Survival analysis, longitudinal analysis and causal inference

Accessing and exploiting organizational resources are essential capabilities for competitive sport organizations, particularly those engaged in motorsports, where teams lacking resources frequently dissolve. Corporate sponsorship represents a common method for resource acquisition, yet not all sponsorships equally benefit the sponsored organization. Sponsorship utility can be dependent on institutional dynamics such as league governance that produces competitive disparities. Through this study we extend the resource-based view to assert that sponsorships vary in their propensity to contribute to team survival, warranting prioritization in sponsorship strategy based on access to different sponsor resources. To empirically investigate the influence of a variety of sponsorships, survival analysis modeling was used to examine 40 years of corporate sponsorship of Formula One racing teams. One finding from the longitudinal analysis was that sponsorships offering financial or performance-based resources enhance team survival to a greater degree than operational sponsorships. However, such prioritization is subject to team experience, changes in institutional monetary allocation, and diminishing returns.

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Causal inference in survival analysis using pseudo-observations

Statistics in Medicine ◽

10.1002/sim.7297 ◽

2017 ◽

Vol 36 (17) ◽

pp. 2669-2681 ◽

Cited By ~ 15

Author(s):

Per K. Andersen ◽

Elisavet Syriopoulou ◽

Erik T. Parner

Keyword(s):

Survival Analysis ◽

Causal Inference

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Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

10.33596/etd-1 ◽

2018 ◽

Author(s):

◽

Min Lu

Keyword(s):

Survival Analysis ◽

Random Forest ◽

Causal Inference ◽

Observational Data ◽

Treatment Effect ◽

Treatment Effects ◽

Individual Therapy ◽

Individual Treatment ◽

P Values ◽

Counterfactual Approach

Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual model which takes the hypothetical stance of asking what if an individual had received both treatments. Making use of random forests (RF) within the counterfactual framework, I estimate individual treatment effects by directly modeling the response. This thesis consists of five Chapters. Chapter 1 reviews the methodology in causal inference and provide mathematical notations. Major approaches reviewed include potential outcome approach, graphical approach and counterfactual approach. Chapter 2 discusses assumptions for counterfactual approach. P-values are useful in causal inference, but whenever it is used, caution must be taken. Section 2.3 and Section 2.4 propose machine learning methods as alternatives to p-values and checking proportional hazards assumption in survival analysis. These two sections are more general in content even beyond the scope of counterfactual approach. Chapter 3 describes six random forest methods for estimating individual treatment effects under counterfactual approach framework and discusses model consistency and convergence of random forest in Section 3.6. Chapter 4 demonstrates the performance of these methods in complex simulations and how the most appropriate method is used in a real dataset for continuous outcome. Chapter 5 addresses causal inference in survival analysis of ischemic cardiomyopathy. Treatment effect is viewed as a dynamic causal procedure. New random forest methods are proposed in this chapter to assess individual therapy overlap. These methods possess the unique feature of being able to incorporate external expert knowledge either in a fully supervised way (i.e., we have a strong belief that knowledge is correct), or in a minimally-supervised fashion (i.e., knowledge is not considered gold-standard).

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A General Implementation of TMLE for Longitudinal Data Applied to Causal Inference in Survival Analysis

The International Journal of Biostatistics ◽

10.1515/1557-4679.1334 ◽

2012 ◽

Vol 8 (1) ◽

Cited By ~ 20

Author(s):

Ori M. Stitelman ◽

Victor De Gruttola ◽

Mark J. van der Laan

Keyword(s):

Survival Analysis ◽

Longitudinal Data ◽

Causal Inference

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Database development and survival analysis in a clinical and historical cohort of dogs affected by myxomatous mitral valve disease treated or not with pimobendan using causal inference techniques

Veterinary Research Communications ◽

10.1007/s11259-021-09875-3 ◽

2022 ◽

Author(s):

Mara Bagardi ◽

Valentina Palermo ◽

Chiara Locatelli ◽

Fabio Maria Colombo ◽

Laura Pazzagli ◽

...

Keyword(s):

Survival Analysis ◽

Mitral Valve ◽

Causal Inference ◽

Mitral Valve Disease ◽

Valve Disease ◽

Historical Cohort ◽

Database Development ◽

Inference Techniques

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Longitudinal analysis of deciduous tooth emergence: II. Parametric survival analysis in Bangladeshi, Guatemalan, Japanese, and Javanese children

American Journal of Physical Anthropology ◽

10.1002/(sici)1096-8644(199802)105:2<209::aid-ajpa8>3.0.co;2-p ◽

1998 ◽

Vol 105 (2) ◽

pp. 209-230 ◽

Cited By ~ 33

Author(s):

Darryl J. Holman ◽

Robert E. Jones

Keyword(s):

Survival Analysis ◽

Longitudinal Analysis ◽

Deciduous Tooth ◽

Tooth Emergence ◽

Parametric Survival ◽

Parametric Survival Analysis

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Beyond metaphors and semantics: A framework for causal inference in neuroscience

Behavioral and Brain Sciences ◽

10.1017/s0140525x19001389 ◽

2019 ◽

Vol 42 ◽

Author(s):

Roberto A. Gulli

Keyword(s):

Causal Inference ◽

Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

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