POST-RANDOMIZATION DATA ANALYSISOGNEN JAKASANOVSKI

26.05.2015

Structure of the presentation1. 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

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)

PP ITT

Per protocol (PP) analysis Main principle Efficacy analysis, explanatory analysis, analysis by treatment administered

ADVANTAGES LIMITATIONS

Maximal efficiency of treatment Non-adherence related to prognosis

Relation to adverse effects

Undermining randomization

Example

Example 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 studyPP analysis: 30% reduction in duration of illnessITT analysis: 22% reduction in duration of illness19% of cases – vomiting and nausea!

ITT analysis Golden standard for data analysis Pragmatic analysis Imputing event rates

ADVANTAGES LIMITATIONS

Preserving randomization, minimizing bias Does not determine maximum efficacy of treatment

Depicting real-life situations Large 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

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

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 research Considerations

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

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.

COST-UTILITY ANALYSISOGNEN JAKASANOVSKI

26.05.2015

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

Cost-effectiveness ratio CER = cost of intervention / health effects produced

ICER = difference in costs P1-P2 / difference in effects eP1-eP2

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 resultsMaximize overall health gain achieved by healthcare systems

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