scholarly journals Re: Visualizing Length of Survival in Time-to-Event Studies: A Complement to Kaplan-Meier Plots

2008 ◽  
Vol 100 (16) ◽  
pp. 1188-1188
Author(s):  
N. Lama ◽  
C. Gallo
Keyword(s):  
Event Studies ◽  
Time To Event ◽  
Kaplan Meier

scholarly journals Review of Outcomes after Diagnosis of Malignancy in Kidney Transplant Patients: UNOS Database

2021 ◽  
Vol 2 (3) ◽  
pp. 253-263
Author(s):  
Het Patel ◽  
Nikhil Agrawal ◽  
Voravech Nissaisorakarn ◽  
Ridhi Gupta ◽  
Francesca Cardarelli
Keyword(s):  
Kidney Transplant ◽  
Graft Failure ◽  
Event Analysis ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients ◽  
Time To Event ◽  
Transplant Patients ◽  
Kaplan Meier ◽  
Lung Malignancy ◽  
Kidney Transplant Patients

Malignancy is the third major cause of death among transplant recipients. Patient and kidney transplant outcomes after the diagnosis of malignancy are not well described. We reviewed incidences and outcomes of colorectal, lung, PTLD, and renal malignancy after transplant among patients who received a transplant from January 2000 to December 2018 using the UNOS/OPTN database. Incidence of each malignancy was measured at 5 years and 10 years of transplant. The Kaplan–Meier curve was used for time-to-event analysis (graft and patient outcomes). Additionally, we sought to identify the causes of graft failure among these recipients. We found that 12,764 (5.5%) patients suffered malignancy, excluding squamous and basal cell skin carcinoma after transplant. During the first 5 years of transplant, incidence of colorectal, lung, PTLD, and renal malignancies was 2.99, 9.21, 15.61, and 8.55 per 10,000 person-years, respectively. Rates of graft failure were 10.3%, 7.6%, 19.9%, and 18.8%, respectively, among these patients at 5 years. Mortality rate was highest among patients who suffered lung malignancy (84%), followed by colorectal (61.5%), PTLD (49.1%), and renal (35.5%) at 5 years after diagnosis of malignancy. In conclusion, kidney transplant recipients diagnosed with lung malignancy have the lowest graft survival, compared to PTLD, colorectal, and renal malignancy. PTLD has the highest incidence rate in the first 5 years of transplant.

Survival analysis

2020 ◽  
pp. 181-218
Author(s):  
Bendix Carstensen
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Cox Model ◽  
Survival Function ◽  
Event Analysis ◽  
Time To Event ◽  
Poisson Models ◽  
Kaplan Meier ◽  
Analysis Survival ◽  
Event Rates

This chapter describes survival analysis. Survival analysis concerns data where the outcome is a length of time, namely the time from inclusion in the study (such as diagnosis of some disease) till death or some other event — hence the term 'time to event analysis', which is also used. There are two primary targets normally addressed in survival analysis: survival probabilities and event rates. The chapter then looks at the life table estimator of survival function and the Kaplan–Meier estimator of survival. It also considers the Cox model and its relationship with Poisson models, as well as the Fine–Gray approach to competing risks.

Pitfalls inherent in retrospective time-to-event studies: The example of time to pregnancy

1993 ◽  
Vol 12 (9) ◽  
pp. 867-879 ◽  
Author(s):  
Clarice R. Weinberg ◽  
Donna Day Baird ◽  
Andrew S. Rowland
Keyword(s):  
Event Studies ◽  
Time To Event ◽  
Time To Pregnancy

scholarly journals Mortality in trials on transcatheter aortic valve implantation versus surgical aortic valve replacement: a pooled meta-analysis of Kaplan–Meier-derived individual patient data

2020 ◽  
Vol 58 (2) ◽  
pp. 221-229 ◽  
Author(s):  
Fabio Barili ◽  
Nicholas Freemantle ◽  
Alberto Pilozzi Casado ◽  
Mauro Rinaldi ◽  
Thierry Folliguet ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data ◽  
Time Varying ◽  
Time To Event ◽  
Surgical Aortic Valve Replacement ◽  
Transcatheter Aortic Valve ◽  
Kaplan Meier ◽  
Valve Implantation

Abstract OBJECTIVES This meta-analysis of Kaplan–Meier-estimated individual patient data was designed to evaluate the effects of transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement (SAVR) on the long-term all-cause mortality rate, to examine the potential time-varying effect and to model their hazard ratios (HRs) over time. Moreover, we sought to compare traditional meta-analytic tools and estimated individual patient data meta-analyses. METHODS Trials comparing TAVI versus SAVR were identified through Medline, Embase, Cochrane databases and specialist websites. The primary outcome was death from any cause at follow-up. Enhanced secondary analyses of survival curves were performed estimating individual patient time-to-event data from published Kaplan–Meier curves. Treatments were compared with the random effect Cox model in a landmark framework and fully parametric models. RESULTS We identified 6 eligible trials that included 6367 participants, randomly assigned to undergo TAVI (3252) or SAVR (3115). According to the landmark analysis, the incidence of death in the first year after implantation was significantly lower in the TAVI group [risk-profile stratified HR 0.85, 95% confidence interval (CI) 0.73–0.99; P = 0.04], whereas there was a reversal of the HR after 40 months (risk-profile stratified HR 1.31, 95% CI 1.01–1.68; P = 0.04) favouring SAVR over TAVI. This time-varying trend of HRs was also confirmed by a fully parametric time-to-event model. Traditional meta-analytic tools were shown to be biased because they did not intercept heterogeneity and the time-varying effect. CONCLUSIONS The mortality rates in trials of TAVI versus SAVR are affected by treatments with a time-varying effect. TAVI is related to better survival in the first months after implantation whereas, after 40 months, it is a risk factor for all-cause mortality.

scholarly journals Estimating COVID-19 hospital demand using a non-parametric model: a case study in Galicia (Spain)

2020 ◽  
Author(s):  
Ana López-Cheda ◽  
María Amalia Jácome ◽  
Ricardo Cao ◽  
Pablo Martínez de Salazar
Keyword(s):  
Length Of Stay ◽  
Prediction Models ◽  
Current Knowledge ◽  
Mitigation Strategies ◽  
Hospital Ward ◽  
Monte Carlo Algorithm ◽  
Time To Event ◽  
Mixture Cure Model ◽  
Lengths Of Stay ◽  
Kaplan Meier

Understanding the demand for hospital beds for COVID-19 patients is key for decision-making and planning mitigation strategies, as overwhelming healthcare systems has critical consequences for disease mortality. However, accurately mapping the time-to-event of hospital outcomes, such as the length-of-stay in the ICU, requires understanding patient trajectories while adjusting for covariates and observation bias, such as incomplete data. Standard methods, like the Kaplan-Meier estimator, require prior assumptions that are untenable given current knowledge. Using real-time surveillance data from the first weeks of the COVID-19 epidemic in Galicia (Spain), we aimed to model the time-to-event and event probabilities of patients hospitalized, without parametric priors and adjusting for individual covariates. We applied a nonparametric Mixture Cure Model and compared its performance in estimating hospital ward/ICU lengths-of-stay to the performances of commonly used methods to estimate survival. We showed that the proposed model outperformed standard approaches, providing more accurate ICU and hospital ward length-of-stay estimates. Finally, we applied our model estimates to simulate COVID-19 hospital demand using a Monte Carlo algorithm. We provided evidence that adjusting for sex, generally overlooked in prediction models, together with age is key for accurately forecasting ICU occupancy, as well as discharge or death outcomes.

scholarly journals Proposals on Kaplan–Meier plots in medical research and a survey of stakeholder views: KMunicate

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e030215 ◽  
Author(s):  
Tim P Morris ◽  
Christopher I Jarvis ◽  
William Cragg ◽  
Patrick P J Phillips ◽  
Babak Choodari-Oskooei ◽  
...  
Keyword(s):  
Outcome Measures ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Time To Event Data ◽  
Kaplan Meier ◽  
Starting Point ◽  
Wide Range ◽  
International Participation ◽  
Over Time

ObjectivesTo examine reactions to the proposed improvements to standard Kaplan–Meier plots, the standard way to present time-to-event data, and to understand which (if any) facilitated better depiction of (1) the state of patients over time, and (2) uncertainty over time in the estimates of survival.DesignA survey of stakeholders’ opinions on the proposals.SettingA web-based survey, open to international participation, for those with an interest in visualisation of time-to-event data.Participants1174 people participated in the survey over a 6-week period. Participation was global (although primarily Europe and North America) and represented a wide range of researchers (primarily statisticians and clinicians).Main outcome measuresTwo outcome measures were of principal importance: (1) participants’ opinions of each proposal compared with a ‘standard’ Kaplan–Meier plot; and (2) participants’ overall ranking of the proposals (including the standard).ResultsMost proposals were more popular than the standard Kaplan–Meier plot. The most popular proposals in the two categories, respectively, were an extended table beneath the plot depicting the numbers at risk, censored and having experienced an event at periodic timepoints, and CIs around each Kaplan–Meier curve.ConclusionsThis study produced a high response number, reflecting the importance of graphics for time-to-event data. Those producing and publishing Kaplan–Meier plots—both authors and journals—should, as a starting point, consider using the combination of the two favoured proposals.

Fractures and health care resource use in cancer patients with chemotherapy-associated peripheral neuropathy (CAPN).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e13038-e13038
Author(s):  
Arijit Ganguli ◽  
Patrick J Reilly ◽  
Saurabh Ray
Keyword(s):  
Peripheral Neuropathy ◽  
Cancer Patients ◽  
Resource Use ◽  
Index Date ◽  
Event Analysis ◽  
Office Visits ◽  
Inclusion Criteria ◽  
Time To Event ◽  
Kaplan Meier ◽  
Increased Risk

e13038 Background: Chemotherapy has been associated with increased risk of fractures1. This study examines the real-world incidence of fractures and healthcare resource use (HRU) that may be associated with CAPN in cancer patients. Methods: A retrospective analysis utilized a national health insurer claims -database (2001-2009), to identify patients ≥18 yrs with a cancer ICD-9-code (140-239) and a chemotherapy drug code (J9xxx). The 1st chemotherapy date was the "index date." Patients with a record of peripheral neuropathy (PN) in the pre-index date were excluded. Patients with a PN post-index were matched with no-PN post-index (non-PN) based on gender, age and index date. Both groups were compared for number of fractures, HRU (hospital outpatient (OP), office, and emergency-room [ER] visits) and all-cause costs in their 365-days post-index period. Time to 1st fracture post-index was compared using Kaplan Meier time to event analysis. Results: Of 34,625 patient meeting the inclusion criteria, 1675 patients (4.3%) formed the PN group and were matched to non-PN group. At baseline, mean age was 54.9 yrs, 62.5% were females, and no difference in % of bone metastasis (p=0.12) between the groups. In PN group, 5.3% (n=87) had a fracture 365-days post-index compared to 3.5% (n=58) in non-PN group (p<0.05). Mean days to fracture from index date in PN group was shorter than the non-PN group (150.9 vs. 153.4, p<0.05). In PN group, annual mean number of OP visit (14.6 vs. 12.0, p<0.0001), ER visit (0.47 vs. 0.30, p<0.001), and office visits (30.4 vs. 23.3, p<0.0001), were higher compared to non-PN group. Annual healthcare cost of PN patients was 21% higher than non-PN patients ($64,578 vs. $53,221) and CAPN-related cost in PN group was estimated to be $5,580 annually. Conclusions: Patients with CAPN were associated with higher incidence of fractures, HRU and cost.

Sign in / Sign up

Export Citation Format

Share Document