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4. Bridging the gap : using evidence synthesis to incorporate information from drug development into Health Technology Appraisal and beyond

Author

Pedder, Hugo, Phillippo, David, and Welton, Nicky

Subjects
evidence synthsis, NMA, MBNMA, HTA, drug development
Abstract

Within the drug development process, drugs are first tested in preclinical trials consisting of animal and/or in vitro studies, followed by several phases of clinical trials in humans. For a new drug to be licensed it must pass through regulatory approval and, for it to be reimbursed by a national healthcare provider such as the UK NHS, be assessed in a Health Technology Appraisal (HTA). Treatments may subsequently be evaluated and recommended as part of national or international guidelines. Synthesis of evidence is performed at different points throughout this process, but often in relative isolation and using different methodologies. Furthermore, available evidence can be sparse, which poses challenges for robust synthesis, particularly if such analyses are intended to inform decision-making. This thesis aims to characterise the use of evidence synthesis within drug development and reimbursement, and to explore which sources of data may be useful at different points for decision-making by addressing the following questions: 1. What are the barriers to using evidence synthesis to support translation of research findings from preclinical studies to human trials? 2. What robust methods can we use to incorporate dose-response and time-course information into evidence synthesis? 3. Can we use early phase evidence to connect evidence networks in reimbursement decision-making? 4. Which tools may be helpful for evidence synthesis of drug development trials? The thesis includes published papers and software to explore these questions, including developing and evaluating a framework for explicitly modelling dose-response and time-course relationships using Model-Based Network Meta-Analysis (MBNMA). The integrative chapter places these developments in the context of other work, discusses strengths and limitations of proposed approaches, and suggests future research that could build upon the work in the thesis.

Published
2023

5. A systematic review and network meta-analysis of psychological, psychosocial, pharmacological, physical and combined treatments for adults with a new episode of depression

Author

Mavranezouli, Ifigeneia, Megnin-Viggars, Odette, Pedder, Hugo, Welton, Nicky J., Dias, Sofia, Watkins, Edward, Nixon, Neil, Daly, Caitlin H., Keeney, Edna, Eadon, Hilary, Caldwell, Deborah M., O'Donoghue, Katriona J.M., Stockton, Sarah, Arnold, Stephanie, Thomas, James, Kapur, Navneet, and Pilling, Stephen

Published
2024
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9. Performance of Model-Based Network Meta-Analysis (MBNMA) of Time-Course Relationships: A Simulation Study

Author

Pedder, Hugo, Boucher, Martin, Dias, Sofia, Bennetts, Margherita, and Welton, Nicky J.

Abstract

Time-course model-based network meta-analysis (MBNMA) has been proposed as a framework to combine treatment comparisons from a network of randomized controlled trials reporting outcomes at multiple time-points. This can explain heterogeneity/inconsistency that arises by pooling studies with different follow-up times and allow inclusion of studies from earlier in drug development. The aim of this study is to explore using simulation: (a) how MBNMA model parameters are affected by the quantity/location of observed time-points across studies/comparisons, (b) how reliably an appropriate MBNMA model can be identified, (c) the robustness of model estimates and predictions under different dataset characteristics. Our results indicate that model parameters for a given treatment comparison are estimated with low mean bias even when no direct evidence was available, provided there was sufficient indirect evidence to estimate the time-course. A staged model selection strategy that selects time-course function, then heterogeneity, then covariance structure, identified the true model most reliably and efficiently. Predictions and parameter estimates from selected models had low mean bias even in the presence of high heterogeneity/correlation between time-points. However, failure to properly account for heterogeneity/correlation could lead to high error in precision of the estimates. Time-course MBNMA provides a statistically robust framework for synthesizing direct and indirect evidence to estimate relative effects and predicted mean responses whilst accounting for time-course and incorporating correlation and heterogeneity. This supports the use of MBNMA in evidence synthesis, particularly when additional studies are available with follow-up times that would otherwise prohibit their inclusion by conventional meta-analysis.

Published
2020
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10. Boosting treatment outcomes via the patient-practitioner relationship, treatment-beliefs or therapeutic setting. A systematic review with meta-analysis of contextual effects in chronic musculoskeletal pain

Author

Saueressig, Tobias, primary, Owen, Patrick J, additional, Pedder, Hugo, additional, Arora, Nitin Kumar, additional, Simons, Marieke, additional, Kaczorowski, Svenja, additional, Miller, Clint T, additional, Donath, Lars, additional, and Belavy, Daniel L, additional

Published
2024
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11. Modelling Time-Course Relationships with Multiple Treatments: Model-Based Network Meta-Analysis for Continuous Summary Outcomes

Author

Pedder, Hugo, Dias, Sofia, Bennetts, Margherita, Boucher, Martin, and Welton, Nicky J.

Abstract

Background: Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models. Methods: We propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis. Results: Of the time-course functions that we explored, the Emax model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET50, due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood. Conclusions: Time-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.

Published
2019
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12. Factors affecting shared decision‐making concerning menopausal hormone therapy.

Author

Koysombat, Kanyada, Mukherjee, Annice, Nyunt, Sandhi, Pedder, Hugo, Vinogradova, Yana, Burgin, Jo, Dave, Harshida, Comninos, Alexander N., Talaulikar, Vikram, Bailey, Julia V., Dhillo, Waljit S., and Abbara, Ali

Subjects
HORMONE therapy for menopause, HORMONE therapy, MEDICAL personnel, SATISFACTION, PATIENT compliance
Abstract

Menopausal hormone therapy (MHT) is an effective treatment for menopause‐related symptoms. Menopause management guidelines recommend a personalized approach to menopause care, including MHT use. Decision‐making around menopause care is a complex, iterative process influenced by multiple factors framed by perspectives from both women and healthcare providers (HCPs). This narrative review aims to summarize evidence around factors affecting decision‐making regarding menopause‐related care. For HCPs, the provision of individualized risk estimates is challenging in practice given the number of potential benefits and risks to consider, and the complexity of the data available, especially within time‐limited consultations. Women seeking menopause care have the difficult task of making sense of the benefit versus risk profiles to make choices in line with their decisional needs influenced by sociocultural/economic, educational, demographic, and personal characteristics. The press, social media, and influential celebrities also impact the perception of menopause and decision‐making around it. Understanding these factors can lead to improved participation in shared decision‐making, satisfaction with the decision and decision‐making process, adherence to treatment, reduced decisional regret, efficient use of resources, and ultimately long‐term satisfaction with care. [ABSTRACT FROM AUTHOR]

Published
2024
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13. DOSage of Exercise for chronic low back pain disorders (DOSE): protocol for a systematic review with dose-response network meta-analysis.

Author

Arora, Nitin Kumar, Donath, Lars, Owen, Patrick J., Miller, Clint T., Kaczorowski, Svenja, Saueressig, Tobias, Pedder, Hugo, Mundell, Niamh L., Tagliaferri, Scott D., Diwan, Ashish, Xiaolong Chen, Xiaohui Zhao, Huessler, Eva-Maria, Ehrenbrusthoff, Katja, Ford, Jon J., Hahne, Andrew J., Hammel, Ludwig, Norda, Heike, and Belavy, Daniel L.

Published
2024
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14. The importance of context (placebo effects) in conservative interventions for musculoskeletal pain: A systematic review and meta‐analysis of randomized controlled trials.

Author

Saueressig, Tobias, Owen, Patrick J., Pedder, Hugo, Tagliaferri, Scott, Kaczorowski, Svenja, Altrichter, Adina, Richard, Antonia, Miller, Clint T., Donath, Lars, and Belavy, Daniel L.

Abstract

Background and Objective: Contextual effects (e.g. patient expectations) may play a role in treatment effectiveness. This study aimed to estimate the magnitude of contextual effects for conservative, non‐pharmacological interventions for musculoskeletal pain conditions. A systematic review and meta‐analysis of randomized controlled trials (RCTs) that compared placebo conservative non‐pharmacological interventions to no treatment for musculoskeletal pain. The outcomes assessed included pain intensity, physical functioning, health‐related quality of life, global rating of change, depression, anxiety and sleep at immediate, short‐, medium‐ and/or long‐term follow‐up. Databases and Data Treatment: MEDLINE, EMBASE, CINAHL, Web of Science Core Collection, CENTRAL and SPORTDiscus were searched from inception to September 2021. Trial registry searches, backward and forward citation tracking and searches for prior systematic reviews were completed. The Cochrane risk of bias 2 tool was implemented. Results: The study included 64 RCTs (N = 4314) out of 8898 records. For pain intensity, a mean difference of (MD: −5.32, 95% confidence interval (CI): −7.20, −3.44, N = 57 studies with 74 outcomes, GRADE: very low) was estimated for placebo interventions. A small effect in favour of the placebo interventions for physical function was estimated (SMD: −0.22, 95% CI: −0.35, −0.09; N = 37 with 48 outcomes, GRADE: very low). Similar results were found for a broad range of patient‐reported outcomes. Meta‐regression analyses did not explain heterogeneity among analyses. Conclusion: The study found that the contextual effect of non‐pharmacological conservative interventions for musculoskeletal conditions is likely to be small. However, given the known effect sizes of recommended evidence‐based treatments for musculoskeletal conditions, it may still contribute an important component. Significance: Contextual effects of non‐pharmacological conservative interventions for musculoskeletal conditions are likely to be small for a broad range of patient‐reported outcomes (pain intensity, physical function, quality of life, global rating of change and depression). Contextual effects are unlikely, in isolation, to offer much clinical care. But these factors do have relevance in an overall treatment context as they provide almost 30% of the minimally clinically important difference. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Optimal dose and type of physical activity to improve functional capacity and minimise adverse events in acutely hospitalised older adults: a systematic review with dose-response network meta-analysis of randomised controlled trials

Author

Gallardo-Gómez, Daniel, primary, del Pozo-Cruz, Jesús, additional, Pedder, Hugo, additional, Alfonso-Rosa, Rosa M, additional, Álvarez-Barbosa, Francisco, additional, Noetel, Michael, additional, Jasper, Unyime, additional, Chastin, Sebastien, additional, Ramos-Munell, Javier, additional, and del Pozo Cruz, Borja, additional

Published
2023
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18. A many-analysts approach to the relation between religiosity and well-being

Author

Hoogeveen, Suzanne, Sarafoglou, Alexandra, Aczel, Balazs, Aditya, Yonathan, Alayan, Alexandra J., Allen, Peter J., Altay, Sacha, Alzahawi, Shilaan, Amir, Yulmaida, Anthony, Francis-Vincent, Appiah, Obed Kwame, Atkinson, Quentin D., Baimel, Adam, Balkaya-Ince, Merve, Balsamo, Michela, Banker, Sachin, Bartos, Frantisek, Becerra, Mario, Beffara, Bertrand, Beitner, Julia, Bendixen, Theiss, Berkessel, Jana B., Berniunas, Renatas, Billet, Matthew, Billingsley, Joseph, Bortolini, Tiago, Breitsohl, Heiko, Bret, Amelie, Brown, Faith L., Brown, Jennifer, Brumbaugh, Claudia C., Buczny, Jacek, Bulbulia, Joseph, Caballero, Saul, Carlucci, Leonardo, Carmichael, Cheryl L., Cattaneo, Marco E. G., Charles, Sarah J., Claessens, Scott, Panagopoulos, Maxinne C., Costa, Angelo Brandelli, Crone, Damien L., Czoschke, Stefan, Czymara, Christian, D'Urso, E. Damiano, Dahlstrom, Orjan, Dalla Rosa, Anna, Danielsson, Henrik, De Ron, Jill, de Vries, Ymkje Anna, Dean, Kristy K., Dik, Bryan J., Disabato, David J., Doherty, Jaclyn K., Draws, Tim, Drouhot, Lucas, Dujmovic, Marin, Dunham, Yarrow, Ebert, Tobias, Edelsbrunner, Peter A., Eerland, Anita, Elbaek, Christian T., Farahmand, Shole, Farahmand, Hooman, Farias, Miguel, Feliccia, Abrey A., Fischer, Kyle, Fischer, Ronald, Fisher-Thompson, Donna, Francis, Zoe, Frick, Susanne, Frisch, Lisa K., Geraldes, Diogo, Gerdin, Emily, Geven, Linda, Ghasemi, Omid, Gielens, Erwin, Gligoric, Vukasin, Hagel, Kristin, Hajdu, Nandor, Hamilton, Hannah R., Hamzah, Imaduddin, Hanel, Paul H. P., Hawk, Christopher E., Himawan, Karel K., Holding, Benjamin C., Homman, Lina E., Ingendahl, Moritz, Inkila, Hilla, Inman, Mary L., Islam, Chris-Gabriel, Isler, Ozan, Izydorczyk, David, Jaeger, Bastian, Johnson, Kathryn A., Jong, Jonathan, Karl, Johannes A., Kaszubowski, Erikson, Katz, Benjamin A., Keefer, Lucas A., Kelchtermans, Stijn, Kelly, John M., Klein, Richard A., Kleinberg, Bennett, Knowles, Megan L., Kolczynska, Marta, Koller, Dave, Krasko, Julia, Kritzler, Sarah, Krypotos, Angelos-Miltiadis, Kyritsis, Thanos, Landes, Todd L., Laukenmann, Ruben, Forsyth, Guy A. Lavender, Lazar, Aryeh, Lehman, Barbara J., Levy, Neil, Lo, Ronda F., Lodder, Paul, Lorenz, Jennifer, Lowicki, Pawel, Ly, Albert L., Maassen, Esther, Magyar-Russell, Gina M., Maier, Maximilian, Marsh, Dylan R., Martinez, Nuria, Martinie, Marcellin, Martoyo, Ihan, Mason, Susan E., Mauritsen, Anne Lundahl, McAleer, Phil, McCauley, Thomas, McCullough, Michael, McKay, Ryan, McMahon, Camilla M., McNamara, Amelia A., Means, Kira K., Mercier, Brett, Mitkidis, Panagiotis, Monin, Benoit, Moon, Jordan W., Moreau, David, Morgan, Jonathan, Murphy, James, Muscatt, George, Nagel, Christof, Nagy, Tamas, Nalborczyk, Ladislas, Nilsonne, Gustav, Noack, Pamina, Norenzayan, Ara, Nuijten, Michele B., Olsson-Collentine, Anton, Oviedo, Lluis, Pavlov, Yuri G., Pawelski, James O., Pearson, Hannah, Pedder, Hugo, Peetz, Hannah K., Pinus, Michael, Pirutinsky, Steven, Polito, Vince, Porubanova, Michaela, Poulin, Michael J., Prenoveau, Jason M., Prince, Mark A., Protzko, John, Pryor, Campbell, Purzycki, Benjamin G., Qiu, Lin, Putter, Julian Quevedo, Rabelo, Andre, Radell, Milen L., Ramsay, Jonathan E., Reid, Graham, Roberts, Andrew J., Luna, Lindsey M. Root, Ross, Robert M., Roszak, Piotr, Roy, Nirmal, Saarelainen, Suvi-Maria K., Sasaki, Joni Y., Schaumans, Catherine, Schivinski, Bruno, Schmitt, Marcel C., Schnitker, Sarah A., Schnuerch, Martin, Schreiner, Marcel R., Schuttengruber, Victoria, Sebben, Simone, Segerstrom, Suzanne C., Seryczynska, Berenika, Shjoedt, Uffe, Simsek, Muge, Sleegers, Willem W. A., Smith, Eliot R., Sowden, Walter J., Spath, Marion, Sporlein, Christoph, Stedden, William, Stoevenbelt, Andrea H., Stuber, Simon, Sulik, Justin, Suwartono, Christiany, Syropoulos, Stylianos, Szaszi, Barnabas, Szecsi, Peter, Tappin, Ben M., Tay, Louis, Thibault, Robert T., Thompson, Burt, Thurn, Christian M., Torralba, Josefa, Tuthill, Shelby D., Ullein, Ann-Marie, Van Aert, Robbie C. M., van Assen, Marcel A. L. M., Van Cappellen, Patty, van den Akker, Olmo R., Van der Cruyssen, Ine, Van der Noll, Jolanda, van Dongen, Noah N. N., Van Lissa, Caspar J., van Mulukom, Valerie, van Ravenzwaaij, Don, van Zyl, Casper J. J., Vaughn, Leigh Ann, Verschuere, Bruno, Vianello, Michelangelo, Vilanova, Felipe, Vishkin, Allon, Vogel, Vera, Vogelsmeier, Leonie V. D. E., Watanabe, Shoko, White, Cindel J. M., Wiebels, Kristina, Wiechert, Sera, Willett, Zachary Z., Witkowiak, Maciej, Witvliet, Charlotte V. O., Wiwad, Dylan, Wuyts, Robin, Xygalatas, Dimitris, Yang, Xin, Yeo, Darren J., Yilmaz, Onurcan, Zarzeczna, Natalia, Zhao, Yitong, Zijlmans, Josjan, van Elk, Michiel, Wagenmakers, Eric-Jan, Hoogeveen, Suzanne, Sarafoglou, Alexandra, Aczel, Balazs, Aditya, Yonathan, Alayan, Alexandra J., Allen, Peter J., Altay, Sacha, Alzahawi, Shilaan, Amir, Yulmaida, Anthony, Francis-Vincent, Appiah, Obed Kwame, Atkinson, Quentin D., Baimel, Adam, Balkaya-Ince, Merve, Balsamo, Michela, Banker, Sachin, Bartos, Frantisek, Becerra, Mario, Beffara, Bertrand, Beitner, Julia, Bendixen, Theiss, Berkessel, Jana B., Berniunas, Renatas, Billet, Matthew, Billingsley, Joseph, Bortolini, Tiago, Breitsohl, Heiko, Bret, Amelie, Brown, Faith L., Brown, Jennifer, Brumbaugh, Claudia C., Buczny, Jacek, Bulbulia, Joseph, Caballero, Saul, Carlucci, Leonardo, Carmichael, Cheryl L., Cattaneo, Marco E. G., Charles, Sarah J., Claessens, Scott, Panagopoulos, Maxinne C., Costa, Angelo Brandelli, Crone, Damien L., Czoschke, Stefan, Czymara, Christian, D'Urso, E. Damiano, Dahlstrom, Orjan, Dalla Rosa, Anna, Danielsson, Henrik, De Ron, Jill, de Vries, Ymkje Anna, Dean, Kristy K., Dik, Bryan J., Disabato, David J., Doherty, Jaclyn K., Draws, Tim, Drouhot, Lucas, Dujmovic, Marin, Dunham, Yarrow, Ebert, Tobias, Edelsbrunner, Peter A., Eerland, Anita, Elbaek, Christian T., Farahmand, Shole, Farahmand, Hooman, Farias, Miguel, Feliccia, Abrey A., Fischer, Kyle, Fischer, Ronald, Fisher-Thompson, Donna, Francis, Zoe, Frick, Susanne, Frisch, Lisa K., Geraldes, Diogo, Gerdin, Emily, Geven, Linda, Ghasemi, Omid, Gielens, Erwin, Gligoric, Vukasin, Hagel, Kristin, Hajdu, Nandor, Hamilton, Hannah R., Hamzah, Imaduddin, Hanel, Paul H. P., Hawk, Christopher E., Himawan, Karel K., Holding, Benjamin C., Homman, Lina E., Ingendahl, Moritz, Inkila, Hilla, Inman, Mary L., Islam, Chris-Gabriel, Isler, Ozan, Izydorczyk, David, Jaeger, Bastian, Johnson, Kathryn A., Jong, Jonathan, Karl, Johannes A., Kaszubowski, Erikson, Katz, Benjamin A., Keefer, Lucas A., Kelchtermans, Stijn, Kelly, John M., Klein, Richard A., Kleinberg, Bennett, Knowles, Megan L., Kolczynska, Marta, Koller, Dave, Krasko, Julia, Kritzler, Sarah, Krypotos, Angelos-Miltiadis, Kyritsis, Thanos, Landes, Todd L., Laukenmann, Ruben, Forsyth, Guy A. Lavender, Lazar, Aryeh, Lehman, Barbara J., Levy, Neil, Lo, Ronda F., Lodder, Paul, Lorenz, Jennifer, Lowicki, Pawel, Ly, Albert L., Maassen, Esther, Magyar-Russell, Gina M., Maier, Maximilian, Marsh, Dylan R., Martinez, Nuria, Martinie, Marcellin, Martoyo, Ihan, Mason, Susan E., Mauritsen, Anne Lundahl, McAleer, Phil, McCauley, Thomas, McCullough, Michael, McKay, Ryan, McMahon, Camilla M., McNamara, Amelia A., Means, Kira K., Mercier, Brett, Mitkidis, Panagiotis, Monin, Benoit, Moon, Jordan W., Moreau, David, Morgan, Jonathan, Murphy, James, Muscatt, George, Nagel, Christof, Nagy, Tamas, Nalborczyk, Ladislas, Nilsonne, Gustav, Noack, Pamina, Norenzayan, Ara, Nuijten, Michele B., Olsson-Collentine, Anton, Oviedo, Lluis, Pavlov, Yuri G., Pawelski, James O., Pearson, Hannah, Pedder, Hugo, Peetz, Hannah K., Pinus, Michael, Pirutinsky, Steven, Polito, Vince, Porubanova, Michaela, Poulin, Michael J., Prenoveau, Jason M., Prince, Mark A., Protzko, John, Pryor, Campbell, Purzycki, Benjamin G., Qiu, Lin, Putter, Julian Quevedo, Rabelo, Andre, Radell, Milen L., Ramsay, Jonathan E., Reid, Graham, Roberts, Andrew J., Luna, Lindsey M. Root, Ross, Robert M., Roszak, Piotr, Roy, Nirmal, Saarelainen, Suvi-Maria K., Sasaki, Joni Y., Schaumans, Catherine, Schivinski, Bruno, Schmitt, Marcel C., Schnitker, Sarah A., Schnuerch, Martin, Schreiner, Marcel R., Schuttengruber, Victoria, Sebben, Simone, Segerstrom, Suzanne C., Seryczynska, Berenika, Shjoedt, Uffe, Simsek, Muge, Sleegers, Willem W. A., Smith, Eliot R., Sowden, Walter J., Spath, Marion, Sporlein, Christoph, Stedden, William, Stoevenbelt, Andrea H., Stuber, Simon, Sulik, Justin, Suwartono, Christiany, Syropoulos, Stylianos, Szaszi, Barnabas, Szecsi, Peter, Tappin, Ben M., Tay, Louis, Thibault, Robert T., Thompson, Burt, Thurn, Christian M., Torralba, Josefa, Tuthill, Shelby D., Ullein, Ann-Marie, Van Aert, Robbie C. M., van Assen, Marcel A. L. M., Van Cappellen, Patty, van den Akker, Olmo R., Van der Cruyssen, Ine, Van der Noll, Jolanda, van Dongen, Noah N. N., Van Lissa, Caspar J., van Mulukom, Valerie, van Ravenzwaaij, Don, van Zyl, Casper J. J., Vaughn, Leigh Ann, Verschuere, Bruno, Vianello, Michelangelo, Vilanova, Felipe, Vishkin, Allon, Vogel, Vera, Vogelsmeier, Leonie V. D. E., Watanabe, Shoko, White, Cindel J. M., Wiebels, Kristina, Wiechert, Sera, Willett, Zachary Z., Witkowiak, Maciej, Witvliet, Charlotte V. O., Wiwad, Dylan, Wuyts, Robin, Xygalatas, Dimitris, Yang, Xin, Yeo, Darren J., Yilmaz, Onurcan, Zarzeczna, Natalia, Zhao, Yitong, Zijlmans, Josjan, van Elk, Michiel, and Wagenmakers, Eric-Jan

Abstract

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported beta = 0.120). For the second research question, this was the case for 65% of the teams (median reported beta = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates.

Published
2023

20. Supplemental Material - An evaluation of menopause service delivery during the COVID-19 pandemic

Author

Pedder, Hugo, Robinson, Lynne, Abernethy, Kathy, Hamoda, Haitham, and Marsden, Jo

Subjects
FOS: Clinical medicine, 111402 Obstetrics and Gynaecology
Abstract

Supplemental Material for An evaluation of menopause service delivery during the COVID-19 pandemic by Hugo Pedder, Lynne Robinson, Kathy Abernethy, Haitham Hamoda and Jo Marsden in Post Reproductive Health

Published
2023
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22. Additional file 1 of Non-optimal apparent temperature and cardiovascular mortality: the association in Puducherry, India between 2011 and 2020

Author

Shrikhande, Shreya S., Pedder, Hugo, Röösli, Martin, Dalvie, Mohammad Aqiel, Lakshmanasamy, Ravivarman, Gasparrini, Antonio, Utzinger, Jürg, and Cissé, Guéladio

Abstract

Additional file 1: Table S1. CVD classification code system used in this study. ICD-10 codes (given in brackets) have been adapted to form the categories used in this study. We divided the CVDs into 3 broad categories, namely ischemic heart diseases, cerebrovascular accidents, and other heart diseases, which included 7 sub-categories, Table S2. Distribution of climate variables based on population characteristics, Figure S1. Comparison of the Tapp-mortality association in models with varying knot placements. Model 1 has 2 equally placed knots, model2 has 3 knots at the 5th, 50th and 95th percentile of the Tapp model3 has 3 knots at the 25th, 50th and 75th percentile and model 4 has 2 knots on the 5th and 95th percentile of the Tapp, Figure S2. Individual exposure-response associations for the 4 models depicted in figure S1, Figure S3. Comparison of the exposure-response association assuming either a Quasi-poisson or conditional logistic regression with binomial likelihood, such as the one we used, Figure S4. Comparison of the exposure-response association using the complete 10 year data set with cases only from the cardiology department for 2011-2015 and from both the cardiology department and all other departments for 2016-2020 vs using only 5 year data with cases from all the departments from 2016-2020, Figure S5. Annual trends in CVD admissions and mortality, Figure S6. 3D- model depicting the RR for the lagged exposure-response association, Figure S7. Comparison of the exposure-response association using the complete 10 year data set vs restricting it to patients who spent less than 10 days in hospital. The black line depicts the overall association while the grey line depicts the patients who spent less than 10 days admitted to hospital before dying, Figure S8. Exposure-response association at different lag days. a.) 1 day, b.) 5 days, c.) 10 days and d.) 20 days, Figure S9. The role of co-morbidities in the exposure-response association. a.) population with co-morbidities and b.) Population without co-morbidities, Figure S11. Comparison of daily Tapp values from the two weather stations we used in our analysis, namely Cuddalore and Puducherry, Figure S12. Comparison of daily Tapp values from the two weather stations we used in our analysis, namely Cuddalore and Puducherry, along with the average of both which we used in our model (depicted in black).

Published
2023
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23. The Association between Apparent Temperature and Hospital Admissions for Cardiovascular Disease in Limpopo Province, South Africa

Author

Bühler, Jacqueline Lisa, primary, Shrikhande, Shreya, additional, Kapwata, Thandi, additional, Cissé, Guéladio, additional, Liang, Yajun, additional, Pedder, Hugo, additional, Kwiatkowski, Marek, additional, Kunene, Zamantimande, additional, Mathee, Angela, additional, Peer, Nasheeta, additional, and Wright, Caradee Y., additional

Published
2022
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25. An evaluation of reproducibility and errors in statistical power calculations performed using G*Power

Author

Thibault, Robert, Zavalis, Emmanuel, Malički, Mario, Goodman, Steve, Munafo, Marcus, and Pedder, Hugo

Subjects
Data_FILES, Medicine and Health Sciences, Life Sciences, meta-research, Social and Behavioral Sciences, sample size, statistical power
Abstract

All preregistration information about this study is available in the archived OSF project. Select the "Files" tab on the left of this webpage to view these files, which include the protocol and protocol code.

Published
2022
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26. Sample size justifications and calculations in top-ranked medical journals

Author

Rivera, Esteban, Ncube, Bongiwe, Pedder, Hugo, and Thibault, Robert

Subjects
power calculations, Medicine and Health Sciences, meta-research, sample size, statistical power
Abstract

In this study, we assess whether articles published in the "big-five" medical journals--as well as their associated registrations and study protocols--contain sample size justifications and/or sample size calculations.

Published
2022
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29. A many-analysts approach to the relation between religiosity and well-being

Author

Hoogeveen, Suzanne, primary, Sarafoglou, Alexandra, additional, Aczel, Balazs, additional, Aditya, Yonathan, additional, Alayan, Alexandra J., additional, Allen, Peter J., additional, Altay, Sacha, additional, Alzahawi, Shilaan, additional, Amir, Yulmaida, additional, Anthony, Francis-Vincent, additional, Kwame Appiah, Obed, additional, Atkinson, Quentin D., additional, Baimel, Adam, additional, Balkaya-Ince, Merve, additional, Balsamo, Michela, additional, Banker, Sachin, additional, Bartoš, František, additional, Becerra, Mario, additional, Beffara, Bertrand, additional, Beitner, Julia, additional, Bendixen, Theiss, additional, Berkessel, Jana B., additional, Berniūnas, Renatas, additional, Billet, Matthew I., additional, Billingsley, Joseph, additional, Bortolini, Tiago, additional, Breitsohl, Heiko, additional, Bret, Amélie, additional, Brown, Faith L., additional, Brown, Jennifer, additional, Brumbaugh, Claudia C., additional, Buczny, Jacek, additional, Bulbulia, Joseph, additional, Caballero, Saúl, additional, Carlucci, Leonardo, additional, Carmichael, Cheryl L., additional, Cattaneo, Marco E. G. V., additional, Charles, Sarah J., additional, Claessens, Scott, additional, Panagopoulos, Maxinne C., additional, Costa, Angelo Brandelli, additional, Crone, Damien L., additional, Czoschke, Stefan, additional, Czymara, Christian, additional, D'Urso, E. Damiano, additional, Dahlström, Örjan, additional, Rosa, Anna Dalla, additional, Danielsson, Henrik, additional, De Ron, Jill, additional, de Vries, Ymkje Anna, additional, Dean, Kristy K., additional, Dik, Bryan J., additional, Disabato, David J., additional, Doherty, Jaclyn K., additional, Draws, Tim, additional, Drouhot, Lucas, additional, Dujmovic, Marin, additional, Dunham, Yarrow, additional, Ebert, Tobias, additional, Edelsbrunner, Peter A., additional, Eerland, Anita, additional, Elbaek, Christian T., additional, Farahmand, Shole, additional, Farahmand, Hooman, additional, Farias, Miguel, additional, Feliccia, Abrey A., additional, Fischer, Kyle, additional, Fischer, Ronald, additional, Fisher-Thompson, Donna, additional, Francis, Zoë, additional, Frick, Susanne, additional, Frisch, Lisa K., additional, Geraldes, Diogo, additional, Gerdin, Emily, additional, Geven, Linda, additional, Ghasemi, Omid, additional, Gielens, Erwin, additional, Gligorić, Vukašin, additional, Hagel, Kristin, additional, Hajdu, Nandor, additional, Hamilton, Hannah R., additional, Hamzah, Imaduddin, additional, Hanel, Paul H. P., additional, Hawk, Christopher E., additional, K. Himawan, Karel, additional, Holding, Benjamin C., additional, Homman, Lina E., additional, Ingendahl, Moritz, additional, Inkilä, Hilla, additional, Inman, Mary L., additional, Islam, Chris-Gabriel, additional, Isler, Ozan, additional, Izydorczyk, David, additional, Jaeger, Bastian, additional, Johnson, Kathryn A., additional, Jong, Jonathan, additional, Karl, Johannes A., additional, Kaszubowski, Erikson, additional, Katz, Benjamin A., additional, Keefer, Lucas A., additional, Kelchtermans, Stijn, additional, Kelly, John M., additional, Klein, Richard A., additional, Kleinberg, Bennett, additional, Knowles, Megan L., additional, Kołczyńska, Marta, additional, Koller, Dave, additional, Krasko, Julia, additional, Kritzler, Sarah, additional, Krypotos, Angelos-Miltiadis, additional, Kyritsis, Thanos, additional, L. Landes, Todd, additional, Laukenmann, Ruben, additional, Forsyth, Guy A. Lavender, additional, Lazar, Aryeh, additional, Lehman, Barbara J., additional, Levy, Neil, additional, Lo, Ronda F., additional, Lodder, Paul, additional, Lorenz, Jennifer, additional, Łowicki, Paweł, additional, Ly, Albert L., additional, Maassen, Esther, additional, Magyar-Russell, Gina M., additional, Maier, Maximilian, additional, Marsh, Dylan R., additional, Martinez, Nuria, additional, Martinie, Marcellin, additional, Martoyo, Ihan, additional, Mason, Susan E., additional, Mauritsen, Anne Lundahl, additional, McAleer, Phil, additional, McCauley, Thomas, additional, McCullough, Michael, additional, McKay, Ryan, additional, McMahon, Camilla M., additional, McNamara, Amelia A., additional, Means, Kira K., additional, Mercier, Brett, additional, Mitkidis, Panagiotis, additional, Monin, Benoît, additional, Moon, Jordan W., additional, Moreau, David, additional, Morgan, Jonathan, additional, Murphy, James, additional, Muscatt, George, additional, Nägel, Christof, additional, Nagy, Tamás, additional, Nalborczyk, Ladislas, additional, Nilsonne, Gustav, additional, Noack, Pamina, additional, Norenzayan, Ara, additional, Nuijten, Michèle B., additional, Olsson-Collentine, Anton, additional, Oviedo, Lluis, additional, Pavlov, Yuri G., additional, Pawelski, James O., additional, Pearson, Hannah I., additional, Pedder, Hugo, additional, Peetz, Hannah K., additional, Pinus, Michael, additional, Pirutinsky, Steven, additional, Polito, Vince, additional, Porubanova, Michaela, additional, Poulin, Michael J., additional, Prenoveau, Jason M., additional, Prince, Mark A., additional, Protzko, John, additional, Pryor, Campbell, additional, Purzycki, Benjamin G., additional, Qiu, Lin, additional, Pütter, Julian Quevedo, additional, Rabelo, André, additional, Radell, Milen L., additional, Ramsay, Jonathan E., additional, Reid, Graham, additional, J. Roberts, Andrew, additional, Luna, Lindsey M. Root, additional, Ross, Robert M., additional, Roszak, Piotr, additional, Roy, Nirmal, additional, Saarelainen, Suvi-Maria K., additional, Sasaki, Joni Y., additional, Schaumans, Catherine, additional, Schivinski, Bruno, additional, Schmitt, Marcel C., additional, Schnitker, Sarah A., additional, Schnuerch, Martin, additional, Schreiner, Marcel R., additional, Schüttengruber, Victoria, additional, Sebben, Simone, additional, Segerstrom, Suzanne C., additional, Seryczyńska, Berenika, additional, Shjoedt, Uffe, additional, Simsek, Müge, additional, Sleegers, Willem W. A., additional, Smith, Eliot R., additional, Sowden, Walter J., additional, Späth, Marion, additional, Spörlein, Christoph, additional, Stedden, William, additional, Stoevenbelt, Andrea H., additional, Stuber, Simon, additional, Sulik, Justin, additional, Suwartono, Christiany, additional, Syropoulos, Stylianos, additional, Szaszi, Barnabas, additional, Szecsi, Peter, additional, Tappin, Ben M., additional, Tay, Louis, additional, Thibault, Robert T., additional, Thompson, Burt, additional, Thurn, Christian M., additional, Torralba, Josefa, additional, Tuthill, Shelby D., additional, Ullein, Ann-Marie, additional, Van Aert, Robbie C. M., additional, van Assen, Marcel A. L. M., additional, Van Cappellen, Patty, additional, van den Akker, Olmo R., additional, Van der Cruyssen, Ine, additional, Van der Noll, Jolanda, additional, van Dongen, Noah N. N., additional, Van Lissa, Caspar J., additional, van Mulukom, Valerie, additional, van Ravenzwaaij, Don, additional, van Zyl, Casper J. J., additional, Ann Vaughn, Leigh, additional, Većkalov, Bojana, additional, Verschuere, Bruno, additional, Vianello, Michelangelo, additional, Vilanova, Felipe, additional, Vishkin, Allon, additional, Vogel, Vera, additional, Vogelsmeier, Leonie V. D. E., additional, Watanabe, Shoko, additional, White, Cindel J. M., additional, Wiebels, Kristina, additional, Wiechert, Sera, additional, Willett, Zachary Z., additional, Witkowiak, Maciej, additional, Witvliet, Charlotte V. O., additional, Wiwad, Dylan, additional, Wuyts, Robin, additional, Xygalatas, Dimitris, additional, Yang, Xin, additional, Yeo, Darren J., additional, Yilmaz, Onurcan, additional, Zarzeczna, Natalia, additional, Zhao, Yitong, additional, Zijlmans, Josjan, additional, van Elk, Michiel, additional, and Wagenmakers, Eric-Jan, additional

Published
2022
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30. Awareness of and engagement with Open Research behaviours: Development of the Brief Open Research Survey (BORS) with the UK Reproducibility Network

Author

Rossit, Stephanie, Jones, Andrew, Evans, Samuel, Munafo, Marcus, Lautarescu, Alexandra, Schultze, Anna, Costello, Rianne, Pennington, Charlotte, Partridge, Adam, Kelson, Mark, Wilkinson, Laura, Burn, Charlotte, Rinke, Eike Mark, Soliman, Nadia, Norris, Emma, Cawthorn, Will, Wise, Emma, Pedder, Hugo, Glover, Scott, Page, Mike, Smailes, David, Baldwin, Jessie, Piccardi, Elena, Clark, Kait, Jay, Caroline, Ballou, Nick, Soltanlou, Mojtaba, and Hulme, Charlotte

Subjects
bepress|Physical Sciences and Mathematics, bepress|Social and Behavioral Sciences|Other Social and Behavioral Sciences, transparency, MetaArXiv|Social and Behavioral Sciences|Sociology, MetaArXiv|Social and Behavioral Sciences, responsible research practices, MetaArXiv|Social and Behavioral Sciences|Social Statistics, bepress|Medicine and Health Sciences, MetaArXiv|Medicine and Health Sciences, MetaArXiv|Physical Sciences and Mathematics, bepress|Social and Behavioral Sciences|Sociology, MetaArXiv|Social and Behavioral Sciences|Other Social and Behavioral Sciences, bepress|Social and Behavioral Sciences|Social Statistics, bepress|Social and Behavioral Sciences, UK Reproducibility Network, meta research, open research
Abstract

Objectives:A need for Open Research practices exists, yet there remains a lack of validated questionnaires to assess Open Research practices. The study aimed to develop a brief (

Published
2022
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31. A many-analysts approach to the relation between religiosity and well-being

Author

Hoogeveen, Suzanne, Sarafoglou, Alexandra, Aczel, Balazs, Aditya, Yonathan, Alayan, Alexandra J., Allen, Peter J., Altay, Sacha, Alzahawi, Shilaan, Amir, Yulmaida, Anthony, Francis-Vincent, Appiah, Obed Kwame, Atkinson, Quentin D., Baimel, Adam, Balkaya-Ince, Merve, Balsamo, Michela, Banker, Sachin, Bartos, Frantisek, Becerra, Mario, Beffara, Bertrand, Beitner, Julia, Bendixen, Theiss, Berkessel, Jana B., Berniunas, Renatas, Billet, Matthew, Billingsley, Joseph, Bortolini, Tiago, Breitsohl, Heiko, Bret, Amelie, Brown, Faith L., Brown, Jennifer, Brumbaugh, Claudia C., Buczny, Jacek, Bulbulia, Joseph, Caballero, Saul, Carlucci, Leonardo, Carmichael, Cheryl L., Cattaneo, Marco E. G., Charles, Sarah J., Claessens, Scott, Panagopoulos, Maxinne C., Costa, Angelo Brandelli, Crone, Damien L., Czoschke, Stefan, Czymara, Christian, D'Urso, E. Damiano, Dahlstrom, Orjan, Dalla Rosa, Anna, Danielsson, Henrik, De Ron, Jill, de Vries, Ymkje Anna, Dean, Kristy K., Dik, Bryan J., Disabato, David J., Doherty, Jaclyn K., Draws, Tim, Drouhot, Lucas, Dujmovic, Marin, Dunham, Yarrow, Ebert, Tobias, Edelsbrunner, Peter A., Eerland, Anita, Elbaek, Christian T., Farahmand, Shole, Farahmand, Hooman, Farias, Miguel, Feliccia, Abrey A., Fischer, Kyle, Fischer, Ronald, Fisher-Thompson, Donna, Francis, Zoe, Frick, Susanne, Frisch, Lisa K., Geraldes, Diogo, Gerdin, Emily, Geven, Linda, Ghasemi, Omid, Gielens, Erwin, Gligoric, Vukasin, Hagel, Kristin, Hajdu, Nandor, Hamilton, Hannah R., Hamzah, Imaduddin, Hanel, Paul H. P., Hawk, Christopher E., Himawan, Karel K., Holding, Benjamin C., Homman, Lina E., Ingendahl, Moritz, Inkila, Hilla, Inman, Mary L., Islam, Chris-Gabriel, Isler, Ozan, Izydorczyk, David, Jaeger, Bastian, Johnson, Kathryn A., Jong, Jonathan, Karl, Johannes A., Kaszubowski, Erikson, Katz, Benjamin A., Keefer, Lucas A., Kelchtermans, Stijn, Kelly, John M., Klein, Richard A., Kleinberg, Bennett, Knowles, Megan L., Kolczynska, Marta, Koller, Dave, Krasko, Julia, Kritzler, Sarah, Krypotos, Angelos-Miltiadis, Kyritsis, Thanos, Landes, Todd L., Laukenmann, Ruben, Forsyth, Guy A. Lavender, Lazar, Aryeh, Lehman, Barbara J., Levy, Neil, Lo, Ronda F., Lodder, Paul, Lorenz, Jennifer, Lowicki, Pawel, Ly, Albert L., Maassen, Esther, Magyar-Russell, Gina M., Maier, Maximilian, Marsh, Dylan R., Martinez, Nuria, Martinie, Marcellin, Martoyo, Ihan, Mason, Susan E., Mauritsen, Anne Lundahl, McAleer, Phil, McCauley, Thomas, McCullough, Michael, McKay, Ryan, McMahon, Camilla M., McNamara, Amelia A., Means, Kira K., Mercier, Brett, Mitkidis, Panagiotis, Monin, Benoit, Moon, Jordan W., Moreau, David, Morgan, Jonathan, Murphy, James, Muscatt, George, Nagel, Christof, Nagy, Tamas, Nalborczyk, Ladislas, Nilsonne, Gustav, Noack, Pamina, Norenzayan, Ara, Nuijten, Michele B., Olsson-Collentine, Anton, Oviedo, Lluis, Pavlov, Yuri G., Pawelski, James O., Pearson, Hannah, Pedder, Hugo, Peetz, Hannah K., Pinus, Michael, Pirutinsky, Steven, Polito, Vince, Porubanova, Michaela, Poulin, Michael J., Prenoveau, Jason M., Prince, Mark A., Protzko, John, Pryor, Campbell, Purzycki, Benjamin G., Qiu, Lin, Putter, Julian Quevedo, Rabelo, Andre, Radell, Milen L., Ramsay, Jonathan E., Reid, Graham, Roberts, Andrew J., Luna, Lindsey M. Root, Ross, Robert M., Roszak, Piotr, Roy, Nirmal, Saarelainen, Suvi-Maria K., Sasaki, Joni Y., Schaumans, Catherine, Schivinski, Bruno, Schmitt, Marcel C., Schnitker, Sarah A., Schnuerch, Martin, Schreiner, Marcel R., Schuttengruber, Victoria, Sebben, Simone, Segerstrom, Suzanne C., Seryczynska, Berenika, Shjoedt, Uffe, Simsek, Muge, Sleegers, Willem W. A., Smith, Eliot R., Sowden, Walter J., Spath, Marion, Sporlein, Christoph, Stedden, William, Stoevenbelt, Andrea H., Stuber, Simon, Sulik, Justin, Suwartono, Christiany, Syropoulos, Stylianos, Szaszi, Barnabas, Szecsi, Peter, Tappin, Ben M., Tay, Louis, Thibault, Robert T., Thompson, Burt, Thurn, Christian M., Torralba, Josefa, Tuthill, Shelby D., Ullein, Ann-Marie, Van Aert, Robbie C. M., van Assen, Marcel A. L. M., Van Cappellen, Patty, van den Akker, Olmo R., Van der Cruyssen, Ine, Van der Noll, Jolanda, van Dongen, Noah N. N., Van Lissa, Caspar J., van Mulukom, Valerie, van Ravenzwaaij, Don, van Zyl, Casper J. J., Vaughn, Leigh Ann, Verschuere, Bruno, Vianello, Michelangelo, Vilanova, Felipe, Vishkin, Allon, Vogel, Vera, Vogelsmeier, Leonie V. D. E., Watanabe, Shoko, White, Cindel J. M., Wiebels, Kristina, Wiechert, Sera, Willett, Zachary Z., Witkowiak, Maciej, Witvliet, Charlotte V. O., Wiwad, Dylan, Wuyts, Robin, Xygalatas, Dimitris, Yang, Xin, Yeo, Darren J., Yilmaz, Onurcan, Zarzeczna, Natalia, Zhao, Yitong, Zijlmans, Josjan, van Elk, Michiel, Wagenmakers, Eric-Jan, Hoogeveen, Suzanne, Sarafoglou, Alexandra, Aczel, Balazs, Aditya, Yonathan, Alayan, Alexandra J., Allen, Peter J., Altay, Sacha, Alzahawi, Shilaan, Amir, Yulmaida, Anthony, Francis-Vincent, Appiah, Obed Kwame, Atkinson, Quentin D., Baimel, Adam, Balkaya-Ince, Merve, Balsamo, Michela, Banker, Sachin, Bartos, Frantisek, Becerra, Mario, Beffara, Bertrand, Beitner, Julia, Bendixen, Theiss, Berkessel, Jana B., Berniunas, Renatas, Billet, Matthew, Billingsley, Joseph, Bortolini, Tiago, Breitsohl, Heiko, Bret, Amelie, Brown, Faith L., Brown, Jennifer, Brumbaugh, Claudia C., Buczny, Jacek, Bulbulia, Joseph, Caballero, Saul, Carlucci, Leonardo, Carmichael, Cheryl L., Cattaneo, Marco E. G., Charles, Sarah J., Claessens, Scott, Panagopoulos, Maxinne C., Costa, Angelo Brandelli, Crone, Damien L., Czoschke, Stefan, Czymara, Christian, D'Urso, E. Damiano, Dahlstrom, Orjan, Dalla Rosa, Anna, Danielsson, Henrik, De Ron, Jill, de Vries, Ymkje Anna, Dean, Kristy K., Dik, Bryan J., Disabato, David J., Doherty, Jaclyn K., Draws, Tim, Drouhot, Lucas, Dujmovic, Marin, Dunham, Yarrow, Ebert, Tobias, Edelsbrunner, Peter A., Eerland, Anita, Elbaek, Christian T., Farahmand, Shole, Farahmand, Hooman, Farias, Miguel, Feliccia, Abrey A., Fischer, Kyle, Fischer, Ronald, Fisher-Thompson, Donna, Francis, Zoe, Frick, Susanne, Frisch, Lisa K., Geraldes, Diogo, Gerdin, Emily, Geven, Linda, Ghasemi, Omid, Gielens, Erwin, Gligoric, Vukasin, Hagel, Kristin, Hajdu, Nandor, Hamilton, Hannah R., Hamzah, Imaduddin, Hanel, Paul H. P., Hawk, Christopher E., Himawan, Karel K., Holding, Benjamin C., Homman, Lina E., Ingendahl, Moritz, Inkila, Hilla, Inman, Mary L., Islam, Chris-Gabriel, Isler, Ozan, Izydorczyk, David, Jaeger, Bastian, Johnson, Kathryn A., Jong, Jonathan, Karl, Johannes A., Kaszubowski, Erikson, Katz, Benjamin A., Keefer, Lucas A., Kelchtermans, Stijn, Kelly, John M., Klein, Richard A., Kleinberg, Bennett, Knowles, Megan L., Kolczynska, Marta, Koller, Dave, Krasko, Julia, Kritzler, Sarah, Krypotos, Angelos-Miltiadis, Kyritsis, Thanos, Landes, Todd L., Laukenmann, Ruben, Forsyth, Guy A. Lavender, Lazar, Aryeh, Lehman, Barbara J., Levy, Neil, Lo, Ronda F., Lodder, Paul, Lorenz, Jennifer, Lowicki, Pawel, Ly, Albert L., Maassen, Esther, Magyar-Russell, Gina M., Maier, Maximilian, Marsh, Dylan R., Martinez, Nuria, Martinie, Marcellin, Martoyo, Ihan, Mason, Susan E., Mauritsen, Anne Lundahl, McAleer, Phil, McCauley, Thomas, McCullough, Michael, McKay, Ryan, McMahon, Camilla M., McNamara, Amelia A., Means, Kira K., Mercier, Brett, Mitkidis, Panagiotis, Monin, Benoit, Moon, Jordan W., Moreau, David, Morgan, Jonathan, Murphy, James, Muscatt, George, Nagel, Christof, Nagy, Tamas, Nalborczyk, Ladislas, Nilsonne, Gustav, Noack, Pamina, Norenzayan, Ara, Nuijten, Michele B., Olsson-Collentine, Anton, Oviedo, Lluis, Pavlov, Yuri G., Pawelski, James O., Pearson, Hannah, Pedder, Hugo, Peetz, Hannah K., Pinus, Michael, Pirutinsky, Steven, Polito, Vince, Porubanova, Michaela, Poulin, Michael J., Prenoveau, Jason M., Prince, Mark A., Protzko, John, Pryor, Campbell, Purzycki, Benjamin G., Qiu, Lin, Putter, Julian Quevedo, Rabelo, Andre, Radell, Milen L., Ramsay, Jonathan E., Reid, Graham, Roberts, Andrew J., Luna, Lindsey M. Root, Ross, Robert M., Roszak, Piotr, Roy, Nirmal, Saarelainen, Suvi-Maria K., Sasaki, Joni Y., Schaumans, Catherine, Schivinski, Bruno, Schmitt, Marcel C., Schnitker, Sarah A., Schnuerch, Martin, Schreiner, Marcel R., Schuttengruber, Victoria, Sebben, Simone, Segerstrom, Suzanne C., Seryczynska, Berenika, Shjoedt, Uffe, Simsek, Muge, Sleegers, Willem W. A., Smith, Eliot R., Sowden, Walter J., Spath, Marion, Sporlein, Christoph, Stedden, William, Stoevenbelt, Andrea H., Stuber, Simon, Sulik, Justin, Suwartono, Christiany, Syropoulos, Stylianos, Szaszi, Barnabas, Szecsi, Peter, Tappin, Ben M., Tay, Louis, Thibault, Robert T., Thompson, Burt, Thurn, Christian M., Torralba, Josefa, Tuthill, Shelby D., Ullein, Ann-Marie, Van Aert, Robbie C. M., van Assen, Marcel A. L. M., Van Cappellen, Patty, van den Akker, Olmo R., Van der Cruyssen, Ine, Van der Noll, Jolanda, van Dongen, Noah N. N., Van Lissa, Caspar J., van Mulukom, Valerie, van Ravenzwaaij, Don, van Zyl, Casper J. J., Vaughn, Leigh Ann, Verschuere, Bruno, Vianello, Michelangelo, Vilanova, Felipe, Vishkin, Allon, Vogel, Vera, Vogelsmeier, Leonie V. D. E., Watanabe, Shoko, White, Cindel J. M., Wiebels, Kristina, Wiechert, Sera, Willett, Zachary Z., Witkowiak, Maciej, Witvliet, Charlotte V. O., Wiwad, Dylan, Wuyts, Robin, Xygalatas, Dimitris, Yang, Xin, Yeo, Darren J., Yilmaz, Onurcan, Zarzeczna, Natalia, Zhao, Yitong, Zijlmans, Josjan, van Elk, Michiel, and Wagenmakers, Eric-Jan

Abstract

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported beta = 0.120). For the second research question, this was the case for 65% of the teams (median reported beta = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates.

Published
2022

32. Participant retention in paediatric randomised controlled trials published in six major journals 2015–2019: systematic review and meta-analysis.

Author

Gaunt, Daisy M., Papastavrou Brooks, Cat, Pedder, Hugo, Crawley, Esther, Horwood, Jeremy, and Metcalfe, Chris

Subjects
RANDOMIZED controlled trials, YOUNG adults, SEQUENTIAL analysis, PEDIATRICS
Abstract

Background: The factors which influence participant retention in paediatric randomised controlled trials are under-researched. Retention may be more challenging due to child developmental stages, involving additional participants, and proxy-reporting of outcomes. This systematic review and meta-analysis explores the factors which may influence retention in paediatric trials. Methods: Using the MEDLINE database, paediatric randomised controlled trials published between 2015 and 2019 were identified from six general and specialist high-impact factor medical journals. The review outcome was participant retention for each reviewed trial's primary outcome. Context (e.g. population, disease) and design (e.g. length of trial) factors were extracted. Retention was examined for each context and design factor in turn, with evidence for an association being determined by a univariate random-effects meta-regression analysis. Results: Ninety-four trials were included, and the median total retention was 0.92 (inter-quartile range 0.83 to 0.98). Higher estimates of retention were seen for trials with five or more follow-up assessments before the primary outcome, those less than 6 months between randomisation and primary outcome, and those that used an inactive data collection method. Trials involving children aged 11 and over had the higher estimated retention compared with those involving younger children. Those trials which did not involve other participants also had higher retention, than those where they were involved. There was also evidence that a trial which used an active or placebo control treatment had higher estimated retention, than treatment-as-usual. Retention increased if at least one engagement method was used. Unlike reviews of trials including all ages of participants, we did not find any association between retention and the number of treatment groups, size of trial, or type of treatment. Conclusions: Published paediatric RCTs rarely report the use of specific modifiable factors that improve retention. Including multiple, regular follow-ups with participants before the primary outcome may reduce attrition. Retention may be highest when the primary outcome is collected up to 6 months after a participant is recruited. Our findings suggest that qualitative research into improving retention when trials involve multiple participants such as young people, and their caregivers or teachers would be worthwhile. Those designing paediatric trials also need to consider the use of appropriate engagement methods. Research on Research (RoR) registry: https://ror-hub.org/study/2561 [ABSTRACT FROM AUTHOR]

Published
2023
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33. Awareness of and engagement with Open Research behaviours: Development of the Brief Open Research Survey (BORS) with the UK Reproducibility Network

Author

Norris, Emma, primary, Clark, Kait, additional, Munafo, Marcus Robert, additional, Jay, Caroline, additional, Baldwin, Jessie, additional, Lautarescu, Alexandra, additional, Pedder, Hugo, additional, Page, Mike, additional, Rinke, Eike Mark, additional, Burn, Charlotte, additional, Cawthorn, Will, additional, Ballou, Nick, additional, Glover, Scott, additional, Evans, Samuel, additional, Rossit, Stephanie, additional, Soltanlou, Mojtaba, additional, Wise, Emma Louise, additional, Kelson, Mark, additional, Soliman, Nadia, additional, Jones, Andrew, additional, Costello, Rianne, additional, Smailes, David, additional, Wilkinson, Laura L., additional, Piccardi, Elena Serena, additional, Partridge, Adam Michael, additional, Hulme, Charlotte, additional, Schultze, Anna, additional, and Pennington, Charlotte Rebecca, additional

Published
2022
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36. Methods to Assess Evidence Consistency in Dose-Response Model Based Network Meta-Analysis

Author

Pedder, Hugo, Dias, Sofia, Boucher, Martin, Bennetts, Meg, Mawdsley, David, and Welton, N J

Subjects
meta-analysis, consistency, synthesis, dose, HEB, NMA
Abstract

Network Meta-Analysis (NMA) simultaneously estimates multiple relative treatment effects based on evidence that forms a network of treatment comparisons. Heterogeneity in treatment definitions, such as dose, can lead to a violation of the consistency assumption that underpins NMA. Model-Based NMA (MBNMA) methods have been proposed that allow functional dose-response relationships to be estimated within an NMA, which avoids lumping different doses together and thereby reduces the likelihood of inconsistency. Dose-response MBNMA relies on appropriate specification of the dose-response relationship as well as consistency of relative effects. In this paper we describe methods to check for inconsistency in dose-response MBNMA models. Global and local (node-splitting) tests for inconsistency are described that account for studies with ≥3 arms that are typical in dose-finding trials. We show that consistency needs to be assessed with respect to the choice of dose-response function. We illustrate the methods using a network comparing biologics for moderate-to-severe psoriasis. By comparing results from an Emax and an exponential dose-response function we show that failure to correctly characterise the dose-response can introduce apparent inconsistency. The number of comparisons for which node-splitting is possible is also shown to be dependent on the complexity of the selected dose-response function. We highlight that the nature of dose-finding studies, which typically compare multiple doses of the same agent, provide limited scope to assess inconsistency, but these study designs help guard against inconsistency in the first place. We highlight the importance of assessing consistency to obtain robust relative effects to inform drug-development and policy decisions.

Published
2022

40. Network meta-analysis for comparative effectiveness of treatments for chronic low back pain disorders: systematic review protocol

Author

Belavy, Daniel L, primary, Diwan, Ashish D, additional, Ford, Jon, additional, Miller, Clint T, additional, Hahne, Andrew J, additional, Mundell, Niamh, additional, Tagliaferri, Scott, additional, Bowe, Steven, additional, Pedder, Hugo, additional, Saueressig, Tobias, additional, Zhao, Xiaohui, additional, Chen, Xiaolong, additional, Balasundaram, Arun Prasad, additional, Arora, Nitin Kumar, additional, and Owen, Patrick J, additional

Published
2021
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41. Lagged association between climate variables and hospital admissions for pneumonia admissions in South Africa

Author

Pedder, Hugo, Kapwata, Thandi, Howard, Guy, Naidoo, Rajen, Medical, South, Morris, Richard W, Mathee, Angela, and Wright, Caradee Y.

Subjects
South Africa, climate change, distributed non-linear lag model, public health, environmental health, pneumonia, meteorology, respiratory disease
Abstract

Pneumonia is a leading cause of hospitalization in South Africa. Climate change could potentially affect its incidence via changes in meteorological conditions. We investigated the delayed effects of temperature and relative humidity on pneumonia hospital admissions at two large public hospitals in Limpopo province, South Africa. Using 4062 pneumonia hospital admission records from 2007 to 2015, a time-varying distributed lag non-linear model was used to estimate temperature-lag and relative humidity-lag pneumonia relationships. Mean temperature, relative humidity and diurnal temperature range were all significantly associated with pneumonia admissions. Cumulatively across the 21-day period, higher mean daily temperature (30 C relative to 21 C) was most strongly associated with a decreased rate of hospital admissions (relative rate ratios (RR): 0.34, 95% confidence interval (CI): 0.14–0.82), whereas results were suggestive of lower mean daily temperature (12 C relative to 21 C) being associated with an increased rate of admissions (RR: 1.27, 95%CI: 0.75–2.16). Higher relative humidity (>80%) was associated with fewer hospital admissions while low relative humidity (

Published
2021
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42. Joining the Dots:Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis

Author

Pedder, Hugo, Dias, Sofia, Bennetts, Meg, Boucher, Martin, and Welton, Nicky J

Subjects
meta-analysis, synthesis, network, dose, HEB, NMA, MBNMA, linking
Abstract

Introduction: Network meta-analysis (NMA) synthesises direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modelling a parametric dose-response relationship within a NMA framework. In this paper, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks.Methods: We created illustrative datasets by removing studies or treatments from a NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For ¬¬disconnected networks, we compared MBNMA estimates with NMA estimates from an “augmented” network connected by adding studies or treatments back into the dataset.Results: In connected networks relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMAvsMBNMA: median=1.13; range=1.04-1.68). In disconnected networks MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15/18 datasets. In the remaining 3/18 datasets a more complex dose-response relationship was required than could be fitted with the available evidence.Conclusions: Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision, whilst making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase-II trials) on multiple doses of agents where available.

Published
2021

44. sj-pdf-3-mdm-10.1177_0272989X20983315 – Supplemental material for Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis

Author

Pedder, Hugo, Dias, Sofia, Bennetts, Meg, Boucher, Martin, and Welton, Nicky J.

Subjects
111708 Health and Community Services, 111799 Public Health and Health Services not elsewhere classified, 160807 Sociological Methodology and Research Methods, FOS: Health sciences, FOS: Sociology
Abstract

Supplemental material, sj-pdf-3-mdm-10.1177_0272989X20983315 for Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis by Hugo Pedder, Sofia Dias, Meg Bennetts, Martin Boucher and Nicky J. Welton in Medical Decision Making

Published
2021
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45. MBNMAdose: An R package for incorporating dose-response information into Network Meta-Analysis. Presentation at ESMARConf2021

Author

Pedder, Hugo

Abstract

Network meta-analysis (NMA) is used to synthesise results from multiple treatments where the RCTs form a connected network of treatments. It provides a framework for comparative effectiveness and assessment of consistency between the direct and indirect evidence and is extensively employed in health economic modelling to inform healthcare policy. Multiple doses of different agents in an NMA are typically ""split"" or ""lumped"". Splitting involves modelling different doses of an agent as independent nodes in the network, making no assumptions regarding how they are related, and can results in sparse or even disconnected networks in which NMA is impossible. Lumping assumes different doses have the same efficacy, which can introduce heterogeneity or inconsistency. MBNMAdose is an R package that allows dose-response relationships to be explicitly modelled using Model-Based NMA (MBNMA). As well as avoiding problems arising from lumping/splitting, this modelling framework can improve precision of estimates over those estimated using standard NMA, allow for interpolation/extrapolation of predicted responses based on the dose-response relationship, and allow for the linking of disconnected networks via the dose-response relationship. MBNMAdose provides a suite of functions that make it easy to implement Bayesian MBNMA models, evaluate their suitability given the data, and produce meaningful outputs from the analyses that can be used in decision-making.

Published
2021
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