) may seem much but the type II error can be small if success criterion is to win in at least one of the m endpoints. Example (2-arm trial, 2 endpoints): Consider a single endpoint trial: alpha = 0.025, test = 1-sided Z-test, power = 90%, and delta (per unit s.d.) = 0.5, then n = 84 per treatment arm. Consider a 2-enpoint trial, each endpoint test at level alpha = 0.025/2 = , delta1 = delta 2 =0.5, r = 0.6, assume n = 84 per treatment arm, then Power (win in at least one of the two endpoints) = 92.7% 23 Benefits of the Bonferroni or Bonferronibased methods Simple to explain to non-statisticians A finding that survives a Bonferroni adjustment is generally considered a credible trial outcome Complex gatekeeping methods simplifies to simple useful shortcut methods. Its critical values can combine with the critical values of alpha-exhaustive methods (e.g., Holm s) leading to (truncated) tests with more power and flexibility to test subsequent families Confidence intervals computation easy. (Very much needed for benefit-risk assessments) Etc. 24

13 Use of resampling methods for endpoints with high correlations (e.g. 0.60) A popular a resampling based step-down procedure: Step 1: Rejects H (1) associated with p (1) if Pr{ min(p 1, P 2,, P m ) p (1) } α Step j = 2,, m: Rejects H (j) associated with p (i) if Pr{ min(p j, P j+1,, P m ) p (j) } α Step m: Rejects H (m) associated with p (m) if Pr{ P m p (m) } α Stop further testing when 1 st time condition not met Probabilities calculated from the resampling distributions of the minimum P-value test statistics 25 Concerns regarding resampling methods Results approximate, requiring large sample sizes and usually simulations are required to validate the results Computation can be difficult (e.g., for time-to-event endpoints) Strong control of the overall type I error rate is achieved under the assumption of subset pivotality condition - hard to justify for some cases Ref: Westfall and Troendle (2008; multiple testing with minimal assumptions); Huang et al. (2006; Bioinformatics; permute or not to permute) 26

14 Co-primary endpoints No reverse multiplicity issue; test each endpoint at 0.05 level to control FWER at the 0.05 level Inflation of the type II error recognized. Limit the number of co-primary endpoints to 2 for the claimed indication (if clinically acceptable) 27 Co-primary endpoints (cont d) More than two co-primary endpoints: When clinically necessary to do so Expected effect sizes are such that trial sample sizes are practical. Cases of strong treatment effects in some (e.g., p-value < 0.01), but weak in some (i.e., p-values slightly > 0.05): OK on case-by-case basis if replicated evidence or presence of other clinical evidence. 28

15 Composite event endpoint as a primary endpoint (Several motivations; Moye, 2003) Reduces multiple endpoints to a single PE (if clinically meaningful to do so) Can reduce the size of the trial if certain conditions met Components increase the number of events in non-overlapping manner (i.e., an event is not a direct consequence of the other) Some homogeneity of treatment effects across components, or components jointly enhance the overall treatment effect Can addresses a broader aspects of a multifaceted disease Can change the focus of the trial in discovering clinically meaningful small treatment effects that collectively demonstrate a statistically significant benefit of the treatment 29 Interpretation of the composite PE result? Example 1: 2-arm trial: treatment A versus control, composite PE = (death, MI and revascularization) Results: Composite: in favor of treatment A (2p=0.008). OR = 0.67 death: in favor of control (2p=0.07) OR = 1.8 MI: no difference (2p=0.9) OR = 0.98 revascularization: in favor of treatment A (2p=0.0001). OR = 0.34 Comment: The composite PE result provides an inflated (and misleading in this case) notion of benefit of treatment A. Clinically relevant component is going in the wrong direction. 30

16 Some ideas (previous example) 1. Do the heterogeneity analysis and reject the composite analysis: No evidence of favorable treatment effect in clinically important components (either individually or jointly) which are like primary endpoints, and Result of the composite driven by soft endpoints which are like secondary endpoints clinically not relevant unless some movement in the direction of benefit for clinically important components E.g., analyze the sub-composite of clinically important components which are like primary endpoints 31 More ideas (composite endpoints) 2. The conventional composite analysis assumes equal weight for the components. Assign clinical utility weights, e.g., death weighted as 0.7, MI as 0.2 and revascularization as 0.1: Issue: How will this impact the power of the study when down weighting the most frequent component? 3. All components clinically important but some occur less frequently: Analyze components and accept p<0.05 for the most frequent component and set a margin for acceptable inferiority for the most relevant (least prevalent) component e.g., upper CI for mortality odds ratio not to exceed

17 Example 2: PROactive trial in type II diabetes (Lancet 2005) Composite (endpoint A)= (all-cause mortality, non-fatal MI, stroke, acute coronary syndrome, endovascular or surgical intervention in the coronary or leg arteries, and amputation above the ankle) Endpoint B = (all-cause mortality, non-fatal MI and stroke). Results: P A = and P B = Statistical methods: Fixed sequence method (problematic), No test for B Fallback method (α = 0.05, α A = 0.04, α B = 0.01), Not Significant 4A method (α = 0.05, α A = 0.04, α B = 0.032), Significant Consistency-ensured method (1-sided test): (α = 0.025, α A = 0.02, α B = ), α* = 0.10), Significant 33 Concluding Remarks 1. PEs vs. SEs differ in concept and purpose Efficacy of a treatment is derived on demonstrating clinically meaningful and statistically results in one or more primary endpoints that satisfies a pre-defined clinical win scenario. Secondary endpoints are not suitable for this special purpose. 2. Multiplicity in efficacy analyses kicks in when multiple ways to win for efficacy Causes inflation of the type I error rate requiring statistical adjustments for its control Many useful statistical approaches to handle this 3. Clinical trials also pose multiple testing situations when multiplicity adjustment in not necessary 34

18 Concluding Remarks 4. Multiplicity adjustment approaches: Necessary to use methods that control FWER control in strong sense for making endpoint specific claims of treatment benefits. The strategy of separate FWER control for the family of secondary endpoints may be reasonable, after clinically meaningful and statistically significant treatment efficacy already concluded based on primary endpoint. For primary endpoint families: use methods that are valid for independent as well as for correlated endpoints and for any joint distribution of test statistics Resampling based methods may not be used for primary endpoints reasons addressed Bonferroni or Bonferroni-based gatekeeping methods have advantages 35 Concluding Remarks 5. Co-primary endpoints issues: No reverse multiplicity adjustment. Control of alpha necessary at 0.05 level. Some flexibility on the case-by-case basis when number of co-primary endpoints greater than 2 with some additional sources of evidence 6. Composite endpoint issues Widespread interest, particularly, for cardiovascular trials Interpretation difficulties when: components of widely different importance; low frequency for important and high frequency for less important components; marked heterogeneity of treatment effects across components Some statistical approaches but more research needed 36

Some key multiplicity questions on primary and secondary endpoints of RCCTs and possible answers

Some key multiplicity questions on primary and secondary endpoints of RCCTs and possible answers Some key multiplicity questions on primary and secondary endpoints of RCCTs and possible answers Mohammad F. Huque, Ph.D. Div of Biometrics IV, Office of Biostatistics OTS, CDER/FDA Basel Statistical Society,

More information

Key multiplicity concepts and principles addressed in the Draft Guidance: Multiple Endpoints in Clinical Trials

Key multiplicity concepts and principles addressed in the Draft Guidance: Multiple Endpoints in Clinical Trials Key multiplicity concepts and principles addressed in the Draft Guidance: Multiple Endpoints in Clinical Trials Mohammad F. Huque, Ph.D. Office of Biostatistics OTS, CDER/FDA DIA Annual Meeting, Boston,

More information

Novel multiple testing procedures for structured study objectives and families of hypotheses a case study

Novel multiple testing procedures for structured study objectives and families of hypotheses a case study Novel multiple testing procedures for structured study objectives and families of hypotheses a case study Guenther Mueller-Velten Novartis Pharma AG EMA Workshop on Multiplicity Issues in Clinical Trials

More information

(Draft) Guideline on multiplicity issues in clinical trials

(Draft) Guideline on multiplicity issues in clinical trials www.pei.de (Draft) Guideline on multiplicity issues in clinical trials Forum Biomedizinische Arzneimittel 14.06.2017 The Paul-Ehrlich-Institut is an Agency of the German Federal Ministry of Health. Statistical

More information

FDA S DRAFT GUIDANCE ON MULTIPLE ENDPOINTS IN CLINICAL TRIALS: OVERVIEW, RECEPTION AND NEXT STEPS. John Scott, Ph.D. FDA/CBER 5 October 2017

FDA S DRAFT GUIDANCE ON MULTIPLE ENDPOINTS IN CLINICAL TRIALS: OVERVIEW, RECEPTION AND NEXT STEPS. John Scott, Ph.D. FDA/CBER 5 October 2017 FDA S DRAFT GUIDANCE ON MULTIPLE ENDPOINTS IN CLINICAL TRIALS: OVERVIEW, RECEPTION AND NEXT STEPS John Scott, Ph.D. FDA/CBER 5 October 2017 Disclaimer 2 This presentation reflects the views of the author

More information

Session 4: Statistical considerations in confirmatory clinical trials II

Session 4: Statistical considerations in confirmatory clinical trials II Session 4: Statistical considerations in confirmatory clinical trials II Agenda Interim analysis data monitoring committees group sequential designs Adaptive designs sample size re-estimation Phase II/III

More information

Analysis of Clinical Trials with Multiple Objectives

Analysis of Clinical Trials with Multiple Objectives Analysis of Clinical Trials with Multiple Objectives Alex Dmitrienko (Mediana Inc) Regulatory Industry Statistics Workshop September 2017 Outline Regulatory guidelines FDA and EMA draft guidance documents

More information

Bios 6648: Design & conduct of clinical research

Bios 6648: Design & conduct of clinical research Bios 6648: Design & conduct of clinical research Section 3 - Essential principle (randomization) 3.4 Trial monitoring: Interim decision and group sequential designs Bios 6648- pg 1 (a) Recruitment and

More information

Published online: 07 Jan 2009.

Published online: 07 Jan 2009. This article was downloaded by: [American University of Beirut] On: 20 September 2014, At: 07:55 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered

More information

Adaptive Dose Ranging Studies:

Adaptive Dose Ranging Studies: Adaptive Dose Ranging Studies: Flexible, Adaptive Dose-Finding Designs Frank Bretz and José Pinheiro Novartis Pharmaceuticals Tokyo University of Science, July 28, 2006 Outline Background and motivation

More information

Missing Data Handling in Non-Inferiority & Equivalence Trials

Missing Data Handling in Non-Inferiority & Equivalence Trials Missing Data Handling in Non-Inferiority & Equivalence Trials A systematic review Brooke A Rabe Graduate Interdisciplinary Program in Statistics The University of Arizona, Tucson, USA PSI s Pharmaceutical

More information

Combining Phase IIb and Phase III. Clinical Trials. Christopher Jennison. Partnerships in Clinical Trials,

Combining Phase IIb and Phase III. Clinical Trials. Christopher Jennison. Partnerships in Clinical Trials, Combining Phase IIb and Phase III Clinical Trials Christopher Jennison Department of Mathematical Sciences, University of Bath, UK http://people.bath.ac.uk/mascj Partnerships in Clinical Trials, Brussels,

More information

How To Design A Clinical Trial. Statistical Analysis. Gynecologic Cancer InterGroup

How To Design A Clinical Trial. Statistical Analysis. Gynecologic Cancer InterGroup How To Design A Clinical Trial Statistical Analysis Andrew Embleton PhD student/medical Statistician MRC Clinical Trials Unit at UCL At what points do you need to consider statistics? At what points do

More information

Clinical Study Synopsis for Public Disclosure

Clinical Study Synopsis for Public Disclosure abcd Clinical Study Synopsis for Public Disclosure This clinical study synopsis is provided in line with s Policy on Transparency and Publication of Clinical Study Data. The synopsis - which is part of

More information

Key Multiplicity Issues in Clinical Trials. Alex Dmitrienko (Mediana Inc) Biopharmaceutical Section s webinar series, June 2017

Key Multiplicity Issues in Clinical Trials. Alex Dmitrienko (Mediana Inc) Biopharmaceutical Section s webinar series, June 2017 Key Multiplicity Issues in Clinical Trials Alex Dmitrienko (Mediana Inc) Biopharmaceutical Section s webinar series, June 2017 Outline Key topics Overview of multiplicity issues in Phase III trials Traditional

More information

Evidentiary Considerations for Integration of Biomarkers in Drug Development : Statistical Considerations

Evidentiary Considerations for Integration of Biomarkers in Drug Development : Statistical Considerations Evidentiary Considerations for Integration of Biomarkers in Drug Development : Statistical Considerations August 21. 2015 Aloka Chakravarty, PhD Office of Biostatistics, OTS, CDER U.S. Food and Drug Administration

More information

Multiplicity Guidelines

Multiplicity Guidelines Multiplicity Guidelines Alex Dmitrienko (Mediana Inc) NISS-Merck Meet-Up September 2017 Regulatory Guidelines Regulatory guidelines FDA guideline Draft guidance on multiplicity issues in clinical trials

More information

Adaptive Design for Clinical Trials

Adaptive Design for Clinical Trials Adaptive Design for Clinical Trials Mark Chang Millennium Pharmaceuticals, Inc., Cambridge, MA 02139,USA (e-mail: Mark.Chang@Statisticians.org) Abstract. Adaptive design is a trial design that allows modifications

More information

Field trial with veterinary vaccine

Field trial with veterinary vaccine ١ Field trial with veterinary vaccine Saeedeh Forghani,, M.D. Clinical Trial and Ethics Department Human Health Management Deputy of Quality Assurance 89/4/2 ٢ ٣ Introduction: The efficacy and safety shall

More information

Moving Forward. Adaptive Eligibility Criteria, Alternate Trial Designs, and Subgroup Analysis. Elizabeth Garrett-Mayer, PhD

Moving Forward. Adaptive Eligibility Criteria, Alternate Trial Designs, and Subgroup Analysis. Elizabeth Garrett-Mayer, PhD Moving Forward Adaptive Eligibility Criteria, Alternate Trial Designs, and Subgroup Analysis Elizabeth Garrett-Mayer, PhD Eligibility Trade-offs Broad eligibility Pros: Heterogeneous group Can generalize

More information

The Promise of Novel Clinical Trial Designs. Michael Parides, Ph.D. Mount Sinai School of Medicine

The Promise of Novel Clinical Trial Designs. Michael Parides, Ph.D. Mount Sinai School of Medicine The Promise of Novel Clinical Trial Designs Michael Parides, Ph.D. Mount Sinai School of Medicine Productivity New Drug Approvals (NMEs) R&D Spending (billions) $12 $13 $13 $15 $49 $38 $39 $43 $30 $32

More information

Multiplicity Considerations in Clinical Trials

Multiplicity Considerations in Clinical Trials The new england journal of medicine Review Article Dan L. Longo, M.D., Editor STATISTICS IN MEDICINE Multiplicity Considerations in Clinical Trials Alex Dmitrienko, Ph.D., and Ralph B. D Agostino, Sr.,

More information

DMC member experience: studies with adaptive designs. P.Bauer Medical University of Vienna December 2007

DMC member experience: studies with adaptive designs. P.Bauer Medical University of Vienna December 2007 DMC member experience: studies with adaptive designs P.Bauer Medical University of Vienna December 2007 A typical application: Dose selection and confirmative inference (the critical issue of combining

More information

Facilitating Antibacterial Drug Development: Bayesian vs Frequentist Methods

Facilitating Antibacterial Drug Development: Bayesian vs Frequentist Methods Facilitating Antibacterial Drug Development: Bayesian vs Frequentist Methods Scott S. Emerson, M.D., Ph.D. Professor of Biostatistics University of Washington The Brookings Institution May 9, 2010 First:

More information

Cost-effectiveness and cost-utility analysis accompanying Cancer Clinical trials. NCIC CTG New Investigators Workshop

Cost-effectiveness and cost-utility analysis accompanying Cancer Clinical trials. NCIC CTG New Investigators Workshop Cost-effectiveness and cost-utility analysis accompanying Cancer Clinical trials NCIC CTG New Investigators Workshop Keyue Ding, PhD. NCIC Clinical Trials Group Dept. of Public Health Sciences Queen s

More information

ONE PART OF THE WHOLE: ANALYTICAL SIMILARITY & TOTALITY OF EVIDENCE

ONE PART OF THE WHOLE: ANALYTICAL SIMILARITY & TOTALITY OF EVIDENCE ONE PART OF THE WHOLE: ANALYTICAL SIMILARITY & TOTALITY OF EVIDENCE KATHERINE GIACOLETTI MIDWEST BIOPHARMACEUTICAL STATISTICS WORKSHOP, MAY 14-16 2018 INDIANAPOLIS, IN AGENDA Regulatory framework for biosimilarity

More information

Eisuke Hida & Yuki Ando Biostatistics group Pharmaceuticals and Medical Devices Agency

Eisuke Hida & Yuki Ando Biostatistics group Pharmaceuticals and Medical Devices Agency Eisuke Hida & Yuki Ando Biostatistics group Pharmaceuticals and Medical Devices Agency This is not an official PMDA guidance or policy statement. No official support or endorsement by the PMDA is intended

More information

Case studies in the design, analysis and interpretation of non-inferiority trials

Case studies in the design, analysis and interpretation of non-inferiority trials Case studies in the design, analysis and interpretation of non-inferiority trials Krishan Singh, Ph.D. GlaxoSmithKline EFSPI Verona, Nov '08 1 Outline Introduction & Background Case Studies Altabax a topical

More information

Dichotomizing Continuous Biomarkers in the Co- Development of Drug and Companion Diagnostics: Practical Considerations

Dichotomizing Continuous Biomarkers in the Co- Development of Drug and Companion Diagnostics: Practical Considerations Dichotomizing Continuous Biomarkers in the Co- Development of Drug and Companion Diagnostics: Practical Considerations Liang Fang, Adarsh Joshi, Huan Wang, Yafeng Zhang Gilead Sciences, Inc. Co-Development

More information

Regulatory aspects of model based dose selection

Regulatory aspects of model based dose selection Regulatory aspects of model based dose selection Norbert Benda Disclaimer: Views expressed in this presentation are the author's personal views and not necessarily the views of BfArM Does the regulator

More information

Heterogeneity Random and fixed effects

Heterogeneity Random and fixed effects Heterogeneity Random and fixed effects Georgia Salanti University of Ioannina School of Medicine Ioannina Greece gsalanti@cc.uoi.gr georgia.salanti@gmail.com Outline What is heterogeneity? Identifying

More information

Short Course: Adaptive Clinical Trials

Short Course: Adaptive Clinical Trials Short Course: Adaptive Clinical Trials Presented at the 2 Annual Meeting of the Society for Clinical Trials Vancouver, Canada Roger J. Lewis, MD, PhD Department of Emergency Medicine Harbor-UCLA Medical

More information

Jennifer Pulley (Statistical Scientist)

Jennifer Pulley (Statistical Scientist) A bioequivalence study design in Immunology which includes the option for sample size reestimation (SSR) at the Interim Analysis Jennifer Pulley (Statistical Scientist) Overview Study design Interactions

More information

Session 302, JSM 2013: Key Subgroup Analysis Issues in Clinical Trials, Discussion

Session 302, JSM 2013: Key Subgroup Analysis Issues in Clinical Trials, Discussion Session 302, JSM 2013: Key Subgroup Analysis Issues in Clinical Trials, Discussion Olga Marchenko CSDD, Innovation Copyright 2013 Quintiles Ilya Lipkovich, Overview of Subgroup Identification Approaches

More information

Response Adjusted for Duration of Antibiotic Risk (RADAR) Scott Evans, Ph.D., M.S. Harvard University

Response Adjusted for Duration of Antibiotic Risk (RADAR) Scott Evans, Ph.D., M.S. Harvard University Response Adjusted for Duration of Antibiotic Risk (RADAR) Scott Evans, Ph.D., M.S. Harvard University CTTI Statistical Think Tank Expert Meeting November 19, 2014 Special Thank You Kunal Merchant Dan Rubin

More information

Bayesian Submissions to FDA and the Evidentiary Standard for Effectiveness: the CDRH Experience

Bayesian Submissions to FDA and the Evidentiary Standard for Effectiveness: the CDRH Experience Bayesian Submissions to FDA and the Evidentiary Standard for Effectiveness: the CDRH Experience Gregory Campbell, Ph.D. President, GCStat Consulting, LLC Former Director, Division of Biostatistics Center

More information

Designs for Clinical Trials

Designs for Clinical Trials Designs for Clinical Trials Design issues Research question Statistical optimality not enough! Use simulation models! Confounding Objective Control Statistics Design, Variables Ethics Variability Cost

More information

STATISTICAL METHODS FOR ADAPTIVE DESIGNS

STATISTICAL METHODS FOR ADAPTIVE DESIGNS STATISTICAL METHODS FOR ADAPTIVE DESIGNS Workshop on flexible designs for diagnostic studies Göttingen, 6-7 November 2017 Tim Friede Department of Medical Statistics University Medical Center Göttingen

More information

DMC membership experience. P.Bauer Basel May 2016

DMC membership experience. P.Bauer Basel May 2016 DMC membership experience P.Bauer Basel May 2016 EMA GUIDELINE ON DATA MONITORING COMMITTEES Clinical trials frequently extend over a long period of time. Thus, for ethical reasons it is desirable to ensure

More information

Nuts & Bolts of Clinical Trials, DSMBs, Event Committees, Core Labs and Data Standards

Nuts & Bolts of Clinical Trials, DSMBs, Event Committees, Core Labs and Data Standards Nuts & Bolts of Clinical Trials, DSMBs, Event Committees, Core Labs and Data Standards Ron Waksman, MD Professor of Medicine, Georgetown University Associate Chief of Cardiology, Washington Hospital Center

More information

Introduction to the Design and Evaluation of Group Sequential

Introduction to the Design and Evaluation of Group Sequential SSCR ntroduction to the Design and Evaluation of Group Sequential Session 1 - Scientific Setting and mplications Presented July 27, 2016 Daniel L. Gillen Department of Statistics University of California,

More information

Formalizing Study Design & Writing Your Protocol. Manish A. Shah, MD Weill Cornell Medicine Center for Advanced Digestive Care

Formalizing Study Design & Writing Your Protocol. Manish A. Shah, MD Weill Cornell Medicine Center for Advanced Digestive Care TWIST: Formalizing Study Design & Writing Your Protocol Manish A. Shah, MD Weill Cornell Medicine Center for Advanced Digestive Care Presented by the Joint Clinical Trials Office/Quality Assurance Unit

More information

Nonparametric Stepwise Procedure for Identification of Minimum Effective Dose (MED)

Nonparametric Stepwise Procedure for Identification of Minimum Effective Dose (MED) International Journal of Statistics and Systems ISSN 097-675 Volume, Number (06), pp. 77-88 Research India Publications http://www.ripublication.com Nonparametric Stepwise Procedure for Identification

More information

BIOSTATISTICAL METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH

BIOSTATISTICAL METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH BIOSTATISTICAL METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH Phase 0 Trials: EARLY-PHASE CLINICAL TRIALS CLINICAL PHASE Clinical Studies: Class of all scientific approaches to evaluate Disease Prevention,

More information

Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial

Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial Gu Mi, Ph.D. Global Statistical Sciences Eli Lilly and Company, Indianapolis, IN 46285 mi_gu@lilly.com

More information

Adaptive Model-Based Designs in Clinical Drug Development. Vlad Dragalin Global Biostatistics and Programming Wyeth Research

Adaptive Model-Based Designs in Clinical Drug Development. Vlad Dragalin Global Biostatistics and Programming Wyeth Research Adaptive Model-Based Designs in Clinical Drug Development Vlad Dragalin Global Biostatistics and Programming Wyeth Research 2007 Rutgers Biostatistics Day February 16, 2007 Outline Definition and general

More information

A Life-cycle Approach to Dose Finding Studies

A Life-cycle Approach to Dose Finding Studies A Life-cycle Approach to Dose Finding Studies Rajeshwari Sridhara, Ph.D. Director, Division of Biometrics V Center for Drug Evaluation and Research, USFDA This presentation reflects the views of the author

More information

SECTION 11 ACUTE TOXICITY DATA ANALYSIS

SECTION 11 ACUTE TOXICITY DATA ANALYSIS SECTION 11 ACUTE TOXICITY DATA ANALYSIS 11.1 INTRODUCTION 11.1.1 The objective of acute toxicity tests with effluents and receiving waters is to identify discharges of toxic effluents in acutely toxic

More information

Docket #: FDA-2018-D-3268

Docket #: FDA-2018-D-3268 Subject: Comment on FDA Draft Guidance for Industry Titled Rare Diseases: Early Drug Development and the Role of Pre-Investigational New Drug Application Meetings Docket #: FDA-2018-D-3268 ARM is an international

More information

EastAdapt: Software for

EastAdapt: Software for EastAdapt: Software for Adaptive Sample Size Re-estimation Cytel Webinar Cambridge, MA Cyrus R. Mehta President, Cytel Corporation May 16, 2009 email: mehta@cytel.com web: www.cytel.com tel: 617-661-2011

More information

Bayesian Designs for Clinical Trials in Early Drug Development

Bayesian Designs for Clinical Trials in Early Drug Development Vol. 3, No. 6, June 007 Can You Handle the Truth? Bayesian Designs for Clinical Trials in Early Drug Development By St. Clare Chung and Miklos Schulz Introduction A clinical trial is designed to answer

More information

Adaptive Design for Medical Device Development

Adaptive Design for Medical Device Development Adaptive Design for Medical Device Development A guide to accelerate clinical development and enhance portfolio value Executive Summary In May 2015, the FDA released a draft guidance document regarding

More information

FDA Drug Approval Process Vicki Seyfert-Margolis, Ph.D.

FDA Drug Approval Process Vicki Seyfert-Margolis, Ph.D. Speaker Comparing The Effectiveness Of New Drugs: Should The FDA Be Asking 'Does It Work' Or 'Does It Work Better'? Vicki L. Seyfert-Margolis, PhD Senior Advisor, Science Innovation and Policy U.S. Food

More information

How to Construct an Optimal Interim Report: What the DMC Does and Doesn t Need to Know

How to Construct an Optimal Interim Report: What the DMC Does and Doesn t Need to Know How to Construct an Optimal Interim Report: What the DMC Does and Doesn t Need to Know April 19, 2017 Jim Neaton University of Minnesota 1 Disclosures Over the last 20+ years for multiple trials of HIV

More information

Interim Analysis of Randomized Clinical Trials. David L DeMets, PhD

Interim Analysis of Randomized Clinical Trials. David L DeMets, PhD Interim Analysis of Randomized Clinical Trials David L DeMets, PhD Need for Data Monitoring Phase I Trials (dose) Monitoring usually at local level Phase II Trials (activity) Most monitoring at local level

More information

Safety Monitoring and Evaluation in Late Phase Clinical Development: An Application in OA Pain

Safety Monitoring and Evaluation in Late Phase Clinical Development: An Application in OA Pain Safety Monitoring and Evaluation in Late Phase Clinical Development: An Application in OA Pain José Pinheiro, Janssen R&D Joint work with Camille Orman, Steven Wang, and Elena Polverejan Janssen R&D Trends

More information

Regulatory Perspective on the Value of Bayesian Methods

Regulatory Perspective on the Value of Bayesian Methods American Course on Drug Development and Regulatory Sciences Substantial Evidence in 21st Century Regulatory Science Borrowing Strength from Accumulating Data April 21, 2016 Regulatory Perspective on the

More information

Scottish Medicines Consortium

Scottish Medicines Consortium Scottish Medicines Consortium fondaparinux sodium, 2.5mg/0.5ml solution for injection, pre-filled syringe (Arixtra ) No. (420/07) GlaxoSmithKline 09 November 2007 The Scottish Medicines Consortium has

More information

8. Clinical Trial Assessment Phase II

8. Clinical Trial Assessment Phase II 8. Clinical Trial Assessment Phase II Junko Sato, PhD Office of New Drug I, PMDA Disclaimer: The information within this presentation is based on the presenter s expertise and experience, and represents

More information

POPULATION ENRICHMENT DESIGNS FOR ADAPTIVE CLINICAL TRIALS. An Aptiv Solutions White Paper

POPULATION ENRICHMENT DESIGNS FOR ADAPTIVE CLINICAL TRIALS. An Aptiv Solutions White Paper FOR ADAPTIVE CLINICAL TRIALS An Aptiv Solutions White Paper EXECUTIVE SUMMARY The increasing pressure on governments caused by the spiraling healthcare costs is leading to a growing demand by payers for

More information

Trial-design in biosimilar research: Equivalence or non-inferiority design

Trial-design in biosimilar research: Equivalence or non-inferiority design Trial-design in biosimilar research: Equivalence or non-inferiority design Prof. Kit C.B. Roes Professor of Clinical Trial Methodology Advisor to MEB-CBG Overview The place(s) of the clinical efficacy

More information

Methodik für Mediziner

Methodik für Mediziner Methodik für Mediziner 19.5.2015 SUPERIORITY AND NON- INFERIORITY PD Dr. Matthias Briel MSc CEB/DKF, Unispital Basel When clinicians bump into a new treatment, they ask is it better than ( superior to

More information

Testimony of Christopher Newton-Cheh, MD, MPH Volunteer for the American Heart Association

Testimony of Christopher Newton-Cheh, MD, MPH Volunteer for the American Heart Association Testimony of Christopher Newton-Cheh, MD, MPH Volunteer for the American Heart Association Before the House Energy and Commerce Subcommittee on Health 21st Century Cures: Examining the Regulation of Laboratory

More information

Evaluating dose-response under model uncertainty using several nested models

Evaluating dose-response under model uncertainty using several nested models Evaluating dose-response under model uncertainty using several nested models Corine Baayen 1,2, Philip Hougaard 1 & Christian Pipper 2 1 H. Lundbeck A/S 2 University of Copenhagen October 2014 Baayen,

More information

3 Phases of Investigation

3 Phases of Investigation Part I: The scientific setting - December 30, 2009, 1 3 Phases of Investigation As discussed in Chapter 1, science is adversarial; scientists should consider all possible hypotheses, and only eliminate

More information

University of Bath, UK

University of Bath, UK Group Sequential Selection Procedures with Elimination and Data-Dependent Treatment Allocation Christopher Jennison, Dept of Mathematical Sciences, University of Bath, UK http:wwwbathacuk mascj Manchester,

More information

Economic evaluations in cancer clinical trials

Economic evaluations in cancer clinical trials What is CREST? The Centre for Health Economics Research and Evaluation (CHERE) at UTS has been contracted by Cancer Australia to establish a dedicated Cancer Research Economics Support Team (CREST) to

More information

Clinical trial design issues and options for study of rare diseases

Clinical trial design issues and options for study of rare diseases Clinical trial design issues and options for study of rare diseases November 3, 2016 Jeffrey Krischer, PhD Rare Diseases Clinical Research Network Rare Diseases Clinical Research Network (RDCRN) is coordinated

More information

Group Sequential Monitoring of Clinical. Trials with Multiple Endpoints. Christopher Jennison, Dept of Mathematical Sciences, University of Bath, UK

Group Sequential Monitoring of Clinical. Trials with Multiple Endpoints. Christopher Jennison, Dept of Mathematical Sciences, University of Bath, UK Group Sequential Monitoring of Clinical Trials with Multiple Endpoints Christopher Jennison, Dept of Mathematical Sciences, University of Bath, UK Stanford February 2004 1 Example 1: A diabetes trial O

More information

Value Assessment: Building Payercentric value propositions to inform decision-making

Value Assessment: Building Payercentric value propositions to inform decision-making Value Assessment: Building Payercentric value propositions to inform decision-making Aris Angelis and Panos Kanavos Medical Technology Research Group, LSE Health Advance-HTA dissemination workshop, Santiago,

More information

Near-Balanced Incomplete Block Designs with An Application to Poster Competitions

Near-Balanced Incomplete Block Designs with An Application to Poster Competitions Near-Balanced Incomplete Block Designs with An Application to Poster Competitions arxiv:1806.00034v1 [stat.ap] 31 May 2018 Xiaoyue Niu and James L. Rosenberger Department of Statistics, The Pennsylvania

More information

Current Trends at FDA: Implications for Data Requirements

Current Trends at FDA: Implications for Data Requirements Introduction The environment surrounding medical device regulation in the United States has always been rigorous, but recent events including well-publicized quality issues associated with implantable

More information

Designing a Disease-Specific Master Protocol

Designing a Disease-Specific Master Protocol Designing a Disease-Specific Master Protocol Lisa M. LaVange, PhD Director, Office of Biostatistics OTS/CDER/FDA Pediatric Master Protocols Workshop September 23, 2016 FDA, White Oak Campus Acknowledgments

More information

INNOVATIVE STATISTICAL DESIGN & ANALYSIS IN PD

INNOVATIVE STATISTICAL DESIGN & ANALYSIS IN PD INNOVATIVE STATISTICAL DESIGN & ANALYSIS IN PD Christopher S. Coffey Department of Biostatistics University of Iowa February 22, 2017 OVERVIEW The traditional approach to clinical trials tends to be large,

More information

June 15, Adaptive Phase I Studies: The IRB Perspective Marilyn Teal, PharmD IRB Member, Schulman IRB

June 15, Adaptive Phase I Studies: The IRB Perspective Marilyn Teal, PharmD IRB Member, Schulman IRB June 15, 2016 Adaptive Phase I Studies: The IRB Perspective Marilyn Teal, PharmD IRB Member, Schulman IRB About Schulman IRB Established in 1983 Superior audit history with FDA five consecutive audits

More information

An Overview of Bayesian Adaptive Clinical Trial Design

An Overview of Bayesian Adaptive Clinical Trial Design An Overview of Bayesian Adaptive Clinical Trial Design Roger J. Lewis, MD, PhD Department of Emergency Medicine Harbor-UCLA Medical Center David Geffen School of Medicine at UCLA Los Angeles Biomedical

More information

Continuous Safety Monitoring in Large Phase I Cancer Clinical Trials with Multiple Expansion Cohorts

Continuous Safety Monitoring in Large Phase I Cancer Clinical Trials with Multiple Expansion Cohorts Continuous Safety Monitoring in Large Phase I Cancer Clinical Trials with Multiple Expansion Cohorts Masha Kocherginsky, PhD 1 Theodore Karrison, PhD 2 1 Northwestern University and 2 The University of

More information

Page 78

Page 78 A Case Study for Radiation Therapy Dose Finding Utilizing Bayesian Sequential Trial Design Author s Details: (1) Fuyu Song and (2)(3) Shein-Chung Chow 1 Peking University Clinical Research Institute, Peking

More information

Antibacterial Drug Development Program Update

Antibacterial Drug Development Program Update Antibacterial Drug Development Program Update Summary of Working Group Webinar held 29, August 2013 Clinical Trials Transformation Initiative Meeting background The Clinical Trials Transformation Initiative

More information

Bios 6648: Design & conduct of clinical research

Bios 6648: Design & conduct of clinical research Bios 6648: Design & conduct of clinical research Section 3 - Essential principle 3.2 Treatment allocation (randomization) 3.3 Study quality control 3.4 Trial monitoring: Interim decision and group sequential

More information

Efficacy, Safety and Futility Stopping Boundaries

Efficacy, Safety and Futility Stopping Boundaries Efficacy, Safety and Futility Stopping Boundaries ExL Pharma Workshop Philadelphia, PA Feb 25-26, 2007 Cyrus R. Mehta President, Cytel Inc. email: mehta@cytel.com web: www.cytel.com tel: 617-661-2011 1

More information

Significant Contributors (p<0.001)

Significant Contributors (p<0.001) Benefit:Risk and Pragmatism: Use Outcomes to Analyze Patients Rather than Patients to Analyze Outcomes Scott Evans, PhD, MS, Harvard University Harvard Catalyst April 4, 2016 Significant Contributors (p

More information

The BEST Platform. A Modular Early-Phase Platform for Seamless Dose Finding and Cohort Expansion Laiya Consulting, Inc. 2018

The BEST Platform. A Modular Early-Phase Platform for Seamless Dose Finding and Cohort Expansion Laiya Consulting, Inc. 2018 The BEST Platform A Modular Early-Phase Platform for Seamless Dose Finding and Cohort Expansion Laiya Consulting, Inc. 2018 Introduction The Bayesian early-phase seamless transformation (BEST) platform

More information

Models of Industry Trials for Regulatory Purposes (Safety) Frank Cerasoli, PhD OREXIGEN Therapeutics

Models of Industry Trials for Regulatory Purposes (Safety) Frank Cerasoli, PhD OREXIGEN Therapeutics Models of Industry Trials for Regulatory Purposes (Safety) Frank Cerasoli, PhD OREXIGEN Therapeutics Safety Evaluation is Not Completed at Approval Phase 3 programs evaluate efficacy and general safety/tolerability

More information

Utilities and Pitfalls of Modeling & Simulation in Pivotal Trials

Utilities and Pitfalls of Modeling & Simulation in Pivotal Trials Utilities and Pitfalls of Modeling & Simulation in Pivotal Trials H.M. James Hung, Ph.D Div. of Biometrics I, OB/OTS/CDER U.S. Food and Drug Administration Presented in PhRMA/FDA Workshop, October 28,

More information

Implementing type I & type II error spending for two-sided group sequential designs

Implementing type I & type II error spending for two-sided group sequential designs Available online at www.sciencedirect.com Contemporary Clinical Trials 29 (2008) 351 358 www.elsevier.com/locate/conclintrial Implementing type I & type II error spending for two-sided group sequential

More information

Basket, umbrella and platform trials: a regulatory perspective. Julia Saperia

Basket, umbrella and platform trials: a regulatory perspective. Julia Saperia Basket, umbrella and platform trials: a regulatory perspective Julia Saperia PSI webinar 18 th April 2018 Acknowledgments David Brown (MHRA, BSWP) Olivier Collignon (LIH, BSWP, EMA) Anja Schiel (NoMA,

More information

The composite success

The composite success The composite success Comparing drug development strategies with probabilities of success including benefit-risk assessment to inform decision-making Gaëlle Saint-Hilary 1 Véronique Robert 2, Mauro Gasparini

More information

Sample Size and Power Calculation for High Order Crossover Designs

Sample Size and Power Calculation for High Order Crossover Designs Sample Size and Power Calculation for High Order Crossover Designs Roger P. Qu, Ph.D Department of Biostatistics Forest Research Institute, New York, NY, USA 1. Introduction Sample size and power calculation

More information

Data Monitoring Committees (DMC)

Data Monitoring Committees (DMC) Data Monitoring Committees (DMC) Mario Chen, PhD Advanced Biostatistics and RCT Workshop Office of AIDS Research, NIH ICSSC, FHI Goa, India, September 2009 1 Overview Why monitor data? When a DMC is needed?

More information

Submission of comments on COMMISSION NOTICE ON THE APPLICATION OF ARTICLES 3, 5 AND 7 OF REGULATION (EC) NO 141/2000 ON ORPHAN MEDICINAL PRODUCTS

Submission of comments on COMMISSION NOTICE ON THE APPLICATION OF ARTICLES 3, 5 AND 7 OF REGULATION (EC) NO 141/2000 ON ORPHAN MEDICINAL PRODUCTS Ref. Ares(2016)807620-16/02/2016 15 February 2016 Submission of comments on COMMISSION NOTICE ON THE APPLICATION OF ARTICLES 3, 5 AND 7 OF REGULATION (EC) NO 141/2000 ON ORPHAN MEDICINAL PRODUCTS Response

More information

FDA Draft Guidance for Industry: Statistical Approaches to Evaluate Analytical Similarity

FDA Draft Guidance for Industry: Statistical Approaches to Evaluate Analytical Similarity Genentech FDA Draft Guidance for Industry: Statistical Approaches to Evaluate Analytical Similarity Docket No. FDA-2017-D-5525: Comments from Genentech a Member of the Roche Group Dear Sir /Madam: Genentech,

More information

BIOSTATISTICAL METHODS

BIOSTATISTICAL METHODS BIOSTATISTICAL METHODS FOR TRANSLATIONAL & CLINICAL RESEARCH Phase 0 Trials: EARLY-PHASE CLINICAL TRIALS Steps to New Drug Discovery Get idea for drug target Develop a bioassay Screen chemical compounds

More information

Leveraging Prognostic Baseline Variables in RCT. Precision in Randomized Trials

Leveraging Prognostic Baseline Variables in RCT. Precision in Randomized Trials Leveraging Prognostic Baseline Variables to Gain Precision in Randomized Trials Michael Rosenblum Associate Professor of Biostatistics Johns Hopkins Bloomberg School of Public Health (JHBSPH) Joint work

More information

Similarity Assessment of Biosimilars. The Past, Present and Future State. Joseph Glajch, Ph.D and Jim Anderson, Ph.D Jan 28, 2016

Similarity Assessment of Biosimilars. The Past, Present and Future State. Joseph Glajch, Ph.D and Jim Anderson, Ph.D Jan 28, 2016 Similarity Assessment of Biosimilars. The Past, Present and Future State Joseph Glajch, Ph.D and Jim Anderson, Ph.D Jan 28, 2016 Disclaimer The views and opinions expressed in the following PowerPoint

More information

Opportunities of Statistics for Precision Medicine in Drug Development. Ivan S.F. Chan, Ph.D. AbbVie Subhead Calibri 14pt, White

Opportunities of Statistics for Precision Medicine in Drug Development. Ivan S.F. Chan, Ph.D. AbbVie Subhead Calibri 14pt, White Opportunities of Statistics for Precision Medicine in Drug Development Ivan S.F. Chan, Ph.D. AbbVie Subhead Calibri 14pt, White IMS Workshop on Perspectives and Analysis Methods for Personalized Medicine

More information

What s New in GCP? FDA Draft Guidance Details FIH Multiple Cohort Trials

What s New in GCP? FDA Draft Guidance Details FIH Multiple Cohort Trials Vol. 14, No. 10, October 2018 Happy Trials to You What s New in GCP? FDA Draft Guidance Details FIH Multiple Cohort Trials While multiple, concurrently accruing patient cohorts in first-in-human (FIH)

More information

An Introduction to Flexible Adaptive Designs

An Introduction to Flexible Adaptive Designs An Introduction to Flexible Adaptive Designs Roger J. Lewis, MD, PhD Department of Emergency Medicine Harbor-UCLA Medical Center David Geffen School of Medicine at UCLA Los Angeles Biomedical Research

More information

Crowe Critical Appraisal Tool (CCAT) User Guide

Crowe Critical Appraisal Tool (CCAT) User Guide Crowe Critical Appraisal Tool (CCAT) User Guide Version 1.4 (19 November 2013) Use with the CCAT Form version 1.4 only Michael Crowe, PhD michael.crowe@my.jcu.edu.au This work is licensed under the Creative

More information

Appendix 5: Details of statistical methods in the CRP CHD Genetics Collaboration (CCGC) [posted as supplied by

Appendix 5: Details of statistical methods in the CRP CHD Genetics Collaboration (CCGC) [posted as supplied by Appendix 5: Details of statistical methods in the CRP CHD Genetics Collaboration (CCGC) [posted as supplied by author] Statistical methods: All hypothesis tests were conducted using two-sided P-values

More information