Statistical analysis of clinical data isi 30 01 07
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Statistical analysis of clinical data isi 30 01 07 Statistical analysis of clinical data isi 30 01 07 Presentation Transcript

  • Statistical Analysis of Clinical DataA Cancer Trials Perspective Presented at the Indian Statistical Institute, Kolkata on 06.02.2007 Dr. Bhaswat S. Chakraborty VP, R&D, Cadila Pharmaceuticals Ltd.
  • Contents Research and Regulations of Cancer Trials Pivotal Cancer Trials (Phase III)  Efficacy end points  Merits and demerits  Optimum Study Designs  Superiority  Non-Inferiority and other designs  Sample Size Considerations  Scientific questions  Basics of sample size calculation Statistical Plan for a Cancer RCT Statistical Analysis of Cancer Data Lead Statisticians & DSMB Special Issues Conclusion
  • Cancer Research Today Research is conducted mainly on  New Drugs  New Combinations  Radiotherapy  Surgery In the West, research is usually done by large co-operative groups, in addition to those mentioned for India In India  Large Pharmaceuticals  Co-operative Groups, e.g., ICON (Indian Co-operative Oncology Network)  Regional Cancer Centres & Govt. sponsored studies  Academia
  • 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
  • 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 Radio- and Chemotherapy Protectants
  • 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
  • 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
  • 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
  • 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
  • What to Measure? 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
  • 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 • Potentially affected by measured crossover therapy • Precisely • Does not capture measured symptom benefit • Includes noncancer deathsDisease-Free Surrogate for • RCT needed • Considered to • Not a validatedSurvival accelerated • Blinding be clinical benefit survival surrogate in most(DFS) approval or preferred by some settings regular • Needs fewer • Subject to assessment approval* patients and bias shorter studies than • Various definitions survival exist
  • Cancer Trials – End PointsEndpoint Evidence Assessment Some Advantages Some DisadvantagesObjective Surrogate for • Single-arm or • Can be assessed • Not a direct measure ofResponse accelerated randomized in single-arm benefitRate (ORR) approval or studies can be studies • Usually reflects drug regular used activity in a minority of approval* • Blinding patients preferred in • Data are moderately comparative complex compared to studies survivalComplete Surrogate for • Single-arm or • Durable CRs • Few drugs produce highResponse accelerated randomized represent obvious rates of CR(CR) approval or studies can be benefit in some • Data are moderately regular used settings (see text) complex compared to approval* • Blinding • Can be assessed survival preferred in in single-arm comparative studies studies
  • Design Concepts Difference in Clinical Efficacy (Є) Non-Inferiority Superiority +δ 0 Equivalence -δ Inferiority Non-Superiority Equality δ = Meaningful Difference
  • Phase III Cancer Trials New Drug (or Regimen) is Compared with a Standard 90 80 New 70 Standard 60 50 40 30 20 10 0 Superiority Trials Survival DFS QoL
  • Phase III Cancer Trials4035 New30 Standard25201510 5 0 Survival DFS QoL  Non-Inferiority or Equivalence Trials
  • Understanding Basics μ0 and μA  Means under Null & Alternate Hypotheses σ02 and σA2  Variances under Null & Alternate Hypotheses (may be the same) N0 and NA  Sample Sizes in two groups (may be the same) H0: Null Hypothesis  μ0 – μA = 0 HA: Alternate Hypothesis  μ0 – μA = δ Type I Error (α): False +ve  Probability of rejecting a true H0 Type II Error (β): False –ve  Probability of rejecting a true HA Power (1-β): True +ve  Probability of accepting a true HA
  • Basics of Sample Size Calculation Answer the scientific questions for the Trial size Understand the distribution and variability of the data Construct correct Null and Alternate hypotheses From the hypotheses derive formula for sample size Also make sure that this size trial has adequate power to establish a true alternate
  • Five Key Questions1. What is the main purpose of the trial?2. What is the principal measure of patient outcome?3. How will the data be analysed to detect a treatment difference?4. What type of results does one anticipate with standard treatment?5. How small a treatment difference is it important to detect and with what degree of certainty? Answers to all of the five questions above enable us to calculate the sample size and analyze the data with most appropriate test of hypothesis. Pocock SJ: Clinical Trials: A Practical Approach Chichester: Wiley; 1983
  • Start Planning Reliable or historical data available? No Yes Use conventional methods for analysis Use bootstrap simulation for sample size Normally distributed continuous data? Summary Yes measure: mean & mean μT – μC difference∆normal = Use parametric methods of σ analysis, two sample ‘t’ or ANOVA Effect Size No 2 [Z1-α/2 + Z1-β/2]2 Use non-parametric methods Nnormal = of analysis, Mann-Whitney U ∆2normal or Proportional Odds Model Sample Size
  • Understanding Sample Size Determination H0: μ0 – μA = 0 HA: μ0 – μA = δ Critical Value S.Error =σ(√2/N) S.Error =σ(√2/N) Power = 1-β β α/2 α/2 0 δ X0–XA 0+Z1-α/2σ√(2/N) δ–Z1-βσ√(2/N)
  • From the Previous Graph, We have 0+Z1-α/2σ√(2/N) = δ–Z1-βσ√(2/N) Upon simplification, 2 [Z1-α/2 + Z1-β/2]2 Nnormal = ∆2normal
  • Sample Size: 2-Sample, Parallel Superiority/Non-Inferiority Trial (z+zβ)2 (p1 (1– p1) + p2(1 – p2))N in each arm = (Є – δ)2
  • Power: 2-Sample, Parallel Superiority/Non-Inferiority Trial Є –δ Φ – z √p1 (1– p1)/n1 + p2(1 – p2)/n2) Where p1 and p2 are true mean response rates from Test & Control & Φ is the cumulative standard normal distribution function
  • Statistical Plan  Primary outcome considerations  Study Design  Sample size calculation  Randomization  Statistical consideration in Inclusion/Exclusion criteria (Homogeneity within centre and strata)  Accrual of patients  Cleaning of data  Interim Analysis  Go/No go criteria  α Considerations  Final analysis  Final conclusions
  • Statistical DesignsCrossover Trials A ABaseline B B Parallel Arm Trials A Baseline B
  • Accrual of Patients Study of the statistical trends in accrual patterns  Seasonal  Planned approaches  Reasons for drop outs and loss to follow up  Motivational factors Monitoring of recruitment progress and strategies  Frequency  Parameters  Duration Understanding natural history and non-cancer, non-intervention deaths Changes in accrual after Interim Analysis
  • Randomisation Generation of randomisation scheme according to  Centre  Block  Strata  Patient  Investigational Product to be given  Measures of ensuring non-bias Allocations What should go on the labels  Primary, secondary, tertiary packaging
  • Blinding Often difficult in oncology trials  Test and control are of different characteristics  Different routes of administration  Different schedules New low toxicity oral treatments are relatively easy to blind In other cases the end-point evaluating investigator must be different from the one administering the drugs
  • Data Capture Manual or Electronic CRF is the main source of raw data capture Data must be quality assured  Integrity, accountability, traceability Data must be validated All production and/or quality system software, purchased or developed in-house  Should document  Intended use, and information against which testing results and other evidence can be compared  To show that the software is validated for its intended use
  • Data Cleaning & Locking Data are cleaned based on a good plan for interim or final analysis  E.g.,  Hundred percent data are made quality checked and assured  Eligibility criteria for data selection  Correction and editing  Double data entry or other methods of data integrity Data will be locked after cleaning the data and resolving all the queries  SOP for data locking  No change after locking Only locked data are used as input into data analysis program
  • Interim Analysis of Data 0.05 0.045 0.04 0.035 0.03 0.025 Nominal p 0.02 0.015 0.01 0.005 0 1 2 3 4 Looks
  • Interim Analysis of Data 2 Looks 3 LooksHow many times 0.05 0.05can you look into Nominal Pvalue Nominal Pvaluethe data? 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 Looks 0.05 0.05Type 1 error at kth Nominal Pvalue Nominal Pvaluetest is NOT the 0.03 0.03same as the 0.01 0.01nominal p value 0.0 0.0for the kth test 1 2 3 4 1 2 3 4 5 Look Look
  • 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?
  • Final Analysis and Conclusion 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  E.g., time to event data
  • Equality Designs(e.g., 2-Sample, Parallel) H0 : Є = 0 HA : Є ≠ 0 Reject 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
  • Superiority/Non-Inferiority Designs(e.g., 2-Sample, Parallel) H0 : Є ≤ δ HA : Є > δ … Superiority H0 : 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
  • Survival Data – The Kaplan-Meier Estimator 1.0 Survival Percentage 0.75 0.50 0.25~40% Patientwill survivebeyond 0.8 year 0.0 Time (Year) 0.0 0.2 0.4 0.6 0.8 1.0
  • 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”
  • 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
  • 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
  • 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
  • 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
  • Practicality of a DSMB If the trial is likely to be completed quickly, the DMC may not have an opportunity to have a meaningful impact In short-term trials with important safety concerns, however, a DMC may still be valuable
  • Outstanding Issues Problems in ‘discounting’ (raising the hurdle to declare non- inferiority in order to account for inherent weaknesses) when indirectly comparing an experimental treatment to a placebo. Confronting weaknesses due to lack of assay sensitivity and constancy. Selection of the primary analysis population (ITT population vs. per protocol population). Inappropriate selection of analysis population will lead to biased results. The impact of trial quality on the results of active controlled trials – what the regulator will want to see substantiated on trial quality. Ethical issues if placebo controlled trials are used vs. statistical issues if they are not.
  • Outstanding Issues Selecting and using surrogate endpoints – the determination of statistical significance. Problems of designing clinical trials involving combination therapies. Statistical analysis and interpretation of complex data. Issues with the choice of control group in dose-finding studies – which doses to test and whether to include a placebo dose. Statistical inference with non-randomized controls - Propensity scores - Paired availability design for historical controls Statistical inference with randomized controls - Adjusting for non-compliance - Surrogate endpoints/missing outcome
  • Conclusions Clinical testing of new Oncology products is very sophisticated and complex A Statistician’s role in Cancer trials is invaluable Statistical considerations must be thoroughly given attention and built in while planning the study design and calculating the sample size Cancer clinical data is very complex (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
  • Conclusions Survival as primary end point is preferred by regulatory agencies 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 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 There are many oustanding statistical issues in Cancer trials that need no be discussed and solved
  • Acknowledgements Dr. Nikunj Patel Dr. Sumit Goyal Dr. Manish Harsh Dr. Nilesh Patel Ms. Darshini Shah Thank You Very Much