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  ["property_scientific_abstract"]=>
  string(2162) "Background: It is increasingly challenging and ethically questionable to recruit patients on control arms when a vast body of information is readily available through electronic health records (EHR). Synthetic Control Arms (SCA) may reduce the time and cost of running a trial, and ultimately alleviate patient burden.
Objectives: This study explores the potential of SCAs derived from EHR to provide additional support for trials investigating treatment efficacy in multiple myeloma (MM).
Study design and participants: De-identified EHR data will be matched with patients from a historical, Phase III randomized control trial investigating Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Relapsed or Refractory Multiple Myeloma (ClinicalTrials.gov Identifier: NCT02076009) and the Phase III study comparing Daratumumab, Lenalidomide, and Dexamethasone With Lenalidomide and Dexamethasone in Participants With Previously Untreated Multiple Myeloma (NCT02252172). Trial specific eligibility criteria will be applied to the EHR dataset to derive patients for the SCA.
Main outcome measures: The primary outcome is progression free survival (PFS), as defined by the International Myeloma Working Group based on relative and absolute changes in serum M protein or kappa/lambda free light chain assay. The observation period will be defined as time from randomization (Trial) or treatment initiation (SCA) to death or disease progression.
Statistical analysis: Patient characteristics will be compared between the trial and SCA using descriptive statistics. The primary and secondary endpoints will be analysed using Kaplan Meier and Cox regression survival analysis methods, where baseline characteristics between the trial treatment arm and SCA will be accounted for using inverse probability of treatment weights (IPTW). Sensitivity analyses will investigate the use of multiple imputation of missing data in EHR, such as ECOG status. Forest plots of the hazard ratios will be used to visually compare the estimates of the primary endpoint under different sensitivity analyses" ["project_brief_bg"]=> string(3070) "Multiple myeloma is the second most common haematological malignancy, accounting for 1% of all cancers and approximately 10% of all hematologic malignancies. The 5-year relative survival rate for all patients with multiple myeloma is 55.6%. No curative therapies are available, however novel treatments in the past few years have increased the survival of patients. Advancements in therapeutic management, namely the use of proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and CD-38-targeting monoclonal antibodies (CD-38 MoABs), has significantly improved overall survival in patients diagnosed with MM over recent decades. However, despite these improvements in treatment, MM remains incurable and most patients experience a relapse of the disease.
The randomised clinical trial (RCT) is the gold standard in drug development. However, for indications where patient recruitment is challenging or where placebo may be unethical, the use of a Synthetic Control Arm (SCA) may reduce time, cost of drug development, and alleviate patient burden. SCA is an external control constructed from patient-level data either from previous clinical trials or Real World Data (RWD) to match the baseline characteristics of the patients in the treatment arm of the trial. An SCA can serve as a sole comparison group in a single-arm trial setting, or supplement data from an RCT which already has a control group5. The addition of an SCA can prove vital in providing evidence of the treatment?s efficacy in regulatory submission. However, challenges remain around accessing viable, usable data to create SCAs, particularly for rare or unusual diseases.
The key benefits of using RWD as an external comparator is that it reflects the typical use of treatments in a clinical setting and tends to encompass patients with widely varying characteristics and co-morbidities. The use of RWD in post-authorization safety studies has become standard practice. However, synthetic control arms may be inadequate substitutes for true control arms generated via RCTs. Specific critiques of multiple myeloma studies which use SCAs involve differences in patient eligibility due to variations in definitions of relapse and refractory status, and the exclusion of unfit patients from trials versus their inclusion in observational datasets. It remains uncertain how much of the results from observational controls can be generalised and compared to more recent trials.
Currently, we have access to de-identified records of 1214 patients with a diagnosis of multiple myeloma. We have carried out a review of multiple myeloma trials reported in ClinicalTrials.gov to identify studies which could potentially match with the types of data that is recorded in EHR. Two Phase III trials (NCT02076009 and NCT02252172) were identified as potential candidates to test the validity of using EHR as a SCA, as the characteristics, criteria and endpoints reported in the trial can be mapped out using data available in EHR, either directly or through the use of surrogate measures." ["project_specific_aims"]=> string(720) "The project aims to evaluate the proof-of-concept and validity of using EHR from an NHS partnership trust as a synthetic control arm. Of particular interest are the number of patients that could be identified that met the inclusion/exclusion criteria, and the feasibility of determining the primary and secondary endpoints. To enable fair comparison of estimates between the SCA and trial data, to refine precision of estimates, and to identify potential sources of bias in EHR, we would need individual level patient data from the trial to apply the best performing confounder adjustment techniques, and to balance our patient characteristics between arms using methods such as inverse probability of treatment weights." ["project_study_design"]=> string(0) "" ["project_study_design_exp"]=> string(0) "" ["project_purposes"]=> array(0) { } ["project_purposes_exp"]=> string(0) "" ["project_software_used"]=> string(0) "" ["project_software_used_exp"]=> string(0) "" ["project_research_methods"]=> string(884) "TRIAL POPULATION: All patient records from both trials will be included as part of the main analysis of the study.
EXTERNAL COMPARATOR: Anonymised EHR data was obtained for patients admitted to one National Health Service Trust in the UK from 09/10/2012 to 09/04/2021. The curated EHR dataset included information on demographics, laboratory tests, diagnostics, procedures, pharmacy, cancer treatment, vitals, and WHO performance status. Diagnoses were coded in accordance with the International Classification of Diseases, tenth revision (ICD-10). Procedures were coded according to the Classification of Interventions and Procedures version 4 (OPCS4). All patients with any recorded diagnosis ICD-10 of C90.0 were included in the cohort.
INCLUSION/EXCLUSION CRITERIA: Patients that would satisfy the original trials? inclusion and exclusion criteria will be selected." ["project_main_outcome_measure"]=> string(309) "The main outcome measure is Progression-free Survival. This is defined as the time from the start of Lenalidomide + Dexamethasone treatment to either disease progression or death. The definition of progression is based on the International Myeloma Working Group Uniform Response Criteria for Multiple Myeloma." ["project_main_predictor_indep"]=> string(273) "The main predictor is the trial treatment arm versus SCA. The trial treatment arm is defined as patients who were randomised to receive daratumumab, lenalidomide, and dexamethasone and the SCA are patients selected from RWD who have received lenalidomide and dexamethasone." ["project_other_variables_interest"]=> string(406) "Baseline characteristics such as age, sex and ethnicity are recorded within the curated demographics dataset.
WHO performance status is determined based on the recorded status closest to the date of the first lenalidomide + dexamethasone treatment.
Prior lines of therapy will be determined based on recorded medications prior to the start of the first lenalidomide + dexamethasone treatment." ["project_stat_analysis_plan"]=> string(2698) "MAIN ANALYSIS
De-identified EHR will be matched with patients from the historical trials. Trial specific eligibility criteria will be applied to the EHR dataset to derive patients for the SCA. As not all data collected for the purpose of an RCT are present in routine RWD, some criteria require surrogate measures. The attached file (Table 1) outlines how some of the trial criteria were screened within EHR data.
Patient characteristics will be compared between the trial and SCA using descriptive statistics (e.g., mean, SD, counts and proportions). The primary endpoint will be analysed using Kaplan-Meier and Cox regression survival analyses. Median survival, 12-month survival rate, and hazards ratios comparing the trial treatment vs control and trial treatment vs SCA, along with 95% confidence intervals will be presented.
SENSITIVITY ANALYSES
1. Missing values in the SCA will be imputed on the start date of lenalidomide + dexamethasone treatment using Multiple Imputation by Chained Equations (MICE), with variables included in the imputation model evaluated based on the Root Mean Square Error (RMSE) and other model diagnostics. The original analysis will be separately repeated in each of 200 imputed datasets. Note that some variables (e.g., WHO performance status) acts as an inclusion criterion, therefore each dataset may contain a different number of patients. The average number of patients included will be calculated from the imputations, whilst the survival analyses will be estimated from pooling the 200 imputations based on Rubin?s Rule. Forest plots of the hazard ratios will be used to visually compare the estimates of the primary endpoint between the trial, SCA, and multiple imputation. These analyses are meant to highlight any systematic differences in findings due to the use of multiple imputation.
2. Subsequent models will explore the use of inverse probability treatment weights (IPTW) and multivariable adjustment to determine their importance on matching the trials control arm median survival estimates, and the trial hazard ratios. A logistic regression model will be used, with trial treatment arm versus SCA as the outcome, and baseline variables such as age, sex, ethnicity, number of prior lines of therapy, proportion of WHO performance status 0 versus 1 or 2, added as predictors. The resulting model will infer probabilities of belonging to the treatment arm, and these will be used as weights in the analysis using Kaplan-Meier and Cox proportional hazards models. Similar to multiple imputation, forest plots will of the hazards and 95% confidence intervals will be presented in comparison to the main analyses." ["project_timeline"]=> string(233) "Subject to data access approval, it is anticipated that the analysis will start by 01/03/2022 and will be completed by 31/03/2022. A draft of the manuscript and initial results will be reported back to the YODA project by 30/04/2022." ["project_dissemination_plan"]=> string(668) "Studies on methodologies and the feasibility of SCA?s using RWD are increasingly becoming of interest to pharmaceutical organisations and life science companies. Public health sectors within the United Kingdom will also be interested to see how anonymised patient derived from hospital records can be used to develop tools aimed at improving their patient care, provide a better understanding of specific treatment outcomes as well as continue to develop more personalised treatment regimes. The results of this study will be reported in the form of a manuscript and submitted to journals such as the British Journal of Haematology or the Journal of Clinical Oncology." ["project_bibliography"]=> string(1776) "

1. Rajkumar SV. Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am J Hematol 2016. DOI: 10.1002/ajh.24402 [doi].
2. Kazandjian D and Landgren O. A look backward and forward in the regulatory and treatment history of multiple myeloma: Approval of novel-novel agents, new drug development, and longer patient survival. Semin Oncol 2016. DOI: https://doi.org/10.1053/j.seminoncol.2016.10.008.
3. Jagannath S, Lin Y, Goldschmidt H, et al. KarMMa-RW: A study of real-world treatment patterns in heavily pretreated patients with relapsed and refractory multiple myeloma (RRMM) and comparison of outcomes to KarMMa. JCO 2020. DOI: 10.1200/JCO.2020.38.15_suppl.8525.
4. Mikhael J. Treatment Options for Triple-class Refractory Multiple Myeloma. Clin Lymphoma Myeloma Leuk 2020. DOI: S2152-2650(19)32008-7 [pii].
5. Thorlund K, Dron L, Park JJH, et al. Synthetic and External Controls in Clinical Trials – A Primer for Researchers. Clin Epidemiol 2020. DOI: 10.2147/CLEP.S242097 [doi].
6. Anonymous Chapter 3 – Data Sources for Post-Authorization Safety Studies. In: Ali AK and Hartzema AG (eds) Post-Authorization Safety Studies of Medicinal Products: Academic Press, 2018, p.49.
7. Banerjee R, Midha S, Kelkar AH, et al. Synthetic control arms in studies of multiple myeloma and diffuse large B-cell lymphoma. Br J Haematol 2021. DOI: https://doi.org/10.1111/bjh.17945.
8. Dimopoulos MA, Oriol A, Nahi H, et al. Daratumumab, Lenalidomide, and Dexamethasone for Multiple Myeloma. N Engl J Med 2016. DOI: 10.1056/NEJMoa1607751.
9. Facon T, Kumar S, Plesner T, et al. Daratumumab plus Lenalidomide and Dexamethasone for Untreated Myeloma. N Engl J Med 2019. DOI: 10.1056/NEJMoa1817249.

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2022-4869

Research Proposal

Project Title: Using Electronic Health Records to derive a Synthetic Control Arm for a historical trial on Relapsed/Refractory Multiple Myeloma

Scientific Abstract: Background: It is increasingly challenging and ethically questionable to recruit patients on control arms when a vast body of information is readily available through electronic health records (EHR). Synthetic Control Arms (SCA) may reduce the time and cost of running a trial, and ultimately alleviate patient burden.
Objectives: This study explores the potential of SCAs derived from EHR to provide additional support for trials investigating treatment efficacy in multiple myeloma (MM).
Study design and participants: De-identified EHR data will be matched with patients from a historical, Phase III randomized control trial investigating Daratumumab, Lenalidomide, and Dexamethasone (DRd) vs Lenalidomide and Dexamethasone (Rd) in Subjects With Relapsed or Refractory Multiple Myeloma (ClinicalTrials.gov Identifier: NCT02076009) and the Phase III study comparing Daratumumab, Lenalidomide, and Dexamethasone With Lenalidomide and Dexamethasone in Participants With Previously Untreated Multiple Myeloma (NCT02252172). Trial specific eligibility criteria will be applied to the EHR dataset to derive patients for the SCA.
Main outcome measures: The primary outcome is progression free survival (PFS), as defined by the International Myeloma Working Group based on relative and absolute changes in serum M protein or kappa/lambda free light chain assay. The observation period will be defined as time from randomization (Trial) or treatment initiation (SCA) to death or disease progression.
Statistical analysis: Patient characteristics will be compared between the trial and SCA using descriptive statistics. The primary and secondary endpoints will be analysed using Kaplan Meier and Cox regression survival analysis methods, where baseline characteristics between the trial treatment arm and SCA will be accounted for using inverse probability of treatment weights (IPTW). Sensitivity analyses will investigate the use of multiple imputation of missing data in EHR, such as ECOG status. Forest plots of the hazard ratios will be used to visually compare the estimates of the primary endpoint under different sensitivity analyses

Brief Project Background and Statement of Project Significance: Multiple myeloma is the second most common haematological malignancy, accounting for 1% of all cancers and approximately 10% of all hematologic malignancies. The 5-year relative survival rate for all patients with multiple myeloma is 55.6%. No curative therapies are available, however novel treatments in the past few years have increased the survival of patients. Advancements in therapeutic management, namely the use of proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and CD-38-targeting monoclonal antibodies (CD-38 MoABs), has significantly improved overall survival in patients diagnosed with MM over recent decades. However, despite these improvements in treatment, MM remains incurable and most patients experience a relapse of the disease.
The randomised clinical trial (RCT) is the gold standard in drug development. However, for indications where patient recruitment is challenging or where placebo may be unethical, the use of a Synthetic Control Arm (SCA) may reduce time, cost of drug development, and alleviate patient burden. SCA is an external control constructed from patient-level data either from previous clinical trials or Real World Data (RWD) to match the baseline characteristics of the patients in the treatment arm of the trial. An SCA can serve as a sole comparison group in a single-arm trial setting, or supplement data from an RCT which already has a control group5. The addition of an SCA can prove vital in providing evidence of the treatment?s efficacy in regulatory submission. However, challenges remain around accessing viable, usable data to create SCAs, particularly for rare or unusual diseases.
The key benefits of using RWD as an external comparator is that it reflects the typical use of treatments in a clinical setting and tends to encompass patients with widely varying characteristics and co-morbidities. The use of RWD in post-authorization safety studies has become standard practice. However, synthetic control arms may be inadequate substitutes for true control arms generated via RCTs. Specific critiques of multiple myeloma studies which use SCAs involve differences in patient eligibility due to variations in definitions of relapse and refractory status, and the exclusion of unfit patients from trials versus their inclusion in observational datasets. It remains uncertain how much of the results from observational controls can be generalised and compared to more recent trials.
Currently, we have access to de-identified records of 1214 patients with a diagnosis of multiple myeloma. We have carried out a review of multiple myeloma trials reported in ClinicalTrials.gov to identify studies which could potentially match with the types of data that is recorded in EHR. Two Phase III trials (NCT02076009 and NCT02252172) were identified as potential candidates to test the validity of using EHR as a SCA, as the characteristics, criteria and endpoints reported in the trial can be mapped out using data available in EHR, either directly or through the use of surrogate measures.

Specific Aims of the Project: The project aims to evaluate the proof-of-concept and validity of using EHR from an NHS partnership trust as a synthetic control arm. Of particular interest are the number of patients that could be identified that met the inclusion/exclusion criteria, and the feasibility of determining the primary and secondary endpoints. To enable fair comparison of estimates between the SCA and trial data, to refine precision of estimates, and to identify potential sources of bias in EHR, we would need individual level patient data from the trial to apply the best performing confounder adjustment techniques, and to balance our patient characteristics between arms using methods such as inverse probability of treatment weights.

Study Design:

What is the purpose of the analysis being proposed? Please select all that apply.:

Software Used:

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: TRIAL POPULATION: All patient records from both trials will be included as part of the main analysis of the study.
EXTERNAL COMPARATOR: Anonymised EHR data was obtained for patients admitted to one National Health Service Trust in the UK from 09/10/2012 to 09/04/2021. The curated EHR dataset included information on demographics, laboratory tests, diagnostics, procedures, pharmacy, cancer treatment, vitals, and WHO performance status. Diagnoses were coded in accordance with the International Classification of Diseases, tenth revision (ICD-10). Procedures were coded according to the Classification of Interventions and Procedures version 4 (OPCS4). All patients with any recorded diagnosis ICD-10 of C90.0 were included in the cohort.
INCLUSION/EXCLUSION CRITERIA: Patients that would satisfy the original trials? inclusion and exclusion criteria will be selected.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: The main outcome measure is Progression-free Survival. This is defined as the time from the start of Lenalidomide + Dexamethasone treatment to either disease progression or death. The definition of progression is based on the International Myeloma Working Group Uniform Response Criteria for Multiple Myeloma.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: The main predictor is the trial treatment arm versus SCA. The trial treatment arm is defined as patients who were randomised to receive daratumumab, lenalidomide, and dexamethasone and the SCA are patients selected from RWD who have received lenalidomide and dexamethasone.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Baseline characteristics such as age, sex and ethnicity are recorded within the curated demographics dataset.
WHO performance status is determined based on the recorded status closest to the date of the first lenalidomide + dexamethasone treatment.
Prior lines of therapy will be determined based on recorded medications prior to the start of the first lenalidomide + dexamethasone treatment.

Statistical Analysis Plan: MAIN ANALYSIS
De-identified EHR will be matched with patients from the historical trials. Trial specific eligibility criteria will be applied to the EHR dataset to derive patients for the SCA. As not all data collected for the purpose of an RCT are present in routine RWD, some criteria require surrogate measures. The attached file (Table 1) outlines how some of the trial criteria were screened within EHR data.
Patient characteristics will be compared between the trial and SCA using descriptive statistics (e.g., mean, SD, counts and proportions). The primary endpoint will be analysed using Kaplan-Meier and Cox regression survival analyses. Median survival, 12-month survival rate, and hazards ratios comparing the trial treatment vs control and trial treatment vs SCA, along with 95% confidence intervals will be presented.
SENSITIVITY ANALYSES
1. Missing values in the SCA will be imputed on the start date of lenalidomide + dexamethasone treatment using Multiple Imputation by Chained Equations (MICE), with variables included in the imputation model evaluated based on the Root Mean Square Error (RMSE) and other model diagnostics. The original analysis will be separately repeated in each of 200 imputed datasets. Note that some variables (e.g., WHO performance status) acts as an inclusion criterion, therefore each dataset may contain a different number of patients. The average number of patients included will be calculated from the imputations, whilst the survival analyses will be estimated from pooling the 200 imputations based on Rubin?s Rule. Forest plots of the hazard ratios will be used to visually compare the estimates of the primary endpoint between the trial, SCA, and multiple imputation. These analyses are meant to highlight any systematic differences in findings due to the use of multiple imputation.
2. Subsequent models will explore the use of inverse probability treatment weights (IPTW) and multivariable adjustment to determine their importance on matching the trials control arm median survival estimates, and the trial hazard ratios. A logistic regression model will be used, with trial treatment arm versus SCA as the outcome, and baseline variables such as age, sex, ethnicity, number of prior lines of therapy, proportion of WHO performance status 0 versus 1 or 2, added as predictors. The resulting model will infer probabilities of belonging to the treatment arm, and these will be used as weights in the analysis using Kaplan-Meier and Cox proportional hazards models. Similar to multiple imputation, forest plots will of the hazards and 95% confidence intervals will be presented in comparison to the main analyses.

Narrative Summary: Multiple myeloma drugs are usually developed within a clinical trial setting, which is considered the gold standard. These trials are very expensive and time-consuming. It can be difficult to recruit patients into these trials, and most of these trials are stopped early as interim results show new treatments are not as effective as current treatments. We want to improve on the process of how these drugs are developed by supplementing trial data with data that we have obtained from routine hospital records. We will assess how well our hospital records emulate patient records from trials, and estimate the benefits of using new drugs by using our hospital records as an external comparator.

Project Timeline: Subject to data access approval, it is anticipated that the analysis will start by 01/03/2022 and will be completed by 31/03/2022. A draft of the manuscript and initial results will be reported back to the YODA project by 30/04/2022.

Dissemination Plan: Studies on methodologies and the feasibility of SCA?s using RWD are increasingly becoming of interest to pharmaceutical organisations and life science companies. Public health sectors within the United Kingdom will also be interested to see how anonymised patient derived from hospital records can be used to develop tools aimed at improving their patient care, provide a better understanding of specific treatment outcomes as well as continue to develop more personalised treatment regimes. The results of this study will be reported in the form of a manuscript and submitted to journals such as the British Journal of Haematology or the Journal of Clinical Oncology.

Bibliography:

1. Rajkumar SV. Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am J Hematol 2016. DOI: 10.1002/ajh.24402 [doi].
2. Kazandjian D and Landgren O. A look backward and forward in the regulatory and treatment history of multiple myeloma: Approval of novel-novel agents, new drug development, and longer patient survival. Semin Oncol 2016. DOI: https://doi.org/10.1053/j.seminoncol.2016.10.008.
3. Jagannath S, Lin Y, Goldschmidt H, et al. KarMMa-RW: A study of real-world treatment patterns in heavily pretreated patients with relapsed and refractory multiple myeloma (RRMM) and comparison of outcomes to KarMMa. JCO 2020. DOI: 10.1200/JCO.2020.38.15_suppl.8525.
4. Mikhael J. Treatment Options for Triple-class Refractory Multiple Myeloma. Clin Lymphoma Myeloma Leuk 2020. DOI: S2152-2650(19)32008-7 [pii].
5. Thorlund K, Dron L, Park JJH, et al. Synthetic and External Controls in Clinical Trials – A Primer for Researchers. Clin Epidemiol 2020. DOI: 10.2147/CLEP.S242097 [doi].
6. Anonymous Chapter 3 – Data Sources for Post-Authorization Safety Studies. In: Ali AK and Hartzema AG (eds) Post-Authorization Safety Studies of Medicinal Products: Academic Press, 2018, p.49.
7. Banerjee R, Midha S, Kelkar AH, et al. Synthetic control arms in studies of multiple myeloma and diffuse large B-cell lymphoma. Br J Haematol 2021. DOI: https://doi.org/10.1111/bjh.17945.
8. Dimopoulos MA, Oriol A, Nahi H, et al. Daratumumab, Lenalidomide, and Dexamethasone for Multiple Myeloma. N Engl J Med 2016. DOI: 10.1056/NEJMoa1607751.
9. Facon T, Kumar S, Plesner T, et al. Daratumumab plus Lenalidomide and Dexamethasone for Untreated Myeloma. N Engl J Med 2019. DOI: 10.1056/NEJMoa1817249.