Chapter 8 Quality of Life Assessment

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Chapter 8Quality of Life Assessment
2
News

• ??????????????!???????????????????????????????????
  ??Lapatinib?????,??????????????????,??????????
• ???HER-2?????,?????????,??????????,????????????

3
Targeted Clinical Trials

• HER2 (the human epidermal growth factor receptor
  2) gene in metastatic breast cancer - Herceptin -
  requirement of screening the patients with
  over-expressed HER2 level (Slamon, 2001).
• Estrogen receptor polymorphism - Estrogen
  Replacement Atherosclerosis trial (ERA,
  Herrington, et al, 2002) a total of 9 SNPs were
  identified and interaction between treatment of
  HRT and some of SNPs in elevation of lipid levels
  is suggested
• Sample size determination Fijal, et al. (2000)
  and Maitournam and Simon (2005).

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Targeted Clinical Trials and EGFR

• Iressa (gefitnib) and Tarceva (Erlotinib) are
  targted at the EGFR pathway.
• Efficacy is correlated to
• race
• number of gene copies
• protein expression
• EGFR mutation
• Gappuzzo et al. (JNCI, 2005), Tsao, et al (NEJM,
  2005)

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Ethnic Difference
IRESSA ------ Placebo
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Non-Asian (n 1350)HR 0.93 (0.81, 1.08), P
.364 RR 6.5
Asian (n 342)HR 0.66 (0.48, 0.91), P
.011 RR 12.0
Patients surviving ()
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Time, mo
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From Tsao, et al (2005, NEJM)
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From Tsao, et al (2005, NEJM)
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Genomic Effect

• The increasing evidence that genetic determinants
  may mediate variability among persons in the
  response to a drug implies that patient responses
  to therapeutics may vary among racial and ethnic
  groups.
• After the intake of identical doses of a given
  agent, some ethnic groups may have clinically
  significant side effects, whereas others may have
  no therapeutic response.

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Genomic Effect

• Caraco (2004) points out that some of this
  diversity in rates of response can be ascribed to
  differences in the rate of drug metabolism,
  particularly by the cytochrome P-450 superfamily
  of enzymes
• While ten isoforms of cytochrome P-450 are
  responsible for the oxidative metabolism of most
  drugs, the effect of genetic polymorphisms on
  catalytic activity is most prominent for three
  isoformsCYP2C9, CYP2C19, and CYP2D6.

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Genomic Effect

• Among these three, CYP2D6 has been most
  extensively studied and is involved in the
  metabolism of about 100 drugs including
  beta-blockers, antiarrhythmic, antidepressant,
  neuroleptic, and opioid agents.
• Several studies revealed that some patients are
  classified as having poor metabolism of certain
  drugs due to lack of CYP2D6 activity.

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Genomic Effect

• Patients having some enzyme activity are
  classified into three subgroups those with
  normal activity (or extensive metabolism),
  those with reduced activity (intermediate
  metabolism), and those with markedly enhanced
  activity (ultrarapid metabolism).

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Genomic Effect

• The distribution of CYP2D6 phenotypes varies with
  race. For instance, the frequency of the
  phenotype associated with poor metabolism is 5 to
  10 percent in the Caucasian population but only 1
  percent in the Chinese and Japanese populations.

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Example

• Drug A is a fixed combination of two
  anti-platelet agents with indication for
  secondary prevention of thromboembolic stroke
  (200mg dipyridamole/25mg aspirin 1bid)
• After the standard process of BSE, we decided to
  request a bridging study due to an ethnic
  difference in medical practice (much lower dose
  for one of the components in Taiwan) and higher
  headache-associated dropout rate in previous
  Philippine study

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Example

• Headache drop out rate Phillipino gt Caucasian
• Local Bridging Study Result first 4 weeks
• Group Placebo Reduced Dose
  2wk Full Dose
• Full
  Dose 2wk 4wk
• Headache 8.7 6.7
  16.3
• drop out rate
• Risk Management Change labelings instruction
  for use

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Why Are We Interested in the Quality of Life?

• The United States Food and Drug Administration
  has stated that efficacy with respect to overall
  survival and/or improvements in QOL might provide
  the basis for drug approval
• Live longer feel better
• Shaughnessy JA, Wittes RE, Burke G et al.
• Commentary concerning demonstration of safety and
  efficacy of investigational anticancer agents in
  clinical trials. J Clin Oncol 1991 92225-32.

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Outline for Quality of Life

• General background
• Data collection considerations

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Your Quality of Life
How are you feeling this afternoon?
Mood
Happy
Miserable
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What is Quality of Life?

• 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
  such as nausea and fatigue
• Fundamental principle quality of life is
  assessed by the patient

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Quality of Life (1)

• Definition depends on context
• Cancer vs. MI vs. hypertension
• Some instruments are disease specific
• Others are "general health status" instruments
• POMS Profile of Mood
• SIP Sickness Impact Profile
• Difficulties with concept
• No agreement on definitions
• Lack of standardized measures

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Quality of Life (2)

• One definition (Levine Croog)
• Two Components
• - Functioning
• 1. Social (Major component)
• - Get along with family friends sense of
  worth(????)
• 2. Physical
• - Perform daily life activities
• 3. Emotional
• - Stability and self control
• 4. Intellectual
• - Decision making ability
• - Perceptions
• 1. Life Satisfaction
• - Sense of well being(????)
• 2. Health Status
• - Compared to others

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Factors Which Influence Quality of Life

• 1. Intervention
• 2. Disease Process
• 3. "Labeling" ? Need a control group
• - Diagnosis brings on change
• 4. Concomitant Care
• 5. Non-related life events
• (e.g. death in family)

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Rationale in Clinical Trials

• Quality of life may assess effect of intervention
• primary response (treatment less toxic?)
• side effects (treatment toxic?)
• economic aspects (low risk/cost treatment but
  benefit high?)

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How To Assess Quality of Life

• Determine your QoL Objective
• Choose an instrument
• Reliable, valid, responsive, feasible
• Global measures, disease-specific measures,
  symptom checklists
• Select your assessment time points and
  administration format
• Develop an analysis plan

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Data Collection Considerations (1)

• Mode
• Self-administered
• glasses, reading skills, fine-motor skills
• Personal interview
• training/background of interviewer
• sensitivity to gender/ethnicity/age
• hearing impairment

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Data Collection Considerations (2)

• Content
• instrument validity, sensitivity specificity
• sensitivity of questions
• frame of reference for answers
• (cognitive skills, privacy, cultural background)
• Source(s)
• participant, family or support network, health
  care providers

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Off the Shelf Instruments

• Off-the-shelf 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 ridiculous questions for trial pop.
• May take hours to complete
• May impact negatively on compliance

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Off-the-Shelf Instruments(Example-NOTT)

• Design
• Advanced Chronic Obstructive Pulmonary Disease
• 24 vs. 12 hours of O2
• Quality of life 10 outcome (No norms in this
  pop!)
• Quality of Life Results ?
• Patients were sick
• Patients got worse
• No treatment difference
• BUT
• Mortality ratio was 2/1 (plt.01)

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More Perspectives

• "Tailor Made" Instruments
• Can be Quick and simple
• Standardized but targeted to disease
• Must be Validated for trial population
• Select subsets of off-the-shelf instruments

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More Perspectives

• Home-made" Instruments
• Often designed by a graduate student
• Often too long
• Often not validated or field tested on your
  patient population

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Quality of Life Instruments

• Can be simple and short
• Classic examples for
• Cancer
• Congestive Heart Failure

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Quality of Life Analysis

• Analytic methods likely to be based on a scoring
  system
• Methods often rank based
• Challenging to design/ compute sample size

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Statistical Consideration

• Multi-trait scaling and other construct
  examination
• Reliability (internal consistency and test-retest
  reliability)
• Correlation analyses with other instruments
• Clinical validity (known group comparisons
  (sensitivity))
• Responsiveness to change over time

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Multi-trait Scaling (1)

• Multi-trait scaling analysis will be employed to
  examine whether the individual items in the
  instrument can be aggregated into hypothesised
  multi-item scales
• Evidence of item convergent validity is defined
  as correlation of 0.40 or greater between an item
  and its own scale (corrected for overlap).

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Multi-trait Scaling (2)

• Evidence of item discriminant validity will be
  based on a comparison of correlation of an item
  with its own scale and with other scales
• Scaling success for any item is defined as an
  item correlated significantly higher with its own
  scale (corrected for overlap) than with another
  scale

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Reliability

• The internal consistency of the multi-item
  questionnaire scales will be assessed by
  Cronbachs alpha coefficient
• A magnitude of gt0.70 is considered acceptable for
  group comparisons
• The test-retest reliability of scales will be
  assessed using intraclass correlations between
  the second and retest assessments in the patients

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Clinical Validity

• Known-group comparison will be used to assess
  whether the questionnaire scores can discriminate
  between subgroups of patients of clinical status

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Responsiveness Over Time

• Mixed effects regression model or generalized
  estimating equations (GEE) will be used to test
  for the significance of changes in QL scores
  before and after treatment

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Sample Size Requirements

• The sample size is based on the recommendation of
  Tabachnik and Fidell that for multivariate
  analysis techniques to obtain reliable estimates
  the number of observations should be 10 times the
  number of variables in the model

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FACT-P (???) (1)
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FACT-P (???) (2)
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FACT-P (???) (3)
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FACT-P (???) (4)
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FACT-P (???) (5)