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  string(82) "Trabectedin: statistical modeling of expanded access programs and clinical trials."
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  string(525) "This project will yield direct insights, via scientific publications, into how to best combine data from expanded access programs with data of (randomized) controlled trials.  Awareness of the potential value of RWD from EA should facilitate that these data are incorporated in decision making whenever this is feasible and appropriate. For patients, this better use of available data can result in speedier access to more diverse treatments. This is directly beneficial to regulators, drug developers and (bio)statisticians."
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      string(12) "Van Rosmalen"
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      string(51) "Erasmus Medical Center, department of Biostatistics"
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  string(71) "Health~Holland Funding from Dutch Government. Grant number: EMCLSH20012"
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      string(125) "NCT01343277 - A Study of Trabectedin or Dacarbazine for the Treatment of Patients With Advanced Liposarcoma or Leiomyosarcoma"
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  ["project_date_type"]=>
  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(1412) "Background:
Considering the increasing interest in both expanded access and real-world data, the question arises whether alternative ways of access to novel treatments can provide clinical information and impact regulatory decision making. [1,2] have shown that there is an increasing trend in the use of RWD from EA by regulators and industry.
Objective:
This research aims to investigate combining two sources of data: (randomized) controlled trials, pre-approval RWD (expanded access) and post-approval RWD. We will do so by evaluating existing methodology of evidence synthesis (e.g. meta-analysis, power priors) and extend such methodology to specifically accommodate for expanded access data.
Study Design:
Statistical application.
Participants:
All patients in the two clinical trials (N=270+577=847) and all patients in the expanded access program (N=1803) making a total of N=2650.
Main Outcome Measures:
Mean Squared Error, Power, Type I error, Effective Sample Size of (Overall Survival, Response)
Statistical Analysis:
We will extend and apply current methods of dynamic borrowing and propensity score matching. Our approach will be compared with these existing alternatives in terms of frequentist characteristics (type I error rate, mean squared error, and statistical power) as well as in terms of performance in real data sets." ["project_brief_bg"]=> string(2810) "Patients suffering from seriously debilitating or life-threatening conditions who are not eligible for further treatments or any clinical trials, may resort to ?expanded access?: pre-approval access to investigational treatments. Expanded access (EA), also known as early access, pre-approval access or compassionate use 1, is the formal regulation adopted by the Food and Drug Administration (FDA) in 1987 2, propelled by the HIV/AIDS crisis. In the United States (US) the FDA regulates this process of formalized non-clinical trial access whereas in the European Union (EU) the responsibility lies with individual member states 3. The numbers of requests for EA are growing 4 and state and federal legislation, such as Right-to-Try laws in the US, stress the need and interest of patients in having earlier access to medicines that are still under clinical investigation.
EA data can be seen as a type of real-world data (RWD). RWD are ?information on health care that is derived from multiple sources outside typical clinical research settings? 5. Recent publications and regulatory frameworks have boosted the promise of RWD 4,6. It can come in many forms and shapes, such as electronic health records, social media or claims databases.
EA programs also form a source of RWD. Historically though, EA programs were only deemed fit for treatment and not for research. Although the primary purpose of EA is treatment, scholars have argued that there is a moral obligation to collect outcome data in all cases where patients are treated with investigational medicines 7?9. The debate on combining data collection and EA has substantially increased in recent years 10,11, with FDA-officials confirming beginning 2018 their willingness to review data from EA programs to support drug applications 7. Considering the increasing interest in both expanded access and real-world data, the question arises whether alternative ways of access to novel treatments can provide clinical information and impact regulatory decision making. 12 have shown that there is an increasing trend in the use of RWD from EA by regulators and industry.
Our research will help clarify the usability of these data. Awareness of the potential value of RWD from EA should facilitate that these data are incorporated in decision making whenever this is feasible and appropriate, and this should impact on traditional clinical development. For patients, this better use of available data can result in speedier access to more diverse treatments. This project will yield direct insights, via scientific publications and statistical methodology, into how to best combine data from expanded access programs with data of controlled trials. This is directly beneficial to regulators, drug developers and (bio)statisticians." ["project_specific_aims"]=> string(629) "The aims of the proposed research is to develop, demonstrate, and evaluate several statistical techniques to a non-simulated data set.
To extend existing methods (e.g. power prior) for borrowing information from historical studies to the context of (expanded access) real world data and investigate methods (e.g. propensity score matching) of adjusting for confounding and other sources of bias specific observational (RWD) data, and combine these methods with methods for borrowing information from historical controls. Recent techniques, such as proposed by propensity score integrated power-priors will also be applied." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(1) { [0]=> array(2) { ["value"]=> string(37) "Develop or refine statistical methods" ["label"]=> string(37) "Develop or refine statistical methods" } } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> array(2) { ["value"]=> string(7) "RStudio" ["label"]=> string(7) "RStudio" } ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(547) "We will be looking at two different sources of data: 2 formal clinical trials (N=577, N=270) and 1 expanded access study(N=1803). The FDA regards expanded access studies to be a source of real-world data[7]. We will be looking at all data from patients that are comparable to the patients in the expanded access program (in terms of indication, histology and prior lines of treatment). We expect patients from the expanded access study to be matched to patients from the clinical trials and aim to develop methodology for improving such matching." ["project_main_outcome_measure"]=> string(187) "(root) Mean Squared Error, Power, Type I error, Effective Sample Sizes of the treatment effect estimates (overall survival, progression-free survival, time to treatment failure, response)" ["project_main_predictor_indep"]=> string(163) "The main estimands will be survival (overall survival, progression-free survival, time to treatment failure) or response (DCR)and treatment-related Adverse Events." ["project_other_variables_interest"]=> string(160) "Age, gender, smoking status, histology, prior therapies, baseline ECOG, Time from last recurrent or metastastic disease to first dose (months), race, ethnicity." ["project_stat_analysis_plan"]=> string(3820) "We aim to develop and evaluate methods for (i) dynamic borrowing of other data scores and (ii) methods for attenuating bias by confounding. We here provide further background on these methods and our plans.
In the analysis of clinical trials, there is often data from relevant previous studies available. A common example is the availability of data from a previous RCT in which the control arm patients received the same treatment as in the new (current) study. Provided that the studies are sufficiently similar in terms of setting and outcomes, the data of the historical controls can be incorporated into the analysis of the current trial. Due to the possibility of between-study heterogeneity (e.g. differences in study populations between trials), a nave pooling of historical controls and the current controls is rarely acceptable. Statistical methods have been devised to appropriately downweight the historical data, based on their characteristics and the observed differences with the current controls. A well-known technique for downweighting the historical controls is the power prior 13?15.
We are proposing to extend the ideas of the power prior and other methods for borrowing historical data to RWD data from EA programs. In this case, we downweight the RWD instead of the historical controls. The downweighting serves to account for a) the lower grade of evidence of EA data (compared to RCT data) due to the lack of randomization and the less controlled setting and b) center-specific and study-specific effects that may differ between EA data and RCT data. In the power prior method, this is done by raising the likelihood for the RWD, ?(??Y_RWD ), to a power of ?, with ? between 0 and 1, whereas the likelihood of the RCT, ?(??Y_RCT ), is not discounted . This leads to
p(???,Y_RCT,Y_RWD )??(??Y_RCT )?(??Y_RWD )^?p(?)
The power of ? can be interpreted as a discounting factor between the data from randomized controlled trials and the data from the real-world. With ? = 0, the real-world data is completely ignored and with ?=1, the datasets are simply pooled. However, in the above specification, the power ? has to be set beforehand. One may resort to Bayesian techniques to estimate ?:
p(???,Y_RCT,Y_RWD )?1/C(?) ?(??Y_RCT )?(??Y_RWD )^?p(?)p(?),
where C(?) is a normalizing constant. Estimation of ? using the available data leads to adaptive borrowing of information from the RWD: when the RCT data and the RWD are sufficiently similar, ? will be estimated as high, and the RWD will be mostly included in the analysis; however when RWD are in conflict with the RCT data, ? will be estimated to have a low value, and the RWD will be effectively discarded. This approach is based on the principle that the RCT data represent the highest level of evidence, whereas the RWD have an observational study design with a lower level of evidence.
It will also be necessary to account for differences in disease severity and other patient characteristics between EA data and RCT data. This can be done by combining the power prior approach either with covariate adjustment or with propensity score methods. This has been recently published by 16?18. and an RPackage ?psrwe? accompanies their findings. They have not, however, evaluated their methods with extensive simulation. We plan to do this and subsequently apply our findings to a non-simulated dataset.
There are also other methods for discounting historical data, such as methods based on a meta-analysis of all available studies, i.e. the meta-analytic-predictive prior 19,20. Our approach will be compared with these existing alternatives in terms of frequentist characteristics (type I error rate, mean squared error, and statistical power) as well as in terms of performance in real data sets." ["project_timeline"]=> string(211) "We aim to have the data request approved and DUA signed around 01APR2021-01MAY201, the analysis to start 15MAY2021, the first results by 01SEP2021. We then expect journal submission around 01OCT2021/01NOV2021. t" ["project_dissemination_plan"]=> string(116) "We aim to publish our findings in biostatistical journals, e.g. Statistics in Medicine or Pharmaceutical Statistics." ["project_bibliography"]=> string(4074) "

1. Kimberly LL, Beuttler MM, Shen M, Caplan AL, Bateman-House A. Pre-approval Access Terminology. Ther Innov Regul Sci. 2017;51(4):494-500. doi:10.1177/2168479017696267
2. Young FE, Norris JA, Levitt JA, Nightingale SL. The FDA?s New Procedures for the Use of Investigational Drugs in Treatment. JAMA J Am Med Assoc. 1988;259(15):2267-2270. doi:10.1001/jama.1988.03720150043034
3. Darrow JJ, Sarpatwari A, Avorn J, Kesselheim AS. Practical, Legal, and Ethical Issues in Expanded Access to Investigational Drugs. Hamel MB, ed. N Engl J Med. 2015;372(3):279-286. doi:10.1056/NEJMhle1409465
4. Jarow JP, Lurie P, Ikenberry SC, Lemery S. Overview of FDA?s Expanded Access Program for Investigational Drugs. Ther Innov Regul Sci. 2017;51(2):177-179. doi:10.1177/2168479017694850
5. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-World Evidence ? What Is It and What Can It Tell Us? N Engl J Med. 2016;375(23):2293-2297. doi:10.1056/NEJMsb1609216
6. Stower H. The promise of real-world data. Nat Med. March 2019. doi:10.1038/d41591-019-00010-z
7. Chapman CR, Moch KI, McFadyen A, et al. What compassionate use means for gene therapies. Nat Biotechnol. 2019;37(4):352-355. doi:10.1038/s41587-019-0081-7
8. Bunnik EM, Aarts N, van de Vathorst S. Little to lose and no other options: Ethical issues in efforts to facilitate expanded access to investigational drugs. Health Policy. 2018;122(9):977-983. doi:10.1016/j.healthpol.2018.06.005
9. Fountzilas E, Said R, Tsimberidou AM. Expanded access to investigational drugs: balancing patient safety with potential therapeutic benefits. Expert Opin Investig Drugs. 2018;27(2):155-162. doi:10.1080/13543784.2018.1430137
10. Sutter S. Expanded Access Programs Eyed For Data-Gathering Purposes. https://pink.pharmaintelligence.informa.com/PS122926/Expanded-Access-Pro….
11. S. Usdin. Beyond compassionate use: the case for using expanded access protocols to generate real world data. https://www.biocentury.com/biocentury/regulation/2017-09-29/case-using-e…. Accessed June 9, 2019.
12. Polak TB, van Rosmalen J, Uyl – de Groot CA. Expanded access as a source of real-world data: An overview of FDA and EMA approvals. Br J Clin Pharmacol. March 2020. doi:10.1111/bcp.14284
13. van Rosmalen J, Dejardin D, van Norden Y, Lwenberg B, Lesaffre E. Including historical data in the analysis of clinical trials: Is it worth the effort? Stat Methods Med Res. 2018;27(10):3167-3182. doi:10.1177/0962280217694506
14. Duan Y, Smith EP, Ye K. Using power priors to improve the binomial test of water quality. J Agric Biol Environ Stat. 2006;11(2):151-168. doi:10.1198/108571106X110919
15. Neuenschwander B, Branson M, Spiegelhalter DJ. A note on the power prior. Stat Med. 2009;28(28):3562-3566. doi:10.1002/sim.3722
16. Wang C, Li H, Chen W, et al. Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat. 2019;00(00):1-18. doi:10.1080/10543406.2019.1657133
17. Wang C, Rosner GL. A Bayesian nonparametric causal inference model for synthesizing randomized clinical trial and real-world evidence. Stat Med. 2019;38(14):2573-2588. doi:10.1002/sim.8134
18. Chen W, Wang C, Li H, et al. Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real- world data. J Biopharm Stat. 2020;30(3):508-520. doi:10.1080/10543406.2020.1730877
19. Neuenschwander B, Capkun-Niggli G, Branson M, Spiegelhalter DJ. Summarizing historical information on controls in clinical trials. Clin Trials. 2010;7(1):5-18. doi:10.1177/1740774509356002
20. Schmidli H, Gsteiger S, Roychoudhury S, O?Hagan A, Spiegelhalter D, Neuenschwander B. Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics. 2014;70(4):1023-1032. doi:10.1111/biom.12242

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2020-4519

Research Proposal

Project Title: Trabectedin: statistical modeling of expanded access programs and clinical trials.

Scientific Abstract: Background:
Considering the increasing interest in both expanded access and real-world data, the question arises whether alternative ways of access to novel treatments can provide clinical information and impact regulatory decision making. [1,2] have shown that there is an increasing trend in the use of RWD from EA by regulators and industry.
Objective:
This research aims to investigate combining two sources of data: (randomized) controlled trials, pre-approval RWD (expanded access) and post-approval RWD. We will do so by evaluating existing methodology of evidence synthesis (e.g. meta-analysis, power priors) and extend such methodology to specifically accommodate for expanded access data.
Study Design:
Statistical application.
Participants:
All patients in the two clinical trials (N=270+577=847) and all patients in the expanded access program (N=1803) making a total of N=2650.
Main Outcome Measures:
Mean Squared Error, Power, Type I error, Effective Sample Size of (Overall Survival, Response)
Statistical Analysis:
We will extend and apply current methods of dynamic borrowing and propensity score matching. Our approach will be compared with these existing alternatives in terms of frequentist characteristics (type I error rate, mean squared error, and statistical power) as well as in terms of performance in real data sets.

Brief Project Background and Statement of Project Significance: Patients suffering from seriously debilitating or life-threatening conditions who are not eligible for further treatments or any clinical trials, may resort to ?expanded access?: pre-approval access to investigational treatments. Expanded access (EA), also known as early access, pre-approval access or compassionate use 1, is the formal regulation adopted by the Food and Drug Administration (FDA) in 1987 2, propelled by the HIV/AIDS crisis. In the United States (US) the FDA regulates this process of formalized non-clinical trial access whereas in the European Union (EU) the responsibility lies with individual member states 3. The numbers of requests for EA are growing 4 and state and federal legislation, such as Right-to-Try laws in the US, stress the need and interest of patients in having earlier access to medicines that are still under clinical investigation.
EA data can be seen as a type of real-world data (RWD). RWD are ?information on health care that is derived from multiple sources outside typical clinical research settings? 5. Recent publications and regulatory frameworks have boosted the promise of RWD 4,6. It can come in many forms and shapes, such as electronic health records, social media or claims databases.
EA programs also form a source of RWD. Historically though, EA programs were only deemed fit for treatment and not for research. Although the primary purpose of EA is treatment, scholars have argued that there is a moral obligation to collect outcome data in all cases where patients are treated with investigational medicines 7?9. The debate on combining data collection and EA has substantially increased in recent years 10,11, with FDA-officials confirming beginning 2018 their willingness to review data from EA programs to support drug applications 7. Considering the increasing interest in both expanded access and real-world data, the question arises whether alternative ways of access to novel treatments can provide clinical information and impact regulatory decision making. 12 have shown that there is an increasing trend in the use of RWD from EA by regulators and industry.
Our research will help clarify the usability of these data. Awareness of the potential value of RWD from EA should facilitate that these data are incorporated in decision making whenever this is feasible and appropriate, and this should impact on traditional clinical development. For patients, this better use of available data can result in speedier access to more diverse treatments. This project will yield direct insights, via scientific publications and statistical methodology, into how to best combine data from expanded access programs with data of controlled trials. This is directly beneficial to regulators, drug developers and (bio)statisticians.

Specific Aims of the Project: The aims of the proposed research is to develop, demonstrate, and evaluate several statistical techniques to a non-simulated data set.
To extend existing methods (e.g. power prior) for borrowing information from historical studies to the context of (expanded access) real world data and investigate methods (e.g. propensity score matching) of adjusting for confounding and other sources of bias specific observational (RWD) data, and combine these methods with methods for borrowing information from historical controls. Recent techniques, such as proposed by propensity score integrated power-priors will also be applied.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.: Develop or refine statistical methods

Software Used: RStudio

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: We will be looking at two different sources of data: 2 formal clinical trials (N=577, N=270) and 1 expanded access study(N=1803). The FDA regards expanded access studies to be a source of real-world data[7]. We will be looking at all data from patients that are comparable to the patients in the expanded access program (in terms of indication, histology and prior lines of treatment). We expect patients from the expanded access study to be matched to patients from the clinical trials and aim to develop methodology for improving such matching.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: (root) Mean Squared Error, Power, Type I error, Effective Sample Sizes of the treatment effect estimates (overall survival, progression-free survival, time to treatment failure, response)

Main Predictor/Independent Variable and how it will be categorized/defined for your study: The main estimands will be survival (overall survival, progression-free survival, time to treatment failure) or response (DCR)and treatment-related Adverse Events.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Age, gender, smoking status, histology, prior therapies, baseline ECOG, Time from last recurrent or metastastic disease to first dose (months), race, ethnicity.

Statistical Analysis Plan: We aim to develop and evaluate methods for (i) dynamic borrowing of other data scores and (ii) methods for attenuating bias by confounding. We here provide further background on these methods and our plans.
In the analysis of clinical trials, there is often data from relevant previous studies available. A common example is the availability of data from a previous RCT in which the control arm patients received the same treatment as in the new (current) study. Provided that the studies are sufficiently similar in terms of setting and outcomes, the data of the historical controls can be incorporated into the analysis of the current trial. Due to the possibility of between-study heterogeneity (e.g. differences in study populations between trials), a nave pooling of historical controls and the current controls is rarely acceptable. Statistical methods have been devised to appropriately downweight the historical data, based on their characteristics and the observed differences with the current controls. A well-known technique for downweighting the historical controls is the power prior 13?15.
We are proposing to extend the ideas of the power prior and other methods for borrowing historical data to RWD data from EA programs. In this case, we downweight the RWD instead of the historical controls. The downweighting serves to account for a) the lower grade of evidence of EA data (compared to RCT data) due to the lack of randomization and the less controlled setting and b) center-specific and study-specific effects that may differ between EA data and RCT data. In the power prior method, this is done by raising the likelihood for the RWD, ?(??Y_RWD ), to a power of ?, with ? between 0 and 1, whereas the likelihood of the RCT, ?(??Y_RCT ), is not discounted . This leads to
p(???,Y_RCT,Y_RWD )??(??Y_RCT )?(??Y_RWD )^?p(?)
The power of ? can be interpreted as a discounting factor between the data from randomized controlled trials and the data from the real-world. With ? = 0, the real-world data is completely ignored and with ?=1, the datasets are simply pooled. However, in the above specification, the power ? has to be set beforehand. One may resort to Bayesian techniques to estimate ?:
p(???,Y_RCT,Y_RWD )?1/C(?) ?(??Y_RCT )?(??Y_RWD )^?p(?)p(?),
where C(?) is a normalizing constant. Estimation of ? using the available data leads to adaptive borrowing of information from the RWD: when the RCT data and the RWD are sufficiently similar, ? will be estimated as high, and the RWD will be mostly included in the analysis; however when RWD are in conflict with the RCT data, ? will be estimated to have a low value, and the RWD will be effectively discarded. This approach is based on the principle that the RCT data represent the highest level of evidence, whereas the RWD have an observational study design with a lower level of evidence.
It will also be necessary to account for differences in disease severity and other patient characteristics between EA data and RCT data. This can be done by combining the power prior approach either with covariate adjustment or with propensity score methods. This has been recently published by 16?18. and an RPackage ?psrwe? accompanies their findings. They have not, however, evaluated their methods with extensive simulation. We plan to do this and subsequently apply our findings to a non-simulated dataset.
There are also other methods for discounting historical data, such as methods based on a meta-analysis of all available studies, i.e. the meta-analytic-predictive prior 19,20. Our approach will be compared with these existing alternatives in terms of frequentist characteristics (type I error rate, mean squared error, and statistical power) as well as in terms of performance in real data sets.

Narrative Summary: This project will yield direct insights, via scientific publications, into how to best combine data from expanded access programs with data of (randomized) controlled trials. Awareness of the potential value of RWD from EA should facilitate that these data are incorporated in decision making whenever this is feasible and appropriate. For patients, this better use of available data can result in speedier access to more diverse treatments. This is directly beneficial to regulators, drug developers and (bio)statisticians.

Project Timeline: We aim to have the data request approved and DUA signed around 01APR2021-01MAY201, the analysis to start 15MAY2021, the first results by 01SEP2021. We then expect journal submission around 01OCT2021/01NOV2021. t

Dissemination Plan: We aim to publish our findings in biostatistical journals, e.g. Statistics in Medicine or Pharmaceutical Statistics.

Bibliography:

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