Zobrazeno 1 - 10
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pro vyhledávání: '"Carlin, John A."'
Autor:
Zhang, Jiaxin, Dashti, S. Ghazaleh, Carlin, John B., Lee, Katherine J., Bartlett, Jonathan W., Moreno-Betancur, Margarita
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposur
Externí odkaz:
http://arxiv.org/abs/2411.13829
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, which occu
Externí odkaz:
http://arxiv.org/abs/2405.15242
Causal inference is the goal of randomized controlled trials and many observational studies. The first step in a formal approach to causal inference is to define the estimand of interest, and in both types of study this can be intuitively defined as
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:
Armstrong, Daniel, Byrnes, Catherine, Carlin, John, Carzino, Rosemary, Cheney, Joyce, Cooper, Peter, George, Narelle, Grimwood, Keith, Martin, James, McKay, Karen, Moodie, Marj, Robertson, Colin, Tiddens, Harm, Vidmar, Suzanna, Wainwright, Claire, Whitehead, Bruce, Anderson, Vicki, Bourgeat, Pierrick, Davidson, Andrew, Gailer, Nicholas, Grayson-Collins, Jasmin, Salvado, Olivier, Quittner, Alexandra, Wainwright, Claire Elizabeth *, Carlin, John Brooke, Zannino, Diana, Armstrong, Floyd Daniel
Publikováno v:
In The Lancet Respiratory Medicine September 2024 12(9):703-713
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