Introduction Objective Methods Results Conclusion

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2 Introduction Objective Methods Results Conclusion 2

3 Malignant pleural mesothelioma (MPM) is the most common form of mesothelioma a rare cancer associated with long latency period (i.e. 20 to 40 years), late diagnosis, poor prognosis, and expected survival of 4 to 12 months without treatment caused by asbestos exposure in up to 80% of cases 3000 new cases and deaths each year in United States no recognized standard of care for second-line treatment (treatment given after initial chemotherapy for advancedstage disease) 3

4 Past studies have analyzed longitudinal data and timeto-event (survival) data separately Methods to demonstrate the association between patient-reported outcomes (PROs) and survival endpoints in oncology are needed Use of latent growth curve models (LGCM) permits assessment of change in PROs both within and between individuals The estimated latent variables can then be incorporated into survival models and analyzed as predictors of survival 4

5 To estimate the pattern of patient-reported outcome (PRO) change and its association with progression-free survival (PFS) to better demonstrate treatment benefit 5

6 Nine questions: Are patient self-reported appetite, fatigue, cough, dyspnea, hemoptysis, pain, symptom distress, interference with activity level, and quality of life (QoL) associated with PFS in patients undergoing second-line treatment for advanced MPM? H A: The PROs are associated ated with PFS H o : The PROs are not associated with PFS 6

7 Post-hoc analysis of secondary PRO and PFS data collected from a phase III randomized controlled trial for second-line treatment t t of MPM (Jassem et al., 2008) Sample consisted of 243 patients randomized to two treatment groups o Best Supportive Care (BSC) 120 o Pemetrexed + BSC 123 PROs measured ~ every 3 weeks using the Lung Cancer Symptom Scale (LCSS) questionnaire LCSS measures on 0-100mm visual analogue scale 7

8 1. Latent growth curve models (LGCMs) constructed for each of the nine PRO (LCSS) items with and without fixed treatment t t effect 2. Survival models for PFS with treatment effect 3. Joint models of PRO and PFS endpoints (Muthén et al., 2009) 8

9 Demographic characteristics: o age (mean - 60 years) o gender (77.8% male) o race (89.7% White) Baseline disease characteristics: o performance status (median score 90) o stage of disease (60.1% Stage IV) o classification of histological diagnosis (72.4% epithelioid subtype) o response to prior chemotherapy (41.6% stable disease) Two-sided t test: o test for group differences 9

10 For additional LGCM interpretation details see Duncan, Duncan, and Strycker (2006, p.25) 10

11 Survival curves for Kaplan-Meier versus Cox proportional hazard (PH) model Median PFS (months) BSC = 1.5 P+BSC = 3.6 Log rank P =.0148 The Cox PH model curves do not represent the KM curves 11

12 Survival curves for Kaplan-Meier versus nonproportional hazard model The non-ph model curves now representative of the KM curves 12

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14 PRO PFS regressed on intercept PFS regressed on slope appetite loss p = p = cough dyspnea symptom distress p = p = p = p = p = p = interference with activity level p = p = pain p = p = QoL p = p = fatigue p = p = * the joint hemoptysis model did not converge 14

15 We jointly estimated a shared parameter (random effects) model using a non-proportional hazards equation and a latent growth curve equation to respectively represent the time-to-event clinical endpoint (PFS) and the repeatedly measured PROs Our conceptual model that shared growth factors (a random intercept and a random slope) resulted in the observed outcomes was supported by the data The associated patient-reported outcomes support the notion that self-reported symptoms correspond to observed clinical outcomes Alternative model specifications can be fit to these data (e.g., an auto-regressive LGCM) and research is ongoing using the current data as well as data from several other Phase III oncology trials 15

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17 Patient-reported outcome (PRO): a measurement of any aspect of a patient s health status that comes directly from the patient (i.e., without the interpretation of the patient s responses by a physician or anyone else), and includes multidimensional data such as functional and health status, well being, symptoms, quality of life (QoL), and satisfaction with treatment (Berger et al., 2003, p. 175; FDA, 2009) Progression-free survival (PFS): the time from randomization in a clinical trial to the date of tumor (disease) progression or death from any cause, whereby tumor (disease) progression is clearly defined in the research protocol (FDA, 2007) 17

18 Multivariate processes of the repeated PRO measurements to confirm whether symptom worsening occurred prior to disease progression Sensitivity analyses to account for any non-ignorable missingness and methods to address missing data Methods to address nonlinearity and nonnormality Joint models adjusted for prognostic factors of performance status, histological i l subtype, prior chemotherapy, stage of disease, gender, and age Use of latent class analysis to identify subpopulations of patients Need to confirm the findings in other cancer indications for generalizability 18

19 Decision-making should be informed by reliable patient self-reported assessment, in addition to clinically- reported symptoms Novel methods to analyze and interpret PRO/PFS data Allowed for better interpretation of PFS as a surrogate endpoint to establish treatment benefit (Laffler, 2009) Directly informs comparative effectiveness research Provides a conceptual and operational framework for integrated and methodological assessment of the clinical value of a medical treatment 19

20 Berger, M. L., Bingefors, K., Hedblom, E. C., Pashos, C. L., Torrance, G. W., & Smith, M. D. (Eds.). (2003). Health care, cost, quality, and outcomes: ISPOR Book of Terms. Lawrenceville, NJ: International Society for Pharmacoeconomics and Outcomes Research. Duncan, T. E., Duncan, S. C., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues and applications (2 nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers. Jassem, J., Ramlau, R., Santoro, A., Schuette, W., Chemaissani, A., Hong, S., et al. (2008). Phase III trial of pemetrexed plus best supportive care compared with best supportive care in previously treated patients with advanced malignant pleural mesothelioma. Journal of Clinical Oncology, 26(10), Laffler, M. J. (2009, September 16). FDA will revisit appropriate use of PFS endpoints at advisory committee. The Pink Sheet. Muthén, B., Asparouhov, T., Boye, M. E., Hackshaw, M. D., & Naegeli, A. N. (2009). Applications of continuous-time survival in latent variable models for the analysis of oncology randomized clinical trial data using Mplus. Technical Report. United States Food and Drug Administration [FDA]. (2007). Guidance for industry: Clinical trial endpoints for the approval of cancer drugs and biologics. United States t Food and Drug Administration i ti [FDA]. (2009). Guidance for industry on patient-reported t t outcome measures: Use in medical product development to support labeling claims. 20