Efficacy analysis and graphical representation in Oncology trials - A case study

Transcription

1 Efficacy analysis and graphical representation in Oncology trials - A case study Anindita Bhattacharjee Vijayalakshmi Indana Cytel, Pune The views expressed in this presentation are our own and do not necessarily represent the views of Cytel Statistical Software & Services Limited 1

2 Agenda Ø Oncology endpoints Ø A Case Study Ø Analysis Ø Graphical Representation Ø Take away points 2

3 Oncology endpoints Ø Early phase maximum tolerated dose/ recommended phase 2 dose, biological drug activity Ø Late phase Clinical benefit Ø Endpoint choice depends on indication, line of therapy, available treatment options, etc. 3

4 Oncology endpoints Time to Event (Survival) Endpoints Endpoint Definition Advantages Disadvantages Overall Survival (OS) Randomization until death Precise and easy to measure most reliable May involve larger studies Progression Free Survival (PFS) Randomization until progression/ death Smaller sample size Not precisely measured 4

5 Oncology endpoints Response and Symptom Endpoints Endpoint Definition Advantages Disadvantages Objective Response Rate (ORR) Proportion of responders (Complete or Partial) Assessed earlier and in smaller studies Not a direct measure of benefit Symptom Endpoints Patient s quality of life (QOL) Patient perspective of direct clinical benefit Data are frequently missing or incomplete 5

6 Time to Event data: Concepts Patients Event X Censored Censored Censored X Event Death, disease occurrence, disease recurrence, recovery, or other experience of interest Censoring When a subject does not have an event of interest during the observation interval Time (months) Analysis Timepoint 6

7 Time to Event data: Concepts Event X Prevent Loss of information Patients Censored Censored Censored X Retain original sample size as decided in the hypothesis in Protocol Time (months) Analysis Timepoint 7

8 Case Study Efficacy Analysis Ø Protocol Ø Analysis Dataset Ø Derivation Ø Graphical Analysis Ø Primary and secondary endpoints 8

9 Protocol Title Ø A Phase III randomized lung cancer study, arms two Primary Endpoint Ø Progression Free Survival (PFS) Secondary Endpoint Ø Objective response rate(orr) 9

10 PFS (with event) Time to First Event occurring Randomization Treatment Start Disease Progression Death Randomization Date(RANDT) Progression Date (PDDT) PFS (in days) = (PDDT - RANDT + 1) Censor = 0 10

11 PFS (with censoring) Time till last tumor assessment indicating lack of progression Randomization Treatment Start Last TA Discontinued Study Randomization Date(RANDT) Last TA (TADT) PFS (in days) = (TADT - RANDT + 1) Censor = 1 11

12 Analysis dataset (ADPFS) Unique Subject Identifier Disposition/ PFS Response/ PFS Time Date (months) Tumor Assessment Treatment Group Censoring Flag 12

13 Graphical Representation Kaplan-Meier Survival Analysis Method: (months) 13

14 Primary Endpoint Kaplan-Meier Survival Analysis Method: Ø Estimates the probability of survival to a given time using the proportion of patients who have survived to that time Ø Accounts for censoring 14

15 100 Trt 1 : (N=42) Trt 2 : (N=43) Trt 1 Trt 2 80 Number of events Trt 1: 26 Trt 2: 39 Survival Probability (%) Median Survival Time Kaplan Meier PFS Trt 1: months Trt 2: 5.95 months proc lifetest data=adpfs method=km outsurv=kmsurv; time pfstime*censor(0); strata trt; run; 20 6 months 14 months Time (months) Patients at risk # Trt 1 Trt

16 100 Trt 1 : (N=42) Trt 2 : (N=43) Trt 1 Trt 2 80 Number of events Trt 1: 26 Trt 2: 39 Survival Probability (%) Kaplan Meier PFS Trt 1: months Trt 2: 5.95 months 20 At month 0 Trt 1=42 Trt 2=43 At month 6 Trt 1=30 Trt 2=20 At month 18 Trt 1=15 Trt 2=3 At month 30 Trt 1=9 Trt 2= Time (months) Patients at risk # Trt 1 Trt

17 Secondary endpoints: Ø Objective Response Rate can be analyzed using a Waterfall plot Ø Depicts increase or decrease in rate for a parameter of interest. 17

18 Waterfall Plot % change from baseline (measurable lesion) PR (Partial Response) PD(Progressive Disease) Decrease in best percentage change from baseline 53.13% (17) Increase in best percentage change from baseline 40.63% (13) Subjects 18

19 Waterfall Plot % change from baseline (measurable lesion) Decrease in best percentage change from baseline 28.21% (11) Increase in best percentage change from baseline 64.10% (25) Subjects 19

20 Waterfall Plot Subjects Subjects Decrease in best percentage change from baseline 53.13%(17) 28.21%(11) Increase in best percentage change from baseline 40.63%(13) 64.10%(25) 20

21 Take away points Ø Understand data and SAP Tumor response data listing 21

22 Take away points Ø Annotate Tables, Listings and Figures CENSOR = 0 PFSTIME FUNC = 1 FUNC = 0 22

23 Take away points Ø Censoring algorithm Ø Latest tumor evaluation Ø Last contact date Ø Randomization date Ø Data checks raise flag Ø Missing data (e. g. missing PFS) Ø Cross check across Tables, Listings and graphs Ø Heavy censoring 23

24 References Ø Guidance for Industry Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics Ø FDA's Richard Pazdur: Drug Approval Entails Evaluation of Clinical Benefit, Not Just Endpoints Ø Oncology Clinical Trials Successful Design, Conduct and Analysis W.M. Kevin Kelly, Susan Halabi Ø Thomas R. Fleming, Mark D. Rothmann, and Hong Laura Lu - Journal Of Clinical Oncology - Issues in Using Progression-Free Survival When Evaluating Oncology Products - J Clin Oncol 27: ,

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