Quality of Life

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Quality of Life

  1. 1. Quality of Life Assessment in Clinical Trials Elizabeth Garrett-Mayer Some slides adapted from www.biostat.wisc.edu/training/courses/542slides/09- qol.pdf
  2. 2. Aside: MIXED procedure  “Mixed” effects model in SAS  Includes both fixed and random effects  Fixed effects  Time  Treatment  Random effects  Person-specific coefficients  Commonly seen  Estimate a common slope over time  Allow each individual to has his/her own intercept
  3. 3. 0 5 10 15 20 -5051015 TIME RESPONSE
  4. 4. Why are we interested in Quality of Life (QOL)?  The FDA has stated that efficacy with respect to overall survival and/or improvements in QOL might provide the basis for drug approval. Shaughnessy JA, Wittes RE, Burke G et al. Commentary concerning demonstration of safety and efficacy of Investigational anticancer agents in clinical trials. Journal of Clinical Oncology 1991 (9) 2225-32
  5. 5. Measuring QOL Happy Miserable How are you feeling today?
  6. 6. What is QoL?  WHO: “Health is not only the absence of infirmity and disease, but also a state of physical, mental and social well-being.”  Multiple domains include: Physical, cognitive, emotional and social functioning, pain, sexual functioning, health perceptions, and symptoms about nausea and fatigue  Fundamental Principle: QoL IS ASSESSED BY THE PATIENT
  7. 7. QoL  Definition depends on context:  Cancer vs. MI vs. hypertension  Early instruments for measuring QoL were disease-specific  Later instruments, “general health status”  POMS = Profile of Mood  SIP = Sickness Impact Profile  Difficulties with concept  No agreement on definition  Lack of standardized measures
  8. 8. QoL  One definition (Levine and Croog) has two components:  Functioning  Social (major component): get along with family and friends  Physical: perform daily activities  Emotional: stability and self-control  Intellectual: decision-making ability  Perceptions  Life satisfaction: sense of well-being  Health Status: compared to others
  9. 9. Factors influencing QoL  Interventions/Treatment  Disease Processes  Labeling: diagnosis brings on ‘change’  Concomitant Care  Non-related life events (e.g. death in the family)
  10. 10. Rationale in Clinical Trials  QOL assesses effect of intervention/treatment  Primary response (treatment improves symptoms?)  Side effects (treatment toxic?)  Economic aspects (low risk/cost of treatment but high benefit?)  Another setting: Treatment for pain  Primary response (pain lessened?)  Side effects (interact with disease? Other side effects?)  Economic aspects
  11. 11. Data Collection  Data collection can add measurement error or bias  Mode: self-administered vs. interview  Self-admin: Reading ability, fine-motor skills  Interview: Hearing problems, age/gender/ethnicity sensitivity, training of interviewer  Either: language  Content  Instrument validity, sensitivity, specificity  Sensitivity of questions  Frame of reference (cognitive skills, privacy, cultural background)  Source(s)  Patient vs family vs health care provider
  12. 12. Assessing QoL  Hardest part!  Determine QoL objective  Choose instrument to measure QoL  Reliable, valid, responsive, feasible  Global measures, disease-specific measures, symptom checklists  Select assessment time points  Develop analysis plan
  13. 13. Choosing your instrument  Off-the-shelf (i.e. general) instruments  Designed to distinguish sickness from wellness  May not be sensitive to particular aspect of a given trial  May not be validated or “normed” in population being tested  May ask silly questions for trial population  May take long time to complete  May impact negatively on compliance
  14. 14. Choosing your instrument  “Tailor Made” Instruments  Quick and simple  Standardized but targeted to disease  Validated, normed to trial population  Select subsets of off-the-shelf instruments  Home-Made Instruments  Often designed by graduate student or comparable  Often too long  Often not validated or ‘normed’ or field tested in the patient population of interest
  15. 15. a. CARES-SF (Schag 1991) - 59 item scale which measures rehabilitation and quality of life in patients with cancer. This has been modified to the HIV Overview of Problems Evaluation Systems (HOPES, Schag 1992) b. City of Hope Quality of Life, Cancer Patient Version (Ferrell 1995) – a 41 item ordinal scale representing the four domains of quality of life including physical well being, psychological well being, and spiritual well being. c. Daily Diary Card-QOL (Gower 1995) - a self-administered card for use in cancer clinical trials that has been shown to demonstrate short-term changes in quality of life related to symptoms induced by chemotherapy. d. EORTC QOL-30 (Aaronson 1993) - this instrument is composed of modules to assess quality of life for specific cancers in clinical trials. The current instrument is 30 items with physical function, role function, cognitive function, emotional function, social function, symptoms, and financial impact. e. FACT-G (Cella 1993) – a 33 item scale developed to measure quality of life in patients undergoing cancer treatment. f. FLIC (Finkelstein 1988) – a 22 item instrument which measures quality of life in the following domains: physical/occupational function, psychological state, sociability, and somatic discomfort. This scale was originally proposed as an adjunct measure to cancer clinical trials. g. Southwest Oncology Group Quality of Life Questionnaire (Moinpour 1990) – a scale developed for cancer patients incorporating questions from various function, symptoms, and global quality of life measures.
  16. 16. Measurement  Look for measures that are proven to be  VALID  RELIABLE  Validity: does measure actually measure the construct it is intended to measure?  Reliability: how much close is does our measure get to the “true” score? (ranges from 0 to 1)
  17. 17. Measurement  RELIABILITY AND VALIDITY DEPEND ON THE SAMPLE TO WHICH YOUR MEASURE IS APPLIED!  Example:  the FACT-G has been shown to have reliability of in 0.87 in Americans undergoing chemotherapy (Cella)  Is it still a reliable measure in Japanese men with esophageal cancer?  Is it a reliable measure in Korean women with breast cancer?  If a measure is to be applied to a different population from which it has been validated on, it needs to be re-assessed.
  18. 18. Measurement  What is the big deal if the reliability is lower in my sample?  Low reliability = Poor measure  Low reliability also implies poor validity.  Think of these scales as “surrogate markers” of quality of life  Would you use surrogate markers that you KNEW were only weakly related to the true outcome of interest?  If reliability is low, then you are not measuring what you are trying to measure.  Look for reliabilities above 0.75
  19. 19. Measurement  What about validity in new population?  The same items/questions may mean different things to different patient populations or cultures  For some latent variables (e.g. mental disorders), the variable of interest manifests itself differently in different cultures or population subgroups.  Translations into different languages can affect results dramatically  If there are items in the scale that are irrelevant for your patient population, then you are compromising your validity by including them.
  20. 20. Analytic Issues  “Measurement”  QOL measured by multiple indicators  Need validated overall ‘score’  Or, can use fancier multivariate methods  Usually, treat ‘score’ as observed level of QoL and proceed with analysis.  Problems:  ‘score’ is often not a valid measure of QoL in the patient population  Score tends to be fraught with measurement error (reliability tells you about this)
  21. 21. Analytic Issues  ATAC study:  Used FACT-G and FACT-B.  Simply added up the responses to each item (but that is shown to be valid and reliable)  Treats each item as “exchangeable:” assumes each item is equally sensitive to changes in QoL  Alternative: develop model to weight each item relative to how informative it is about QoL (latent variable methods…..).
  22. 22. Latent Variables  QoL is, by definition, a “latent variable:” it cannot be directly measured.  We measure it using “symptoms” of QoL  Statistical methods help us make inference about state of QoL via the symptoms.  Develop models/scales for measuring QoL  We can maximize reliability and evaluate validity.  Issues to consider:  What if our “symptoms” are not tapping into QoL like we think?  What if patients’ perceptions of the questions we ask are different?  How can we find out about these things????
  23. 23. Quality Of Life Have you felt nauseated? Have you had problems sleeping? Have you had pain? Have you lacked appetite? Have you felt depressed? Latent Variable Depiction
  24. 24. Other latent variables in medical research  Pain  Mental disorders  Depression  Schizophrenia  Autism  Mobility/Function (gerontology)  Arthritis
  25. 25. Other QOL issues  Often interested in whether or not survival with poor quality of life is better than death without suffering.  “QALY”= Quality Adjusted Life Years  Example:  Cancer: many patients would rather not get toxic therapies and have more enjoyable end of life  The general idea is to down-weight time spent in periods of poor quality of life.  Methodologically challenging:  How to determine the weights?  Different settings might need different weights.
  26. 26. QTWIST: Quality-Adjusted Time Without Symptoms of disease and Toxicity. Quality Adjusted Survival  Evaluate therapies based on both quantity and quality of life through survival analysis  Based on QALYs.  Define QOL health states, including one with good health (minimal symptoms).  Patients progress through health states and never back-track.  Partition the area under the Kaplan- Meier Curve and calculate the average time spent in each clinical health state.  Compare treatment regimens using weighted sums durations, weights are utility based.  Example: 5 year survival 0 6 1 0 8 2 0 6 1 0 2 1 3. . . .× + × + × + × = 3 adjusted years of life Compare the average QTWIST in two treatment groups. Could be that on treatment A, people live longer, but QOL is worse. Quality of Life for Individual
  27. 27. References:  www.biostat.wisc.edu/training/courses/542s lides/09-qol.pdf  Fairclough and Gelber, “Quality of Life: Statistical Issues and Analysis.” From Quality of Life and Pharmacoeconomics in Clinical Trials, Second Edition, ed. B. Spiker. 1996.

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