scholarly journals Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method

2021 ◽  
Vol 9 ◽  
pp. 28-40
Author(s):  
Toni Monleón-Getino ◽  
Keyword(s):  
Polynomial Regression ◽  
Parametric Method ◽  
Survival Function ◽  
Parametric Function ◽  
Numerical Function ◽  
Time Points ◽  
Event Time ◽  
Kaplan Meier ◽  
Competing Events ◽  
Cardiac Illness

Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.

scholarly journals Cox proportion hazard model for patients with hepatitis disease in Iraq

2009 ◽  
Vol 6 (3) ◽  
pp. 612-617
Author(s):  
Baghdad Science Journal
Keyword(s):  
Regression Model ◽  
Survival Time ◽  
Cox Regression ◽  
Survival Function ◽  
Hazard Model ◽  
Cox Regression Model ◽  
Hepatic Diseases ◽  
Kaplan Meier

Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.

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.

Survival Analysis

2018 ◽  
pp. 243-256
Author(s):  
Jorge Leite ◽  
Sandra Carvalho ◽  
Munir Boodhwani ◽  
Felipe Fregni
Keyword(s):  
Survival Analysis ◽  
Time Window ◽  
Cumulative Probability ◽  
Specific Time ◽  
Basic Principles ◽  
Hazard Functions ◽  
Time Points ◽  
Clinical Investigator ◽  
Probability Of Survival ◽  
Kaplan Meier

This chapter focuses on basic principles of survival analysis for the clinical investigator. Survival analysis is a specific type of standardized statistical analysis that focuses on assessing the time elapsed since the exposure/intervention to the occurrence of an event. Important concepts such as median survival time, cumulative probability of survival at specific time points by using Kaplan-Meier estimators, and the use of the use of log rank (Mantel–Cox) to compare survival functions are discussed. This chapter also discusses the concept of censoring, which happens when the event occurs outside the pre-specified time window, and how to develop hazard functions when there are several interrelated factors that can contribute to the increase or decrease of survival probability.

P1129PERITONEAL DIALYSIS TECHNIQUE FAILURE IN A LARGE POPULATION IN COLOMBIA

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Jasmin Vesga ◽  
Nelcy Rodriguez ◽  
Angela Rivera ◽  
Mauricio Sanabria
Keyword(s):  
Large Population ◽  
Wilcoxon Test ◽  
Technique Survival ◽  
Kaplan Meier ◽  
History Of ◽  
Therapy Services ◽  
Competing Events ◽  
Catheter Complications ◽  
Definition Of ◽  
Technique Failure

Abstract Background and Aims Peritoneal dialysis (PD) is a renal replacement therapy widely used in the world; about 197,000 patients use it. The main limitation of the use is related to technique failure. Extending time free of technique failure remains a long-term challenge in PD. To estimate the incidence of technique failure in PD and, describe the main reasons for failure. Method A historical, multicenter, observational cohort study of all adult patients starting PD between January 1, 2010, and December 31, 2015, with follow-up until December 31, 2018, at Renal Therapy Services (RTS) network. The definition of technique failure applied when a patient was switched to Hemodialysis (HD) for at least 30 days. Socio-demographic and clinical characteristics of all patients were summarized descriptively by PD modality. We estimate the failure rate with an analysis of Kaplan Meier and analysis that incorporate competing risks as death and transplant. Results 6452 patients met the inclusion criteria for data analysis, 67% were in CAPD modality. The mean age was 59 years, 54% were male, and 53.7% had a history of diabetes, see Table 1. There were 1462 technique failures events with a rate of 10.67 events per 100 patients-year [95% CI, 10.14 – 11.24]. The median technique survival time was 5.8 years [ 95% CI: 5.11 to 5.45]. Technique failure rates did not differ by PD modality (Wilcoxon test = 0.4986), Figure 1. In the presence of competing events, the cumulative incidence of technique failure for the first year was 6.9 events per 100 persons-risk [95% CI: 6.27 to 7.56], Table 2. The most common causes of technique failure were catheter complications, followed by psychological and medical indications, Table 3. Conclusion The incidence of PD technique failure is around ten events per 100 patient-year, and this is lower than the previous cohort reports in the literature. Catheter problems remain the leading cause of technique failure

scholarly journals Hidden Imputations and the Kaplan-Meier Estimator

2020 ◽  
Vol 189 (11) ◽  
pp. 1408-1411 ◽  
Author(s):  
Stephen R Cole ◽  
Jessie K Edwards ◽  
Ashley I Naimi ◽  
Alvaro Muñoz
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Data Set ◽  
Kaplan Meier ◽  
Event Times

Abstract The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.

scholarly journals The analysis of competing events like cause-specific mortality--beware of the Kaplan-Meier method

2010 ◽  
Vol 26 (1) ◽  
pp. 56-61 ◽  
Author(s):  
M. Verduijn ◽  
D. C. Grootendorst ◽  
F. W. Dekker ◽  
K. J. Jager ◽  
S. le Cessie
Keyword(s):  
Kaplan Meier ◽  
Specific Mortality ◽  
Competing Events

scholarly journals The what, when and how of orthopaedic registers: an introduction into register-based research

2019 ◽  
Vol 4 (6) ◽  
pp. 337-343 ◽  
Author(s):  
Claus Varnum ◽  
Alma Bečić Pedersen ◽  
Per Hviid Gundtoft ◽  
Søren Overgaard
Keyword(s):  
Survival Analysis ◽  
Selection Bias ◽  
Survival Data ◽  
Cox Regression ◽  
Research Question ◽  
Cox Regression Analysis ◽  
Information Bias ◽  
Kaplan Meier ◽  
Routinely Collected Health Data ◽  
Competing Events

Establishment of orthopaedic registers started in 1975 and many registers have been initiated since. The main purpose of registers is to collect information on patients, implants and procedures in order to monitor and improve the outcome of the specific procedure. Data validity reflects the quality of the registered data and consists of four major aspects: coverage of the register, registration completeness of procedures/patients, registration completeness of variables included in the register and accuracy of registered variables. Survival analysis is often used in register studies to estimate the incidence of an outcome. The most commonly used survival analysis is the Kaplan–Meier survival curves, which present the proportion of patients who have not experienced the defined event (e.g. death or revision of a prosthesis) in relation to the time. Depending on the research question, competing events can be taken into account by using the cumulative incidence function. Cox regression analysis is used to compare survival data for different groups taking differences between groups into account. When interpreting the results from observational register-based studies a number of factors including selection bias, information bias, chance and confounding have to be taken into account. In observational register-based studies selection bias is related to, for example, absence of complete follow-up of the patients, whereas information bias is related to, for example, misclassification of exposure (e.g. risk factor of interest) or/and outcome. The REporting of studies Conducted using Observational Routinely-collected Data guidelines should be used for studies based on routinely-collected health data including orthopaedic registers. Linkage between orthopaedic registers, other clinical quality databases and administrative health registers may be of value when performing orthopaedic register-based research. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180097

scholarly journals Normalized Apparent Diffusion Coefficient in the Prognostication of Patients with Glioblastoma Multiforme

2016 ◽  
Vol 43 (1) ◽  
pp. 127-133 ◽  
Author(s):  
Jai Jai Shiva Shankar ◽  
Adil Bata ◽  
Krista Ritchie ◽  
Andrea Hebb ◽  
Simon Walling
Keyword(s):  
Diffusion Coefficient ◽  
Glioblastoma Multiforme ◽  
Apparent Diffusion Coefficient ◽  
Survival Function ◽  
Correlation Coefficients ◽  
Diffusion Weighted Images ◽  
Institutional Ethics ◽  
Normal Appearing White Matter ◽  
Kaplan Meier ◽  
Apparent Diffusion

AbstractBackground: Glioblastoma multiforme (GBM) is known to have poor prognosis, with no available imaging marker that can predict survival at the time of diagnosis. Diffusion weighted images are used in characterisation of cellularity and necrosis of GBM. The purpose of this study was to assess whether pattern or degree of diffusion restriction could help in the prognostication of patients with GBM. Material and Methods: We retrospectively analyzed 84 consecutive patients with confirmed GBM on biopsy or resection. The study was approved by the institutional ethics committee. The total volume of the tumor and total volume of tumor showing restricted diffusion were calculated. The lowest Apparent Diffusion Coefficient (ADC) in the region of the tumor and in the contralateral Normal Appearing White Matter were calculated in order to calculate the nADC. Treatment and follow-up data in these patients were recorded. Multivariate analsysis was completed to determine significant correlations between different variables and the survival of these patients. Results: Patient survival was significantly related to the age of the patient (p<0.0001; 95% CI-1.022-1.043) and the nADC value (p=0.014; 95% CI-0.269-0.860) in the tumor. The correlation coefficients of age and nADC with survival were −0.335 (p=0.002) and 0.390 (p<0.001), respectively. Kaplan Meier survival function, grouped by normalized Apparent Diffusion Coefficient cut off value of 0.75, was significant (p=0.007). Conclusion: The survival of patients with GBM had small, but significant, correlations with the patient’s age and nADC within the tumor.

Cabazitaxel or mitoxantrone with prednisone in patients with metastatic castration-resistant prostate cancer (mCRPC) previously treated with docetaxel: Estimating mean overall survival (OS) for health economic analyses from a phase III trial (TROPIC).

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 128-128 ◽  
Author(s):  
S. Oudard ◽  
F. Joulain ◽  
A. De Geer ◽  
A. O. Sartor
Keyword(s):  
Goodness Of Fit ◽  
Graphical Method ◽  
Survival Function ◽  
Information Criteria ◽  
Phase Iii ◽  
Point Estimate ◽  
Weibull Function ◽  
Term Survival ◽  
Kaplan Meier ◽  
Previously Treated

128^ Background: TROPIC evaluated the efficacy and safety of the novel taxane cabazitaxel in men with mCRPC previously treated with docetaxel. Median OS was significantly improved, as previously reported (12.7 months in mitoxantrone arm vs 15.1 months in cabazitaxel arm, HR=0.72 [0.61 – 0.84], p<0.0001- updated OS results). Median OS is the most useful descriptive statistic for physicians and patients as it reflects a point estimate in time by which 50% patients may survive regardless of disease status or progression. It avoids assumptions on long-term survival beyond the follow-up period of the clinical trial. Payers however are interested in making a coverage and reimbursement decisions based on the overall therapeutic benefit relative to its risk, and the expected impact on healthcare expenditures. Analyses such as cost-effectiveness analysis therefore require the estimation of mean OS. Methods: Mean OS is only observable when the last patient has died. Its estimation can be derived via an extrapolation of the trial Kaplan-Meier curve using a survival function. Several parametric distributions (exponential, weibull, lognormal, loglogistic and Gompertz) were tested. The parametric distribution that best fitted the trial Kaplan-Meier curves was selected using the Akaike's Information criteria (AIC), the Bayesian Information Criteria (BIC) and graphical method to evaluate the goodness of fit of the distributions. Mean OS from the best fitted model was generated to support payer decision making. Results: Using AIC/BIC and graphical method, the Weibull survival function, S(t)=exp(-l ts) where l is a scale parameter and s a shape parameter, was selected as the distribution that best fitted the TROPIC data. Results of the estimated mean survival assuming a Weibull function are described in the table. Conclusions: Assuming a Weibull distribution, mean OS is estimated at 14.5 months in mitoxantrone arm vs 18.5 months in cabazitaxel arm, leading to 4 months OS difference in favour of cabazitaxel. [Table: see text] [Table: see text]

Sign in / Sign up

Export Citation Format

Share Document