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2. MulTI-domain self-management in older People wiTh OstEoarthritis and multi-morbidities: protocol for the TIPTOE randomised controlled trial

Author

Deere, Rachel, Pallmann, Philip, Shepherd, Victoria, Brookes-Howell, Lucy, Carson-Stevens, Andrew, Davies, Ffion, Dunphy, Emma, Gupta, Preeti, Hickson, Mary, Hill, Val, Ingarfield, Kate, Ivins, Nicola, Jones, Fiona, Letchford, Robert, Lowe, Rachel, Nash, Sarah, Otter, Paula, Prout, Hayley, Randell, Elizabeth, Sewell, Bernadette, Smith, Debs, Trubey, Robert, Wainwright, Tom, Busse, Monica, and Button, Kate

Published
2024
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3. Point estimation for adaptive trial designs II: practical considerations and guidance

Author

Robertson, David S., Choodari-Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, and Jaki, Thomas

Subjects
Statistics - Methodology, Statistics - Applications, 62F10
Abstract

In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This paper is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realisation they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy pre-specified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred., Comment: Part II of a two-part series, part I can be found at arXiv:2105.08836

Published
2022

4. Albumin Versus Balanced Crystalloid for the Early Resuscitation of Sepsis: An Open Parallel-Group Randomized Feasibility Trial. The ABC-Sepsis Trial

Author

Gray, Alasdair J., Oatey, Katherine, Grahamslaw, Julia, Irvine, Sîan, Cafferkey, John, Kennel, Titouan, Norrie, John, Walsh, Tim, Lone, Nazir, Horner, Daniel, Appelboam, Andy, Hall, Peter, Skipworth, Richard J. E., Bell, Derek, Rooney, Kevin, Shankar-Hari, Manu, Corfield, Alasdair R., Newcombe, Virginia, Chapman, Robert, Hutton, Corinne, Perkins, Gavin, Gordon, Anthony, Keating, Liza, Pallmann, Philip, Armstrong, Ruth, Pollock, Roz, Wackett, Tony, Chapman, Robert, Hutton, Corrine, Kale, Jane, Kale, Neville, Anderson, Rory, Birrell, David, Black, Polly, Blackstock, Caroline, Carter, Jonathan, Clark, Donna, Davidson, Craig, Davie, Carlyn, Rollo, Nicola Di, Ferguson, Andrew, Fethers, Nicholas, Freeman, Nicola, Gibson, Louise, Grant, Alison, Harris, Marie-Clare, James, Ed, Letham, Katy, Lynch, Stephen, McCurrach, Fiona, McKenzie, Suzi, McLaughlin, Connor, Miell, Anna, Moncur, Lyle, O’Brien, Rachel, Pickering, Gillian, Reed, Matt, Rezai, Roozbeh, Robinson, Sara, Skinner, Janet, Walker, Craig, Williams, Alison, Wilson, Emma-Beth, Di Rollo, Nicola, Carruthers, Jennifer, Cheyne, Claire, McGeough, Angela, Lidstone-Scott, Rebecca, Lynch, Stephen, Abel, Lynn, Bongale, Santosh, Currie, Susie, Kane, Donna, Keen, Rachel, Orlikowska, Izabella, Rodden, Natalie, Tait, Jennifer, Thomson, Nicola, Walker, Lauren, Smith, Kate, Brown, Ammani, Cathcart, Susanne, Maguire, Donogh, O’Mailley, Patrick, Rimmer, Dominic, Ward, Catherine, Baxter, Nicola, Denny, Fraser, Doran, Kayleigh, Dunlop, Lisa, Dunn, Jessica, Finnen, Lisa, Green, Darren, Hughes, Emma, Lowe, David, McArthur, Courtney, McLaren, Barbara, McLeish, Kirsty, McMunigal, Linda, McParland, Chris, O’Shaughnessy, Ornagh, Wilson, Mark, Armes, Roland, Brown, Craig, Cooper, Jamie, Connor, David, Collier-Wakefield, Otto, Coutts, Mandi, Cruickshank, Mairi, Ebtehadj, Bianca, Ganley, Pauline, Hiscox, John, Hartman, Catharina, Kane, Leia, Kastora, Stavroula, Lamb, Sarah, McCarthy, Sinead, Mulholland, Conal, Nadim, Imran, Noble, Jennifer, Patton, Gareth, Savage, David, Spowage-Delaney, Benjamin, Doonan, Reece, Lee, Stephanie, Strange, Joshua, Barham, Ffion, Bosanko, Clare, Bullough, Richard, Danglosi, Joan, Freeman, Elaine, Jackson, Luci-Adele, James, Robert, Jose, Louise, Lowe, Fionna, Lowther, Ashleigh, McCarthy, Ian, McConnell, David, Parry, Richard, Shirreff, Henry, Smith, Jason, Squire, Rosalyn, Battishill, Wayne, Boggaram, Girish, Chilcott, Gemma, Gagg, James, Machin, Emma, Shovelton, Charmaine, Purnell, Rebecca, Crosthwaite-Eyre, Richard, Page, George, Pitt, Andrew, Riou, Peter, Sykes, Daisy, Worth, Cassie, Zitter, Lily, Baron, Tanya, Baskaran, Dineshbabu, Beer, Sally, Bhargav, Akshay, Blamey, Helen, Brown, Charlotte, Clare-Gray, Benjamin, Davies, Matthew, Dawson, Neil, Dineen, Karen, Edgar, Iain, Erasu, Vishakha, Faisal, Muhammad, Georgiou, Dominique, Gibson, Alice, Ghataoura, Rashpal, Glover, Samuel, Gordon, Olivia, Ilyas, Sadaf, Jeffs, Aimee, Knott, Henry, Kundoor, Sneha, Martinez, Jose, McCallum, Andrew, Novak, Alex, Panduro, Tine, Pattanshetty, Ravi, Paul, Immanuel, Magallano, Rufino Roger, Razdan, Pamposh, Sambo, Tinelly, Shah, Sumaiyah, Shaikh, Ayisha, Smith, Simon, Thraves, Hannah, Tickle, Charlotte, Ang, Justin, Boyle, Adrian, Hardwick, Susie, Meynell, Kerry, Miller, Samuel, Charsley, Julia, Jolly, Sarah, Mahony, Faye, Moseley-Rodgers, Jade, Sivayoham, Narani, Sheerin, Thomas, Seel, Claire, Abdalla, Mohamed, Cameron, Gregor, Harnett, James, Ramos, Daryl, Perez, Fabiola Sevilla, White, Rachel, Abdullah, Wajiha, Ballard, Suzanna, Barnes, Katie, Clegg, Rebecca, Cousins, Elisha, Cumiskey, Simon, Elwir, Bashar, Ferriman, Alexander, Fletcher, Alison, Griffiths, Charlotte, Guiling, Paolyn, Johnson, Graham, Loach, Benjamin, Matthews, Alison, May-Moulden, Lucy, Norman, Rebecca, Seelly, Helen, Smith, Richard, Tabner, Andrew, Tilbury, Nicholas, Ward, Thomas, Wright, Lianne, Wroblewski, Heather, and Zawadzka, Marcelina

Published
2024
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5. CoBWeb: a user-friendly web application to estimate causal treatment effects from observational data using multiple algorithms

Author

Markoulidakis, Andreas, Holmans, Peter, Pallmann, Philip, Busse, Monica, and Griffin, Beth-Ann

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Background/aims: While randomized controlled trials are the gold standard for measuring causal effects, robust conclusions about causal relationships can be obtained using data from observational studies if proper statistical techniques are used to account for the imbalance of pretreatment confounders across groups. Propensity score (PS) and balance weighting are useful techniques that aim to reduce the observed imbalances between treatment groups by weighting the groups to be as similar as possible with respect to observed confounders. Methods: We have created CoBWeb, a free and easy-to-use web application for the estimation of causal treatment effects from observational data, using PS and balancing weights to control for confounding bias. CoBWeb uses multiple algorithms to estimate the PS and balancing weights, to allow for more flexible relations between the treatment indicator and the observed confounders (as different algorithms make different (or no) assumptions about the structural relationship between the treatment covariate and the confounders). The optimal algorithm can be chosen by selecting the one that achieves the best trade-off between balance and effective sample size. Results: CoBWeb follows all the key steps required for robust estimation of the causal treatment effect from observational study data and includes sensitivity analysis of the potential impact of unobserved confounders. We illustrate the practical use of the app using a dataset derived from a study of an intervention for adolescents with substance use disorder, which is available for users within the app environment. Conclusion: CoBWeb is intended to enable non-specialists to understand and apply all the key steps required to perform robust estimation of causal treatment effects using observational data., Comment: 16 pages

Published
2021

7. How balance and sample size impact bias in the estimation of causal treatment effects: A simulation study

Author

Markoulidakis, Andreas, Holmans, Peter, Pallmann, Philip, Busse, Monica, and Griffin, Beth Ann

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unless statistical techniques are used to account for the imbalance of confounders across exposure groups. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the imbalances between exposure groups by weighting the groups to look alike on the observed confounders. Despite the plethora of available methods to estimate PSBW, there is little guidance on what one defines as adequate balance, and unbiased and robust estimation of the causal treatment effect is not guaranteed unless several conditions hold. Accurate inference requires that 1. the treatment allocation mechanism is known, 2. the relationship between the baseline covariates and the outcome is known, 3. adequate balance of baseline covariates is achieved post-weighting, 4. a proper set of covariates to control for confounding bias is known, and 5. a large enough sample size is available. In this article, we use simulated data of various sizes to investigate the influence of these five factors on statistical inference. Our findings provide evidence that the maximum Kolmogorov- Smirnov statistic is the proper statistical measure to assess balance on the baseline covariates, in contrast to the mean standardised mean difference used in many applications, and 0.1 is a suitable threshold to consider as acceptable balance. Finally, we recommend that 60-80 observations, per confounder per treatment group, are required to obtain a reliable and unbiased estimation of the causal treatment effect.

Published
2021

8. Correction: Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

Author

Law, Martin, Couturier, Dominique-Laurent, Choodari-Oskooei, Babak, Crout, Phillip, Gamble, Carrol, Jacko, Peter, Pallmann, Philip, Pilling, Mark, Robertson, David S., Robling, Michael, Sydes, Matthew R., Villar, Sofía S., Wason, James, Wheeler, Graham, Williamson, S. Faye, Yap, Christina, and Jaki, Thomas

Published
2023
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9. Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

Author

Law, Martin, Couturier, Dominique-Laurent, Choodari-Oskooei, Babak, Crout, Phillip, Gamble, Carrol, Jacko, Peter, Pallmann, Philip, Pilling, Mark, Robertson, David S., Robling, Michael, Sydes, Matthew R., Villar, Sofía S., Wason, James, Wheeler, Graham, Williamson, S. Faye, Yap, Christina, and Jaki, Thomas

Published
2023
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11. Point estimation for adaptive trial designs I: a methodological review

Author

Robertson, David S., Choodari-Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, and Jaki, Thomas

Subjects
Statistics - Methodology, Statistics - Applications, 62F10
Abstract

Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value", and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realised trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This paper is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this paper can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature., Comment: Part I of a two-part series; accepted in Statistics in Medicine

Published
2021

13. A tutorial comparing different covariate balancing methods with an application evaluating the causal effects of substance use treatment programs for adolescents

Author

Markoulidakis, Andreas, Taiyari, Khadijeh, Holmans, Peter, Pallmann, Philip, Busse, Monica, Godley, Mark D., and Griffin, Beth Ann

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Other Statistics
Abstract

Randomized controlled trials are the gold standard for measuring causal effects. However, they are often not always feasible, and causal treatment effects must be estimated from observational data. Observational studies do not allow robust conclusions about causal relationships unless statistical techniques account for the imbalance of pretreatment confounders across groups while key assumptions hold. Propensity score and balance weighting (PSBW) are useful techniques that aim to reduce the imbalances between treatment groups by weighting the groups to look alike on the observed confounders. There are many methods available to estimate PSBW. However, it is unclear a priori which will achieve the best trade-off between covariate balance and effective sample size. Moreover, it is critical to assess the validity of key assumptions required for robust estimation of the needed treatment effects, including the overlap and no unmeasured confounding assumptions. We present a step-by-step guide to covariate balancing strategies, including how to evaluate overlap, obtain estimates of PSBW, check for covariate balance, and assess sensitivity to unobserved confounding. We compare the performance of several estimation methods using a case study examining the relative effectiveness of substance use treatment programs and provide a user-friendly web application that can implement the proposed steps.

Published
2020

14. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

Author

Burnett, Thomas, Mozgunov, Pavel, Pallmann, Philip, Villar, Sofia S., Wheeler, Graham M., and Jaki, Thomas

Subjects
Statistics - Applications
Abstract

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory Phase III studies., Comment: 35 pages, 9 figures

Published
2020

15. Family group conferencing for children and families: Evaluation of implementation, context and effectiveness (Family VOICE). Study protocol

Author

Scourfield, Jonathan, primary, Evans, Rhiannon, additional, Pallmann, Philip, additional, Petrou, Stavros, additional, Robling, Michael, additional, Au, Kar-Man, additional, Jones-Williams, Delyth, additional, Lugg-Widger, Fiona, additional, Meindl, Melissa, additional, Schroeder, Elizabeth-Ann, additional, Wood, Sophie, additional, and Wilkins, David, additional

Published
2024
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17. Practical guidance for running late-phase platform protocols for clinical trials: lessons from experienced UK clinical trials units

Author

Love, Sharon B., Cafferty, Fay, Snowdon, Claire, Carty, Karen, Savage, Joshua, Pallmann, Philip, McParland, Lucy, Brown, Louise, Masters, Lindsey, Schiavone, Francesca, Hague, Dominic, Townsend, Stephen, Amos, Claire, South, Annabelle, Sturgeon, Kate, Langley, Ruth, Maughan, Timothy, James, Nicholas, Hall, Emma, Kernaghan, Sarah, Bliss, Judith, Turner, Nick, Tutt, Andrew, Yap, Christina, Firth, Charlotte, Kong, Anthony, Mehanna, Hisham, Watts, Colin, Hills, Robert, Thomas, Ian, Copland, Mhairi, Bell, Sue, Sebag-Montefiore, David, Jones, Robert, Parmar, Mahesh K. B., and Sydes, Matthew R.

Published
2022
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19. The cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study.

Author

Webb, Edward J D, Howdon, Daniel, Bestwick, Rebecca, King, Natalie, Sandoe, Jonathan A T, Euden, Joanne, Grozeva, Detelina, West, Robert, Howard, Philip, Powell, Neil, Albur, Mahableshwar, Bond, Stuart, Brookes-Howell, Lucy, Dark, Paul, Hellyer, Thomas, Llewelyn, Martin, McCullagh, Iain J, Ogden, Margaret, Pallmann, Philip, and Parsons, Helena

Subjects
COVID-19 pandemic, COVID-19, QUALITY-adjusted life years, COST estimates, HEALTH boards
Abstract

Background Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. Objectives Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. Methods Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a £20 000/QALY threshold. Uncertainty was characterized using bootstrapping. Results People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups' 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (£9830 versus £10 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. Conclusions Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Family Group Conference Provision in UK Local Authorities and Associations with Children Looked after Rates.

Author

Wood, Sophie, Scourfield, Jonathan, Meindl, Melissa, Au, Kar Man, Evans, Rhiannon, Jones-Willams, Delyth, Lugg-Widger, Fiona, Pallmann, Philip, Robling, Michael, Schroeder, Elizabeth-Ann, Petrou, Stavros, and Wilkins, David

Subjects
CHILD welfare, SELF-evaluation, RESEARCH funding, T-test (Statistics), EXECUTIVES, GOVERNMENT agencies, PATIENT-family relations, QUESTIONNAIRES, SOCIAL services, DECISION making, FOSTER home care, DESCRIPTIVE statistics, FAMILIES, ASSOCIATIONS, institutions, etc., FAMILY reunification, COMPARATIVE studies, FOSTER children, DATA analysis software, CONFIDENCE intervals, PATIENT care conferences, SOCIAL isolation, REGRESSION analysis
Abstract

Family group conferences (FGCs) in child welfare share decision-making with family members by bringing the immediate and wider family together to make a plan to meet a child's needs. This paper reports survey findings on FGC provision in the UK in 2022 and explores whether in England the presence of an FGC service and the rate of FGC provision is associated with the rate of children in care, entering care, in kinship foster care and leaving care. Seventy-nine per cent (n  = 167) of local authorities in the UK provided FGCs to families, and 14 per cent (n  = 29) did not. Services that were more established offered a more diverse range of FGCs. The introduction of FGCs in English local authorities was associated with a higher rate of children in care, but also higher rates of kinship foster care, a key goal of FGCs where it is not possible for children to stay with their parents. Higher rates of FGCs were associated with more children leaving care, possibly due to reunification with birth families. To understand in more detail, the circumstances of children in and leaving care in local authorities with FGCs, individual data linkage studies are needed. [ABSTRACT FROM AUTHOR]

Published
2024
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22. The Cost-Effectiveness and Cost-Consequences of a School-Based Social Worker Intervention: A Within-Trial Economic Evaluation

Author

Schroeder, Elizabeth-Ann, primary, Westlake, David, additional, Daher, Shahd, additional, Adara, Linda, additional, Ayayo, Sharon, additional, Bennett, Verity, additional, Kim, sungwook, additional, Lugg-Widger, Fiona, additional, Meindl, Melissa, additional, Meister, Lena, additional, Munnery, Kim, additional, Pallmann, Philip, additional, Roberts, Louise, additional, Rawlinson, Sarah, additional, White, James, additional, and Petrou, Stavros, additional

Published
2024
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23. The Adaptive designs CONSORT Extension (ACE) statement : a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Author

ACE Consensus Group, Dimairo, Munyaradzi, Pallmann, Philip, Wason, James, Todd, Susan, Jaki, Thomas, Julious, Steven A, Mander, Adrian P, Weir, Christopher J, Koenig, Franz, Walton, Marc K, Nicholl, Jon P, Coates, Elizabeth, Biggs, Katie, Hamasaki, Toshimitsu, Proschan, Michael A, Scott, John A, Ando, Yuki, Hind, Daniel, and Altman, Douglas G

Published
2020

26. Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

Author

Henley, Josie, primary, Brookes-Howell, Lucy, additional, Euden, Joanne, additional, Pallmann, Philip, additional, Llewelyn, Martin, additional, Howard, Philip, additional, Powell, Neil, additional, Dark, Paul, additional, Szakmany, Tamas, additional, Hellyer, Thomas P, additional, Albur, Mahableshwar, additional, Hamilton, Ryan, additional, Prestwich, Graham, additional, Ogden, Margaret, additional, Maboshe, Wakunyambo, additional, Sandoe, Jonathan, additional, Thomas-Jones, Emma, additional, and Carrol, Enitan, additional

Published
2023
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27. Protocol for a randomised controlled feasibility trial of HD-DRUM, a rhythmic movement training application for cognitive and motor symptoms in people with Huntington’s disease

Author

Ioakeimidis, Vasileios, primary, Busse, Monica E., additional, Drew, Cheney J. G., additional, Pallmann, Philip, additional, Jones, Derek K., additional, Rosser, Anne E., additional, Palombo, Marco, additional, Schubert, Robin, additional, and Metzler-Baddeley, Claudia, additional

Published
2023
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29. Reduction in massive postpartum haemorrhage and red blood cell transfusion during a national quality improvement project, Obstetric Bleeding Strategy for Wales, OBS Cymru: an observational study

Author

Bell, Sarah F., Collis, Rachel E., Pallmann, Philip, Bailey, Christopher, James, Kathryn, John, Miriam, Kelly, Kevin, Kitchen, Thomas, Scarr, Cerys, Watkins, Adam, Edey, Tracey, Macgillivray, Elinore, Greaves, Kathryn, Volikas, Ingrid, Tozer, James, Sengupta, Niladri, Roberts, Iolo, Francis, Claire, and Collins, Peter W.

Published
2021
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30. Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project

Author

Wilson, Nina, Biggs, Katie, Bowden, Sarah, Brown, Julia, Dimairo, Munyaradzi, Flight, Laura, Hall, Jamie, Hockaday, Anna, Jaki, Thomas, Lowe, Rachel, Murphy, Caroline, Pallmann, Philip, Pilling, Mark A., Snowdon, Claire, Sydes, Matthew R., Villar, Sofía S., Weir, Christopher J., Welburn, Jessica, Yap, Christina, Maier, Rebecca, Hancock, Helen, and Wason, James M. S.

Published
2021
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32. Long-term risk prediction after major lower limb amputation: 1-year results of the PERCEIVE study.

Author

Gwilym, Brenig Llwyd, Pallmann, Philip, Waldron, Cherry-Ann, Thomas-Jones, Emma, Milosevic, Sarah, Brookes-Howell, Lucy, Harris, Debbie, Massey, Ian, Burton, Jo, Stewart, Phillippa, Samuel, Katie, Jones, Sian, Cox, David, Clothier, Annie, Prout, Hayley, Edwards, Adrian, Twine, Christopher P, Bosanquet, David Charles, and group, Vascular and Endovascular Research Network (VERN) and the PERCEIVE study

Subjects
LEG amputation, MEDICAL personnel, DEATH forecasting, NURSE practitioners, FORECASTING
Abstract

Background Decision-making when considering major lower limb amputation is complex and requires individualized outcome estimation. It is unknown how accurate healthcare professionals or relevant outcome prediction tools are at predicting outcomes at 1-year after major lower limb amputation. Methods An international, multicentre prospective observational study evaluating healthcare professional accuracy in predicting outcomes 1 year after major lower limb amputation and evaluation of relevant outcome prediction tools identified in a systematic search of the literature was undertaken. Observed outcomes at 1 year were compared with: healthcare professionals' preoperative predictions of death (surgeons and anaesthetists), major lower limb amputation revision (surgeons) and ambulation (surgeons, specialist physiotherapists and vascular nurse practitioners); and probabilities calculated from relevant outcome prediction tools. Results A total of 537 patients and 2244 healthcare professional predictions of outcomes were included. Surgeons and anaesthetists had acceptable discrimination (C-statistic = 0.715), calibration and overall performance (Brier score = 0.200) when predicting 1-year death, but performed worse when predicting major lower limb amputation revision and ambulation (C-statistics = 0.627 and 0.662 respectively). Healthcare professionals overestimated the death and major lower limb amputation revision risks. Consultants outperformed trainees, especially when predicting ambulation. Allied healthcare professionals marginally outperformed surgeons in predicting ambulation. Two outcome prediction tools (C-statistics = 0.755 and 0.717, Brier scores = 0.158 and 0.178) outperformed healthcare professionals' discrimination, calibration and overall performance in predicting death. Two outcome prediction tools for ambulation (C-statistics = 0.688 and 0.667) marginally outperformed healthcare professionals. Conclusion There is uncertainty in predicting 1-year outcomes following major lower limb amputation. Different professional groups performed comparably in this study. Two outcome prediction tools for death and two for ambulation outperformed healthcare professionals and may support shared decision-making. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Point estimation for adaptive trial designs I:A methodological review

Author

Robertson, David S., Choodari‐Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, Jaki, Thomas, Robertson, David S., Choodari‐Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, and Jaki, Thomas

Abstract

Recent FDA guidance on adaptive clinical trial designs defines bias as “a systematic tendency for the estimate of treatment effect to deviate from its true value,” and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end‐of‐trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two‐part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias‐reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

Published
2023

37. Effectiveness and Cost-Effectiveness of a Personalised Self-management Intervention for living with long COVID: protocol for the LISTEN randomised controlled trial

Author

Potter, Claire, primary, Leggat, Fiona, additional, Lowe, Rachel, additional, Pallmann, Philip, additional, Riaz, Muhammad, additional, Barlow, Christy, additional, Edwards, Adrian, additional, Siriwardena, Aloysius Niroshan, additional, Sevdalis, Nick, additional, Sewell, Bernadette, additional, McRae, Jackie, additional, Fish, Jessica, additional, de Abreu, Maria Ines de Sousa, additional, Jones, Fiona, additional, and Busse, Monica, additional

Published
2023
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39. Additional file 1 of Effectiveness and cost-effectiveness of a personalised self-management intervention for living with long COVID: protocol for the LISTEN randomised controlled trial

Author

Potter, Claire, Leggat, Fiona, Lowe, Rachel, Pallmann, Philip, Riaz, Muhammad, Barlow, Christy, Edwards, Adrian, Siriwardena, Aloysius Niroshan, Sevdalis, Nick, Sewell, Bernadette, McRae, Jackie, Fish, Jessica, de Sousa de Abreu, Maria Ines, Jones, Fiona, and Busse, Monica

Abstract

Additional file 1: Appendix 1. Model Informed Consent Form. Appendix 2. Model Participant Information Sheet.

Published
2023
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40. Additional file 2 of Effectiveness and cost-effectiveness of a personalised self-management intervention for living with long COVID: protocol for the LISTEN randomised controlled trial

Author

Potter, Claire, Leggat, Fiona, Lowe, Rachel, Pallmann, Philip, Riaz, Muhammad, Barlow, Christy, Edwards, Adrian, Siriwardena, Aloysius Niroshan, Sevdalis, Nick, Sewell, Bernadette, McRae, Jackie, Fish, Jessica, de Sousa de Abreu, Maria Ines, Jones, Fiona, and Busse, Monica

Abstract

Additional file 2. SPIRIT Checklist for Trials.

Published
2023
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44. Procalcitonin Evaluation of Antibiotic Use in COVID-19 Hospitalised Patients (PEACH): Protocol for a Retrospective Observational Study

Author

Euden, Joanne, primary, Pallmann, Philip, additional, Grozeva, Detelina, additional, Albur, Mahableshwar, additional, Bond, Stuart E., additional, Brookes-Howell, Lucy, additional, Dark, Paul, additional, Hellyer, Thomas, additional, Hopkins, Susan, additional, Howard, Philip, additional, Llewelyn, Martin J., additional, Maboshe, Wakunyambo, additional, McCullagh, Iain J., additional, Ogden, Margaret, additional, Parsons, Helena, additional, Partridge, David, additional, Powell, Neil, additional, Shaw, Dominick, additional, Shinkins, Bethany, additional, Szakmany, Tamas, additional, Todd, Stacy, additional, Thomas-Jones, Emma, additional, West, Robert M., additional, Carrol, Enitan D., additional, and Sandoe, Jonathan A. T., additional

Published
2022
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45. Response to Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”

Author

Law, Martin, primary, Couturier, Dominique-Laurent, additional, Choodari-Oskooei, Babak, additional, Crout, Phillip, additional, Gamble, Carrol, additional, Pallmann, Philip, additional, Pilling, Mark, additional, Robertson, David, additional, Robling, Michael, additional, Sydes, Matthew R., additional, Villar, Sofía S., additional, Wason, James, additional, Wheeler, Graham, additional, Williamson, S. Faye, additional, Yap, Christina, additional, and Jaki, Thomas, additional

Published
2022
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47. Point estimation for adaptive trial designs I: A methodological review

Author

Robertson, David S, Choodari-Oskooei, Babak, Dimairo, Munya, Flight, Laura, Pallmann, Philip, and Jaki, Thomas

Subjects
FOS: Computer and information sciences, bias-correction, Statistics - Applications, Methodology (stat.ME), Bias, Research Design, adaptive design, Humans, Applications (stat.AP), point estimation, conditional bias, Statistics - Methodology, 62F10, Software, flexible design
Abstract

Funder: Biometrika Trust, Funder: Health and Care Research Wales; Id: http://dx.doi.org/10.13039/100012068, Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

Published
2022
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