Search Results - "Seaman, Shaun R"

Introduction to Double Robust Methods for Incomplete Data by Seaman, Shaun R., Vansteelandt, Stijn

“…Most methods for handling incomplete data can be broadly classified as inverse probability weighting (IPW) strategies or imputation strategies. The former…”
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Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model by Bartlett, Jonathan W, Seaman, Shaun R, White, Ian R, Carpenter, James R

“…Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially…”
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Correcting for Optimistic Prediction in Small Data Sets by SMITH, Gordon C. S, SEAMAN, Shaun R, WOOD, Angela M, ROYSTON, Patrick, WHITE, Ian R

“…The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of…”
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Combining Multiple Imputation and Inverse‐Probability Weighting by Seaman, Shaun R., White, Ian R., Copas, Andrew J., Li, Leah

“…Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse‐probability weighting (IPW). IPW is also used to adjust for…”
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An evaluation of sample size requirements for developing risk prediction models with binary outcomes by Pavlou, Menelaos, Ambler, Gareth, Qu, Chen, Seaman, Shaun R, White, Ian R, Omar, Rumana Z

“…Risk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance…”
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Prediction of five-year mortality after COPD diagnosis using primary care records by Kiddle, Steven J, Whittaker, Hannah R, Seaman, Shaun R, Quint, Jennifer K, Kostikas, Konstantinos

“…Accurate prognosis information after a diagnosis of chronic obstructive pulmonary disease (COPD) would facilitate earlier and better informed decisions about…”
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Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods by Seaman, Shaun R, Bartlett, Jonathan W, White, Ian R

“…Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to…”
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Handling missing data in matched case‐control studies using multiple imputation by Seaman, Shaun R, Keogh, Ruth H

“…Analysis of matched case‐control studies is often complicated by missing data on covariates. Analysis can be restricted to individuals with complete data, but…”
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Review of inverse probability weighting for dealing with missing data by Seaman, Shaun R, White, Ian R

“…The simplest approach to dealing with missing data is to restrict the analysis to complete cases, i.e. individuals with no missing values. This can induce…”
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How to develop a more accurate risk prediction model when there are few events by Pavlou, Menelaos, Ambler, Gareth, Seaman, Shaun R, Guttmann, Oliver, Elliott, Perry, King, Michael, Omar, Rumana Z

“…When the number of events is low relative to the number of predictors, standard regression could produce overfitted risk models that make inaccurate…”
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Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study by Nyberg, Tommy, Ferguson, Neil M, Nash, Sophie G, Webster, Harriet H, Flaxman, Seth, Andrews, Nick, Hinsley, Wes, Bernal, Jamie Lopez, Kall, Meaghan, Bhatt, Samir, Blomquist, Paula, Zaidi, Asad, Volz, Erik, Aziz, Nurin Abdul, Harman, Katie, Funk, Sebastian, Abbott, Sam, Nyberg, Tommy, Ferguson, Neil M, Nash, Sophie G, Webster, Harriet H, Flaxman, Seth, Andrews, Nick, Hinsley, Wes, Lopez Bernal, Jamie, Kall, Meaghan, Bhatt, Samir, Blomquist, Paula, Zaidi, Asad, Volz, Erik, Abdul Aziz, Nurin, Harman, Katie, Funk, Sebastian, Abbott, Sam, Hope, Russell, Charlett, Andre, Chand, Meera, Ghani, Azra C, Seaman, Shaun R, Dabrera, Gavin, De Angelis, Daniela, Presanis, Anne M, Thelwall, Simon, Hope, Russell, Charlett, Andre, Chand, Meera, Ghani, Azra C, Seaman, Shaun R, Dabrera, Gavin, De Angelis, Daniela, Presanis, Anne M, Thelwall, Simon

“…The omicron variant (B.1.1.529) of SARS-CoV-2 has demonstrated partial vaccine escape and high transmissibility, with early studies indicating lower severity…”
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Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis by Nyberg, Tommy, Twohig, Katherine A, Harris, Ross J, Seaman, Shaun R, Flannagan, Joe, Allen, Hester, Charlett, Andre, De Angelis, Daniela, Dabrera, Gavin, Presanis, Anne M

“…To evaluate the relation between diagnosis of covid-19 with SARS-CoV-2 variant B.1.1.7 (also known as variant of concern 202012/01) and the risk of hospital…”
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Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study by Aliabadi, Shirin, Lopez-Bernal, Jamie, Moore, Nathan, Golubchik, Tanya, Cogger, Benjamin, Wantoch, Michelle, Spellman, Karla, McCluggage, Kathryn, Munn, Robert, Fuller, William, Charalampous, Themoula, Robson, Samuel, Abudahab, Khalil, Willingham, Iona, Raviprakash, Veena, Loose, Matthew, Coll, Francesc, Wilson, Harry, Pybus, Oliver, Zarebski, Alex, Jahun, Aminu, Fisher, Chloe, Siveroni, Igor, Boyd, Olivia, Smollett, Katherine, Poplawski, Radoslaw, Sluga, Graciela, Nichols, Jenna, Johnson, Natasha, Singer, Joshua, Nickbakhsh, Seema, Carden, Holli, Taha, Yusri, Blacow, Rachel, McHugh, Martin, Stanley, Rachael, Meader, Emma, Coupland, Lindsay, Peto, Timothy, Sloan, Tim, Chauhan, Anoop, Pearson, Clare, Robinson, Esther, Muir, Peter, Pymont, Hannah, Hutchings, Stephanie, Bibby, David, Lee, David, Ellaby, Nicholas, Manesis, Nikos, Bishop, Chloe, Gaskin, Amy, Gatica-Wilcox, Bree, Cronin, Michelle, Cottrell, Simon, Edwards, Sue, Aydin, Alp, Ratcliffe, Liz, Acheson, Erwan, Ellard, Sian, Irish-Tavares, Dianne, Hart, Jennifer, Symmonds, Amanda, Bourgeois, Yann, Dent, Hannah, Paul, Hannah, Partridge, David, Bonner, Stephen, Silveira, Siona, Williams, Charlotte, Jeremiah, Sarah, Berry, Lisa, Jones, Katie, Percival, Benita, Hesketh, Andrew, Girgis, Sophia, Forrest, Sally, Gupta, Ravi, Ludden, Catherine, Bewshea, Claire, Temperton, Ben, Warwick-Dugdale, Joanna, Studholme, David, Jeffries, Aaron, Jackson, Kathryn, Gregory, Richard, Wierzbicki, Claudia, Green, Luke, Freeman, Timothy, Cerda, Alberto, Alderton, Alex, Amato, Roberto, Ariani, Cristina, Beale, Mathew, Bellis, Katherine, Kwiatkowski, Dominic, McGuigan, Samantha, Prestwood, Liam, Spencer Chapman, Michael, Tonkin-Hill, Gerry

“…The SARS-CoV-2 delta (B.1.617.2) variant was first detected in England in March, 2021. It has since rapidly become the predominant lineage, owing to high…”
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Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models by Keogh, Ruth H., Gran, Jon Michael, Seaman, Shaun R., Davies, Gwyneth, Vansteelandt, Stijn

“…Longitudinal observational data on patients can be used to investigate causal effects of time‐varying treatments on time‐to‐event outcomes. Several methods…”
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Semi-Parametric Methods of Handling Missing Data in Mortal Cohorts under Non-Ignorable Missingness by Wen, Lan, Seaman, Shaun R.

“…We propose semi-parametric methods to model cohort data where repeated outcomes may be missing due to death and non-ignorable dropout. Our focus is to obtain…”
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Propensity score analysis with partially observed covariates: How should multiple imputation be used? by Leyrat, Clémence, Seaman, Shaun R, White, Ian R, Douglas, Ian, Smeeth, Liam, Kim, Joseph, Resche-Rigon, Matthieu, Carpenter, James R, Williamson, Elizabeth J

“…Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk…”
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A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data by Tompsett, Daniel, Sutton, Stephen, Seaman, Shaun R., White, Ian R.

“…We develop and demonstrate methods to perform sensitivity analyses to assess sensitivity to plausible departures from missing at random in incomplete repeated…”
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Using generalized linear models to implement g‐estimation for survival data with time‐varying confounding by Seaman, Shaun R., Keogh, Ruth H., Dukes, Oliver, Vansteelandt, Stijn

“…Using data from observational studies to estimate the causal effect of a time‐varying exposure, repeatedly measured over time, on an outcome of interest…”
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Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods by Seaman, Shaun R, Presanis, Anne, Jackson, Christopher

“…Time-to-event data are right-truncated if only individuals who have experienced the event by a certain time can be included in the sample. For example, we may…”
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Nowcasting COVID‐19 deaths in England by age and region by Seaman, Shaun R., Samartsidis, Pantelis, Kall, Meaghan, De Angelis, Daniela

“…Understanding the trajectory of the daily number of COVID‐19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory…”
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