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Clinically Meaningful Change and Clinical Relevance of the Functional Assessment of Cancer Therapy-Lung: Analysis of ECOG 5592 Data. David Cella & David T. Eton, Evanston Northwestern Healthcare & Northwestern University Diane L. Fairclough, AMC Cancer Research Center

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slide1

Clinically Meaningful Change and Clinical Relevance of the Functional Assessment of Cancer Therapy-Lung: Analysis of ECOG 5592 Data

David Cella & David T. Eton, Evanston Northwestern Healthcare & Northwestern University

Diane L. Fairclough, AMC Cancer Research Center

Philip Bonomi, Rush-Presbyterian St Luke’s Medical Center

David H. Johnson, Vanderbilt University

Anne Heyes, Cheryl Silberman, & Mike Wolf, AstraZeneca

acknowledgements
Acknowledgements
  • National Cancer Institute (grants CA 23318, CA66636, CA21115, CA 17145, CA 49957)
  • AstraZeneca Pharmaceuticals
what is a clinically meaningful change
What is a (clinically) meaningful change?
  • Meaningful change: A difference or change in score on a health-related quality of life (HRQoL) questionnaire that is important to the involved person or people
  • “Clinically” meaningful corresponds to a clinically important difference or change in patient status.
how are cmcs determined
How are CMCs determined?
  • Anchor-based methods

- Anchoring score differences to

traditional clinical parameters

(e.g., tumor response, time to progression)

  • Distribution-based methods

- Standard deviation

- Standard error of measurement

the present study
The Present Study
  • Purpose: To determine CMCs in two score aggregates of the FACT-L (the Lung Cancer Subscale & the Trial Outcomes Index) in advanced non-small cell lung cancer patients.
  • Data source: Eastern Cooperative Oncology Group study 5592
slide6

Sample Characteristics E5592 (N = 573)

  • 63% Female; 37% Male
  • Mean age = 60 years; range = 32-81 years
  • 87% Caucasian; 10% Afric-Am; 3% Other
  • 81% Stage IV; 19% Stage IIIB
  • Baseline Performance Status:

- 68% ECOG 1

- 32% ECOG 0

  • Treatment Arm:

- 34% cisplatin + etoposide

- 33% cisplatin + paclitaxel (high dose) + g-csf

- 33% cisplatin + paclitaxel (std dose)

hrqol assessment
HRQoL Assessment
  • The Functional Assessment of Cancer Therapy - Lung (FACT-L) Questionnaire

- Physical well-being (PWB) (7 items)

- Social/family well-being (SWB) (7 items)

- Emotional well-being (EWB) (5 items)

- Functional well-being (FWB) (7 items)

- Lung Cancer Subscale (LCS) (7 items)

- Trial Outcome Index (TOI) (21 items)

hrqol assessment1
HRQoL Assessment
  • The Functional Assessment of Cancer Therapy - Lung Questionnaire (FACT-L)

- Lung Cancer Subscale (LCS): 7 items

- Trial Outcome Index (TOI): 21 items

  • Baseline & 12-week assessments used
slide9

Anchor-based methods

- Independent samples t-tests (baseline)

- One-way ANCOVAs on changes in HRQoL

over time (controlling for baseline clinical

factors)

  • Distribution-based methods

- 1/3 and 1/2 standard deviation (SD)

- Standard error of measurement (SEM)

SEM = SD (1 - reliability)1/2

Data Analysis

slide12

Baseline to 12-week change inLung Cancer Subscale score(time to progression: < 116 days vs. > 116 days)

slide14

Baseline to 12-week change inTrial Outcome Index score(time to progression: < 116 days vs. > 116 days)

summary
Summary

Based on anchor &

distribution-based methods...

  • A 2 to 3 point score difference approximates a CMC on the LCS of the FACT-L
  • A 5 to 6 point score difference approximates a CMC on the TOI of the FACT-L
practical implications
Practical Implications
  • Determine sample size in clinical trials
  • Evaluate treatment efficacy
summary1
Summary
  • Baseline HRQL predicts outcome in advanced NSCLC
  • Longitudinal HRQL adds to the prognostic ability of baseline HRQL
  • Physical well-being, functional well-being and pt. reported symptoms are reliable predictors of outcome in advanced NSCLC
the present study1
The Present Study
  • Data source: Eastern Cooperative Oncology Group study 5592
  • Objectives (3)

- Show that HRQL predicts outcome

- Show that changes in HRQL add to the

prediction of outcome

- Show that longitudinal HRQL data have clinical import

hrqol assessment2
HRQoL Assessment
  • The Functional Assessment of Cancer Therapy - Lung Questionnaire (FACT-L)

- Physical well-being (PWB) (7 items)

- Social/family well-being (SWB) (7 items)

- Emotional well-being (EWB) (5 items)

- Functional well-being (FWB) (7 items)

- Lung Cancer Subscale (LCS) (7 items)

- Trial Outcome Index (TOI) (21 items)

  • Baseline & 6-week assessments used
outcomes
Outcomes
  • Time to disease progression
  • Survival duration
slide26

Data Analysis

  • Spearman correlation ()
  • Cox proportional hazards regression
  • Survival curves
slide31

1.0

.8

.6

.4

.2

0.0

0

200

400

600

800

Time to progression based on Trial Outcome Index scores

-2 Log likelihood = 2940.24

Overall 2 (7) = 67.67, p<.001

Hi baseline - improve

Hi baseline - decline

Proportion not progressing

Lo baseline - improve

Lo baseline - decline

Time to progression in days

slide32

Survival duration based on Physical Well-Being scores

-2 Log likelihood = 3058.81

Overall 2 (8) = 67.21, p<.001

Hi baseline - improve

Hi baseline - decline

Proportion surviving

Lo baseline - improve

Lo baseline - decline

Survival post-randomization in days

summary2
Summary
  • Baseline HRQL predicts outcome in advanced NSCLC
  • Longitudinal HRQL adds to the prognostic ability of baseline HRQL
  • Physical well-being, functional well-being and pt. reported symptoms are reliable predictors of outcome in advanced NSCLC
practical implications1
Practical Implications
  • Patient stratification in clinical trials
  • Treatment planning & adjustment
slide35
Our next step: ALCaMP-2Real-time (weekly) assessment of all lung cancer patients beginning chemotherapy