Common Misunderstandings of Survival Time Analysis

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1 Common isunderstandings of Survival Time Analysis ilensu Shanyinde Centre for Statistics in edicine University of Oxford 2 nd April 2012

2 Outline Introduction Essential features of the Kaplan-eier survival curves edian survival times edian follow-up times Forest plots to present outcomes by subgroups

3 Introduction Survival data is common in cancer clinical trials Survival analysis methods are necessary for such data Primary endpoints are considered to be time until an event of interest occurs Some evidence to indicate inconsistencies or insufficient information in presenting survival data Poor presentation could lead to misinterpretation of the data 3

4 Paper by DG Altman et al (1995) Systematic review of appropriateness and presentation of survival analyses in clinical oncology journals Assessed; - Description of data analysed (length and quality of follow-up) - Graphical presentation 4

5 Cohort sample size 132, paper publishing survival analysis Results; - 48% did not include summary of follow-up time - edian follow-up was frequently presented (58%) but method used to compute it was rarely specified (31%) Graphical presentation; - 95% used the K- method to present survival curves - Censored observations were rarely marked (29%) - Number at risk presented (8%) 5

6 Paper by S athoulin-pelissier et al (2008) Systematic review of RCTs, evaluating the reporting of time to event end points in cancer trials Assessed the reporting of; - Number of events and censoring information - Number of patients at risk - Effect size by median survival time or Hazard ratio - Summarising Follow-up 6

7 Cohort sample size 125 Results; - Survival analysis cited with K- method (92%) - Censoring was less defined (47%) - Number of patients at risk was less defined (45%) - edian Follow-up was reported (71%), but method used was not specified in almost all the papers 7

8 Presenting Kaplan eier survival curves 8

9 Case 1: Presenting K- survival curves Patients with first relapse of acute lymphoblastic leukaemia aged 1-18 years Experiencing two types of relapse, isolated or combined relapse Cohort size N = 123, of which 80 (65%) had i-cns relapse 58 patients experienced the event (death) 9

10 Case 1 :- Presenting K- survival curves Assess overall survival by type of relapse (isolated and combined relapse) K- method estimates survival probability in both groups Graphical display of the survival curves using the K- method To effectively display an informative plot as recommended 10

11 Overall survival probability Isolated CNS Combined CNS Isolated CNS Combined CNS Log rank p = Log rank p = Time (months) Number at risk Isolated CNS Combined CNS Time (months) Number at risk Isolated CNS Combined CNS

12 edian survival times 12

13 edian Survival time Effect size is sometimes determined using edian survival time, if incorrectly presented could mislead results edian survival time : - Time when half of the patients are event free edian survival time estimated from the K- survival curves. Takes into account patients who have been censored, so all patients are included 13

14 Case 2: edian Survival time - Retrospective study of patients newly diagnosed with Hodgkin Lymphoma (HL) - Cohort size N=224, data collected from from five hospitals - Patients comprising of 93 (42%) HIV+ patients - 31 events (deaths) of which 15(48%) were HIV+ patients - To compare overall survival of patients according to HIV status 14

15 Case 2: edian Survival time Principal Investigator proposed summary of observed survival times excluding censored patients 16 HIV and 15 HIV + patients have died at a median time of 32 months (range: 5-97) and 9 months (range: 1-75) respectively, p= xx 15

16 Cumulative percentage Case 2: edian Survival time 16 HIV and 15 HIV + patients have died at a median time of 32 months (range: 5-97) and 9 months (range: 1-75) respectively, p= xx Five-year overall survival (OS) was 81% (95%CI: 69-89) and 88% (95%CI:80-93) for HIV positive and negative patients respectively. HIV+ HIV- HIV+ HIV- log rank p= Time (years) Number at risk HIV HIV Time (years) Number at risk HIV HIV

17 edian follow-up 17

18 edian follow-up Quantify length of follow-up of patients The median follow-up is an indicator of how mature your survival data is (e.g. how many months on average the patients were followed since randomisation into the study). Interpretation depends greatly on the time frame in which the study was carried out i.e. did we observe enough events Several methods (yielding different results) could be used and need to report method used in analysis 18

19 ethod 1: edian follow-up ethod 1 - edian follow-up using all patients - Have data on date of event or date patient last seen - Estimated from observed follow-up times - Advantages; - Includes all patients Disadvantages - Unstable and biased towards patients with short follow-up - Directly affected by times of observed events 19

20 ethod 2: edian follow-up ethod 2 - edian follow-up for censored patients or survivors - Estimated from observed follow-up times, excluding patients with events - Advantages; - We are not aware of how long we could have followed the patient if they had not experienced the event - Disadvantages; - Loss of information - Unstable estimate when number of survivors is small 20

21 ethod 3: edian follow-up ethod 3 - Reverse K- method - Estimated from the from K- method, but events are reversed - The event of interest here becomes being alive and death is censored Advantages; - Analogous to the K- estimator - Robust Disadvantages; - Challenging to understand 21

22 Case 3: edian follow-up Patients with first relapse of acute lymphoblastic leukaemia. Two types of relapse, isolated or combined relapse Cohort size N = 123, of which 80 (65%) had i-cns relapse To summarise median follow-up for the whole cohort 22

23 Case 3: edian follow-up ethod All patients Censored patients only edian follow-up estimate 33.9 months 36.3 months Reverse K months (95%CI; ) Depending on which method you use different results are obtained. Consider this in your analysis. 23

24 Forest plots 24

25 Forest Plots Recently used method of displaying lots of information in small space and getting the bigger picture across groups We present Relative Risk, but could also be used to present Hazard Ratios or Odd Ratios 25

26 Overall survival probability Case 4: Forest plot Patients with first relapse of acute lymphoblastic leukaemia, aged 1-18 years Some concerns that age at relapse might have a big effect on Figure 3B outcome <10 10 Log rank p = Time (months) Number at risk <

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29 Summary Some recommended essential features when graphically displaying the survival curves edian survival times and what it actually means Report method used to obtain median follow-up Graphical display of subgroups using Forest Plots to present outcomes 29

30 References DG Altman et al (1995), Review of survival analyses published in cancer journals. British Journal of Cancer 72: S athoulin-pelissier et al (2008) Survival End Point Reporting in Randomized Cancer Clinical Trials: A Review of ajor Journals. J Clin Oncol 26: I Zweiner et al (2011), Survival Analysis. edicine 108: Schemper and TL Smith (1996), A Note on Quantifying Followup Studies of Failure Time. Controlled Clinical Trials 17:

31 Acknowledgements Sharon Love Senior Statistician Centre for Statistics in edicine, University of Oxford Dr Saha Vaskar CRUK Professor of Paediatric Oncology Academic Unit of Paediatric and Adolescent Oncology, anchester Dr Silvia ontoto Clinical Senior Lecturer/ Honorary Consultant Barts Cancer Institute- a CR-UK Centre of Excellence, Queen ary University 31

Kaspar Rufibach (F. Hoffmann-La Roche) Joint work with Mouna Akacha (Novartis)

Treatment effect measures for time-to-event endpoints - Estimands and beyond Kaspar Rufibach (F. Hoffmann-La Roche) Joint work with Mouna Akacha (Novartis) Frankfurt, June 13 th, 2017 Problem statement