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.

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.

scholarly journals Germination Data Analysis by Time-to-Event Approaches

Plants ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 617
Author(s):  
Alessandro Romano ◽  
Piergiorgio Stevanato
Keyword(s):  
Survival Analysis ◽  
Data Analysis ◽  
Sugar Beet ◽  
Biological Data ◽  
Event Analysis ◽  
Time To Event ◽  
Advantages And Disadvantages ◽  
Regression Techniques ◽  
Parametric Functions ◽  
Scientific Fields

Germination data are analyzed by several methods, which can be mainly classified as germination indexes and traditional regression techniques to fit non-linear parametric functions to the temporal sequence of cumulative germination. However, due to the nature of germination data, often different from other biological data, the abovementioned methods may present some limits, especially when ungerminated seeds are present at the end of an experiment. A class of methods that could allow addressing these issues is represented by the so-called “time-to-event analysis”, better known in other scientific fields as “survival analysis” or “reliability analysis”. There is relatively little literature about the application of these methods to germination data, and some reviews dealt only with parts of the possible approaches such as either non-parametric and semi-parametric or parametric ones. The present study aims to give a contribution to the knowledge about the reliability of these methods by assessing all the main approaches to the same germination data provided by sugar beet (Beta vulgaris L.) seeds cohorts. The results obtained confirmed that although the different approaches present advantages and disadvantages, they could generally represent a valuable tool to analyze germination data providing parameters whose usefulness depends on the purpose of the research.

A global test for competing risks survival analysis

2020 ◽  
Vol 29 (12) ◽  
pp. 3666-3683
Author(s):  
Dominic Edelmann ◽  
Maral Saadati ◽  
Hein Putter ◽  
Jelle Goeman
Keyword(s):  
Survival Analysis ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Competing Risks ◽  
Cox Model ◽  
Wald Test ◽  
Ratio Test ◽  
Type I ◽  
Multistate Models ◽  
Global Test

Standard tests for the Cox model, such as the likelihood ratio test or the Wald test, do not perform well in situations, where the number of covariates is substantially higher than the number of observed events. This issue is perpetuated in competing risks settings, where the number of observed occurrences for each event type is usually rather small. Yet, appropriate testing methodology for competing risks survival analysis with few events per variable is missing. In this article, we show how to extend the global test for survival by Goeman et al. to competing risks and multistate models[Per journal style, abstracts should not have reference citations. Therefore, can you kindly delete this reference citation.]. Conducting detailed simulation studies, we show that both for type I error control and for power, the novel test outperforms the likelihood ratio test and the Wald test based on the cause-specific hazards model in settings where the number of events is small compared to the number of covariates. The benefit of the global tests for competing risks survival analysis and multistate models is further demonstrated in real data examples of cancer patients from the European Society for Blood and Marrow Transplantation.

scholarly journals The duration of export trade relations and its influential factors in China's wooden floor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ruonan Liu ◽  
Yuhui Yue ◽  
Dongling Miao ◽  
Baodong Cheng
Keyword(s):  
Survival Analysis ◽  
Influencing Factors ◽  
Survival Function ◽  
Forest Products ◽  
Export Trade ◽  
Wooden Floor ◽  
Content Type ◽  
Trade Relations ◽  
Kaplan Meier ◽  
Wood Flooring

PurposeThis article will select 25 years of subdivided data to perform Kaplan–Meier survival analysis on the export trade relations of Chinese wooden flooring, use discrete-time cloglog models to analyze influencing factors, use logit and probit models to test the robustness, and try to systematically reveal the duration of China's wood flooring export trade and its influencing factors.Design/methodology/approachThis study used Kaplan–Meier survival function estimation method. In the survival analysis, survival function and hazard rate function are often used to characterize the distribution of survival time.FindingsThe continuous average export time of China's wooden flooring is relatively long, about 14 years. China's wooden flooring has a negative time dependency. After the export trade exceeds the threshold value of 15 years, the failure rate of trade greatly decreases, which has a “threshold effect.” Gravity model variables have a significant impact on the duration of China's wooden floor export.Originality/valueStudying the duration of forest products trade is of great significance for clearing deep-level trade relations and promoting sustainable development of forest products trade.

scholarly journals Competing Risks Models for an Enterprises Duration on the Market

2020 ◽  
Vol 20 (1) ◽  
pp. 456-473
Author(s):  
Dominika M. Urbańczyk
Keyword(s):  
Survival Analysis ◽  
Competing Risks ◽  
Waiting Time ◽  
Research Topic ◽  
Business Activity ◽  
Full Understanding ◽  
Kaplan Meier ◽  
Duration Distribution ◽  
The Impact ◽  
Valuable Result

AbstractResearch background: Enterprises are an important element of the economy, which explains that the analysis of their duration on the market is an important and willingly undertaken research topic. In the case of complex problems like this, considering only one type of event, which ends the duration, is often insufficient for full understanding.Purpose: In this paper there is an analysis of the duration of enterprises on the market, taking into account various reasons for the termination of their business activity as well as their characteristics.Research methodology: A survival analysis can be used to study duration on the market. However, the possibility of considering the waiting time for only one type of event is its important limitation. One solution is to use competing risks. Various competing risks models (naive Kaplan-Meier estimator, subdistribution model, subhazard and cause-specific hazard) are presented and compared with an indication of their advantages and weakness.Results: The competing risks models are estimated to investigate the impact of the causes of an enterprises liquidation on duration distribution. The greatest risk concerns enterprises with a natural person as the owner (regardless of the reason of failure). For each of the competing risks, it is also indicated that there is a section of activity which adversely affects the ability of firms to survive on the market.Novelty: A valuable result is considering the reasons for activity termination in the duration analysis for enterprises from the Mazowieckie Voivodeship.

scholarly journals Competing risks’ analysis in cardiovascular disease epidemiology; – Τhe case of ATTICA study (2002-2012)

2020 ◽  
pp. 19-25
Author(s):  
Thomas Tsiampalis ◽  
Demosthenes Panagiotakos
Keyword(s):  
Cardiovascular Disease ◽  
Competing Risks ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Survival Function ◽  
Gray Model ◽  
Disease Epidemiology ◽  
Hazards Model ◽  
One To One

Background: In studies of all-cause mortality, a one-to-one relation connects the hazard with the survival and as a consequence the regression models which focus on the hazard, such as the proportional hazards model, immediately dictate how the covariates relate to the survival function, as well. However, these two concepts and their one-to-one relation are totally different in the context of competing risks, where the terms of cause-specific hazard and cumulative incidence function appear. Objective: The aim of the present work was to present two of the most popular methods (cause-specific hazard model and Fine & Gray model) through an application on cardiovascular disease epidemiology (CVD), as well as, to narratively review more recent publications, based on either the frequentist, or the Bayesian approach to inference. Methods: A narrative review of the most widely used methods in the competing risks setting was conducted, extended to more recent publications. For the application, our interest lied in modeling the risk of Coronary Heart Disease in the presence of vascular stroke, by using the cause-specific hazard and the Fine & Gray models, two of most commonly encountered approaches. Results-Conclusions: After the implementation of these two approaches in the context of competing risks in CVD epidemiology, it is noted that while the use of the Fine & Gray model includes information about the existence of a competing risk, the interpretation of the results is not as easy as in the case of the cause-specific risk Cox model.

scholarly journals Survival analysis of renal patients underwent transplantation in Kyrgyz Republic and various countries by 10 years follow-up

2019 ◽  
Vol 3 (Issue 4) ◽  
pp. 243
Author(s):  
Zhanybek Gaibyldaev ◽  
Zhamalbek Ashimov ◽  
Damirbek Abibillaev ◽  
Fuat Kocyigit
Keyword(s):  
Survival Analysis ◽  
Heart Surgery ◽  
Cox Regression ◽  
Statistical Significance ◽  
Survival Function ◽  
Strong Impact ◽  
Kyrgyz Republic ◽  
Cox Regression Analysis ◽  
Kaplan Meier

In our study we conducted survival analysis of 204 patients visited Scientific-Research Institute of Heart Surgery and Organs transplantation and who underwent renal transplantation in Kyrgyzstan and other Eurasian countries between 2005 and 2016 years (age range: 9-71 years, mean: 38.21 (12.74) years, median: 34.0 (0.89) years; gender: 142 male (69.6%)). During follow-up period, mortality event was observed in 16 (7.84%) patients. Survival function probabilities of patients and rational risk factors of survival functions were evaluated by Kaplan-Meier and Cox regression analyses, respectively. According to Kaplan-Meier results survival probabilities calculated for 1st year: 0.96 (0.014), for 3rd year: 0.94 (0.018), for 5th year: 0.86 (0.04), for 7th year: 0.75 (0.10). Among age groups 28-39 age ranges prevailed by 11 patients. Nevertheless, that difference did not show statistical significance: p˃0.322. The intensity of transplantation also analyzed according to years, which revealed increasing in numbers of operations by time. For instance, when in 2006 only two cases were registered in our center, but numbers of transplanted patients reached up to 48 in 2015. The association of mortality states and years of transplantation found significantly by Kaplan-Meier test (Breslow p˂0.001). The survival analysis was compared according to countries and revealed significant results (Breslow p˂0.05). From other factors influencing mortality, sex did not show strong impact on survival by Kaplan-Meier analysis, but significant association was found by Cox regression analysis.

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.

Time-to-Event Analyses: Return to Unrestricted Participation Following Sport-Related Concussion in a Cohort of High School Athletes

2020 ◽  
Author(s):  
Tracey Covassin ◽  
Abigail C. Bretzin ◽  
Erica Beidler ◽  
Jessica Wallace
Keyword(s):  
High School ◽  
High School Student ◽  
Student Athletes ◽  
Event Analysis ◽  
Time To Event ◽  
Injury Surveillance System ◽  
Kaplan Meier ◽  
Injury Reporting ◽  
Recovery Trajectory ◽  
High School Athletic

Abstract Context: Understanding time-loss resulting from sport-related concussion (SRC) within individual sports allows high school athletic trainers to provide accurate and clinically evidence-based information. Currently there is a lack of research regarding patterns of clinical recovery outcomes in high school student-athletes across sports. Objective: To describe the time to authorized unrestricted RTP following SRC in a large cohort of high school student-athletes in variety of sports using a time-to-event analysis. Design: Descriptive Epidemiology Study. Setting: Aggregate injury and player exposure data from the STATE-XXX High School Athletic Association (XHSAA) Head Injury Reporting System (HIRS). Patients or Other Participants: High school student-athletes. Main Outcome Measure(s): Dates for SRC injury events and authorized unrestricted RTP were entered into the HIRS for each case, and were used to calculate time to unrestricted RTP. Survival analysis determined time to authorized RTP for males and females in weekly increments across sports and academic years. Separate Kaplan-Meier analyses adjusted for SRC cases with a history of concussion also identified the proportions of student-athletes that obtained authorized medical clearance in weekly increments. Results: There was a total of 15,821 SRC cases, 10,375 (65.6%) male and 5,446 (34.4%) female, reported during the 2015–16 through 2018–19 academic years. The median time to authorized unrestricted RTP was 11 days for all cases. Approximately, 30% of concussed student-athletes were not cleared for unrestricted RTP by 14 days following their SRC diagnosis, with 13% taking longer than 21 days to unrestricted RTP after SRC. Conclusions: The results from this multi-site, State-based injury surveillance system indicate that it is not abnormal for high school student-athletes to take longer than 14 days to fully recovery from a SRC. This information may be useful for educating high school student-athletes and sport stakeholders, normalizing SRC recovery trajectory perceptions, and establishing realistic RTP timeline expectations.

scholarly journals Observational Study of Chlorpromazine in Hospitalized Patients with Covid-19

2020 ◽  
Author(s):  
Nicolas Hoertel ◽  
Marina Sanchez Rico ◽  
Raphael Vernet ◽  
Anne-Sophie Jannot ◽  
Antoine Neuraz ◽  
...  
Keyword(s):  
Primary Endpoint ◽  
Cox Regression ◽  
Cox Model ◽  
Event Analysis ◽  
Time To Event ◽  
Rt Pcr ◽  
University Hospitals ◽  
Inverse Probability ◽  
Antiviral Effects

On the grounds of its anti-inflammatory and potential antiviral effects, chlorpromazine has been suggested to be effective treatment for Covid-19. We examined the association between chlorpromazine use and respiratory failure among all hospitalized adults with Covid-19 at the 39 Greater Paris University hospitals since the beginning of the epidemic. Study baseline was defined as the date of hospital admission. The primary endpoint was a composite of intubation or death in a time-to-event analysis adjusting for numerous potential confounders. We used a multivariable Cox model with inverse probability weighting according to the propensity score. Of the 12,217 adult inpatients with a positive Covid-19 RT-PCR test included in the analyses, 57 (0.47%) received chlorpromazine. Over a mean follow-up of 20.8 days, the primary endpoint occurred in 29 patients (50.9%) exposed to chlorpromazine and 1,899 patients (15.6%) who were not. In the main analysis, there was a positive significant association between chlorpromazine use and the outcome (HR, 1.67; 95% CI, 1.09 to 2.56, p=0.019), while a Cox regression in a matched analytic sample yielded non-significant association (1.38; 95% CI, 0.91 to 2.09, p=0.123). These findings suggest that chlorpromazine is unlikely to have a clinical efficacy for Covid-19.

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