Survival and Event History Analysis: A Process Point of View

By O. Aalen, O. Borgan, Håkon K. Gjessing. 2008

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “Causal inference for longitudinal studies has been studied extensively, too [e.g. Vanderweele, 2015, Aalen et al., 2008, Robins, 1997], where the results are often formulated in the language of potential outcomes [Imbens and Rubin, 2015], rather than SCMs.”
A martingale approach to continuous-time marginal structural models (2009, unknown): “We assume that the counterfactual intensity follows Allen’s additive hazard regression model, see [ABGK93] and [ABG08].”
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey (2020, background): “Survival probability indicates the probability of an event not happening [96].”
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (2016, background): “Particular functional forms of the conditional intensity function λ∗(t) are often designed to capture the phenomena of interests [1].”
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017, background): “From the survival analysis theory (Aalen et al., 2008), given the history T = { t 1 , . . . , t n } , for any t > t n , we characterize the conditional probability that no event happens during [ t n , t ) as S ( t |T ) = exp − t t n λ ( τ ) d τ .”
An empirical comparison of dependency network evolution in seven software packaging ecosystems (2017, methodology): “ssumed if they are clear from the context. 3.2 Statistical analysis techniques One of the statistical techniques that will be used in this article is survival analysis (a.k.a. event history analysis) [1]. It is a technique that models \time to event" data with the aim to estimate the survival r”
Structure and dynamics of information pathways in online media (2012, methodology): “3 and rearrange with hazard functions: Perhaps surprisingly, our continuous time model of diffusion is a particular case of Aalen’s additive regression model, frequently used in survival theory analysis [3].”
Reductions in Risk Based on Time Offense-Free in the Community: Once a Sexual Offender, Not Always a Sexual Offender (2017, methodology): “…part of the effect is attributable to individuals with the greatest propensity for sexual crime reoffending shortly after release (and often), making them, consequently, most likely to be caught and removed from the follow-up sample (the effect of frailty in survival analysis [Aalen et al., 2008]).”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, methodology): “The survival function of a process [1] indicates the probability that no event will occur until t ≥ t n given the history H t n and is computed as S ( t |H t n ) = 1 − F ( t |H t n ) .”
On Estimation in Relative Survival (2012, methodology): “Assuming equal excess hazard λEi for all i and following the same arguments as in the derivation of the Nelson–Aalen estimator (Aalen et al., 2008), the cumulative excess hazard can be estimated as (Andersen and Vaeth, 1989) Λ̂∗E (t) = ∫ t 0 dN (u) Y (u) − ∫ t 0 n∑ i=1 Yi (u)dΛP i (u) Y (u) .”
flexsurv: A Platform for Parametric Survival Modeling in R. (2016, background): “Fully-specified models can be more convenient for representing complex data structures and processes (Aalen et al. 2008), e.g. hazards that vary predictably, interval censoring, frailties, multiple responses, datasets or time scales, and can help with out-of-sample prediction.”
Regular Article (2004, unknown): “, 2011a) also consider the additive Aalen function (Aalen et al., 2008) for counting process models for large networks, in this study we consider only the multiplicative Cox (Andersen et al.”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, methodology): “Combining the models (6) and (7) allows the construction of dynamic path models 15,37 where a series of DAGs can be deﬁned more or less continuously over time, making estimation of immediate structure a more likely possibility.”
Modeling Information Propagation with Survival Theory (2013, methodology): “…by computing the cumulative probability F i ( t | s ( t i )) of infection of a susceptible node i at any given time t using the hazard function (Aalen et al., 2008): In the remainder of the paper, we propose an additive and a multiplicative model of hazard functions α i ( t | s ) and validate…”
History of applications of martingales in survival analysis. (2010, unknown): “ametric models, as well as chapters giving preliminary glimpses into topics to come: semiparametric eﬃciency, frailty models (for more elaborate introductions of frailty models see Hougaard, 2002; or Aalen et al., 2008) and multiple timescales. Fleming and Harrington had published their monograph Co”