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- 1. Biostatistics in Cancer Clinical Trials Presented at the “Recent Trends in Bio-Medical Biostatistics”, Gujarat University, Ahmedabad on 24.02.2007 Dr. Bhaswat S. Chakraborty VP, R&D, Cadila Pharmaceuticals Ltd.
- 2. 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 Tumor Data Analysis – an Example Conclusion
- 3. Worldwide Cancer Statistics (All Types)From Parkin, D. M. et al.CA Cancer J Clin 2005;55:74-108.
- 4. From Parkin, D. M. et al.CA Cancer J Clin 2005;55:74-108.
- 5. PopulationBasedCancerRegistries inIndia(PBCR)
- 6. 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
- 7. 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
- 8. 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
- 9. 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
- 10. 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
- 11. 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
- 12. 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
- 13. 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
- 14. 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 Various definitions than survival exist
- 15. 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
- 16. Design Concepts Difference in Clinical Efficacy (Є) Non-Inferiority Superiority + 0 Equivalence - Inferiority Non-Superiority Equality = Meaningful Difference
- 17. 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
- 18. Phase III Cancer Trials4035 New30 Standard25201510 5 0 Survival DFS QoL Non-Inferiority or Equivalence Trials
- 19. 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
- 20. 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
- 21. 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
- 22. 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 = ∆2normal of analysis, Mann-Whitney U or Proportional Odds Model Sample Size
- 23. 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)
- 24. 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
- 25. Sample Size: 2-Sample, Parallel Superiority/Non-Inferiority Trial (z+zβ)2 (p1 (1– p1) + p2(1 – p2))N in each arm = (Є – )2
- 26. Power: 2-Sample, Parallel Superiority/Non-Inferiority Trial
- 27. Sample and Power for Simulated TumorData Expected Relative Risk 10.80.6 860.4 64 500.2 110 0 0.3 0.4 0.5 0.6 0.7 0.8 Relative risk
- 28. 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
- 29. 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
- 30. 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
- 31. 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
- 32. 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
- 33. 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
- 34. 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
- 35. Interim Analysis of Data 2 Look 3s LoHow many times NominalPvue 0.3 0.5 NominalPvue 0.3 0.5can you look intothe data? o p o c o c k o o b + fle o fle + h a r + o b 0. 0.1 0. 0.1 1 2 1 2 3 L o o k L o o k 4 Look 5s Lo NominalPvue 0.3 0.5 NominalPvue 0.3 0.5Type 1 error at kthtest is NOT thesame as thenominal p value 0. 0.1for the kth test 1 2 3 L o o k 0. 0.1 4 1 2 3 4 L o o k 5
- 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. 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
- 38. 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”
- 39. 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
- 40. 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
- 41. 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
- 42. 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
- 43. time death group futime number reduction in size 0 0 1 0 1 1 1 0 1 1 1 3 4 0 1 4 2 1 7 0 1 7 1 1Simulated Tumor Data: An Example 10 6 14 0 1 0 1 1 1 10 10 14 5 4 1 1 1 1 18 0 1 18 1 1 5 1 1 18 1 3 12 1 1 18 1 1 23 0 1 23 3 3 10 1 1 23 1 3 3 1 1 23 1 1 3 1 1 23 3 1 7 1 1 24 2 3 3 1 1 25 1 1 26 0 1 26 1 2 1 1 1 26 8 1 2 1 1 26 1 4 25 1 1 28 1 2 29 0 1 29 1 4 29 0 1 29 1 2 29 0 1 29 4 1 28 1 1 30 1 6 2 1 1 30 1 5 3 1 1 30 2 1 12 1 1 31 1 3 32 0 1 32 1 2 34 0 1 34 2 1 36 0 1 36 2 1 29 1 1 36 3 1 37 0 1 37 1 2 9 1 1 40 4 1 16 1 1 40 5 1 41 0 1 41 1 2 3 1 1 43 1 1 6 1 1 43 2 6 3 1 1 44 2 1 9 1 1 45 1 1 18 1 1 48 1 1 49 0 1 49 1 3
- 44. time death group futime number reduction in size 1 0 2 1 1 3 210 0 2 210 1 10 180 1 2 180 8 8Simulated Tumor Data: An Example 180 10 13 0 0 0 2 2 2 180 10 13 1 1 1 6 1 1 221 1 2 365 2 7 1 1 2 17 5 3 18 0 2 18 5 1 142 1 2 365 1 5 2 1 2 19 5 1 76 1 2 21 1 4 22 0 2 22 1 1 25 0 2 25 1 10 25 0 2 25 1 5 25 0 2 25 1 1 6 1 2 26 1 1 6 1 2 27 1 6 2 1 2 29 2 6 2 1 2 36 8 8 38 0 2 38 1 1 22 1 2 39 1 11 4 1 2 39 6 5 24 1 2 40 3 1 41 0 2 41 3 2 41 0 2 41 1 1 1 1 2 43 1 1 44 0 2 44 1 1 2 1 2 44 6 1 45 0 2 45 1 2 2 1 2 46 1 4 46 0 2 46 1 4 49 0 2 49 3 3 50 0 2 50 1 1 87 1 2 100 4 6 54 0 2 54 3 4 38 1 2 54 2 1 59 0 2 59 1 3
- 45. Control GroupSimulated Tumor Data: An Example 70Survival Time (Days) 60 50 40 30 20 10 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 Patient No Experimental Group 250 Survival Time (days) 200 150 100 50 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Patient No
- 46. Descriptive Statistics ± Standard deviation 120 100 80 time 60 Variable: time grouped by: group 40 95% N Mean Conf. (±) Std.Error Std.Dev. 20 1 48 15.77083333 4.241619672 2.108375644 14.60725495 0 2 38 47.73684211 19.66266124 9.704135603 59.8203094 1 2 Entire sample 86 29.89534884 9.420677178 4.738064537 43.93900293 group
- 47. Log-rank Test (Cox-Mantel) Events EventsKaplan Meier observed expected 1 29 21.09256306 2 18 25.90743694 Degrees of 1.2 Chi-square Freedom P 1 6.369814034 1 0.011607777 0.8Probability Censored 0.6 1 2 0.4 0.2 0 0 50 100 150 200 250 tim e
- 48. Cox Regression at Mean 1.2 1 0.8 Probability 0.6 0.4 0.2 0 0 50 100 150 200 250 tim e 95% Hazard =Coefficient Conf. (±) Std.Error P Exp(Coef.)-0.823394288 0.667410889 0.340517244 0.015603315 0.438939237
- 49. Conclusion of Tumor Data Kaplan Meier Two survival patterns are different with a median of 12 and 70 days for the Control and Experimental Groups Log-Rank Test The p-value of 0.0116 indicates significantly higher survival experience of the experimental group Cox Regression Hazard of death for the Experimental group is estimated to be about 44% that of the Control group The log hazard coefficient is – 0.8234 (hence, e-0.8234=0.4389, which gives us the estimated unadjusted Experimental hazard ratio). It means that the expected log hazard for the Experimental group is .8234 lower than it is for the Control group Difference in survival time in Experimental & Control groups is highly significant (p=0.0156)
- 50. 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
- 51. 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
- 52. Acknowledgements Dr. Nikunj Patel Dr. Sumit Goyal Dr. Manish Harsh Dr. Nilesh Patel Ms. Darshini Shah Thank You Very Much

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