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Overview Of Ich New E9

Overview Of Ich New E9



An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.

An overview of the ICH E9 guidance. Easy to follow, and I can provide a live presentation of this to your team! Great for those who are not familiar with statistics.



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    Overview Of Ich New E9 Overview Of Ich New E9 Presentation Transcript

    • Overview of ICH E9: Statistical Principles for Clinical Trials Presented by Jeff Davidson
    • ICH E9 cover page
      • E9
      • Current Step 4 version dated 5 February 1998
      • This Guideline has been developed by the appropriate ICH Expert Working Group and has been subject to consultation by the regulatory parties, in accordance with the ICH Process. At Step 4 of the Process the final draft is recommended for adoption to the regulatory bodies of the European Union, Japan and USA.
      • The great tragedy of science -
      • the slaying of a beautiful hypothesis
      • by an ugly fact. 
      • - Aldous Huxley
      • The most important science in the whole world:
      • for upon it depends the practical application of every other science
      • and of every art: the one science essential to all political and social
      • administration, all education, all organization based on
      • experience, for it only gives results of our experience. 
      • - Florence Nightingale
      • Focus on statistical principles
        • Gives direction to researchers in design, conduct, analysis, and evaluation of trials
        • Does not address use of specific statistical tests
        • Emphasis on later phase, confirmatory trials
      • Target audience: individuals from a broad range of scientific disciplines
        • Statisticians, clinicians, pharmacologists, epidemiologists
      Scope and Direction
      • Trial Statistician:
        • Responsible for all the statistical work associated with the trial
        • Ensures statistical principles are appropriately applied
        • Has the proper training and experience to implement the principles in this guidance
      Unjustified statistics are like smiling cats - not to be trusted. Scope and Direction
      • Minimizing bias
        • Systematic tendency of any factors associated with design, conduct, analysis and interpretation
        • To lead to an estimate of treatment effect
        • Different from the true value
      • Maximizing precision
        • Obtaining small standard errors and narrow confidence intervals
      • Evaluating robustness
        • Sensitivity of overall conclusions to various limitations of the data, assumptions, analysis procedures used
      Scope and Direction
      • Controlling the type I error
        • Ensuring that the chance of declaring a treatment efficacious when it in fact does not work is low (e.g., α ≤ 0.05)
      • “ Multiplicity” refers to having more than one opportunity to detect a difference between drugs (e.g., interim analyses, multiple endpoints of interest)
      Scope and Direction
      • Principal features of statistical analysis should be clearly specified in the protocol
      • Protocol (and amendments) should be approved by a trained statistician
      • A detailed Analysis Plan should be written before data analysis begins
      Protocol and Analysis Plan
    • Types of Trials
      • Exploratory Trial
      Confirmatory Trial
    • Exploratory Trial
      • Clear and precise objectives, however, tests of hypothesis may be data dependent
      • Such trials cannot be the basis of the formal proof of efficacy
    • Confirmatory Trial
      • An adequately controlled trial in which the hypotheses are stated in advance and evaluated
      • Key hypothesis of interest
        • Follows directly from the trial’s primary objective
        • Is always pre-defined
        • Is the hypothesis that is subsequently tested when the trial is complete
      • Adherence to protocols and SOPs is particularly important
    • Population
      • Earlier phases may focus on a very narrow subgroup
      • Confirmatory trials should more closely mirror target population of the therapy under study
        • Issues of Generalizability
      • Clear Inclusion/Exclusion criteria
    • Outcome Variables
      • Primary variable(s)
        • Directly related to the primary objective
        • Preferable to specify only one reliable and validated variable
        • Used in earlier studies or in published literature
        • Used when estimating the sample size
      • Secondary variables
        • Either supportive measurements related to the primary objective, or measurements of effects related to the secondary objectives
    • Avoiding Bias by Design: Blinding
      • Blinding limits the occurrence of conscious and unconscious bias arising from the influence one’s knowledge of treatment may have on
        • Recruitment and allocation of subjects
        • Their subsequent care
        • Attitudes of subjects to the treatments
        • Assessment of endpoints
        • Handling of withdrawals
        • Exclusion of data from analysis
        • Choice of analysis methods
    • Avoiding Bias by Design: Randomization
      • Introduces a deliberate element of chance into the assignment of treatments
      • Provides a sound statistical basis for the comparison of treatment groups
      • Tends to produce treatment groups with distributions of prognostic factors (measured and unmeasured) are similar
      • Parallel group: random assignment to A vs B
      • Randomization
      Design Configuration Population A B
      • Crossover:
      • random assignment to AB or BA
      • subject serves as own control
      • Randomization Washout period
      Design Configuration Population A B B A
      • Factorial Designs:
      Design Configuration Population A (A1, A2) B (B1,B2) A1, B1 A1, B2 A2, B1 A2, B2
    • Other Study Designs
      • Cohort Studies
      • Case-Control Studies
      • Descriptive Studies (e.g., surveys)
      • Registries
      • Adaptive
      • Etc., etc…
    • Type of Comparison
      • Trials to show “superiority”
        • New treatment vs. placebo, or new treatment vs. active control
        • Test (and hopefully reject) the null hypothesis that there is no difference in outcomes between groups
        • vs. the alternative hypothesis that there is a difference between the groups
        • One-sided or two-sided
    • Type of Comparison
      • Trials to show “ equivalence ”
        • New treatment vs. active control
        • Test (and hopefully reject) the null hypothesis that the new treatment performs differently than the active control by at least some small amount
        • vs. the alternative hypothesis that the difference between the groups is no greater than this small amount
          • An amount that is sufficiently small that the treatments are considered equivalent for all practical purposes if the difference between the treatments is smaller than this amount (equivalence margin)
    • Type of Comparison
      • Trials to show “noninferiority”
        • New treatment vs. active control
        • Test (and hopefully reject) the null hypothesis that the active control performs better than the new treatment by at least some small amount
        • vs. the alternative hypothesis that the new treatment does not perform worse than the active control by more than this small amount
    • Type of comparison
      • Trials to show “equivalence”
        • e.g., test null hypothesis that % cured with active
        • Control is ≥ 5% more than the % cured with the new treatment and vice versa
      • Trials to show “noninferiority”
        • e.g., test null hypothesis that % cured with active
        • Control is ≥ 5% than the % cured with the new treatment
    • Sample Size
      • Determine based on:
      • A primary endpoint
      • The null hypothesis
      • The test statistic (e.g., t-test, chi-square test, logrank test)
      • The treatment difference to be detected (the “alternative hypothesis”)
      • Significant level (Type I error)
      • Desired power (Type II error)
      • Variability assumptions
      • The plan for handling treatment withdrawals and protocol violations
    • Data Monitoring
      • Oversight of Trial Quality
      • Monitoring of Treatment Effects
    • Oversight of Trial Quality
      • Checks performed in a blinded manner:
        • Whether the protocol is being followed
        • The acceptability of data being accrued
        • The success of planned accrual targets
        • The appropriateness of the design assumptions
        • Success in keeping patients in the trials
      • Has no impact on Type I error
    • Monitoring Treatment Effects: Interim Analysis
      • Usually for serious outcomes
      • Requires unblinded access to treatment group summary data
      • Should only be done if included in the protocol
      • Goal, stop the trial early if:
        • Superiority of the new treatment is clear
        • Future demonstration of a treatment effect is unlikely
        • Unacceptable adverse effects are apparent
    • Monitoring Treatment Effects: Interim Analysis
      • May require a DMC, which should approve interim plans
      Independent Statistical Team Study Team IRB Sponsor DMC
    • Monitoring Treatment Effects: Interim Analysis
      • Repeated testing of outcome data increases the chance of a
      • Type I error
      • Test for difference in proportions failing in two groups without adjustment for multiple testing
      • Decision Rule : Reject null if |Z| ≥1.96 Overall Type I Error Rate
      • Single test at end of study -----> 0.05
      • Two tests, equally spaced -----> 0.08
      • Five tests, equally spaced -----> 0.14
      • -(Friedman, Furberg and DeMets, 1996)
    • Data Analysis
      • Include main features of analysis in protocol
      • For confirmatory trial, include statistical methods to be used for the primary variable(s)
      • For exploratory trials, include general principles and directions
      • Additional ‘statistical analysis plan’
        • Detailed procedures for primary and secondary variables
        • Do blind review of data, record date of breaking blind
    • Analysis Sets
      • Disposition of participants enrolled, summary of protocol violations
      • Degree of compliance and missing data lead to different Analysis Sets:
        • Full Analysis Set
        • Per Protocol Set
        • Rationale:
          • Minimize bias (Analysis Sets defined a priori)
          • Demonstrate lack of sensitivity
    • Full Analysis Set
      • ‘ Full analysis set’ = the analysis set which is as complete as possible and as close as possible to the intention-to-treat ideal of including all randomized subjects, it may exclude, for example:
        • Participants who failed to meet a major entry criteria
        • Participants who lack any data post randomization
    • Full Analysis Set
      • If specified in the plan, subjects who fail to meet an entry criterion may be excluded without the possibility of introducing bias under the following circumstances:
        • The entry criterion was measured prior to randomization
        • The detection of the relevant eligibility violations can be made completely objectively
        • All subjects receive equal scrutiny for eligibility violations
        • All detected violations of the particular entry criterion are excluded
    • Per Protocol Set
      • ‘ Per protocol set’ = subset of the participants in the full analysis set who are more compliant with the protocol
        • Complete a certain pre-specified minimal exposure to the treatment regimen
        • Have some minimum number of measurements of the primary variable(s)
        • Have no major protocol violations
      • May give overly optimistic results in superiority trials
      • May be the more conservative analysis set for equivalence or non-inferiority trials
    • Estimation and Confidence Intervals
      • Not just p-values, include confidence intervals for estimated treatment effects
      • Prespecify any covariates to be controlled for in primary or secondary analysis
        • To improve precision
        • To adjust for potential imbalances
        • To account for stratified designs
        • Never adjust for post-randomization variables
      • Prespecify interactions and subgroups of interest if treatment effect is likely to vary by baseline factors (e.g., gender)
    • Evaluation of Safety
      • Choice of variables: Laboratory tests, vital signs, adverse events
      • Safety Analysis Set: Usually those who received at least one dose of the investigational drug
      • Statistical Analysis
    • Example of an unacceptable adverse event
    • Evaluation of Safety is critical to the success of a new drug
    • Reporting
      • Document deviations from analysis plan, when and why they occurred
      • Account for all subjects who entered the study
      • Describe all reasons for exclusion from analysis dataset and all protocol violations
      • Summarize measurements of all important variables
      • Consider the effect of loss of subjects, violations and missing data on analysis results
      • Describe participants lost, withdrawn, etc.
      • Thank you!
      • Questions?