1. POST- RANDOMIZATION DATA ANALYSIS OGNEN JAKASANOVSKI 26.05.2015

2. Structure of the presentation 1.Approaches to analysis of clinical trials with regards to loss to follow-up patients 2.Per protocol (PP) analysis 3.Intention to Treat (ITT) analysis 4.Considerations for ITT analysis

3. Approaches to analysis with regards to loss to follow-up patients  What are loss to follow-up patients?  Do we preserve randomization?  Explanatory vs. pragmatic approach  2 most common approaches  Per protocol (PP)  Intention to treat (ITT)

4. PP ITT

5. Per protocol (PP) analysis  Main principle  Efficacy analysis, explanatory analysis, analysis by treatment administered ADVANTAGESLIMITATIONS Maximal efficiency of treatmentNon-adherence related to prognosis Relation to adverse effects Undermining randomization

6. Example

7.  Excluding patients who do not meet eligibility criteria post- randomization  629 randomized patients for oseltamivir trial  255 (40%) were not found to have influenza during the study  PP analysis: 30% reduction in duration of illness  ITT analysis: 22% reduction in duration of illness  19% of cases – vomiting and nausea!

8. ITT analysis  Golden standard for data analysis  Pragmatic analysis  Imputing event rates ADVANTAGESLIMITATIONS Preserving randomization, minimizing biasDoes not determine maximum efficacy of treatment Depicting real-life situationsLarge loss to follow-up leads to inconclusive results Uses information from all subjects at any given time Might not show the potential benefit or show smaller benefit compared to PP Gives practical information on treatment administration

9. Comparison of PP and ITT analysis  PP gives slightly more significant results than ITT  PP results are much more significant than ITT  PP results are not significant, but ITT are – confounding?  ITT analysis is not significant, PP results are - crossover

10. Research considerations ITT is better regarded as a complete trial strategy for design, conduct and analysis rather than as an approach to analysis alone Phase of researchConsiderations Design Pragmatic/explanatory aim? Inclusion criteria that would justify exclusion to ITT? Conduct Minimise missing responses Follow up subjects who withdraw from treatment Analysis Investigate cause for missing response Reporting Explicitly state that ITT analysis has been done Discuss the potential effect of missing response Base conclusions on ITT analysis Report deviations from randomized allocation and missing response

11. References 1. Wang (2006). Clinical Trials – A Practical Guide to Design, Analysis, and Reporting, Intention- To-Treat analysis; Pages 255 - 263. 2. Montori, Guyatt (2001). Intention-to-treat principle. Canadian Medical Association or its licensors. 3. Fergusson, Aaron, Guyatt, Hebert (2002). Post-randomization exclusions: the intention-to- treat principle and excluding patients from analysis. British Medical Journal Volume 325; 4. Newell (1992). Intention-to-Treat analysis: Implications for Quantitative and Qualitative Research. International Journal of Epidemiology, vol. 21, no. 5. 5. Hollis, Campbell (1999). What is meant by intention-to-treat analysis? Survey of published randomized controlled trials. British Medical Journal, Volume 319.

12. COST-UTILITY ANALYSIS OGNEN JAKASANOVSKI 26.05.2015

13. Definition  Cost-utility analysis (CUA) is a form of evaluation that focuses particular attention on the quality of the health outcome produced or forgone by health programmes or treatments. It has many similarities to cost-effectiveness analysis (CEA), because they have similar underlying principles of conducting.  Similarities between CUA and CEA

15. Cost-effectiveness ratio  CER = cost of intervention / health effects produced  ICER = difference in costs P1-P2 / difference in effects eP1-eP2

16. Advantages of cost-utility analysis  When health-related quality of life is important  Common unit of outcome (QALYs, DALYs…)  Measuring mortality and morbidity  Comparability and transferability of results  Maximize overall health gain achieved by healthcare systems

17. Methods of measuring preferences Response method Queston framing Certainty (values) Uncertainty (utilities) Scaling 1 – rating scale, category scaling, visual analogue scale, ratio scale 2 Choice 3 – time trade off, paired comparison, equivalence, person trade-off 4 – standard gamble