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pro vyhledávání: '"CARLIN, JOHN B."'
Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, induced wh
Externí odkaz:
http://arxiv.org/abs/2405.15242
The target trial is an increasingly popular conceptual device for guiding the design and analysis of observational studies that seek to perform causal inference. As tends to occur with concepts like this, there is variability in how certain aspects o
Externí odkaz:
http://arxiv.org/abs/2405.10026
Autor:
Wijesuriya, Rushani, Moreno-Betancur, Margarita, Carlin, John B, White, Ian R, Quartagno, Matteo, Lee, Katherine J
Longitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clust
Externí odkaz:
http://arxiv.org/abs/2404.06967
Autor:
Dashti, S. Ghazaleh, Lee, Katherine J., Simpson, Julie A., Carlin, John B., Moreno-Betancur, Margarita
Mediation analysis is commonly used in epidemiological research, but guidance is lacking on how multivariable missing data should be dealt with in these analyses. Multiple imputation (MI) is a widely used approach, but questions remain regarding impa
Externí odkaz:
http://arxiv.org/abs/2403.17396
Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for which such modelling might be useful. More broadly, statistics is widel
Externí odkaz:
http://arxiv.org/abs/2309.06668
Autor:
Zhang, Jiaxin, Dashti, S. Ghazaleh, Carlin, John B., Lee, Katherine J., Moreno-Betancur, Margarita
In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends on causal and missingness assumptions. The latter can be depicted by adding variable-specific missingness indicators to causal diagrams
Externí odkaz:
http://arxiv.org/abs/2301.06739
Autor:
Middleton, Melissa, Nguyen, Cattram, Carlin, John B., Moreno-Betancur, Margarita, Lee, Katherine J.
Case-cohort studies are conducted within cohort studies, wherein collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to
Externí odkaz:
http://arxiv.org/abs/2210.11013
Autor:
Mainzer, Rheanna M., Nguyen, Cattram D., Carlin, John B., Moreno-Betancur, Margarita, White, Ian R., Lee, Katherine J.
Multiple imputation (MI) is a popular method for handling missing data. Auxiliary variables can be added to the imputation model(s) to improve MI estimates. However, the choice of which auxiliary variables to include in the imputation model is not al
Externí odkaz:
http://arxiv.org/abs/2203.16717
Autor:
Dashti, S. Ghazaleh, Lee, Katherine J., Simpson, Julie A., White, Ian R., Carlin, John B., Moreno-Betancur, Margarita
Publikováno v:
Am J Epidemiol. 2024 Feb 22:kwae012. Epub ahead of print. PMID: 38400653
Targeted Maximum Likelihood Estimation (TMLE) is increasingly used for doubly robust causal inference, but how missing data should be handled when using TMLE with data-adaptive approaches is unclear. Based on the Victorian Adolescent Health Cohort St
Externí odkaz:
http://arxiv.org/abs/2112.05274
Autor:
Lange, Katherine, Pham, Cindy, Fedyszyn, Izabela E., Cook, Fallon, Burgner, David P., Olsson, Craig A., Downes, Marnie, Priest, Naomi, Mansell, Toby, Tang, Mimi L.K., Ponsonby, Anne-Louise, Symeonides, Christos, Loughman, Amy, Vuillermin, Peter, Kerr, Jessica A., Gray, Lawrence, Sly, Peter D., Lycett, Kate, Carlin, John B., Saffery, Richard, Wake, Melissa, O'Connor, Meredith
Publikováno v:
In Journal of Affective Disorders 1 January 2024 344:356-364