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1. Recommendations to address respondent burden associated with patient-reported outcome assessment

Author

Aiyegbusi, Olalekan Lee, Cruz Rivera, Samantha, Roydhouse, Jessica, Kamudoni, Paul, Alder, Yvonne, Anderson, Nicola, Baldwin, Robert Mitchell, Bhatnagar, Vishal, Black, Jennifer, Bottomley, Andrew, Brundage, Michael, Cella, David, Collis, Philip, Davies, Elin-Haf, Denniston, Alastair K., Efficace, Fabio, Gardner, Adrian, Gnanasakthy, Ari, Golub, Robert M., Hughes, Sarah E., Jeyes, Flic, Kern, Scottie, King-Kallimanis, Bellinda L., Martin, Antony, McMullan, Christel, Mercieca-Bebber, Rebecca, Monteiro, Joao, Peipert, John Devin, Quijano-Campos, Juan Carlos, Quinten, Chantal, Rantell, Khadija Rerhou, Regnault, Antoine, Sasseville, Maxime, Schougaard, Liv Marit Valen, Sherafat-Kazemzadeh, Roya, Snyder, Claire, Stover, Angela M., Verdi, Rav, Wilson, Roger, and Calvert, Melanie J.

Published
2024
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3. Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures

Author

Bui, Cat, Emechebe, Nnadozie, Kamalakar, Rajesh, Papadopoulos, Elektra, Sail, Kavita, Sen, Rohini, Turner, Sean C, Cocks, Kim, Reijneveld, Jaap, Gerlinger, Christoph, Keating, Karen, Su, Yun, Wolf, Birgit, Ge, Miaomiao, Ingelgaard, Anders, Peil, Barbara, Voorhaar, Maarten, Wong, Brendon, Dekanic Arbanas, Gracia, Kuljanic, Karin, Petranovic, Duska, Rede, Ivana, Arraras, Juan, Coons, Stephen Joel, Eremenco, Sonya, Murray, Lindsey, Reeve, Bryce, De Vries, Corinne, Herold, Ralf, Pignatti, Francesco, Machingura, Abigirl, Martinelli, Francesca, Musoro, Jammbe, Piccart, Martine, Barriuso, Jorge, Cherny, Nathan, Dafni, Ourania, De Vries, Elisabeth, Gyawali, Bishal, Kiesewetter, Barbara, Oosting, Sjoukje, Roitberg, Felipe, Rumpold, Gerhard, Schoepf, Felix, Tschuggnall, Michael, Black, Jennifer, Sasseville, Maxime, Soltys, Katherine, Ferrer, Montserrat, Garin, Olatz, Vilagut, Gemma, Schürmann, Christoph, Thomas, Stefanie, Wieseler, Beate, Snyder, Claire, Alonso Abad, Ariel, Bogaerts, Kris, Brackx, Febe, Molenberghs, Geert, Verbeke, Geert, Frigolett Catalan, Cristián, Choi, Jan, Thomassen, Doranne, Geissler, Jan, Sauerbrei, Willi, Gross, Franziska, Pilz, Micha Johannes, Barbachano, Yolanda, Campbell, Lisa, Rantell, Khadija, Desplanques, Gregoire, Regnault, Antoine, Morgan, Kate, Plate, Ananda, ten Seldam, Silene, Terada, Mitsumi, Mizusawa, Junki, Mitchell, Sandra, Wilder Smith, Ashley, Reksten, Tove Ragna, Schiel, Anja, Hansen Joseph, Kenth Louis, Campbell, Alicyn, Cappelleri, Joseph, de Besi, Patrizia, Russell-Smith, Alexander, Sandin, Rickard, Mamolo, Carla, Brundage, Michael, Tu, Dongsheng, Groenvold, Mogens, Petersen, Morten, Cleeland, Charlie, Williams, Lori, Shelley Wang, Xin, Ringash, Jolie, Calvert, Melanie, Cruz Rivera, Samantha, Aiyegbusi, Olalekan Lee, Goetghebeur, Els, Liu, Limin, Van Lancker, Kelly, Boele, Florien, Gilbert, Alexandra, Peacock, Rosemary, Basch, Ethan, King, Madeleine, Rutherford, Claudia, Bhatnagar, Vishal, Fiero, Mallorie, Horodniceanu, Erica, Johnson, Laura Lee, Kluetz, Paul, Rodriguez, Lisa, Pe, Madeline, Alanya, Ahu, Falk, Ragnhild Sorum, Amdal, Cecilie Delphin, Bjordal, Kristin, Chang, Jane, Cislo, Paul, Coens, Corneel, Dirven, Linda, Speck, Rebecca M, Fitzgerald, Kristina, Galinsky, Jayne, Giesinger, Johannes M, Holzner, Bernhard, Le Cessie, Saskia, O'Connor, Daniel, Oliver, Kathy, Pawar, Vivek, Quinten, Chantal, Schlichting, Michael, Ren, Jinma, Roychoudhury, Satrajit, Taphoorn, Martin J B, Velikova, Galina, Wintner, Lisa M, Griebsch, Ingolf, and Bottomley, Andrew

Published
2023
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4. Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice

Author

Aiyegbusi, Olalekan Lee, Alanya, Ahu, Barbachano, Yolanda, Basch, Ethan, Calvert, Melanie, Campbell, Alicyn, Cappelleri, Joseph, Choi, Jungyeon, Cruz Rivera, Samantha, Delphin Amdal, Cecilie, Fiero, Mallorie, Goetghebeur, Els, Kamalakar, Rajesh, Keating, Karen, Kluetz, Paul, le Cessie, Saskia, Liu, Limin, Molenberghs, Geert, Musoro, Jammbe, Ness, David, Rantell, Khadija, Regnault, Antoine, Reijneveld, Jaap, Roychoudhury, Satrajit, Rumpold, Gerhard, Russell-Smith, Alexander, Rutherford, Claudia, Sail, Kavita, Sandin, Rickard, Sasseville, Maxime, Sauerbrei, Willi, Schiel, Anja, Schlichting, Michael, Soltys, Kathy, Sorum Falk, Ragnhild, Su, Yun, ten Seldam, Silene, Thomassen, Doranne, Van Lancker, Kelly, Musoro, Jammbe Z, Amdal, Cecilie Delphin, Cappelleri, Joseph C, Falk, Ragnhild Sørum, Fiero, Mallorie H, and Reijneveld, Jaap C

Published
2023
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5. Maximising the impact of patient-reported outcome trial results to benefit patients and society

Author

Cruz Rivera, Samantha

Subjects
RZ Other systems of medicine
Abstract

There is an increasing need to demonstrate the return on medical research investment through benefit to patients, society and the economy. Patient-reported outcomes (PROs), such as quality of life and symptom data, are increasingly collected in clinical trials and may provide evidence, which can lead to a range of impacts. However it is unclear whether PRO impact is realised in practice. In addition, the different types of impact associated with PRO trial results, their barriers and facilitators, and appropriate impact metrics are not well defined. The doctoral research constituting this thesis adopted a mixed-methods approach with the aim of: a) synthesising existing methodological frameworks for healthcare research impact; b) determining the range of potential impacts associated with PRO data collected in trials, identifying potential PRO impact metrics and barriers and defining common facilitators to maximise PRO impact and; c) examining real-world evidence of PRO trial data impact and highlight optimal pathways to such impact. A number of studies were undertaken to address the aims. First, a systematic review of the literature identified 24 existing non-PRO-specific frameworks and over 80 impact metrics, which were then synthesised into a novel impact matrix and a simplified consolidated methodological framework for use by researchers and other stakeholders to help maximise the impact of healthcare research. Second, informed by this framework, an additional systematic review sought to determine the potential impact of PRO data collected in clinical trials and examined real world evidence of PRO trial data impact based on Research Excellence Framework (REF). This systemic review suggested that PRO trial data has the potential to inform clinical practice, clinical guidelines and, health policy; support drug approval, pricing and reimbursement decisions and; inform clinical and shared decision-making and consent for treatment. Furthermore, this second systematic review highlighted perceived methodological problems regarding the design, conduct and analysis and reporting of PRO data from clinical trials; which may hinder the impact of PRO data from clinical findings. Potential facilitators aimed at maximising PRO trial impact were also identified. The review identified 12 (n=69, 17%) REF 2014 impact case studies, which reported impact directly attributable to PRO findings. Including changes to international clinical guidelines and national guidelines, influencing cost-effectiveness analysis and drug approvals. Finally, in order to gain deeper understanding about the topics identified in the second systematic review, 24 semi-structured qualitative interviews were conducted with international stakeholders. Interviewees suggested PRO trial findings could lead to impact in the five impact categories identified in the aforementioned 'pathways to research impact' methodological framework. However, it was suggested that broader international stakeholder collaboration is required to tackle existing barriers and maximise the realisation of PRO trial impact on patients and society. In conclusion, this thesis has identified a range of potential impacts from PRO data which may benefit patients and society. However, a number of barriers need to be addressed to fully realise these benefits. This research highlights that the measurement of research impact, and specifically PRO research impact, is an essential exercise to better allocate limited funding, provide accountability and minimise research waste. Nonetheless, determining and implementing impact metrics is a complex task and will require greater stakeholder collaboration and engagement throughout the research pathway.

Published
2019

6. Patient reported outcome assessment must be inclusive and equitable

Author

Calvert, Melanie J., Cruz Rivera, Samantha, Retzer, Ameeta, Hughes, Sarah E., Campbell, Lisa, Molony-Oates, Barbara, Aiyegbusi, Olalekan Lee, Stover, Angela M., Wilson, Roger, McMullan, Christel, Anderson, Nicola E., Turner, Grace M., Davies, Elin Haf, Verdi, Rav, Velikova, Galina, Kamudoni, Paul, Muslim, Syed, Gheorghe, Adrian, O’Connor, Daniel, Liu, Xiaoxuan, Wu, Albert W., and Denniston, Alastair K.

Published
2022
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10. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Author

Ashrafian, Hutan, Beam, Andrew L, Collins, Gary S, Darzi, Ara, Deeks, Jonathan J, ElZarrad, M Khair, Espinoza, Cyrus, Esteva, Andre, Faes, Livia, Ferrante di Ruffano, Lavinia, Fletcher, John, Golub, Robert, Harvey, Hugh, Haug, Charlotte, Holmes, Christopher, Jonas, Adrian, Keane, Pearse A, Kelly, Christopher J, Lee, Aaron Y, Lee, Cecilia S, Manna, Elaine, Matcham, James, McCradden, Melissa, Moher, David, Monteiro, Joao, Mulrow, Cynthia, Oakden-Rayner, Luke, Paltoo, Dina, Panico, Maria Beatrice, Price, Gary, Rowley, Samuel, Savage, Richard, Sarkar, Rupa, Vollmer, Sebastian J, Yau, Christopher, Cruz Rivera, Samantha, Liu, Xiaoxuan, Chan, An-Wen, Denniston, Alastair K, and Calvert, Melanie J

Published
2020
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11. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Author

Ashrafian, Hutan, Beam, Andrew L, Chan, An-Wen, Collins, Gary S, Deeks, Ara DarziJonathan J, ElZarrad, M Khair, Espinoza, Cyrus, Esteva, Andre, Faes, Livia, Ferrante di Ruffano, Lavinia, Fletcher, John, Golub, Robert, Harvey, Hugh, Haug, Charlotte, Holmes, Christopher, Jonas, Adrian, Keane, Pearse A, Kelly, Christopher J, Lee, Aaron Y, Lee, Cecilia S, Manna, Elaine, Matcham, James, McCradden, Melissa, Monteiro, Joao, Mulrow, Cynthia, Oakden-Rayner, Luke, Paltoo, Dina, Panico, Maria Beatrice, Price, Gary, Rowley, Samuel, Savage, Richard, Sarkar, Rupa, Vollmer, Sebastian J, Yau, Christopher, Liu, Xiaoxuan, Cruz Rivera, Samantha, Moher, David, Calvert, Melanie J, and Denniston, Alastair K

Published
2020
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12. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Author

Cruz Rivera, Samantha, Liu, Xiaoxuan, Chan, An-Wen, Denniston, Alastair K., Calvert, Melanie J., and Darzi, Ara

Subjects
Artificial intelligence -- Usage -- Health aspects, Clinical trials -- Standards -- Technology application, Artificial intelligence, Technology application, Biological sciences, Health
Abstract

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions., Author(s): Samantha Cruz Rivera [sup.1] [sup.2] [sup.3] , Xiaoxuan Liu [sup.3] [sup.4] [sup.5] [sup.6] [sup.7] , An-Wen Chan [sup.8] , Alastair K. Denniston [sup.1] [sup.3] [sup.4] [sup.5] [sup.6] [sup.9] , [...]

Published
2020
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13. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Author

Liu, Xiaoxuan, Cruz Rivera, Samantha, Moher, David, Calvert, Melanie J., Denniston, Alastair K., Chan, An-Wen, Darzi, Ara, Holmes, Christopher, Yau, Christopher, Ashrafian, Hutan, Deeks, Jonathan J., di Ruffano, Lavinia Ferrante, Faes, Livia, Keane, Pearse A., and Vollmer, Sebastian J.

Subjects
Artificial intelligence -- Standards -- Usage, Practice guidelines (Medicine), Clinical trials -- Standards, Artificial intelligence, Biological sciences, Health
Abstract

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions., Author(s): Xiaoxuan Liu [sup.1] [sup.2] [sup.3] [sup.4] [sup.5] , Samantha Cruz Rivera [sup.5] [sup.6] [sup.7] , David Moher [sup.8] [sup.9] , Melanie J. Calvert [sup.4] [sup.5] [sup.6] [sup.7] [sup.10] [sup.11] [...]

Published
2020
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15. Setting International Standards in Analyzing Patient-Reported Outcomes and Quality of Life Endpoints in Cancer Clinical Trials-Innovative Medicines Initiative (SISAQOL-IMI): stakeholder views, objectives, and procedures

Author

Pe, Madeline, primary, Alanya, Ahu, additional, Falk, Ragnhild Sorum, additional, Amdal, Cecilie Delphin, additional, Bjordal, Kristin, additional, Chang, Jane, additional, Cislo, Paul, additional, Coens, Corneel, additional, Dirven, Linda, additional, Speck, Rebecca M, additional, Fitzgerald, Kristina, additional, Galinsky, Jayne, additional, Giesinger, Johannes M, additional, Holzner, Bernhard, additional, Le Cessie, Saskia, additional, O'Connor, Daniel, additional, Oliver, Kathy, additional, Pawar, Vivek, additional, Quinten, Chantal, additional, Schlichting, Michael, additional, Ren, Jinma, additional, Roychoudhury, Satrajit, additional, Taphoorn, Martin J B, additional, Velikova, Galina, additional, Wintner, Lisa M, additional, Griebsch, Ingolf, additional, Bottomley, Andrew, additional, Bui, Cat, additional, Emechebe, Nnadozie, additional, Kamalakar, Rajesh, additional, Papadopoulos, Elektra, additional, Sail, Kavita, additional, Sen, Rohini, additional, Turner, Sean C, additional, Cocks, Kim, additional, Reijneveld, Jaap, additional, Gerlinger, Christoph, additional, Keating, Karen, additional, Su, Yun, additional, Wolf, Birgit, additional, Ge, Miaomiao, additional, Ingelgaard, Anders, additional, Peil, Barbara, additional, Voorhaar, Maarten, additional, Wong, Brendon, additional, Dekanic Arbanas, Gracia, additional, Kuljanic, Karin, additional, Petranovic, Duska, additional, Rede, Ivana, additional, Arraras, Juan, additional, Coons, Stephen Joel, additional, Eremenco, Sonya, additional, Murray, Lindsey, additional, Reeve, Bryce, additional, De Vries, Corinne, additional, Herold, Ralf, additional, Pignatti, Francesco, additional, Machingura, Abigirl, additional, Martinelli, Francesca, additional, Musoro, Jammbe, additional, Piccart, Martine, additional, Barriuso, Jorge, additional, Cherny, Nathan, additional, Dafni, Ourania, additional, De Vries, Elisabeth, additional, Gyawali, Bishal, additional, Kiesewetter, Barbara, additional, Oosting, Sjoukje, additional, Roitberg, Felipe, additional, Rumpold, Gerhard, additional, Schoepf, Felix, additional, Tschuggnall, Michael, additional, Black, Jennifer, additional, Sasseville, Maxime, additional, Soltys, Katherine, additional, Ferrer, Montserrat, additional, Garin, Olatz, additional, Vilagut, Gemma, additional, Schürmann, Christoph, additional, Thomas, Stefanie, additional, Wieseler, Beate, additional, Snyder, Claire, additional, Alonso Abad, Ariel, additional, Bogaerts, Kris, additional, Brackx, Febe, additional, Molenberghs, Geert, additional, Verbeke, Geert, additional, Frigolett Catalan, Cristián, additional, Choi, Jan, additional, Thomassen, Doranne, additional, Geissler, Jan, additional, Sauerbrei, Willi, additional, Gross, Franziska, additional, Pilz, Micha Johannes, additional, Barbachano, Yolanda, additional, Campbell, Lisa, additional, Rantell, Khadija, additional, Desplanques, Gregoire, additional, Regnault, Antoine, additional, Morgan, Kate, additional, Plate, Ananda, additional, ten Seldam, Silene, additional, Terada, Mitsumi, additional, Mizusawa, Junki, additional, Mitchell, Sandra, additional, Wilder Smith, Ashley, additional, Reksten, Tove Ragna, additional, Schiel, Anja, additional, Hansen Joseph, Kenth Louis, additional, Campbell, Alicyn, additional, Cappelleri, Joseph, additional, de Besi, Patrizia, additional, Russell-Smith, Alexander, additional, Sandin, Rickard, additional, Mamolo, Carla, additional, Brundage, Michael, additional, Tu, Dongsheng, additional, Groenvold, Mogens, additional, Petersen, Morten, additional, Cleeland, Charlie, additional, Williams, Lori, additional, Shelley Wang, Xin, additional, Ringash, Jolie, additional, Calvert, Melanie, additional, Cruz Rivera, Samantha, additional, Aiyegbusi, Olalekan Lee, additional, Goetghebeur, Els, additional, Liu, Limin, additional, Van Lancker, Kelly, additional, Boele, Florien, additional, Gilbert, Alexandra, additional, Peacock, Rosemary, additional, Basch, Ethan, additional, King, Madeleine, additional, Rutherford, Claudia, additional, Bhatnagar, Vishal, additional, Fiero, Mallorie, additional, Horodniceanu, Erica, additional, Johnson, Laura Lee, additional, Kluetz, Paul, additional, and Rodriguez, Lisa, additional

Published
2023
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16. Single-arm studies involving patient-reported outcome data in oncology: a literature review on current practice

Author

Liu, Limin, primary, Choi, Jungyeon, additional, Musoro, Jammbe Z, additional, Sauerbrei, Willi, additional, Amdal, Cecilie Delphin, additional, Alanya, Ahu, additional, Barbachano, Yolanda, additional, Cappelleri, Joseph C, additional, Falk, Ragnhild Sørum, additional, Fiero, Mallorie H, additional, Regnault, Antoine, additional, Reijneveld, Jaap C, additional, Sandin, Rickard, additional, Thomassen, Doranne, additional, Roychoudhury, Satrajit, additional, Goetghebeur, Els, additional, le Cessie, Saskia, additional, Aiyegbusi, Olalekan Lee, additional, Basch, Ethan, additional, Calvert, Melanie, additional, Campbell, Alicyn, additional, Cappelleri, Joseph, additional, Cruz Rivera, Samantha, additional, Delphin Amdal, Cecilie, additional, Fiero, Mallorie, additional, Kamalakar, Rajesh, additional, Keating, Karen, additional, Kluetz, Paul, additional, Liu, Limin, additional, Molenberghs, Geert, additional, Musoro, Jammbe, additional, Ness, David, additional, Rantell, Khadija, additional, Reijneveld, Jaap, additional, Rumpold, Gerhard, additional, Russell-Smith, Alexander, additional, Rutherford, Claudia, additional, Sail, Kavita, additional, Sasseville, Maxime, additional, Schiel, Anja, additional, Schlichting, Michael, additional, Soltys, Kathy, additional, Sorum Falk, Ragnhild, additional, Su, Yun, additional, ten Seldam, Silene, additional, and Van Lancker, Kelly, additional

Published
2023
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21. Development of a core outcome set and identification of patient-reportable outcomes for primary brain tumour trials: protocol for the COBra study

Author

Retzer, Ameeta, primary, Sivell, Stephanie, additional, Scott, Hannah, additional, Nelson, Annmarie, additional, Bulbeck, Helen, additional, Seddon, Kathy, additional, Grant, Robin, additional, Adams, Richard, additional, Watts, Colin, additional, Aiyegbusi, Olalekan Lee, additional, Kearns, Pamela, additional, Cruz Rivera, Samantha, additional, Dirven, Linda, additional, Baddeley, Elin, additional, Calvert, Melanie, additional, and Byrne, Anthony, additional

Published
2022
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22. Listening to the Patient Voice Adds Value to Cancer Clinical Trials

Author

Brundage, Michael D, primary, Crossnohere, Norah L, additional, O’Donnell, Jennifer, additional, Cruz Rivera, Samantha, additional, Wilson, Roger, additional, Wu, Albert W, additional, Moher, David, additional, Kyte, Derek, additional, Reeve, Bryce B, additional, Gilbert, Alexandra, additional, Chen, Ronald C, additional, Calvert, Melanie J, additional, and Snyder, Claire, additional

Published
2022
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23. Ethical Considerations for the Inclusion of Patient-Reported Outcomes in Clinical Research

Author

Cruz Rivera, Samantha, primary, Aiyegbusi, Olalekan Lee, additional, Ives, Jonathan, additional, Draper, Heather, additional, Mercieca-Bebber, Rebecca, additional, Ells, Carolyn, additional, Hunn, Amanda, additional, Scott, Jane A., additional, Fernandez, Conrad V., additional, Dickens, Andrew P., additional, Anderson, Nicola, additional, Bhatnagar, Vishal, additional, Bottomley, Andrew, additional, Campbell, Lisa, additional, Collett, Clive, additional, Collis, Philip, additional, Craig, Kathrine, additional, Davies, Hugh, additional, Golub, Robert, additional, Gosden, Lesley, additional, Gnanasakthy, Ari, additional, Haf Davies, Elin, additional, von Hildebrand, Maria, additional, Lord, Janet M., additional, Mahendraratnam, Nirosha, additional, Miyaji, Tempei, additional, Morel, Thomas, additional, Monteiro, Joao, additional, Zwisler, Ann-Dorthe Olsen, additional, Peipert, John Devin, additional, Roydhouse, Jessica, additional, Stover, Angela M., additional, Wilson, Roger, additional, Yap, Christina, additional, and Calvert, Melanie J., additional

Published
2022
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24. Non-pharmacological therapies for postviral syndromes, including Long COVID: a systematic review and meta-analysis protocol

Author

Chandan, Joht Singh, primary, Brown, Kirsty, additional, Simms-Williams, Nikita, additional, Camaradou, Jenny, additional, Bashir, Nasir, additional, Heining, Dominic, additional, Aiyegbusi, Olalekan Lee, additional, Turner, Grace, additional, Cruz Rivera, Samantha, additional, Hotham, Richard, additional, Nirantharakumar, Krishnarajah, additional, Sivan, Manoj, additional, Khunti, Kamlesh, additional, Raindi, Devan, additional, Marwaha, Steven, additional, Hughes, Sarah E, additional, McMullan, Christel, additional, Calvert, Melanie, additional, and Haroon, Shamil, additional

Published
2022
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26. ‘Give Us The Tools!’: development of knowledge transfer tools to support the involvement of patient partners in the development of clinical trial protocols with patient-reported outcomes (PROs), in accordance with SPIRIT-PRO Extension

Author

Cruz Rivera, Samantha, primary, Stephens, Richard, additional, Mercieca-Bebber, Rebecca, additional, Retzer, Ameeta, additional, Rutherford, Claudia, additional, Price, Gary, additional, Slade, Anita, additional, Aiyegbusi, Olalekan Lee, additional, Edge, Philip, additional, Roberts, Lesley, additional, Gosden, Lesley, additional, Verdi, Rav, additional, Wilson, Roger, additional, and Calvert, Melanie, additional

Published
2021
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27. A systematic review assessing the quality of patient reported outcomes measures in dry eye diseases

Author

Recchioni, Alberto, Aiyegbusi, Olalekan Lee, Cruz-Rivera, Samantha, Rauz, Saaeha, Slade, Anita, Recchioni, Alberto, Aiyegbusi, Olalekan Lee, Cruz-Rivera, Samantha, Rauz, Saaeha, and Slade, Anita

Abstract

Background: Patient-reported outcome measures (PROMs) can provide valuable insights on the impact of a disease or treatment on a patient’s health-related quality of life. In ophthalmology, particularly in dry eye disease (DED) and ocular surface disease (OSD), it is unclear whether the available PROMs were developed using comprehensive guidelines. To address this, we evaluated the methodological quality of studies assessing the psychometric properties of PROMs in DED and OSD [PROSPERO registration number CRD42019142328]. Methods: Four databases were searched; reference list and citation searching of included studies was also conducted. The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist was used to appraise the quality of the studies evaluating the psychometric properties of PROMs used in DED and OSD. Results: The search strategy (S3 Table) retrieved 5,761 records, 573 duplicates were removed, 5,188 abstracts were screened and 127 full-text articles were retrieved for further review. Of these, 118 full-text articles did not meet the eligibility criteria and were excluded. Reference list and citation searching, identified an additional 8 articles bringing the total numbers of papers reviewed to 17. In general, psychometric properties such as content validity, measurement error and structural validity were not assessed by the studies included in this review. Studies reviewing The Impact of Dry Eye on Everyday Life (IDEEL) presented with the highest quality scores together with the Ocular Surface Disease Index (OSDI) questionnaire. Conclusions: The quality of studies evaluating PROMs in DED and OSD was considered using the COSMIN standards. The majority of the studies evaluating PROMs included in this review did not meet the recommended COSMIN criteria and the quality of the PROMs evaluated is not assured. Further evaluation of their psychometric properties is required if these are going to be used in clinical practice o

Published
2021

28. Directrices para los protocolos de ensayos clínicos de intervenciones con inteligencia artificial: la extensión SPIRIT-AI.

Author

Cruz Rivera, Samantha, Xiaoxuan Liu, An-Wen Chan, Denniston, Alastair K., and Calvert, Melanie J.

Subjects
*MEDICAL protocols, *ARTIFICIAL intelligence, *CLINICAL trials, *EVALUATION of medical care, *DELPHI method, *STAKEHOLDER analysis
Abstract

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial. [ABSTRACT FROM AUTHOR]

Published
2023
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30. Assessing the impact of healthcare research: A systematic review of methodological frameworks

Author

Cruz Rivera, Samantha, Kyte, Derek G., Aiyegbusi, Olalekan Lee, Keeley, Thomas J., and Calvert, Melanie J.

Subjects
Medical research -- Influence -- Measurement, Biological sciences
Abstract

Background Increasingly, researchers need to demonstrate the impact of their research to their sponsors, funders, and fellow academics. However, the most appropriate way of measuring the impact of healthcare research is subject to debate. We aimed to identify the existing methodological frameworks used to measure healthcare research impact and to summarise the common themes and metrics in an impact matrix. Methods and findings Two independent investigators systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), the Excerpta Medica Database (EMBASE), the Cumulative Index to Nursing and Allied Health Literature (CINAHL+), the Health Management Information Consortium, and the Journal of Research Evaluation from inception until May 2017 for publications that presented a methodological framework for research impact. We then summarised the common concepts and themes across methodological frameworks and identified the metrics used to evaluate differing forms of impact. Twenty-four unique methodological frameworks were identified, addressing 5 broad categories of impact: (1) 'primary research-related impact', (2) 'influence on policy making', (3) 'health and health systems impact', (4) 'health-related and societal impact', and (5) 'broader economic impact'. These categories were subdivided into 16 common impact subgroups. Authors of the included publications proposed 80 different metrics aimed at measuring impact in these areas. The main limitation of the study was the potential exclusion of relevant articles, as a consequence of the poor indexing of the databases searched. Conclusions The measurement of research impact is an essential exercise to help direct the allocation of limited research resources, to maximise research benefit, and to help minimise research waste. This review provides a collective summary of existing methodological frameworks for research impact, which funders may use to inform the measurement of research impact and researchers may use to inform study design decisions aimed at maximising the short-, medium-, and long-term impact of their research., Author(s): Samantha Cruz Rivera, Derek G. Kyte *, Olalekan Lee Aiyegbusi, Thomas J. Keeley, Melanie J. Calvert Introduction In 2010, approximately US$240 billion was invested in healthcare research worldwide [1]. [...]

Published
2017
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31. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Author

Cruz Rivera, Samantha, primary, Liu, Xiaoxuan, additional, Chan, An-Wen, additional, Denniston, Alastair K, additional, Calvert, Melanie J, additional, Ashrafian, Hutan, additional, Beam, Andrew L, additional, Collins, Gary S, additional, Darzi, Ara, additional, Deeks, Jonathan J, additional, ElZarrad, M Khair, additional, Espinoza, Cyrus, additional, Esteva, Andre, additional, Faes, Livia, additional, Ferrante di Ruffano, Lavinia, additional, Fletcher, John, additional, Golub, Robert, additional, Harvey, Hugh, additional, Haug, Charlotte, additional, Holmes, Christopher, additional, Jonas, Adrian, additional, Keane, Pearse A, additional, Kelly, Christopher J, additional, Lee, Aaron Y, additional, Lee, Cecilia S, additional, Manna, Elaine, additional, Matcham, James, additional, McCradden, Melissa, additional, Moher, David, additional, Monteiro, Joao, additional, Mulrow, Cynthia, additional, Oakden-Rayner, Luke, additional, Paltoo, Dina, additional, Panico, Maria Beatrice, additional, Price, Gary, additional, Rowley, Samuel, additional, Savage, Richard, additional, Sarkar, Rupa, additional, Vollmer, Sebastian J, additional, and Yau, Christopher, additional

Published
2020
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32. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Author

Liu, Xiaoxuan, primary, Cruz Rivera, Samantha, additional, Moher, David, additional, Calvert, Melanie J, additional, Denniston, Alastair K, additional, Ashrafian, Hutan, additional, Beam, Andrew L, additional, Chan, An-Wen, additional, Collins, Gary S, additional, Deeks, Ara DarziJonathan J, additional, ElZarrad, M Khair, additional, Espinoza, Cyrus, additional, Esteva, Andre, additional, Faes, Livia, additional, Ferrante di Ruffano, Lavinia, additional, Fletcher, John, additional, Golub, Robert, additional, Harvey, Hugh, additional, Haug, Charlotte, additional, Holmes, Christopher, additional, Jonas, Adrian, additional, Keane, Pearse A, additional, Kelly, Christopher J, additional, Lee, Aaron Y, additional, Lee, Cecilia S, additional, Manna, Elaine, additional, Matcham, James, additional, McCradden, Melissa, additional, Monteiro, Joao, additional, Mulrow, Cynthia, additional, Oakden-Rayner, Luke, additional, Paltoo, Dina, additional, Panico, Maria Beatrice, additional, Price, Gary, additional, Rowley, Samuel, additional, Savage, Richard, additional, Sarkar, Rupa, additional, Vollmer, Sebastian J, additional, and Yau, Christopher, additional

Published
2020
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33. Core outcomes in brain tumour trials – the COBra study review of glioma trial registration data

Author

Retzer, Ameeta, Baddeley, Elin, Sivell, Stephanie, Seddon, Kathy, Bulbeck, Helen, Nelson, Annmarie, Calvert, Melanie, Grant, Robin, Adams, Richard, Watts, Colin, Aiyegbusi, Olalekan Lee, Cruz Rivera, Samantha, Kearns, Pamela, Dirven, Linda, Byrne, Anthony, Retzer, Ameeta, Baddeley, Elin, Sivell, Stephanie, Seddon, Kathy, Bulbeck, Helen, Nelson, Annmarie, Calvert, Melanie, Grant, Robin, Adams, Richard, Watts, Colin, Aiyegbusi, Olalekan Lee, Cruz Rivera, Samantha, Kearns, Pamela, Dirven, Linda, and Byrne, Anthony

Abstract

AIMS Trial registration supports unbiased reporting of research studies. Despite inconsistent use globally and sub-optimal completeness, registration is associated with publication of the same outcomes as defined in trial protocols, though these are not necessarily reported in published results. In the core outcomes in brain tumour trials (COBra) study a registry review was undertaken, alongside a systematic review of qualitative literature and semi-structured interviews, to develop a core outcome set (COS) outcome longlist. The study aims to identify all outcomes reported across all glioma trial registration data. METHOD Phase 3 interventional glioma trials involving patients aged 18 and over, registered on clinicaltrials.gov or the ISRCTN registry were included. Two researchers independently searched the databases and registered data and available protocols were retrieved. Two reviewers independently extracted basic trial data including primary and secondary outcomes. Outcomes were categorised according to an ontology developed through literature review. RESULTS 91 trials were identified. Primary outcome categories reported were survival (84%), disease activity (14%), symptoms (4%), adverse events (3%) and resource use (1%). Secondary outcome categories reported were survival (60%), adverse events (52%), quality of life (QoL, 41%), disease activity (30%), symptoms (20%), function (12%), resource use (7%), health status (7%), and economic (3%). The review contributed 15 unique outcomes to the final outcome longlist of 35. CONCLUSION Trial registries represent a pragmatic solution in COS development and enabled identification of outcomes not present in other data sources. Under half of registered glioma studies included QoL outcomes; a small minority included functional outcomes or economic evaluation.

34. Development of a core outcome set and identification of patient-reportable outcomes for primary brain tumour trials

Author

Ameeta Retzer, Stephanie Sivell, Hannah Scott, Annmarie Nelson, Helen Bulbeck, Kathy Seddon, Robin Grant, Richard Adams, Colin Watts, Olalekan Lee Aiyegbusi, Pamela Kearns, Samantha Cruz Rivera, Linda Dirven, Elin Baddeley, Melanie Calvert, Anthony Byrne, Retzer, Ameeta [0000-0002-4156-8386], Watts, Colin [0000-0003-3531-8791], Aiyegbusi, Olalekan Lee [0000-0001-9122-8251], Cruz Rivera, Samantha [0000-0002-1566-6804], Baddeley, Elin [0000-0002-7571-4820], Calvert, Melanie [0000-0002-1856-837X], Apollo - University of Cambridge Repository, and Neurology

Subjects
Clinical Trials as Topic, Delphi Technique, Brain Neoplasms, General Medicine, Glioma, QUALITATIVE RESEARCH, Adult palliative care, Treatment Outcome, Clinical trials, Oncology, Research Design, Stakeholder Participation, Outcome Assessment, Health Care, Quality of Life, Humans, Neurological oncology
Abstract

Peer reviewed: True, INTRODUCTION: Primary brain tumours, specifically gliomas, are a rare disease group. The disease and treatment negatively impacts on patients and those close to them. The high rates of physical and cognitive morbidity differ from other cancers causing reduced health-related quality of life. Glioma trials using outcomes that allow holistic analysis of treatment benefits and risks enable informed care decisions. Currently, outcome assessment in glioma trials is inconsistent, hindering evidence synthesis. A core outcome set (COS) - an agreed minimum set of outcomes to be measured and reported - may address this. International initiatives focus on defining core outcomes assessments across brain tumour types. This protocol describes the development of a COS involving UK stakeholders for use in glioma trials, applicable across glioma types, with provision to identify subsets as required. Due to stakeholder interest in data reported from the patient perspective, outcomes from the COS that can be patient-reported will be identified. METHODS AND ANALYSIS: Stage I: (1) trial registry review to identify outcomes collected in glioma trials and (2) systematic review of qualitative literature exploring glioma patient and key stakeholder research priorities. Stage II: semi-structured interviews with glioma patients and caregivers. Outcome lists will be generated from stages I and II. Stage III: study team will remove duplicate items from the outcome lists and ensure accessible terminology for inclusion in the Delphi survey. Stage IV: a two-round Delphi process whereby the outcomes will be rated by key stakeholders. Stage V: a consensus meeting where participants will finalise the COS. The study team will identify the COS outcomes that can be patient-reported. Further research is needed to match patient-reported outcomes to available measures. ETHICS AND DISSEMINATION: Ethical approval was obtained (REF SMREC 21/59, Cardiff University School of Medicine Research Ethics Committee). Study findings will be disseminated widely through conferences and journal publication. The final COS will be adopted and promoted by patient and carer groups and its use by funders encouraged. PROSPERO REGISTRATION NUMBER: CRD42021236979.

Published
2022

35. [Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionDiretrizes para protocolos de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão SPIRIT-AI].

Author

Cruz Rivera S, Liu X, Chan AW, Denniston AK, and Calvert MJ

Abstract

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

Published
2023
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36. [Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionDiretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI].

Author

Liu X, Cruz Rivera S, Moher D, Calvert MJ, and Denniston AK

Abstract

The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

Published
2023
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37. Listening to the Patient Voice Adds Value to Cancer Clinical Trials.

Author

Brundage MD, Crossnohere NL, O'Donnell J, Cruz Rivera S, Wilson R, Wu AW, Moher D, Kyte D, Reeve BB, Gilbert A, Chen RC, Calvert MJ, and Snyder C

Subjects
Drug Development, Health Policy, Humans, Neoplasms therapy, Patient Reported Outcome Measures
Abstract

Randomized clinical trials are critical for evaluating the safety and efficacy of interventions in oncology and informing regulatory decisions, practice guidelines, and health policy. Patient-reported outcomes (PROs) are increasingly used in randomized trials to reflect the impact of receiving cancer therapies from the patient perspective and can inform evaluations of interventions by providing evidence that cannot be obtained or deduced from clinicians' reports or from other biomedical measures. This commentary focuses on how PROs add value to clinical trials by representing the patient voice. We employed 2 previously published descriptive frameworks (addressing how PROs are used in clinical trials and how PROs have an impact, respectively) and selected 9 clinical trial publications that illustrate the value of PROs according to the framework categories. These include 3 trials where PROs were a primary trial endpoint, 3 trials where PROs as secondary endpoints supported the primary endpoint, and 3 trials where PROs as secondary endpoints contrast the primary endpoint findings in clinically important ways. The 9 examples illustrate that PROs add valuable data to the care and treatment context by informing future patients about how they may feel and function on different treatments and by providing clinicians with evidence to support changes to clinical practice and shared decision making. Beyond the patient and clinician, PROs can enable administrators to consider the cost-effectiveness of implementing new interventions and contribute vital information to policy makers, health technology assessors, and regulators. These examples provide a strong case for the wider implementation of PROs in cancer trials., (© The Author(s) 2022. Published by Oxford University Press.)

Published
2022
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