Up-to-date and projected estimates of survival for people with cystic fibrosis using baseline characteristics: A longitudinal study using UK patient registry data von Keogh, Ruth H., Szczesniak, Rhonda, Taylor-Robinson, David, Bilton, Diana

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Data Resource Profile: The UK Cystic Fibrosis Registry von Taylor-Robinson, David, Archangelidi, Olia, Carr, Siobhán B, Cosgriff, Rebecca, Gunn, Elaine, Keogh, Ruth H, MacDougall, Amy, Newsome, Simon, Schlüter, Daniela K, Stanojevic, Sanja, Bilton, Diana

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Implementation of a dynamic model updating pipeline provides a systematic process for maintaining performance of prediction models von Tanner, Kamaryn T., Diaz-Ordaz, Karla, Keogh, Ruth H.

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Epidemiologic analyses with error-prone exposures: review of current practice and recommendations von Shaw, Pamela A., Deffner, Veronika, Keogh, Ruth H., Tooze, Janet A., Dodd, Kevin W., Küchenhoff, Helmut, Kipnis, Victor, Freedman, Laurence S.

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Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models von Keogh, Ruth H., Gran, Jon Michael, Seaman, Shaun R., Davies, Gwyneth, Vansteelandt, Stijn

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Trial emulation with observational data in cystic fibrosis von Davies, Gwyneth, Keogh, Ruth H

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Introduction to the special issue based on contributions from ‘A celebration of 50 Years of the Cox model in memory of Sir David Cox’, 10 November 2022, London School of Hygiene &... von Keogh, Ruth H

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Treatment patterns in people with cystic fibrosis: have they changed since the introduction of ivacaftor? von Granger, Emily, Davies, Gwyneth, Keogh, Ruth H.

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Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods von Keogh, Ruth H, Daniel, Rhian M, VanderWeele, Tyler J, Vansteelandt, Stijn

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Estimating long‐term treatment effects in observational data: A comparison of the performance of different methods under real‐world uncertainty von Newsome, Simon J., Keogh, Ruth H., Daniel, Rhian M.

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Simulating Data From Marginal Structural Models for a Survival Time Outcome von Seaman, Shaun R., Keogh, Ruth H.

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Handling missing data in matched case‐control studies using multiple imputation von Seaman, Shaun R, Keogh, Ruth H

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Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression von Szczesniak, Rhonda D., Su, Weiji, Brokamp, Cole, Keogh, Ruth H., Pestian, John P., Seid, Michael, Diggle, Peter J., Clancy, John P.

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Use of multiple imputation in supersampled nested case‐control and case‐cohort studies von Borgan, Ørnulf, Keogh, Ruth H., Njøs, Aleksander

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Multiple imputation in Cox regression when there are time‐varying effects of covariates von Keogh, Ruth H., Morris, Tim P.

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Diagnostic performance of the IMMY cryptococcal antigen lateral flow assay on serum and cerebrospinal fluid for diagnosis of cryptococcosis in HIV-negative patients: a systematic r... von Macrae, Catriona, Ellis, Jayne, Keddie, Suzanne H, Falconer, Jane, Bradley, John, Keogh, Ruth, Baerenbold, Oliver, Hopkins, Heidi, Jarvis, Joseph N

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Using Negative Control Outcomes and Difference-in-Differences Analysis to Estimate Treatment Effects in an Entirely Treated Cohort: The Effect of Ivacaftor in Cystic Fibrosis von Newsome, Simon J, Daniel, Rhian M, Carr, Siobhán B, Bilton, Diana, Keogh, Ruth H

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Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison von Tanner, Kamaryn T., Sharples, Linda D., Daniel, Rhian M., Keogh, Ruth H.

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Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding von Seaman, Shaun R., Keogh, Ruth H., Dukes, Oliver, Vansteelandt, Stijn

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Using full-cohort data in nested case-control and case-cohort studies by multiple imputation von Keogh, Ruth H., White, Ian R.

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