Author of

• 11692

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  P2.10 - Poster Session 2 - Chemotherapy (ID 207)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Medical Oncology
  • Presentations: 2
  • Moderators:
  • Coordinates: 10/29/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 11693

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    P2.10-001 - Modelling of cost effectiveness in advance NSCLC of a four arm treatment sequence with biomarkers. Using a Filemaker app (ID 162)

    09:30 - 09:30  |  Author(s): J.A. Davidson
    • Abstract

    Background
    The addition of treatment predicitive biomarkers and the sequencing of treatments should effect outcomes in terms of survival and cost. in NSCLC. Clinical trials are expensive in time and resources. Generally one biomarker and a treatment at a particular sequence is tested in a clinical trial. Estimating the cost benefit of combining biomarkers and sequencing of treatment in a computer model as a teaching tool and for the design of trials would be useful. The adaption of a STATA model into a Filemaker program suitable for use on a personal computer or tablet would provide greater use for this modelling.

    Methods
    A decision analysis model in STATA 9 has been adapted to run on Filemaker which can be run on an IPAD. Biomarkers used in the model are histology and Epidermal Growth Factor Receptor mutation status . The model uses reported response rates and disease free survival for platinum doublet, erlotinib, Pemetrexed and Vinorelbine. Two trials of two hundred patients are created half are treated with a platinum agent first followed by treatments selected using no additional biomarker information. The other half are treated using the best suitable treatment as informed by knowing the biomarker information where 75% of the patients have Adenocarcinoma and 15% are EGFR mutation positive. Costs for treatments and the use of biomarker are used to establish cost benefits.

    Results
    The modelling has 5.9 months improved survival for Adenocarcinomas for the use of biomarkers but no significant improvement for Squamous cell cancer. The added cost per life year saved for the arm which used histology and EGFR1 mutation testing to control the sequence of therapy from first to fourth line therapy was $37,401.

    Conclusion
    The model showed a meaningful educational and design of a trial outcome for managment of NSCLC. The result was specific for the parameters in the model but can be adapted quickly to investigate other hypothesis. The adaption to Filemaker a data tool suitable for tablets should allow clinicians, students and researches to access the modelling to simulate other senarios.

  • 11732

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    P2.10-040 - Prognostic significance, accuracy and usefulness of oncologists' estimates of survival time for patients starting first-line chemotherapy for advanced non-small-cell lung cancer (ANSCLC) (ID 2560)

    09:30 - 09:30  |  Author(s): J.A. Davidson
    • Abstract

    Background
    Oncologists are frequently required to provide estimates of survival time for their patients with advanced cancer. The aims of this study were to determine the accuracy and prognostic significance of oncologists’ estimates of survival time above and beyond conventional prognostic factors.

    Methods
    Medical oncologists from 26 sites in Australia and New Zealand recorded the “expected survival time in months” for individual patients with ANSCLC prior to randomisation in a trial of first-line chemotherapy with a platinum-based doublet. Blood samples, demographics, tumour and treatment characteristics were collected at baseline along with the oncologist’s rating of each patient using Spitzer’s Quality of Life Index (SQLI). Based on previous studies, we deemed estimates within 0.75-1.33 times observed survival as precise, and expected 50% of patients to live longer (or shorter) than their oncologist’s estimate, 50% to live from half to double their oncologist’s estimate (typical scenario); 5-10% to live ≤¼ of their estimate (worst-case scenario); and, 5-10% to live ≥3 times their estimate (best-case scenario). Associations between estimated and observed survival times in months were assessed with Cox proportional hazards regression before and after adjustment for baseline prognostic factors including age, gender, Eastern Cooperative Oncology Group performance status (ECOG PS), cancer extent, histology, co-morbidities, laboratory results and SQLI.

    Results
    Estimates of survival were available for 244 (98%) of the first 250 patients randomised. Patient characteristics were: median age 64 years; female 40%; adenocarcinoma 64%; ECOG PS 0-1 92%; and distant metastases 71%. After a median follow-up of 21 months there were 172 deaths (69%). The median (interquartile range, IQR) for observed survival was 10 months (5-20) and for estimated survival was 11 months (9-12). Oncologists’ estimates were imprecise (22% from 0.75-1.33 times observed) but well calibrated (47% of patients lived shorter than expected and 53% lived longer than expected). The proportions of patients with observed survival times falling within ranges bounded by simple multiples of their estimated survival times corresponded closely with our a-priori hypotheses: 10% lived ≤1/4 of their estimated survival time, 53% lived from half to double their estimated survival time, and 13% lived ≥3 times their estimated survival time. The oncologist’s estimate of survival time at baseline was the strongest predictor of observed survival in both univariable analysis (HR 0.90, 95% CI 0.86-0.95, p<0.001) and multivariable analysis (HR 0.90, 95% CI 0.86-0.95, p<0.001) accounting for all other independently significant predictors, namely: estimated neutrophil-lymphocyte ratio >5 (HR 3.15, 95% CI 1.76-5.64, p<0.001); haemoglobin <120g/L (HR 1.93, 95% CI 1.3-2.9, p=0.001) and total white cell count >11x10[9]/L (HR 1.55, 95% CI 1.05-2.27, p=0.03).

    Conclusion
    Oncologists' estimates of survival time were independently associated with observed survival time and provided a reasonable basis for estimating worst-case, typical and best-case scenarios for survival. Oncologists’ estimates provide useful additional prognostic information, above and beyond that provided by established prognostic factors.