S. Seaman

发表

Methods for Observed-Cluster Inference When Cluster Size Is Informative: A Review and Clarifications

Andrew J Copas, Shaun R Seaman, Menelaos Pavlou, 2014, Biometrics.

Review of inverse probability weighting for dealing with missing data

I. White, S. Seaman, 2013, Statistical methods in medical research.

Estimating the relative incidence of heroin use: application of a method for adjusting observed reports of first visits to specialized drug treatment agencies.

D. De Angelis, S. Seaman, D. Angelis, 2001, American journal of epidemiology.

Propensity scores using missingness pattern information: a practical guide

H. A. Blake, J. Carpenter, L. Tomlinson, 2019, Statistics in medicine.

Inverse Probability Weighting with Missing Predictors of Treatment Assignment or Missingness

I. White, S. Seaman, 2014 .

Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment

Thomas Meitinger, Florian Holsboer, Benno Pütz, 2004, Nature Genetics.

Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

Ian R White, Jonathan W Bartlett, Shaun R Seaman, 2012, BMC Medical Research Methodology.

Rapid simulation of P values for product methods and multiple-testing adjustment in association studies.

B Müller-Myhsok, B. Müller-Myhsok, S. Seaman, 2005, American journal of human genetics.

What Is Meant by "Missing at Random"?

Dan Jackson, John Carlin, Shaun Seaman, 2013, 1306.2812.

A synthesis of convenience survey and other data to estimate undiagnosed HIV infection among men who have sex with men in England and Wales.

Anne M Johnson, A. Copas, A. Presanis, 2011, International journal of epidemiology.

Bayesian trio models for association in the presence of genotyping errors

P. Holmans, L. Bernardinelli, S. Seaman, 2004, Genetic epidemiology.

Multiple imputation: a mature approach to dealing with missing data

S. Chevret, S. Chevret, S. Seaman, 2015, Intensive Care Medicine.

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

James R Carpenter, Ian R White, Jonathan W Bartlett, 2012, Statistical methods in medical research.

A latent variable model for improving inference in trials assessing the effect of dose on toxicity and composite efficacy endpoints

J. Wason, S. Seaman, 2019, Statistical methods in medical research.

Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods

A. Presanis, S. Seaman, Christopher Jackson, 2021, Statistical methods in medical research.

Methods for handling longitudinal outcome processes truncated by dropout and death

S. Seaman, G. M. Terrera, Lan Wen, 2017, Biostatistics.

Linear Increments with Non‐monotone Missing Data and Measurement Error

I. White, D. Farewell, S. Seaman, 2016, Scandinavian journal of statistics, theory and applications.

Nowcasting COVID‐19 deaths in England by age and region

D. De Angelis, S. Seaman, P. Samartsidis, 2020, medRxiv.

Dynamic Prediction of Survival in Cystic Fibrosis

R. Szczesniak, R. Keogh, D. Taylor-Robinson, 2018, Epidemiology.

Equivalence of prospective and retrospective models in the Bayesian analysis of case-control studies

S. Richardson, S. Seaman, 2004 .

The West of Scotland Twenty-07 Study: Health in the Community - Inverse Probability Weights for Wave 5

S. Seaman, M. Benzeval, 2011 .

A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital

K. Stirrups, N. Walker, G. Dougan, 2020, Journal of Infection.

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

Joshua B. Singer, Darren L. Smith, Shane A. McCarthy, 2021, The Lancet Infectious Diseases.

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

S. Bhatt, S. Flaxman, W. Hinsley, 2022, The Lancet.

Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis

Ross J. Harris, A. Charlett, A. Presanis, 2021, BMJ.

Conflicting evidence in a Bayesian synthesis of surveillance data to estimate human immunodeficiency virus prevalence

David J. Spiegelhalter, Daniela De Angelis, A E Ades, 2008 .

Hospitalization and Mortality Risk for COVID-19 Cases With SARS-CoV-2 AY.4.2 (VUI-21OCT-01) Compared to Non-AY.4.2 Delta Variant Sublineages

S. Gharbia, A. Charlett, A. Presanis, 2022, The Journal of infectious diseases.

Analysis of Randomised Trials Including Multiple Births When Birth Size Is Informative.

S. Seaman, M. Pavlou, L. Yelland, 2015, Paediatric and perinatal epidemiology.

Using continuous data on tumour measurements to improve inference in phase II cancer studies

J. Wason, S. Seaman, 2013, Trials.

A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data

Ian R White, Shaun R Seaman, Daniel Tompsett, 2020, Statistics in medicine.

Using generalized linear models to implement g-estimation for survival data with time-varying confounding

S. Vansteelandt, R. Keogh, S. Seaman, 2021, Statistics in medicine.

Multiple Imputation of Missing Data in Nested Case-Control and Case-Cohort Studies

Angela M Wood, Jonathan W Bartlett, Ruth H Keogh, 2018, Biometrics.

Multiple imputation for an incomplete covariate that is a ratio

Patrick Royston, Tim P Morris, Ian R White, 2013, Statistics in medicine.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models

Ruth H. Keogh, Stijn Vansteelandt, Jon Michael Gran, 2021, Statistics in medicine.

Simulating longitudinal data from marginal structural models using the additive hazard model

Ruth H. Keogh, Stijn Vansteelandt, Shaun R. Seaman, 2020, Biometrical journal. Biometrische Zeitschrift.

Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic

A. Presanis, D. De Angelis, S. Seaman, 2021, medRxiv.

Gaining power and precision by using model–based weights in the analysis of late stage cancer trials with substantial treatment switching

Jack Bowden, Xin Huang, Ian R White, 2015, Statistics in medicine.

Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

James Carpenter, Dan Jackson, Ian R White, 2014, Statistics in medicine.

Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events

Gareth Ambler, Maria De Iorio, Rumana Z Omar, 2015, Statistics in medicine.

The window of therapeutic opportunity in multiple sclerosis

H. Waldmann, S. Seaman, A. Coles, 2005, Journal of Neurology.

Multiple sclerosis recurrence risk for siblings in an isolated population of Central Sardinia, Italy

L. Bernardinelli, I. Prokopenko, S. Seaman, 2002, Genetic epidemiology.

Dynamic Prediction of Survival in Cystic Fibrosis

Rhonda, Jessica K Barrett, Ruth H Keogh, 2018, Epidemiology.

Bayesian analysis of case-control studies with categorical covariates

Sylvia Richardson, Shaun R. Seaman, S. Richardson, 2001 .

Correcting for Optimistic Prediction in Small Data Sets

Patrick Royston, Ian R. White, Gordon C. S. Smith, 2014, American journal of epidemiology.

Mortality from overdose among injecting drug users recently released from prison: database linkage study

R. Brettle, S. Gore, S. Seaman, 1998, BMJ.

Injecting drug users in Edinburgh have a high rate of recidivism.

R. Brettle, S. Gore, S. Seaman, 2000, Addiction.

Joint modelling rationale for chained equations

James R Carpenter, Jonathan AC Sterne, Ian R White, 2014, BMC Medical Research Methodology.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model

Shaun R Seaman, Rachael A Hughes, S. Seaman, 2016, Statistical methods in medical research.

Combining Multiple Imputation and Inverse-Probability Weighting

Andrew J Copas, Ian R White, Shaun R Seaman, 2012, Biometrics.

How to develop a more accurate risk prediction model when there are few events

Gareth Ambler, Rumana Z Omar, Shaun R Seaman, 2015, BMJ : British Medical Journal.

Multiple Sclerosis Severity Score: Using disability and disease duration to rate disease severity

G. Salemi, M. Trojano, G. Edan, 2005, Neurology.

Evaluation of Nyholt’s Procedure for Multiple Testing Correction

Frank Dudbridge, Bertram Müller-Myhsok, Brian L. Browning, 2005, Human Heredity.

Effect of Genotyping Error on Type-I Error Rate of Affected Sib Pair Studies with Genotyped Parents

P. Holmans, S. Seaman, S.R. Seaman, 2005, Human Heredity.

An evaluation of penalised survival methods for developing prognostic models with rare events

R Z Omar, G Ambler, S Seaman, 2012, Statistics in medicine.

Drug use in prison

S. Gore, S. Seaman, 1996, BMJ.

Missing data methods in Mendelian randomization studies with multiple instruments.

Stephen Burgess, Juan P Casas, Simon G Thompson, 2011, American journal of epidemiology.

Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated Units

A. Presanis, S. Seaman, D. Angelis, 2018, Statistical Science.

Factors affecting repeated cessations of injecting drug use and relapses during the entire injecting career among the Edinburgh Addiction Cohort

Matthew Hickman, Daniela De Angelis, Shaun Seaman, 2015, Drug and alcohol dependence.

A two-step method for variable selection in the analysis of a case-cohort study

P J Newcombe, S Richardson, S Seaman, 2017, International journal of epidemiology.

Single-dose BNT162b2 vaccine protects against asymptomatic SARS-CoV-2 infection

R. Samworth, C. Workman, M. Weekes, 2021, eLife.

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

S. Bhatt, S. Flaxman, W. Hinsley, 2022, SSRN Electronic Journal.

Review of methods for handling confounding by cluster and informative cluster size in clustered data

Andrew Copas, Shaun Seaman, Menelaos Pavlou, 2014, Statistics in medicine.

An examination of a method for marginal inference when the cluster size is informative

A. Copas, S. Seaman, M. Pavlou, 2013 .

Familial effects on the clinical course of multiple sclerosis

O. Andersen, S. Sawcer, G. Stewart, 2006, Neurology.

Hospitalisation risk for COVID-19 patients infected with SARS-CoV-2 variant B.1.1.7: cohort analysis

United Kingdom, University of Cambridge, Andre Charlett, 2021, 2104.05560.

Demographic, behavioural and occupational risk factors associated with SARS-CoV-2 infection in UK healthcare workers: a retrospective observational study

M. Weekes, S. Seaman, I. Goodfellow, 2022, BMJ Open.

Re-interpreting conventional interval estimates taking into account bias and extra-variation

M. Höfler, S. Seaman, Michael Höfler, 2006, BMC medical research methodology.

A Bayesian multivariate factor analysis model for evaluating an intervention by using observational time series data on multiple outcomes

Andre Charlett, Daniela De Angelis, Shaun R. Seaman, 2020, Journal of the Royal Statistical Society. Series A,.

Estimation of required sample size for external validation of risk models for binary outcomes

I. White, E. Steyerberg, R. Omar, 2021, Statistical methods in medical research.

Propensity score analysis with partially observed covariates: How should multiple imputation be used?

L. Smeeth, I. White, J. Carpenter, 2017, Statistical methods in medical research.

Directions of change in intrinsic case severity across successive SARS-CoV-2 variant waves have been inconsistent

J. Haughney, S. Seaman, J. Hughes, 2023, The Journal of infection.

Osteopontin gene and clinical severity of multiple sclerosis

S. Sawcer, S. Seaman, R. Roxburgh, 2003, Journal of Neurology.

Directions of change in intrinsic case severity across successive SARS-CoV-2 variant waves have been inconsistent

J. Haughney, S. Seaman, J. Hughes, 2023, The Journal of infection.

Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data

Daniela De Angelis, Matthew Hickman, Pantelis Samartsidis, 2018 .

No evidence of a significant role for CTLA-4 in multiple sclerosis

S. Sawcer, A. Compston, M. Maranian, 2006, Journal of Neuroimmunology.

Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data

Shaun R. Seaman, Andrew J. Copas, A. Copas, 2010 .

Doubly robust generalized estimating equations for longitudinal data

Andrew Copas, Shaun R. Seaman, A. Copas, 2009, Statistics in medicine.

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19

B. Tom, A. Presanis, D. De Angelis, 2022, Statistical methods in medical research.

Handling missing data in matched case‐control studies using multiple imputation

Ruth H Keogh, Shaun R Seaman, R. Keogh, 2015, Biometrics.

Comparative Effectiveness of Adalimumab vs Tofacitinib in Patients With Rheumatoid Arthritis in Australia

S. Seaman, B. D. De Stavola, C. Deakin, 2023, JAMA network open.

Introduction to Double Robust Methods for Incomplete Data.

Stijn Vansteelandt, Shaun R Seaman, S. Vansteelandt, 2018, Statistical science : a review journal of the Institute of Mathematical Statistics.

A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes

Gareth Ambler, Rumana Z Omar, Shaun Seaman, 2015, BMC Medical Research Methodology.

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

I. White, J. Carpenter, J. Bartlett, 2012, Statistical methods in medical research.

O2-2.1 Maximising the return from cohort studies

S. Seaman, A. Leyland, S. Harding, 2011, Journal of Epidemiology and Community Health.

Maximising the return from cohort studies

I. White, A. Leyland, S. Seaman, 2011 .

Bayesian trio models for association in the presence of missing and imprecise genotype information

Carlo Berzuini, Peter Holmans, Luisa Bernardinelli, 2003 .

Familial effects on the clinical course of multiple sclerosis

S. Sawcer, G. Stewart, D. Booth, 2006, Neurology.

The multiple sclerosis severity score

S. Sawcer, X. Montalban, E. Celius, 2003 .

Inconsistent directions of change in case severity across successive SARS-CoV-2 variant waves suggests an unpredictable future

R. Forrest, J. Haughney, E. Goldstein, 2022, medRxiv.

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

I. White, J. Carpenter, J. Bartlett, 2012, Statistical methods in medical research.

Introduction to Double Robust Methods for Incomplete Data.

S. Vansteelandt, S. Seaman, 2018, Statistical science : a review journal of the Institute of Mathematical Statistics.

Propensity scores using missingness pattern information: a practical guide

J. Carpenter, L. Tomlinson, S. Seaman, 2019, Statistics in medicine.

Adjusting for time‐varying confounders in survival analysis using structural nested cumulative survival time models

S. Vansteelandt, R. Keogh, S. Seaman, 2019, Biometrics.

Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout

S. Seaman, S. Yiu, L. Su, 2022, Statistical methods in medical research.

Pre-AIDS mortality in the Edinburgh City Hospital HIV cohort.

R. Brettle, S. Gore, S. Seaman, 1997, Statistics in medicine.