Survival Analysis: Techniques for Censored and Truncated Data

By Jun Yan. 2004

2,632 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c1878eddd6f7026fb4930b4ed26c5ef3e692c99b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Contemporary Patterns of Spontaneous Labor With Normal Neonatal Outcomes (2010, background): “5 (2, 8) 5 (2, 8) Effacement at admission (%) 90 (60, 100) 90 (50, 100) 80 (50, 100) Station at admission 1 ( 3, 0) 1 ( 3, 0) 2 ( 3, 0) Oxytocin use in spontaneous labor 47 45 45 Epidural analgesia 84 77 71 Total number of vaginal examinations in first stage 5 (1, 9) 4 (1, 7) 4 (1, 7) Instrumental d”
Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting (2012, methodology): “These models are an alternative to Cox proportional hazard models and are a popular choice for modelling survival data parametrically (Klein and Moeschberger, 2003).”
Mining Electronic Health Records: A Survey (2017, methodology): “Kleing et al. [Klein and Moeschberger 2005] provides a comprehensive overview of various techniques used to handle survival and censored data.”
The Role of Dynamic Renegotiation and Asymmetric Information in Financial Contracting (2015, neutral): “The bandwidth is denoted b. Confidence bands for the smoothed hazard are estimated using the log transformation discussed in Klein and Moeschberger (2003).”
Maximum likelihood estimation in Dagum distribution with censored samples (2011, methodology): “The empirical hazard function is drawn using the cohort life table technique according to Klein and Moeschberger [17].”
Post-listing performance and private sector regulation: The experience of London's Alternative Investment Market $ (2013, positive): “Table 6 presents parameter estimates from the maximum likelihood estimation of an Accelerated Failure Time model (Klein and Moeschberger, 2003).”
Machine Learning for Survival Analysis: A Survey (2017, methodology): “In the healthcare domain, the starting point of the observation is usually a particular medical intervention such as a hospitalization admission, the beginning of taking a certain medication or a diagnosis of a given disease [Klein and Moeschberger 2005; Miller Jr 2011].”
Understanding, modelling and managing longevity risk: key issues and main challenges (2012, methodology): “In 2009 many companies based in developed countries had closed the defined benefit retirement plans previously offered to their employees (such as the 401(K) pension plans in the United States).”
Effects of South Carolina’s Sex Offender Registration and Notification Policy on Adult Recidivism (2010, methodology): “Second, the “counting process” approach (Klein & Moeschberger, 2003) was used to remove incarceration periods from the competing risks analyses (Scrucca, Santucci, & Aversa, 2007).”
After the pink slip: Applying dynamic motivation frameworks to the job search experience. (2012, methodology): “For Hypotheses 9 and 10, we used Cox regression (i.e., proportional hazard model [Klein & Moeschberger, 1997]) and ordinary least squares (OLS) regression to predict the speed of reemployment and number of interviews.”
The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments (2013, methodology): “Thus, we think that, while the log-rank test is frequently used for testing the equality of survival curves in propensity score matched samples [9, 10, 35], such an approach is inappropriate, because it requires that the samples be independent of one another [36, 37].”
A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003 (2008, positive): “Alternatively, Kaplan–Meier survival curves between treated and untreated subjects in the matched sample can be compared using a test described by Klein and Moeschberger [81].”
Bankruptcy Prediction With Industry Effects (2004, positive): “Following the standard survival analysis literature (see Kiefer (1988), Klein and Moeschberger (1997)), we define the hazard rate or intensity rate for the bankruptcy time τ iB , a random variable, as: λiB(t) = lim t→0 Pr(t ≤ τ iB t + t | τ ′B ≥ t) t .”