P. Philipson

发表

Joint modelling of longitudinal and competing risks data

R. Kolamunnage-Dona, A. Marson, P. Williamson, 2008, Statistics in medicine.

Evaluating the Quality of Research into a Single Prognostic Biomarker: A Systematic Review and Meta-analysis of 83 Studies of C-Reactive Protein in Stable Coronary Artery Disease

Keith R. Abrams, Harry Hemingway, Stephen Palmer, 2010, PLoS medicine.

Planning for optimal performance – What happens before the taper?

P. Philipson, P. Hayes, K. Johns, 2019, International Journal of Sports Science & Coaching.

Mid-regional proadrenomedullin (MR-proADM), C-reactive protein (CRP) and other biomarkers in the early identification of disease progression in patients with COVID-19 in the acute NHS setting

P. Philipson, S. Kidd, K. Saeed, 2022, Journal of Clinical Pathology.

Mid-Regional pro-Adrenomedullin (MR-proADM), C-Reactive Protein (CRP) and Other Biomarkers in the Early Identification of Disease Progression in COVID-19 Patients in the Acute NHS Setting

Christopher M Thomas, P. Philipson, S. Kidd, 2021, medRxiv.

JointModels of Longitudinal and Time-to-Event

R. Kolamunnage-Dona, P. Philipson, A. Jorgensen, 2018 .

Impact arising from sustained public engagement: A measured increase in learning outcomes

L. Boothroyd, P. Philipson, J. McLaughlin, 2018, Research for All.

A truncated mean-parameterised Conway-Maxwell-Poisson model for the analysis of Test match bowlers

Pete Philipson, P. Philipson, 2021, 2109.00378.

Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues

Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, 2016, BMC Medical Research Methodology.

HIGHER EDUCATION OUTREACH: EXAMINING KEY CHALLENGES FOR ACADEMICS

P. Garrud, P. Philipson, David Walker, 2019, British Journal of Educational Studies.

Optimal designs for threshold-determining limiting dilution assays

J. Matthews, P. Philipson, 2012 .

Reducing inequalities in care for patients with non-malignant diseases: Insights from a realist evaluation of an integrated palliative care pathway

Pete Philipson, Diana Jones, Monique Lhussier, 2016, Palliative medicine.

‘Everything takes too long and nobody is listening’: Developing theory to understand the impact of advice on stress and the ability to cope

Philip Hodgson, Pete Philipson, Monique Lhussier, 2018, Health & social care in the community.

Translational phases of evidence in a prognostic biomarker: a systematic review and meta-analysis of natriuretic peptides and the prognosis of stable coronary disease

Santiago G. Moreno, A. Hingorani, H. Hemingway, 2012, Heart.

A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial

Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, 2018 .

Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review

R. Kolamunnage-Dona, P. Philipson, A. Jorgensen, 2018, The international journal of biostatistics.

South Asians and coronary disease: is there discordance between effects on incidence and prognosis?

Ruoling Chen, M. Marmot, H. Hemingway, 2013, Heart.

Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data

Pete Philipson, Ruwanthi Kolamunnage-Dona, Graeme L. Hickey, 2020, Comput. Stat. Data Anal..

Faster Monte Carlo estimation of semiparameteric joint models of time-to-event and multivariate longitudinal data

P. Philipson, M. Crowther, G. Hickey, 2020 .

Measuring the Impact of Research: Lessons from the UK’s Research Excellence Framework 2014

Pete Philipson, Kushwanth Koya, Gobinda Chowdhury, 2016, PloS one.

Joint Modelling of Repeated Measurements and Time-to-Event Data [R package joineR version 1.2.5]

Peter J. Diggle, Inês Sousa, Robin Henderson, 2020 .

joineR: Joint modelling of repeated measurements and time-to-event data

Peter J. Diggle, Inês Sousa, Robin Henderson, 2012 .

Comparative review of methods for handling drop‐out in longitudinal studies

Robin Henderson, Weang Kee Ho, Peter M Philipson, 2008, Statistics in medicine.

joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

Graeme L. Hickey, Pete Philipson, Andrea Jorgensen, 2018, BMC Medical Research Methodology.

A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data

P. Philipson, James Murray, 2022, Comput. Stat. Data Anal..

Fast estimation for generalised multivariate joint models using an approximate EM algorithm

P. Philipson, James Murray, 2023, Comput. Stat. Data Anal..

Risk factors associated with readmission within 30 days following stoma surgery: Development of a ‘traffic light’ prediction model

P. Philipson, R. Brady, Callum Allison, 2022, Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland.

Reducing inequalities in care for patients with non-malignant diseases: Insights from a realist evaluation of an integrated palliative care pathway

P. Philipson, M. Lhussier, Diana E Jones, 2016, Palliative medicine.

Joint modelling of longitudinal and competing risks data

R. Kolamunnage-Dona, A. Marson, P. Williamson, 2008, Statistics in Medicine.

Tests for Hazard Transformation

Robin Henderson, Pete Philipson, Weang Kee Ho, 2010 .

Random effects models for joint analysis of repeated measurement and time-to-event outcomes

Peter J. Diggle, Pete Philipson, Robin Henderson, 2008 .

Joint Models of Longitudinal and Time-to-Event Data with More Than One Event Time Outcome: A Review

R. Kolamunnage-Dona, P. Philipson, A. Jorgensen, 2018, The international journal of biostatistics.