Integrated aspects of rct niper feb 2009 final
Upcoming SlideShare
Loading in...5
×
 

Integrated aspects of rct niper feb 2009 final

on

  • 401 views

 

Statistics

Views

Total Views
401
Views on SlideShare
401
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • 10/12/12
  • 10/12/12
  • 10/12/12
  • 10/12/12
  • 10/12/12

Integrated aspects of rct niper feb 2009 final Integrated aspects of rct niper feb 2009 final Presentation Transcript

  • Integrated Aspects of Phase III RCTsPresented at the NIPER Symposium on Clinical Research and Training, February 21-22, 2009 Dr. Bhaswat S. Chakraborty Senior VP, Cadila Pharmaceuticals Ltd.
  • Contents• Clinical Trials – Regulatory phases• Phase 3 Trials• Data Requirements• Study Design Considerations• Controls – Placebo, Active, NI• Assay Sensitivity, Multiplicity of Analyses• Interim Analysis• Intent to Treat & Final Analyses• Missing Data, Multiple Analyses & Assay Sensitivity• Prediction of Success of a Trial• Evidence Based Medicine• Conclusions 2
  • Global Ethical Medicine
  • 4
  • Source: MDS, New Zealand 5
  • Investigational New Drug• The pharmaceutical industry begins talking to FDA prior to submission of an IND• Show FDA the results of preclinical testing in animals and any prior human experience• What is being proposed to do for human testing• At this stage, the FDA decides whether it is reasonably safe for the company to move forward with testing the drug in humans 6
  • Phase 1• Phase 1 studies are usually conducted in healthy volunteers• Goal: – determine what the drugs most frequent side effects are – how the drug is metabolized and excreted• The number of subjects typically ranges from 20 to 100 7
  • Early Phase Clinical Trials 8
  • Phase 2• Phase 2 begins if Phase 1 studies dont show unacceptable toxicity• Phase 1 → Safety; the emphasis in Phase 2 → Effectiveness – Obtaining data on whether the drug works in people with a certain disease or condition• RCTs – Gold standard for evidence of efficacy – Patients receiving the drug vs patients receiving a placebo or a different drug – Randomized, well designed• Safety continues to be evaluated• N = a few dozen to about 300 9
  • Phase 3• Phase 3 studies begin if evidence of effectiveness is shown in Phase 2• Phase 3: large-scale efficacy & safety studies• Jointly decided by FDA & sponsor• A is pre-NDA meeting with FDA is common, right before a new drug application is submitted• Gather more information about safety and effectiveness – different populations and different dosages – using the drug in combination with other drugs• N = several hundred to about ≥4,000 patients 10
  • 11
  • Phase 4• Postmarketing study commitments are called Phase 4 commitments – Studies conducted after the FDA has approved a product for marketing• The FDA uses postmarketing study commitments to gather additional information about a products – Safety (mainly) – also efficacy or optimal use 12
  • 13
  • Regulatory Approvals: USFDA 2008: 22 NMEs; 3 BLAs 14
  • Clinical Trials: Testing Medical Products in Humans• Clinical studies, test potential treatments in human volunteers to see whether they should be approved for wider use in the general population – A treatment could be a drug, medical device, or biologic, such as a vaccine, blood product, or gene therapy – A new treatment may or may not be “better” – Complete and accurate research – Protection and well being of participants • Ethics, consent, audit – Documentation 15
  • Clinical Trials..• Drug studies in humans can begin only after an IND is reviewed by the FDA and a local institutional review board (IRB)• The board is a panel of scientists and non- scientists in hospitals and research institutions that oversees clinical research 16
  • Institutional (Ethical) Review• IRBs approve the clinical trial protocols – the type of people who may participate in the clinical trial – the schedule of tests and procedures – the medications and dosages to be studied – the length of the study – the studys objectives – other details• IRBs make sure the study is – acceptable – participants have given consent – Participants are fully informed of their risks – researchers take appropriate steps to protect patients from harm 17
  • Data• There are legal and ethical reasons for reporting all relevant data collected during the drug development process• Some reporting strategies already exist in the 1988 Guidelines, ICH E3 and E9• Electronic Submissions and desktop review capabilities will help all of us make better use of clinical data in NDA’s• There may be better strategies and these should be considered 18
  • What does FDA Look for?• FDA approves a drug application based on – Substantial evidence of efficacy & safety from “adequate and well-controlled investigations” – A valid comparison to a control – Quantitative assessment of the drug’s effect • (21 CFR 314.126.)• The design of cancer trials intended to support drug approval is very important 19
  • Adequate and Well-Controlled Studies• Because the course of most diseases is variable, you need a control group, a group treated just like the test group, except that they don’t get the drug, to distinguish the effect of the drug from spontaneous change, placebo effect, observer expectations• 21 CFR 314.126 describes the following controls – Placebo – No treatment – Dose response – Active control • Superiority of non-inferiority – Historical• Placebo, dose response or superiority are usually convincing studies 20
  • Adequate and Well-Controlled Studies..• Minimization of Bias: a unidirectional tilt favoring one group, a non-random difference in how test and control group are selected, treated, observed, and analyzed – These are the 4 main places bias can enter• Remedies: – Blinding (patient and observer bias) – Randomization (treatment and control start out equal) – Careful specification of procedures and analyzes in a protocol to avoid • Choosing the most favorable analysis out of many (bias) • Having so many analyses that one is favorable by chance (multiplicity) Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 21 2007
  • Design Concepts Non-Inferiority Difference in Clinical Efficacy (Є) Superiority +δ 0 Equivalence -δ Inferiority Non-SuperiorityEquality δ = Meaningful Difference 22
  • Purposes of Active Trials• The purpose of an active control trial could be to demonstrate that a new experimental treatment is either • superior to the control • equivalent to the control, or • non-inferior to the control • superior to a virtual placebo 23
  • Study Design: Approaches• Randomised Controlled Trials (RCT) most preferred approach – Demonstrating superiority of the new therapy• Other approaches – Single arm studies (e.g., Phase II) • e.g., when many complete responses were observed or when toxicity was minimal or modest – Equivalence Trials – No Treatment or Placebo Control Studies – Isolating Drug Effect in Combinations – Studies for Radiotherapy Protectants and Chemotherapy Protectants 24
  • Randomized Clinical Trials• Gold standard in Phase III• Single centre CT – Primary and secondary indications – Safety profile in patients – Pharmacological / toxicological characteristics• Multi-centre CT – Confirmation of the above – Effect size – Site, care and demographic differences – Epidemiological determination – Complexity – Far superior to meta-analyzed determination of effect 25
  • Placebo Control Equality Trials• No anticancer drug treatment in the control arm is unethical• Sometimes acceptable – E.g., in early stage cancer when standard practice is to give no treatment – Add-on design (also for adjuvants) • all patients receive standard treatment plus either no additional treatment or the experimental drug – Placebos preferred to no-treatment controls because they permit blinding – Unless very low toxicity, blinding may not be feasible because of a relatively high rate of recognizable toxicities 26
  • Reasons for Active Control1. Ethics – For trials involving mortality or serious morbidity outcome, it is unethical to use placebo when there are available active drugs on the market2. Assay sensitivity – In trials involving psychotropic drugs, placebo often has large effect. An active control is sometime used to demonstrate that the trial has assay sensitivity3. Comparative purpose – To show how the experimental drug compares to another known active drug or a competitor 27
  • Non-Inferiority Trials• New drug not less effective by a predefined amount, the noninferiority (NI) margin – NI margin cannot be larger than the effect of the control drug in the new study – If the new drug is inferior by more than the NI margin, it would have no effect at all – NI margin is some fraction of (e.g., 50 percent) of the control drug effect 28
  • Drug or Therapy Combinations• Use the add-on design – Standard + Placebo – Standard + Drug X• Effects seen in early phases of development – Establish the contribution of a drug to a standard regimen – Particularly if the combination is more effective than any of the individual components 29
  • What to Measure?• Primary outcome measure: The health parameter measured in all study participants to detect a response to treatment• Secondary outcomes measure: Other parameters that are measured in all study participants to help describe the effect of treatment• Baseline variables: The characteristics of each participant measured at the time of random allocation. – This information is documented to allow the trial results to be generalised to the appropriate population/s – Specific characteristics associated with the patient’s response to treatment (such as age and sex) are known as prognostic factors 30
  • What to Measure? E.g., Cancer Trials• Time to event end points – Survival – Disease free survival – Progress (of disease) free survival• Objective response rates – Complete – Partial – Stable disease – Progressive disease• Symptom end points• Palliation• QoL 31
  • Cancer Trials – End PointsEndpoint Evidence Assessment Some Advantages Some DisadvantagesSurvival Clinical benefit • RCT needed • Direct measure of • Requires larger and • Blinding not benefit longer studies essential • Easily measured • Potentially affected by • Precisely crossover therapy measured • Does not capture symptom benefit • Includes noncancer deathsDisease-Free Surrogate for • RCT needed • Considered to be • Not a validated survivalSurvival (DFS) accelerated • Blinding clinical benefit by surrogate in most settings approval or preferred some • Subject to assessment regular • Needs fewer bias approval* patients and shorter • Various definitions exist studies than survival 32
  • Interim analysis after each new response or group of responses an interim analysis is performed ⇓ enough evidence to stop the trial or continue the trial→ continuous sequential or group sequential analysis 33
  • Why Interim Analyses?• Ethics: superiority of a treatment• Safety: inferiority of a treatment / toxicity of a treatment• Economy: costly therapy no clinically relevant difference in effect between treatments 34
  • False Positives in Interim Analyses Interim analysis for a trial in non-Hodgkins lymphoma; n=130, IA after enrolment of each 25 patients Response Rate Response Rate CP CVP Analysis 1 3/14 5/11 1.63 Analysis 2 11/27 13/24 0.92 Analysis 3 18/40 17/36 0.04 Analysis 4 18/54 24/48 3.25 0.05<P<0.1 Analysis 5 23/67 31/59 4.25 0.016<P<0.05 CP=Cytoxan-prednisone CVP=Cytoxan-vincristine-prednisone Source: Stuart J. Pocock, Clinical Trials 35
  • Interim Analysis of Data 2 Looks 3 LooksHow many times can 0.05 0.05you look into thedata? Nominal Pvalue Nominal Pvalue 0.03 0.03 o pocock o ob+fle 0.01 0.01 o fle+har+ob 0.0 0.0 1 2 1 2 3 Look Look 4 Looks 5 LooksType 1 error at kth 0.05 0.05test is NOT the same Nominal Pvalue Nominal Pvalueas the nominal p 0.03 0.03value for the kth test 0.01 0.01 0.0 0.0 1 2 3 4 1 2 3 4 5 Look Look 36
  • Considerations for IA– Stopping rules for significant efficacy– Stopping rules for futility– Measures taken to minimize bias– A procedure/method for preparation of data for analysis– Data has to be centrally pooled, cleaned and locked– Data analysis - blinded or unblinded?– To whom the interim results will be submitted? • DSMB • Expert Steering Group– What is the scope of recommendations from IA results?– Safety? Efficacy? Both? Futility? Sample size readjustment for borderline results? 37
  • Equality Designs(e.g., 2-Sample, Parallel)H0 : Є = 0 HA : Є ≠ 0Reject H0 when p1— p2 ˆ ˆ > z/2 √p1(1—p1)/n1 + p2(1— p2)/n2 ˆ ˆ ˆ ˆ Where p1— p2 are true mean response rates ˆ ˆ from Test & Control 38
  • Superiority/Non-Inferiority Designs (e.g., 2-Sample, Parallel)H0 : Є ≤ δ HA : Є > δ … SuperiorityH0 : Є ≥ δ HA : Є < δ … Non-Inferiority Reject H0 when p1— p2 – δ ˆ ˆ > z √p1(1—p1)/n1 + p2(1— p2)/n2 ˆ ˆ ˆ ˆ 39
  • Survival Data – The Kaplan-Meier Estimator 1.0 0.75 Survival 0.50 0.25~35% Patient willsurvive beyond 0.8years 0.0 Time (Year) 0.0 0.2 0.4 0.6 0.8 1.0 40
  • Include all Patients: ITT• It can be justified to look at data and drop the “outliers”, poor compliers, inappropriately entered patients• It is even plausible and acceptable as an academic principle• But if not rigorously planned, such exclusion can lead to bias• Even when planned, it can lead to imbalances that also introduce bias 41
  • Intent-to-Treat Principle• All randomized patients• Exclusions on prespecified baseline criteria permissible – also known as Modified Intent-to-Treat• Confusion regarding intent-to-treat population: define and agree upon in advance based upon desired indication• Advantages: – Comparison protected by randomization • Guards against bias when dropping out is realted to outcome – Can be interpreted as comparison of two strategies – Failure to take drug is informative – Refects the way treatments will perform in population• Concerns: – “Difference detecting ability” 42
  • Per Protocol Analyses• Focuses on the outcome data• Addresses what happens to patients who remain on therapy• Typically excludes patients with missing or problematic data• Statistical concerns: – Selection bias – Bias difficult to assess 43
  • Intent to Treat & Per Protocol Analyses• Both types of analyses are important for approval• Results should be logically consistent• Design protocol and monitor trial to minimize exclusions• Substantial missing data and poor drug compliance weaken trial’s ability to demonstrate efficacy 44
  • Missing Data• Protocol should specify preferred method for dealing with missing primary endpoint – ITT • e.g., treat missing as failures • e.g., assign outcome based on blinded case-by-case review – Per Protocol • e.g., exclusion of patients with missing endpoint 45
  • Multiple Analyses• The two main problems introduced by multiple analyses are – firstly, the increased probability of detecting intervention effects where none exist (“false positives” owing to multiple comparisons — type I errors) – secondly, the limited capability (“power”) of trials to detect a true treatment effect in secondary outcomes if not enough participants are enrolled to show a statistically significant difference in these outcomes (“false negatives” — type II errors) 46
  • Assay Sensitivity• The critical question is whether a non-inferiority trial, for example, could distinguish the control from placebo and shown an effect of the non-inferiority margin• If it could – the trial is said to have said to have “assay sensitivity”• If a trial a trial has assay sensitivity – then if C-T < M, T had an effect• If the trial did not have assay sensitivity – then even if C-T < M, we have learned nothing• If you don’t know whether the trial had assay sensitivity, finding no difference between C and T means – Both drugs were effective – Neither drug was effective Source: RJ Temple, US FDA, Unapproved Drugs Workshop 47
  • Assay Sensitivity: Major Problem in NI• In a non a non--inferiority trial, the trial itself does not show the study’s ability to distinguish active from inactive therapy. Assay ability to distinguish active from inactive therapy• Assay sensitivity must, therefore, be deduced or assumed, based on 1. historical experience showing sensitivity to drug effects 2. a close evaluation of study quality and, particularly important 3. the similarity of the current trial to trials that were able to distinguish the active control drug from placebo• In many symptomatic conditions, such as depression, pain, allergic rhinitis, IBS, angina, the assumption of assay sensitivity cannot be made Source: RJ Temple, US FDA, Unapproved Drugs Workshop January 48 2007
  • Data Safety and Monitoring Board (DSMB)• All trials may not need a DSMB• DSMB Membership – Medical Oncologist, Biostatistician and Ethicist• Statistical expertise is a key constituent of a DSMB• Three Critical Issues – Risk to participants – Practicality of Periodic Review of a Trial – Scientific Validity of the Trial 49
  • Bayesian Prediction of Trial Success
  • Conclusion: Effect of Expertise Trial Success Based on Centre Expertise 100 90 80 70Success (%) 60 50 40 30 20 10 0 0 0.2 0.4 0.6 0.8 1 Probability of Expertise Source: Unpublished Results, D. Chakraborty, University of Toronto
  • Conclusions• Randomized Phase 3 Clinical Trials are very sophisticated and complex• Principal Investigators’, Monitoring Teams’, Biostatisticians’, DSMB’s … all roles are important – Phase 3 Trials are team effort• Phase 3 RCTs provide for the main evidence of efficacy and safety• Clinical data is very complex (confounded, censored, skewed, often fraught with missing data point), therefore, proper hypothesization and statistical treatment of data are required• Prospective RCTs are usually the preferred approach for evaluation of new therapies 52
  • Conclusions• Clinically meaningful margins must be well defined in Control trials prospectively – Superiority and non-inferiority margins must not be confused• Two or one-sidedness of α should also be prospectively defined• Power must be adequate• Variance must be analysed using the right model• Strategy for dealing with multiple end points must be prespecified – Too many end points ot tests will increase the false positive (α) error• Sometimes (e.g., in equality trials) statistically significant results may not be medically significant• Data censoring or skewed data must be well defined – E.g., time to event data 53
  • Conclusions• Randomisation and blinding offer a robust way to remove bias in end-point estimations• Data must be accurately captured without any bias and analysed by prospectively described methods• Interim analysis should carefully plan ‘ spending’ function and the outcome measure to be analyzed• Final analysis should be done carefully, independently and meaningfully (medical as well as scientific)• Choose clinically relevant delta• Design, conduct, and monitor trials to minimize missing data and poor compliance to drug• Analysis – Both intent-to-treat and per protocol analyses should be conducted – Sensitivity analyses• Outstanding medical and statistical issues must be brought to the fore• Trial success can be predicted in many cases using Bayesian models 54
  • Thank You Very Much