Conditional Survival Probabilities for Patients With Resected Pancreatic Adenocarcinoma

2014 ◽  
Vol 37 (2) ◽  
pp. 107-111 ◽  
Author(s):  
Mark V. Mishra ◽  
Colin E. Champ ◽  
Scott W. Keith ◽  
Timothy N. Showalter ◽  
Pramila R. Anne ◽  
...  
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Conditional Survival ◽  
Survival Probabilities

Conditional Survival in Pancreatic Adenocarcinoma Patients Treated with Neoadjuvant FOLFIRINOX and Chemoradiation

2020 ◽  
Vol 231 (4) ◽  
pp. S155-S156
Author(s):  
Yurie Sekigami ◽  
Theodoros Michelakos ◽  
Filippos Kontos ◽  
Motaz Qadan ◽  
Onofrio Antonio Catalano ◽  
...  
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Conditional Survival

scholarly journals Causes of Death and Conditional Survival Estimates of Medium- and Long-term Survivors of Pancreatic Adenocarcinoma

JAMA Oncology ◽  
2018 ◽  
Vol 4 (8) ◽  
pp. 1129 ◽  
Author(s):  
Douglas S. Swords ◽  
Sean J. Mulvihill ◽  
Matthew A. Firpo ◽  
Courtney L. Scaife
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Causes Of Death ◽  
Conditional Survival ◽  
Long Term Survivors

scholarly journals Conditional Probability of Survival in Patients With Newly Diagnosed Glioblastoma

2011 ◽  
Vol 29 (31) ◽  
pp. 4175-4180 ◽  
Author(s):  
Mei-Yin C. Polley ◽  
Kathleen R. Lamborn ◽  
Susan M. Chang ◽  
Nicholas Butowski ◽  
Jennifer L. Clarke ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Conditional Probability ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Ratio Test ◽  
Karnofsky Performance Score ◽  
Conditional Survival ◽  
Survival Probabilities

Purpose The disease outcome for patients with cancer is typically described in terms of estimated survival from diagnosis. Conditional probability offers more relevant information regarding survival for patients once they have survived for some time. We report conditional survival probabilities on the basis of 498 patients with glioblastoma multiforme receiving radiation and chemotherapy. For 1-year survivors, we evaluated variables that may inform subsequent survival. Motivated by the trend in data, we also evaluated the assumption of constant hazard. Patients and Methods Patients enrolled onto seven phase II protocols between 1975 and 2007 were included. Conditional survival probabilities and 95% CIs were calculated. The Cox proportional hazards model was used to evaluate prognostic values of age, Karnofsky performance score (KPS), and prior progression 1-year post diagnosis. To assess the constant hazard assumption, we used a likelihood-ratio test to compare the Weibull and exponential distributions. Results The probabilities of surviving an additional year given survival to 1, 2, 3, and 4 years were 35%, 49%, 69%, and 93%, respectively. For patients who survived for 1 year, lower KPS and progression were significantly predictive of shorter survival (both P < .001), but age was not (hazard ratio, 1.22 for a 10-year increase; P = .25). The Weibull distribution fits the data significantly better than exponential (P = .02), suggesting nonconstant hazard. Conclusion Conditional probabilities provide encouraging information regarding life expectancy to survivors of glioblastoma multiforme. Our data also showed that the constant hazard assumption may be violated in modern brain tumor trials. For single-arm trials, we advise using individual patient data from historical data sets for efficacy comparisons.

scholarly journals Conditional Survival Probabilities for Previously Untreated Advanced Melanoma Patients Receiving Ipilimumab: Model Based Analysis

2015 ◽  
Vol 18 (3) ◽  
pp. A195
Author(s):  
D. Lee ◽  
S. Kotapati ◽  
J. Porter ◽  
N. Hertel ◽  
A. Zagorska ◽  
...  
Keyword(s):  
Advanced Melanoma ◽  
Conditional Survival ◽  
Survival Probabilities ◽  
Model Based ◽  
Melanoma Patients ◽  
Model Based Analysis

scholarly journals Conditional survival after a diagnosis of malignant brain tumour in Canada: 2000–2008

2017 ◽  
Vol 24 (5) ◽  
pp. 341 ◽  
Author(s):  
Y. Yuan ◽  
J. Ross ◽  
Q. Shi ◽  
F.G. Davis
Keyword(s):  
Brain Cancer ◽  
Survival Probability ◽  
Population Based ◽  
Diffuse Astrocytoma ◽  
Conditional Survival ◽  
Additional Time ◽  
Survival Probabilities ◽  
Cancer Registry Data ◽  
Kaplan Meier ◽  
Patient Will

Background “Conditional survival probability” is defined as the probability that a patient will survive an additional time, given that the patient has already survived a defined period of time after diagnosis. Such estimates might be more relevant for clinicians and patients during post-diagnosis care, because survival probability projections are based on the patient’s survival to date. Here, we provides the first population-based estimates of conditional survival probabilities by histology for brain cancer in Canada.Methods Canadian Cancer Registry data were accessed for patients diagnosed with primary brain cancers during 2000–2008. Kaplan–Meier survival probabilities were estimated by histology. Conditional survival probabilities at 6 months (short-term, denoted scs) and 2 years (long-term, denoted lcs) were derived from the Kaplan–Meier survival estimates for a range of time periods.Results Among the 20,875 patients who met the study criteria, scs increased by a margin of 16–18 percentage points from 6-month survivors to 2-year survivors for the three most aggressive brain cancers. The lcs for 2-year survivors was 66% or greater for all tumour groups except glioblastoma. The lcs for 4-year survivors was 62% or greater for all histologies. For glioblastoma and diffuse astrocytoma, the lcs increased each year after diagnosis. For all other histologies, the lcs first increased and then plateaued from 2 years after diagnosis. The lcs and scs both worsened with increasing older age at diagnosis.Summary We report histologically specific conditional survival probabilities that can have value for clinicians practicing in Canada as they plan the course of follow-up for individual patients with brain cancer.

Conditional survival probability of patients with resected pancreatic adenocarcinoma.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 284-284
Author(s):  
Mark Vikas Mishra ◽  
Colin Eamon Champ ◽  
Timothy Norman Showalter ◽  
Scott W. Keith ◽  
Pramila R. Anne ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Pancreatic Adenocarcinoma ◽  
Proportional Hazards ◽  
Female Gender ◽  
Cox Proportional Hazards ◽  
Conditional Survival ◽  
Prognostic Information ◽  
Kaplan Meier ◽  
Cox Proportional Hazards Models ◽  
Younger Age

284 Background: The purpose of this study is to evaluate conditional survival (CS) probabilities for patients with resected pancreatic adenocarcinoma (PC) amongst patients who have survived ≥1 more year(s) after diagnosis. Methods: Patients with resected PC from 1998-2008 were identified from the Surveillance, Epidemiology and End Results Database. Data on patient, tumor and treatment characteristics were extracted. Overall survival (OS) rates were calculated using the Kaplan-Meier method. A multivariate analysis (MVA) at different time points from survival was performed to determine independent prognostic factors associated with all-cause mortality hazard ratios (HRs) using Cox proportional hazards models. Results: A total of 4,883 patients with resected PC were identified. Fourteen percent of patients had Stage IA/B disease, 23% Stage IIA, 53% Stage IIB, and 2% Stage IIIA, and 6% with unknown stage. The 1-, 3-, and 5-year survival estimates for patients at diagnosis were 67%, 29%, and 21%, respectively. One, 3- and 5-year survival probabilities conditional upon number of years already survived are shown in table 1. Prognostic factors significantly correlated with improved OS at the time of diagnosis on MVA include: earlier stage, younger age, later year of diagnosis, white race, female gender, and residence in a high income district (p < 0.05). After already surviving 3 years following diagnosis, younger age was the only prognostic factor correlated with improved OS (p<0.05). Conclusions: CS estimates provide additional prognostic information that may be used to counsel PC patients on how their prognosis may change over time. Further research utilizing prospectively-collected data is warranted to help determine recommended follow-up intervals and benchmarks for future clinical trials. [Table: see text]

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