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1. On model-based time trend adjustments in platform trials with non-concurrent controls

Author

Roig, Marta Bofill, Krotka, Pavla, Burman, Carl-Fredrik, Glimm, Ekkehard, Gold, Stefan M., Hees, Katharina, Jacko, Peter, Koenig, Franz, Magirr, Dominic, Mesenbrink, Peter, Viele, Kert, and Posch, Martin

Subjects
Statistics - Methodology
Abstract

Platform trials can evaluate the efficacy of several treatments compared to a control. The number of treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel-group trials because of using shared control groups. For arms entering the trial later, not all patients in the control group are randomised concurrently. The control group is then divided into concurrent and non-concurrent controls. Using non-concurrent controls (NCC) can improve the trial's efficiency, but can introduce bias due to time trends. We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added later, we assess the robustness of model-based approaches to adjust for time trends when using NCC. We consider approaches where time trends are modeled as linear or as a step function, with steps at times where arms enter or leave the trial. For trials with continuous or binary outcomes, we investigate the type 1 error (t1e) rate and power of testing the efficacy of the newly added arm under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with trends that are different or not additive in the model scale. A step function model fitted on data from all arms gives increased power while controlling the t1e, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the trend's shape deviates from a step function if block randomisation is used. But if trends differ between arms or are not additive on the model scale, t1e control may be lost. The efficiency gained by using step function models to incorporate NCC can outweigh potential biases. However, the specifics of the trial, plausibility of different time trends, and robustness of results should be considered

Published
2021
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2. Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

Author

Sánchez-Montalva, Adrian, Estevez, Ana Belén, Sánchez, Àlex, Sanjuan, Anna, Sena, Elena, Granados, Emma, Arévalo de Andrés, Esther, Nuñez, Fátima, Arteaga, Gara, Fuentes Ruiz, Gabriela Perez, Fernández, Guillermo, Rivera-Esteban, Jesus, Comella, Joan, Ramos-Quiroga, Josep Antoni, Genescà, Joan, Espinosa, Juan, Pericàs, Juan Manuel, Murcia, Lada, Cash-Gibson, Lucinda, de Valles Silvosa, Maria, Barroso de Sousa, María Fernanda, Sánchez-Maroto Carrizo, Olga, Ibañez-Jiménez, Pol, Augustin, Salvador, Perez-Hoyos, Santiago, Rodríguez-Navarro, Sarai, Muñoz-Martínez, Sergio, Serres, Silvia, Kalko, Susana, Michon, Amelie, Ussi, Anton, Lydall, Ben, van de Ketterij, Edwin, Quiles, Ignacio, Carapina, Tamara, Kumaus, Constantin, Ramazanova, Dariga, Meyer, Elias Laurin, Koenig, Franz, Roig, Marta Bofill, Brunner, Martin, Posch, Martin, Krotka, Pavla, Zehetmayer, Sonja, Carton, Charlotte, Legius, Eric, Begum, Amina, Pariante, Carmine, Worrell, Courtney, Lombardo, Giulia, Sforzini, Luca, Brown, Mollie, Gullet, Nancy, Amasi-Hartoonian, Nare, Ferner, Rosalie, Kose, Melisa, Spitaleri, Andrea, Ghodousi, Arash, Di Serio, Clelia, Cirillo, Daniela, Cugnata, Federica, Saluzzo, Francesca, Benedetti, Francesco, Scarale, Maria Giovanna, Zini, Michela, Rancoita, Paola Maria, Alagna, Riccardo, Poletti, Sara, Dhaenens, Britt, Van Der Lei, Johan, de Steenwinkel, Jurriaan, Moinat, Maxim, Oostenbrink, Rianne, Hoogendijk, Witte, Hölscher, Michael, Heinrich, Norbert, Otte, Christian, Potratz, Cornelia, Zocholl, Dario, Kulakova, Eugenia, Tacke, Frank, Brasanac, Jelena, Leubner, Jonas, Krajewska, Maja, Freitag, Michaela Maria, Gold, Stefan, Zoller, Thomas, Chae, Woo Ri, Daniel, Christel, Kara, Leila, Vaterkowski, Morgan, Griffon, Nicolas, Wolkenstein, Pierre, Pais, Raluca, Ratziu, Vlad, Voets, David, Maes, Christophe, Kalra, Dipak, Thienpoint, Geert, Deckerck, Jens, Lea, Nathan, Singleton, Peter, Viele, Kert, Jacko, Peter, Berry, Scott, Parke, Tom, Aydin, Burç, Kubiak, Christine, Demotes, Jacques, Ueda, Keiko, Matei, Mihaela, Contrino, Sergio, Röhl, Claas, Cordero, Estefania, Greenhalgh, Fiona, Jarke, Hannes, Angelova, Juliana, Boudes, Mathieu, Dressler, Stephan, Strammiello, Valentina, Anstee, Quentin, Gutierrez-Ibarluzea, Iñaki, Otte, Maximilian, Heimbach, Natalie, Hofner, Benjamin, Burgwinkel, Cora, Kaestel, Hue, Hees, Katharina, Nguyen, Quynh, Prieto-Alhambra, Daniel, Tan, Eng Hooi (Cheryl), Raviglione, Mario, de Colombani, Pierpaolo, Villa, Simone, Maron, Eduard, Evans, Gareth, Savitz, Adam J., Van Dessel, Ann, Duca, Anna, Kaminski, Anne, Wouters, Bie, Porter, Brandon, Charron, Catherine, Spiertz, Cecile, Zizzamia, Christopher, Millar, Daniel, Hasselbaink, Danny, Orr, David, Kesters, Divya, Hubin, Ellen, Davies, Emma, Didden, Eva-Maria, Guz, Gabriela, Verstraete, Evelyn, Mao, Gary, Capuano, George, Martynowicz, Heddie, De Smedt, Heidi, Larsson, Ingela, Bruegelmans, Ines, Coste, Isabelle, Gonzalez Moreno, Jesus Maria, Niewczas, Julia, Xu, Jiajun, Rombouts, Karin, Woo, Katherine, Wuyts, Kathleen, Hersh, Kathryn, Oldenburg, Khrista, Zhang, Lingjiao, Schmidt, Mark, Szuch, Mark, Todorovic, Marija, Mangelaars, Maartje, Grewal, Melissa, Sandor, Molli, Di Prospero, Nick, Van Houten, Pamela, Minnick, Pansy, Bastos, Polyana, Patrizi, Robert, Morello, Salvatore, De Wilde, Severijn, Sun, Tao, Kline, Timothy, de Marez, Tine, Mielke, Tobias, Reijns, Tom, Popova, Vanina, Flossbach, Yanina, Tymofyeyev, Yevgen, De Groote, Zeger, Sverdlov, Alex, Bobirca, Alexandra, Krause, Annekatrin, Bobrica, Catalin, Heintz, Daniela, Magirr, Dominic, Glimm, Ekkehard, Baffert, Fabienne, Castiglione, Federica, Caruso, Franca, Patalano, Francesco, Bretz, Frank, Heimann, Guenter, Carbarns, Ian, Rodríguez, Ignacio, Ratescu, Ioana, Hampson, Lisa, Pedrosa, Marcos, Hark, Mareile, Mesenbrink, Peter, Penna, Sabina Hernandez, Bergues-Lang, Sarah, Baltes-Engler, Susanne, Arsiwala, Tasneem, Mondragon, Valeria Jordan, Guo, Hua, Da Costa, Jose Leite, Burman, Carl-Fredrik, Kirk, George, Aaes-Jørgensen, Anders, Dirach, Jorgen, Kjær, Mette Skalshøi, Martin, Alexandra, Hristov, Diyan, Rousseaux, Florent, Hittel, Norbert, Dornheim, Robert, Evans, Daniel, Sykes, Nick, Couvert, Camille, Leuven, Catherine, Notelet, Loïc, Gidh-Jain, Madhavi, Jouannin, Mathieu, Ammour, Nadir, Pierre, Suzanne, Haufe, Volker, Dong, Yingwen, Dubanchet, Catherine, de Préville, Nathalie, Baltauss, Tania, Jian, Zhu, Shnider, Sara, Bar-El, Tal, Bakker, Annette, Nievo, Marco, Iloeje, Uche, Conradie, Almari, Auffarrth, Ece, Lombard, Leandra, Benhayoun, Majda, Olugbosi, Morounfolu, Seidel, Stephanie S., Gumí, Berta, Guzmán, Claudia García, Molero, Eva, Pairó, Gisela, Machin, Núria, Cardelús, Raimon, Ramasastry, Saira, Pelzer, Saskia, Kremer, Andreas, Lindfors, Erno, Lynch, Chris, Spiertz, Cécile, Machín, Núria, and Pericàs, Juan M.

Published
2024
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3. A modified weighted log-rank test for confirmatory trials with a high proportion of treatment switching

Author

Jiménez, José L., Niewczas, Julia, Bore, Alexander, and Burman, Carl-Fredrik

Subjects
Statistics - Methodology
Abstract

In confirmatory cancer clinical trials, overall survival (OS) is normally a primary endpoint in the intention-to-treat (ITT) analysis under regulatory standards. After the tumor progresses, it is common that patients allocated to the control group switch to the experimental treatment, or another drug in the same class. Such treatment switching may dilute the relative efficacy of the new drug compared to the control group, leading to lower statistical power. It would be possible to decrease the estimation bias by shortening the follow-up period but this may lead to a loss of information and power. Instead we propose a modified weighted log-rank test (mWLR) that aims at balancing these factors by down-weighting events occurring when many patients have switched treatment. As the weighting should be pre-specified and the impact of treatment switching is unknown, we predict the hazard ratio function and use it to compute the weights of the mWLR. The method may incorporate information from previous trials regarding the potential hazard ratio function over time. We are motivated by the RECORD-1 trial of everolimus against placebo in patients with metastatic renal-cell carcinoma where almost 80\% of the patients in the placebo group received everolimus after disease progression. Extensive simulations show that the new test gives considerably higher efficiency than the standard log-rank test in realistic scenarios.

Published
2020
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5. Modestly Weighted Logrank Tests

Author

Magirr, Dominic and Burman, Carl-Fredrik

Subjects
Statistics - Applications
Abstract

We propose a new class of weighted logrank tests (WLRT) that control the risk of concluding that a new drug is more efficacious than standard of care, when, in fact, it is uniformly inferior. Perhaps surprisingly, this risk is not controlled for WLRT in general. Tests from this new class can be constructed to have high power under a delayed-onset treatment effect scenario, as well as being almost as efficient as the standard logrank test under proportional hazards.

Published
2018

8. Optimizing Trial Designs for Targeted Therapies

Author

Ondra, Thomas, Jobjörnsson, Sebastian, Beckman, Robert A., Burman, Carl-Fredrik, König, Franz, Stallard, Nigel, and Posch, Martin

Subjects
Statistics - Methodology
Abstract

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor's as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

Published
2016
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9. Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL

Author

Koenig, Franz, primary, Spiertz, Cécile, additional, Millar, Daniel, additional, Rodríguez-Navarro, Sarai, additional, Machín, Núria, additional, Van Dessel, Ann, additional, Genescà, Joan, additional, Pericàs, Juan M., additional, Posch, Martin, additional, Sánchez-Montalva, Adrian, additional, Estevez, Ana Belén, additional, Sánchez, Àlex, additional, Sanjuan, Anna, additional, Sena, Elena, additional, Granados, Emma, additional, Arévalo de Andrés, Esther, additional, Nuñez, Fátima, additional, Arteaga, Gara, additional, Fuentes Ruiz, Gabriela Perez, additional, Fernández, Guillermo, additional, Rivera-Esteban, Jesus, additional, Comella, Joan, additional, Ramos-Quiroga, Josep Antoni, additional, Espinosa, Juan, additional, Pericàs, Juan Manuel, additional, Murcia, Lada, additional, Cash-Gibson, Lucinda, additional, de Valles Silvosa, Maria, additional, Barroso de Sousa, María Fernanda, additional, Sánchez-Maroto Carrizo, Olga, additional, Ibañez-Jiménez, Pol, additional, Augustin, Salvador, additional, Perez-Hoyos, Santiago, additional, Muñoz-Martínez, Sergio, additional, Serres, Silvia, additional, Kalko, Susana, additional, Michon, Amelie, additional, Ussi, Anton, additional, Lydall, Ben, additional, van de Ketterij, Edwin, additional, Quiles, Ignacio, additional, Carapina, Tamara, additional, Kumaus, Constantin, additional, Ramazanova, Dariga, additional, Meyer, Elias Laurin, additional, Koenig, Franz, additional, Roig, Marta Bofill, additional, Brunner, Martin, additional, Krotka, Pavla, additional, Zehetmayer, Sonja, additional, Carton, Charlotte, additional, Legius, Eric, additional, Begum, Amina, additional, Pariante, Carmine, additional, Worrell, Courtney, additional, Lombardo, Giulia, additional, Sforzini, Luca, additional, Brown, Mollie, additional, Gullet, Nancy, additional, Amasi-Hartoonian, Nare, additional, Ferner, Rosalie, additional, Kose, Melisa, additional, Spitaleri, Andrea, additional, Ghodousi, Arash, additional, Di Serio, Clelia, additional, Cirillo, Daniela, additional, Cugnata, Federica, additional, Saluzzo, Francesca, additional, Benedetti, Francesco, additional, Scarale, Maria Giovanna, additional, Zini, Michela, additional, Rancoita, Paola Maria, additional, Alagna, Riccardo, additional, Poletti, Sara, additional, Dhaenens, Britt, additional, Van Der Lei, Johan, additional, de Steenwinkel, Jurriaan, additional, Moinat, Maxim, additional, Oostenbrink, Rianne, additional, Hoogendijk, Witte, additional, Hölscher, Michael, additional, Heinrich, Norbert, additional, Otte, Christian, additional, Potratz, Cornelia, additional, Zocholl, Dario, additional, Kulakova, Eugenia, additional, Tacke, Frank, additional, Brasanac, Jelena, additional, Leubner, Jonas, additional, Krajewska, Maja, additional, Freitag, Michaela Maria, additional, Gold, Stefan, additional, Zoller, Thomas, additional, Chae, Woo Ri, additional, Daniel, Christel, additional, Kara, Leila, additional, Vaterkowski, Morgan, additional, Griffon, Nicolas, additional, Wolkenstein, Pierre, additional, Pais, Raluca, additional, Ratziu, Vlad, additional, Voets, David, additional, Maes, Christophe, additional, Kalra, Dipak, additional, Thienpoint, Geert, additional, Deckerck, Jens, additional, Lea, Nathan, additional, Singleton, Peter, additional, Viele, Kert, additional, Jacko, Peter, additional, Berry, Scott, additional, Parke, Tom, additional, Aydin, Burç, additional, Kubiak, Christine, additional, Demotes, Jacques, additional, Ueda, Keiko, additional, Matei, Mihaela, additional, Contrino, Sergio, additional, Röhl, Claas, additional, Cordero, Estefania, additional, Greenhalgh, Fiona, additional, Jarke, Hannes, additional, Angelova, Juliana, additional, Boudes, Mathieu, additional, Dressler, Stephan, additional, Strammiello, Valentina, additional, Anstee, Quentin, additional, Gutierrez-Ibarluzea, Iñaki, additional, Otte, Maximilian, additional, Heimbach, Natalie, additional, Hofner, Benjamin, additional, Burgwinkel, Cora, additional, Kaestel, Hue, additional, Hees, Katharina, additional, Nguyen, Quynh, additional, Prieto-Alhambra, Daniel, additional, Tan, Eng Hooi (Cheryl), additional, Raviglione, Mario, additional, de Colombani, Pierpaolo, additional, Villa, Simone, additional, Maron, Eduard, additional, Evans, Gareth, additional, Savitz, Adam J., additional, Duca, Anna, additional, Kaminski, Anne, additional, Wouters, Bie, additional, Porter, Brandon, additional, Charron, Catherine, additional, Spiertz, Cecile, additional, Zizzamia, Christopher, additional, Hasselbaink, Danny, additional, Orr, David, additional, Kesters, Divya, additional, Hubin, Ellen, additional, Davies, Emma, additional, Didden, Eva-Maria, additional, Guz, Gabriela, additional, Verstraete, Evelyn, additional, Mao, Gary, additional, Capuano, George, additional, Martynowicz, Heddie, additional, De Smedt, Heidi, additional, Larsson, Ingela, additional, Bruegelmans, Ines, additional, Coste, Isabelle, additional, Gonzalez Moreno, Jesus Maria, additional, Niewczas, Julia, additional, Xu, Jiajun, additional, Rombouts, Karin, additional, Woo, Katherine, additional, Wuyts, Kathleen, additional, Hersh, Kathryn, additional, Oldenburg, Khrista, additional, Zhang, Lingjiao, additional, Schmidt, Mark, additional, Szuch, Mark, additional, Todorovic, Marija, additional, Mangelaars, Maartje, additional, Grewal, Melissa, additional, Sandor, Molli, additional, Di Prospero, Nick, additional, Van Houten, Pamela, additional, Minnick, Pansy, additional, Bastos, Polyana, additional, Patrizi, Robert, additional, Morello, Salvatore, additional, De Wilde, Severijn, additional, Sun, Tao, additional, Kline, Timothy, additional, de Marez, Tine, additional, Mielke, Tobias, additional, Reijns, Tom, additional, Popova, Vanina, additional, Flossbach, Yanina, additional, Tymofyeyev, Yevgen, additional, De Groote, Zeger, additional, Sverdlov, Alex, additional, Bobirca, Alexandra, additional, Krause, Annekatrin, additional, Bobrica, Catalin, additional, Heintz, Daniela, additional, Magirr, Dominic, additional, Glimm, Ekkehard, additional, Baffert, Fabienne, additional, Castiglione, Federica, additional, Caruso, Franca, additional, Patalano, Francesco, additional, Bretz, Frank, additional, Heimann, Guenter, additional, Carbarns, Ian, additional, Rodríguez, Ignacio, additional, Ratescu, Ioana, additional, Hampson, Lisa, additional, Pedrosa, Marcos, additional, Hark, Mareile, additional, Mesenbrink, Peter, additional, Penna, Sabina Hernandez, additional, Bergues-Lang, Sarah, additional, Baltes-Engler, Susanne, additional, Arsiwala, Tasneem, additional, Mondragon, Valeria Jordan, additional, Guo, Hua, additional, Da Costa, Jose Leite, additional, Burman, Carl-Fredrik, additional, Kirk, George, additional, Aaes-Jørgensen, Anders, additional, Dirach, Jorgen, additional, Kjær, Mette Skalshøi, additional, Martin, Alexandra, additional, Hristov, Diyan, additional, Rousseaux, Florent, additional, Hittel, Norbert, additional, Dornheim, Robert, additional, Evans, Daniel, additional, Sykes, Nick, additional, Couvert, Camille, additional, Leuven, Catherine, additional, Notelet, Loïc, additional, Gidh-Jain, Madhavi, additional, Jouannin, Mathieu, additional, Ammour, Nadir, additional, Pierre, Suzanne, additional, Haufe, Volker, additional, Dong, Yingwen, additional, Dubanchet, Catherine, additional, de Préville, Nathalie, additional, Baltauss, Tania, additional, Jian, Zhu, additional, Shnider, Sara, additional, Bar-El, Tal, additional, Bakker, Annette, additional, Nievo, Marco, additional, Iloeje, Uche, additional, Conradie, Almari, additional, Auffarrth, Ece, additional, Lombard, Leandra, additional, Benhayoun, Majda, additional, Olugbosi, Morounfolu, additional, Seidel, Stephanie S., additional, Gumí, Berta, additional, Guzmán, Claudia García, additional, Molero, Eva, additional, Pairó, Gisela, additional, Machin, Núria, additional, Cardelús, Raimon, additional, Ramasastry, Saira, additional, Pelzer, Saskia, additional, Kremer, Andreas, additional, Lindfors, Erno, additional, and Lynch, Chris, additional

Published
2024
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17. A framework for assessing the impact of accelerated approval

Author

Gould, A. Lawrence, primary, Campbell, Robert K., additional, Loewy, John W., additional, Beckman, Robert A., additional, Dey, Jyotirmoy, additional, Schiel, Anja, additional, Burman, Carl-Fredrik, additional, Zhou, Joey, additional, Antonijevic, Zoran, additional, Miller, Eva R., additional, and Tang, Rui, additional

Published
2022
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18. Discussion on the paper by Hampson and Jennison

Author

Julious, Steven A., Dimairo, Munyaradzi, Stallard, Nigel, Glimm, Ekkehard, Bretz, Frank, Gallo, Paul, Minas, Giorgos, Aston, John A. D., Vandemeulebroecke, Marc, Posch, Martin, Gilmour, Steven, Bauer, Peter, Clarke, Bertrand, Bartroff, Jay, Burman, Carl-Fredrik, Coad, D. S., Draper, David, Mateu, Jorge, Rodríguez-Cortés, Francisco J., Roes, Kit C. B., Wong, Weng Kee, Sverdlov, Oleksandr, Lewis, Roger, Zini, Alessandro, Porcu, Emilio, and D'Ercole, Roberto

Published
2013
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19. On model-based time trend adjustments in platform trials with non-concurrent controls

Author

Roig, Marta Bofill, Krotka, Pavla, Burman, Carl-Fredrik, Glimm, Ekkehard, Gold, Stefan M., Hees, Katharina, Jacko, Peter, Koenig, Franz, Magirr, Dominic, Mesenbrink, Peter, Viele, Kert, Posch, Martin, Roig, Marta Bofill, Krotka, Pavla, Burman, Carl-Fredrik, Glimm, Ekkehard, Gold, Stefan M., Hees, Katharina, Jacko, Peter, Koenig, Franz, Magirr, Dominic, Mesenbrink, Peter, Viele, Kert, and Posch, Martin

Abstract

Background: Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods: We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results: A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from

Published
2022

31. Improved group sequential Holm procedures for testing multiple correlated hypotheses over time.

Author

Öhrn, Fredrik, Niewczas, Julia, and Burman, Carl-Fredrik

Abstract

Clinical trials can typically feature two different types of multiple inference: testing of more than one null hypothesis and testing at multiple time points. These modes of multiplicity are closely related mathematically but distinct statistically and philosophically. Regulatory agencies require strong control of the family-wise error rate (FWER), the risk of falsely rejecting any null hypothesis at any analysis. The correlations between test statistics at interim analyses and the final analysis are therefore routinely used in group sequential designs to achieve less conservative critical values. However, the same type of correlations between different comparisons, endpoints or sub-populations are less commonly used. As a result, FWER is in practice often controlled conservatively for commonly applied procedures. Repeated testing of the same null hypothesis may give changing results, when the hypothesis is rejected at an interim but accepted at the final analysis. The mathematically correct overall rejection is at odds with an inference theoretic approach and with common sense. We discuss these two issues, of incorporating correlations and how to interpret time-changing conclusions, and provide case studies where power can be increased while adhering to sound statistical principles. [ABSTRACT FROM AUTHOR]

Published
2022
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34. A decision theoretical modeling for Phase III investments and drug licensing

Author

Miller, Frank, Burman, Carl-Fredrik, Miller, Frank, and Burman, Carl-Fredrik

Abstract

For a new candidate drug to become an approved medicine, several decision points have to be passed. In this article, we focus on two of them: First, based on Phase II data, the commercial sponsor decides to invest (or not) in Phase III. Second, based on the outcome of Phase III, the regulator determines whether the drug should be granted market access. Assuming a population of candidate drugs with a distribution of true efficacy, we optimize the two stakeholders' decisions and study the interdependence between them. The regulator is assumed to seek to optimize the total public health benefit resulting from the efficacy of the drug and a safety penalty. In optimizing the regulatory rules, in terms of minimal required sample size and the Type I error in Phase III, we have to consider how these rules will modify the commercial optimization made by the sponsor. The results indicate that different Type I errors should be used depending on the rarity of the disease.

Published
2018
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35. Optimized adaptive enrichment designs

Author

Ondra, Thomas, primary, Jobjörnsson, Sebastian, additional, Beckman, Robert A, additional, Burman, Carl-Fredrik, additional, König, Franz, additional, Stallard, Nigel, additional, and Posch, Martin, additional

Published
2017
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37. The Strong Null Hypothesis and the MaxCombo Test: Comment on "Robust Design and Analysis of Clinical Trials with Nonproportional Hazards: A Straw Man Guidance form a Cross-Pharma Working Group.".

Author

Magirr, Dominic and Burman, Carl-Fredrik

Subjects
*NULL hypothesis, *EXPERIMENTAL design, *HAZARDS, *PROGRESSION-free survival, *LOG-rank test
Abstract

Roychoudhury et al. ([5]) presented a "straw-man guidance" on the design and analysis of clinical trials with non-proportional hazards. Compare the behavior of the Graph HT G(0,1) ht statistic to that of a milestone analysis at a late follow-up time. The Strong Null Hypothesis and the MaxCombo Test: Comment on "Robust Design and Analysis of Clinical Trials with Nonproportional Hazards: A Straw Man Guidance form a Cross-Pharma Working Group". [Extracted from the article]

Published
2023
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38. Optimized adaptive enrichment designs.

Author

Ondra, Thomas, Jobjörnsson, Sebastian, Beckman, Robert A, Burman, Carl-Fredrik, König, Franz, Stallard, Nigel, and Posch, Martin

Subjects
NET present value, EXPECTED utility, UTILITY functions, DYNAMIC programming
Abstract

Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup. [ABSTRACT FROM AUTHOR]

Published
2019
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