Abstract 1652
Background
In cancer patients undergoing myelosuppressive chemotherapy, granulocyte colony-stimulating factor (G-CSF) reduces the risk of neutropenic complications and decreases need for dose reductions,1 allowing potential improvement of survival. A recent meta-analysis found that those receiving G-CSF have significantly improved overall survival (OS) versus those who do not.2 Deemed a “gold standard” approach for estimating probabilities of survival for time-to-event data,3 Kaplan-Meier (KM) estimates are a non-parametric method with no underlying distributional assumptions, allowing comparison between studies. Meta-analysis methods based on KM curves have been proposed.4,5 We present a new approach of pooling KM curves, improving their accuracy in assessing survival probabilities.
Methods
Our new approach pinpoints specific coordinates on KM curves using web-based digital graphics data extraction tools. When the amount of at risk patients during a specific time period is known, the number of patients experiencing events can be calculated, or if unknown, imputed by “borrowing” event percentages from a study with a similar KM survival profile. Using minimum squared distances between pairs, the similarity between two KM curves can be determined. Reconstructed event times can be used for meta-analyses, using fixed or random effect modeling with weights for each study.
Results
This approach was applied to 70 reports of OS in cancer patients receiving chemotherapy with or without G-CSF prophylaxis, including recent meta-analysis data.2 Preliminary results are in line with published data. Supporting reports of better survival in patients receiving G-CSF, the new approach provides synthesized KM curves, allowing comparisons of survival probabilities between treatments at any given time points.
Conclusions
Our preliminary results support findings of significantly improved OS in cancer patients receiving G-CSF. References: 1. Aapro et al. Support Care Cancer 2010;18:529–41. 2. Lyman et al. Blood 2017:130;3424. 3. Land et al. Procedia Comput Sci. 2011;6:267–72. 4. Parmar et al. Statist Med. 1998; 17:2815–34. 5. Tierney et al. Trials 2007;8:16.
Clinical trial identification
Legal entity responsible for the study
Hexal AG, Holzkirchen, Germany.
Funding
Hexal AG, Holzkirchen, Germany.
Editorial Acknowledgement
Terri Penfold, Spirit Medical Communications.
Disclosure
A. Krendyukov, L. Yau: Employee: Hexal AG.
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