1 QOL in oncology clinical trials: Now that we have the data what do we do?

2 What is the question? Objective –Treatment comparison(s) –Changes in QOL over time Population –All subjects (Intent-to-Treat) –Surviving (responding) patients –Patients remaining on therapy

3 What is the question? Time frame –Early, Later or Overall? –Limited to ‘on therapy’ vs. a more global view –Is more intensive early toxicity balanced by longer survival? –Does therapy have a palliative effect? –What is the impact of therapy on the QOL of survivors?

4 Clear and specific objectives define both the DESIGN and ANALYSIS

5 Missing Data Why is missing QOL data a problem? Loss of power to detect differences (minor) Potential for biased estimates and treatment comparisons (major)

6 Missing Data How much missing data should be allowed? There is no magic rule! Depends on setting and research question Setting one rule may lead to restricted QOL evaluations (e.g. off therapy, post relapse), such that one can not answer the relevant question(s)

7 Missing Data Missing Completely at Random (MCAR) –Missingness independent of (previously) observed QOL and (current) missing QOL Missing at Random (MAR) –Missingness may depend on previously observed QOL, independent of current QOL Missing Not at Random (MNAR) –Missingness depends on current QOL

8 Missing Data MCAR vs. MAR: This is testable because it depends on the observed QOL data MAR vs. MNAR: This is NOT testable because it depends on the missing QOL data. However, if missingness is associated with clinical events (toxicity, disease progression, metastasis) that are likely to affect QOL, one should consider MNAR!

9 Missing Data (MCAR) MCAR is very unlikely for QOL in oncology setting. (More missing FU for patients with poorer QOL) => Avoid using methods that assume MCAR (MANOVA, repeated univariate t-tests, complete case analysis and other strategies that exclude cases with fewer observations)

10 Missing Data (MAR) MAR may be a reasonable assumption when minimal missing data (<5%), in settings with minimal mortality/morbidity or for certain restricted questions => MLE (REML) of all available data –mixed effects models, –repeated measures model for incomplete designs

11 Missing Data (MNAR) Missing QOL associated with morbidity and mortality is likely to be MNAR All MNAR methods of analysis are based on untestable assumptions (however, some will be more reasonable than others) Sensitivity analyses are necessary If missingness is equivalent across arms, treatment comparisons based on MLE (REML) may (not guaranteed) be unbiased even though estimates of change within arms are biased

12 Multiple Endpoints Multiple QOL domains Longitudinal assessment => Major concern about multiple testing

13 Multiple Endpoints Possible solutions : Limit number of primary hypotheses Alpha adjustments and closed testing procedures Use of summary measures Some combination of all of the above will be useful; best strategy will depend on the research question

14 Multiple Endpoints Limit number of primary hypotheses Advantages –Reduce Type I errors –Focused objectives Disadvantages –What do you do with the rest of the data?

15 Multiple Endpoints Alpha adjustments/closed testing procedures Advantages –Reduce Type I errors Disadvantages –Loss of power (often very conservative) –Difficult to interpret large number of tests

16 Multiple Endpoints Summary measures/statistics Advantages –Increased power to detect smaller effects that persist over long periods of time (domains) –Fewer tests to interpret Disadvantages –May obscure differences across domains/time

17 Summary “One size” will NOT fit all … Careful planning at the design stage is essential While some analysis decisions can be delayed (detailed analysis plans for QOL before unblinding study), delay may equal ignoring problem/unanswered questions

18 Longitudinal vs. Univariate Repeated Univariate (ANOVA) –MCAR assumptions –Multiple Endpoints (Type I error/Interpretation) Longitudinal Models –MAR assumption (MLE available data) Summary Measures (AUC, Time to Event) –Appropriate choice => Easier Interpretation –Individual vs. Population Means

19 Missing Data Detection of non-ignorable missing data –Not formally testable –Conditions likely associated with QOL Analysis when suspected –No simple /single approach –Critical to understand assumptions –Sensitivity analysis