Application of dynamic modeling for survival estimation in advanced renal cell carcinoma.
OBJECTIVE:In oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We eval...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC6117067?pdf=render |
| _version_ | 1818192809711108096 |
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| author | Baris Deniz Arman Altincatal Apoorva Ambavane Sumati Rao Justin Doan Bill Malcolm M Dror Michaelson Shuo Yang |
| author_facet | Baris Deniz Arman Altincatal Apoorva Ambavane Sumati Rao Justin Doan Bill Malcolm M Dror Michaelson Shuo Yang |
| author_sort | Baris Deniz |
| collection | DOAJ |
| description | OBJECTIVE:In oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach-dynamic modeling-to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study. METHODS:We developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up. RESULTS:Both statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab. CONCLUSIONS:Dynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies. |
| first_indexed | 2024-12-12T00:36:24Z |
| format | Article |
| id | doaj.art-3485163a5e8243b6af12d64d4c95953c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T00:36:24Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3485163a5e8243b6af12d64d4c95953c2022-12-22T00:44:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01138e020340610.1371/journal.pone.0203406Application of dynamic modeling for survival estimation in advanced renal cell carcinoma.Baris DenizArman AltincatalApoorva AmbavaneSumati RaoJustin DoanBill MalcolmM Dror MichaelsonShuo YangOBJECTIVE:In oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach-dynamic modeling-to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study. METHODS:We developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up. RESULTS:Both statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab. CONCLUSIONS:Dynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies.http://europepmc.org/articles/PMC6117067?pdf=render |
| spellingShingle | Baris Deniz Arman Altincatal Apoorva Ambavane Sumati Rao Justin Doan Bill Malcolm M Dror Michaelson Shuo Yang Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. PLoS ONE |
| title | Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. |
| title_full | Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. |
| title_fullStr | Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. |
| title_full_unstemmed | Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. |
| title_short | Application of dynamic modeling for survival estimation in advanced renal cell carcinoma. |
| title_sort | application of dynamic modeling for survival estimation in advanced renal cell carcinoma |
| url | http://europepmc.org/articles/PMC6117067?pdf=render |
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