array(44) {
  ["project_title"]=>
  string(102) "Comparison of patients characteristics and outcomes between clinical trials and real-world populations"
  ["project_narrative_summary"]=>
  string(714) "We are looking to explore and understand the differences between patients treated in clinical trials compared to real-world patients treated in the typical clinical setting. We have access the entire US VA database of patients treated for prostate cancer and would like to compared baseline characteristics and outcomes with treatment between clinical trials and routine care.
Exposure variables of interest:
Age, race, BMI, PSA at diagnosis, charlson comorbidities/ index (or equivalent), volume of disease (based on imaging), hemoglobin, albumin, creatinine, bilirubin.
Outcome variables of interests:
Duration of treatment, progression free survival, overall survival, rate of change" ["project_learn_source"]=> string(9) "colleague" ["project_learn_source_exp"]=> string(0) "" ["project_key_personnel"]=> array(2) { [0]=> array(6) { ["p_pers_f_name"]=> string(6) "Martin" ["p_pers_l_name"]=> string(6) "Schoen" ["p_pers_degree"]=> string(2) "MD" ["p_pers_pr_affil"]=> string(22) "Saint Louis University" ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } [1]=> array(6) { ["p_pers_f_name"]=> string(5) "Tarek" ["p_pers_l_name"]=> string(9) "Abdulhadi" ["p_pers_degree"]=> string(2) "MD" ["p_pers_pr_affil"]=> string(21) "Washington University" ["p_pers_scop_id"]=> string(0) "" ["requires_data_access"]=> string(0) "" } } ["project_ext_grants"]=> array(2) { ["value"]=> string(2) "no" ["label"]=> string(68) "No external grants or funds are being used to support this research." } ["project_funding_source"]=> string(0) "" ["project_assoc_trials"]=> array(1) { [0]=> object(WP_Post)#3712 (24) { ["ID"]=> int(1845) ["post_author"]=> string(4) "1363" ["post_date"]=> string(19) "2019-12-12 12:23:00" ["post_date_gmt"]=> string(19) "2019-12-12 12:23:00" ["post_content"]=> string(0) "" ["post_title"]=> string(257) "NCT01715285 - A Randomized, Double-blind, Comparative Study of Abiraterone Acetate Plus Low-Dose Prednisone Plus Androgen Deprivation Therapy (ADT) Versus ADT Alone in Newly Diagnosed Subjects With High-Risk, Metastatic Hormone-naive Prostate Cancer (mHNPC)" ["post_excerpt"]=> string(0) "" ["post_status"]=> string(7) "publish" ["comment_status"]=> string(4) "open" ["ping_status"]=> string(4) "open" ["post_password"]=> string(0) "" ["post_name"]=> string(193) "nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h" ["to_ping"]=> string(0) "" ["pinged"]=> string(0) "" ["post_modified"]=> string(19) "2023-02-06 13:28:21" ["post_modified_gmt"]=> string(19) "2023-02-06 13:28:21" ["post_content_filtered"]=> string(0) "" ["post_parent"]=> int(0) ["guid"]=> string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h/" ["menu_order"]=> int(0) ["post_type"]=> string(14) "clinical_trial" ["post_mime_type"]=> string(0) "" ["comment_count"]=> string(1) "0" ["filter"]=> string(3) "raw" } } ["project_date_type"]=> string(18) "full_crs_supp_docs" ["property_scientific_abstract"]=> string(2071) "Background: Clinical trials are often conducted with strict inclusion and exclusion criteria, which may limit the generalizability of their results to the broader patient population. Understanding the differences in demographics and outcomes between clinical trial patients and real-world patients can inform clinical decision-making and improve the external validity of clinical trial results.
Objective: The objective of this study is to explore and compare the demographics and outcomes of patients with prostate cancer treated in clinical trials versus those treated in the typical clinical setting.
Study Design: This is a retrospective cohort study using electronic medical record data from patients diagnosed with prostate cancer. Patients will be divided into two groups: those treated in clinical trials and those treated in the typical clinical setting. Demographic variables including age, race, BMI, PSA at diagnosis, Charlson comorbidity index, volume of disease (based on imaging), hemoglobin, albumin, creatinine, and bilirubin will be compared between the two groups. Outcome variables of interest include duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
Participants: The study will include all patients diagnosed with prostate cancer who received treatment.
Primary and Secondary Outcome Measure(s): The primary outcome of this study is to understand the differences in characteristics of patients in trials with those in the real world. The secondary outcomes are to compare the duration of treatment and survival.
Statistical Analysis: Descriptive statistics will be used to summarize the demographic characteristics of patients in each group. Multivariable regression models will be used to adjust for potential confounding factors and to determine the association between treatment setting and outcomes of interest. Propensity score matching will also be conducted to account for differences in baseline characteristics between the two groups." ["project_brief_bg"]=> string(2945) "Prostate cancer is one of the most common cancers among men worldwide. Clinical trials are often conducted to test new treatments, and their results can guide clinical decision-making. However, patients in clinical trials may not be representative of the broader patient population, potentially limiting the generalizability of trial results. Understanding the differences in demographics and outcomes between clinical trial patients and real-world patients is crucial to improve the external validity of clinical trial results and inform clinical practice. This study aims to explore and compare the demographics and outcomes of patients with prostate cancer treated in clinical trials versus those treated in the real-world setting. The findings of this study can help clinicians and researchers better understand the limitations of clinical trial results and guide the development of more representative and effective treatments for prostate cancer patients.The specific aims of the project are:
1. To compare the demographic and clinical characteristics of patients with prostate cancer treated in clinical trials versus real-world settings.
2. To evaluate the impact of demographic and clinical characteristics on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
3. To compare the differences in treatment results between the two settings and how patients respond to different treatments for prostate cancer.
4. To investigate the differences in adverse events, progression-free survival, treatment duration, and overall survival between different kinds of treatments for prostate cancer.
5. If adequate numbers of patients are available, to employ a propensity score matching algorithm to match patients based on baseline characteristics and compare outcomes.
The specific hypotheses to be evaluated are:
1. Patients with prostate cancer treated in clinical trials will have different demographic and clinical characteristics compared to those treated in real-world settings.
2. Certain demographic and clinical characteristics will be associated with better treatment outcomes, including longer progression-free survival, overall survival, and better response to treatment.
3. Patients treated in clinical trials will have different treatment outcomes compared to those treated in real-world settings, potentially due to differences in patient characteristics or treatment protocols.
4. Different types of treatments for prostate cancer will have different rates of adverse events, progression-free survival, treatment duration, and overall survival.
Propensity score matching will help account for potential confounding variables and provide a more accurate comparison of treatment outcomes between patients treated in clinical trials and real-world settings." ["project_specific_aims"]=> string(1341) "The specific aims of the project are:
1. To compare the demographic and clinical characteristics of patients with prostate cancer treated in clinical trials versus real-world settings.
2. To evaluate the impact of demographic and clinical characteristics on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
3. To compare the differences in treatment results between the two settings and how patients respond to different treatments for prostate cancer.
4. To investigate the differences in adverse events, progression-free survival, treatment duration, and overall survival between different kinds of treatments for prostate cancer.
5. If adequate numbers of patients are available, to employ a propensity score matching algorithm to match patients based on baseline characteristics and compare outcomes.
The specific hypotheses to be evaluated are:
1. Patients with prostate cancer treated in clinical trials will have different demographic and clinical characteristics compared to those treated in real-world settings.
2. Certain demographic and clinical characteristics will be associated with better treatment outcomes, including longer progression-free survival, overall survival" ["project_study_design"]=> array(2) { ["value"]=> string(7) "meta_an" ["label"]=> string(52) "Meta-analysis (analysis of multiple trials together)" } ["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(413) "We will use the dataset from studies to compare data relating to the subject.
There will be no specific inclusion/exclusion criteria, the study will include all patients who are diagnosed with prostate cancer and received treatment.
The data selection process may still involve certain criteria to ensure data quality and availability of required variables(Age, Race, BMI, PSA, setting of treatment)" ["project_main_outcome_measure"]=> string(1048) "The primary outcome of our study is to understand the differences in baseline characteristics of patients in clinical trials compared to real-world patients. This will allow us to identify potential factors that may influence the outcomes of prostate cancer treatment and determine whether clinical trial patients are representative of the general population of prostate cancer patients.
The secondary outcomes focus on treatment outcomes and include duration of treatment, progression-free survival, overall survival, rate of change of PSA, other available assessments of tumor response (imaging), and subsequent treatments. These outcomes will help us understand whether there are differences in treatment response and outcomes between clinical trial patients and real-world patients. We will also be able to compare the effectiveness of different treatments for prostate cancer and identify potential differences in adverse events, progression-free survival, treatment duration, and overall survival between different types of treatments." ["project_main_predictor_indep"]=> string(730) "The main independent variables for this study are the setting of treatment (clinical trial vs. real-world setting) and the type of treatment for prostate cancer. Other independent variables of interest include age, race, BMI, PSA at diagnosis, Charlson comorbidity index (or equivalent), volume of disease (based on imaging), hemoglobin, albumin, creatinine, and bilirubin. These independent variables will be used to compare the demographic and clinical characteristics of patients treated in clinical trials and real-world settings and to assess the impact of these variables on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response." ["project_other_variables_interest"]=> string(1263) "The independent variables of interest for this study include:
1. Age: a continuous variable representing the age of the patient at diagnosis.
2. Race: a categorical variable representing the patient's self-reported race/ethnicity.
3. BMI: a continuous variable representing the patient's body mass index at diagnosis.
4. PSA at diagnosis: a continuous variable representing the initial prostate-specific antigen level at diagnosis.
5. Charlson comorbidity index (or equivalent): a continuous variable representing the severity of comorbidities at diagnosis.
6. Volume of disease (based on imaging): a continuous variable representing the extent of disease at diagnosis.
7. Hemoglobin: a continuous variable representing the patient's hemoglobin level at diagnosis.
8. Albumin: a continuous variable representing the patient's albumin level at diagnosis.
9. Creatinine: a continuous variable representing the patient's creatinine level at diagnosis.
10. Bilirubin: a continuous variable representing the patient's bilirubin level at diagnosis.
11. Setting of Treatment: a categorical variable representing whether the patients were treated in a clinical trial or in a real-world setting." ["project_stat_analysis_plan"]=> string(767) "Descriptive statistics will be used to summarize the demographics and clinical characteristics of patients treated in clinical trials and real-world settings. Chi-square tests and t-tests will be used to compare differences in demographic and clinical characteristics between the two groups. Cox proportional hazards regression models will be used to compare differences in treatment outcomes, including progression-free survival and overall survival, between the two groups. Logistic regression models will be used to compare differences in adverse events between treatment types. Propensity score matching will be employed to match patients based on their baseline characteristics, and sensitivity analyses will be conducted to assess the robustness of the results." ["project_timeline"]=> string(164) "1 year, we plan to start the project after we receive the data and analysis completion date 6 months after and 1 year for the manuscript and to report back to YODA." ["project_dissemination_plan"]=> string(118) "Physicians specialized in Oncology, we plan to submit the data to Frontier and Clinical GU, European Oncology Urology." ["project_bibliography"]=> string(2508) "

Schoen, Martin & Carson, Kenneth & Eisen, Seth & Bennett, Charles & Luo, Suhong & Reimers, Melissa & Knoche, Eric & Whitmer, Alison & Yan, Yan & Drake, Bettina & Sanfilippo, Kristen. (2022). Survival of veterans treated with enzalutamide and abiraterone for metastatic castrate resistant prostate cancer based on comorbid diseases. Prostate Cancer and Prostatic Diseases. 1-8. 10.1038/s41391-022-00588-5.
Schoen, Martin & Carson, Kenneth & Eisen, Seth & Bennett, Charles & Luo, Suhong & Reimers, Melissa & Knoche, Eric & Whitmer, Alison & Yan, Yan & Drake, Bettina & Sanfilippo, Kristen. (2022). Survival of Veterans Treated with Enzalutamide and Abiraterone for Metastatic Castrate Resistant Prostate Cancer based on Comorbid Diseases. 10.21203/rs.3.rs-1624365/v1.
Yoon, Harrison & Luo, Suhong & Sanfilippo, Kristen & Linneman, Travis & Whitmer, Alison & Schoen, Martin. (2022). Statin type and survival of patients with metastatic castrate-resistant prostate cancer receiving abiraterone and enzalutamide: A nationwide retrospective cohort study.. Journal of Clinical Oncology. 40. 50-50. 10.1200/JCO.2022.40.6_suppl.050.
Cheranda, Nina & Luo, Suhong & Riekhof, Forest & Govindan, Srinivas & Sanfilippo, Kristen & Schoen, Martin. (2022). Survival of patients with metastatic prostate cancer and comorbid obesity.. Journal of Clinical Oncology. 40. 116-116. 10.1200/JCO.2022.40.6_suppl.116.
Govindan, Srinivas & Luo, Suhong & Cheranda, Nina & Riekhof, Forest & Schoen, Martin. (2022). Treatment outcomes of patients with metastatic prostate cancer and co-morbid diabetes mellitus.. Journal of Clinical Oncology. 40. 113-113. 10.1200/JCO.2022.40.6_suppl.113.
Schoen, Martin & Carson, Kenneth & Luo, Suhong & Eisen, Seth & Reimers, Melissa & Drake, Bettina & Bennett, Charles & Knoche, Eric & Yan, Yan & Sanfilippo, Kristen. (2021). Survival of veterans treated with enzalutamide and abiraterone in advanced prostate cancer.. Journal of Clinical Oncology. 39. 5032-5032. 10.1200/JCO.2021.39.15_suppl.5032.
Patel, Mukti & Riekhof, Forest & Sanfilippo, Kristen & Carson, Kenneth & Schoen, Martin. (2021). Characteristics and comorbidities of veterans treated with enzalutamide or abiraterone.. Journal of Clinical Oncology. 39. e17022-e17022. 10.1200/JCO.2021.39.15_suppl.e17022.

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2023-5234

General Information

How did you learn about the YODA Project?: Colleague

Conflict of Interest

Request Clinical Trials

Associated Trial(s):
  1. NCT01715285 - A Randomized, Double-blind, Comparative Study of Abiraterone Acetate Plus Low-Dose Prednisone Plus Androgen Deprivation Therapy (ADT) Versus ADT Alone in Newly Diagnosed Subjects With High-Risk, Metastatic Hormone-naive Prostate Cancer (mHNPC)
What type of data are you looking for?: Individual Participant-Level Data, which includes Full CSR and all supporting documentation

Request Clinical Trials

Data Request Status

Status: Withdrawn/Closed

Research Proposal

Project Title: Comparison of patients characteristics and outcomes between clinical trials and real-world populations

Scientific Abstract: Background: Clinical trials are often conducted with strict inclusion and exclusion criteria, which may limit the generalizability of their results to the broader patient population. Understanding the differences in demographics and outcomes between clinical trial patients and real-world patients can inform clinical decision-making and improve the external validity of clinical trial results.
Objective: The objective of this study is to explore and compare the demographics and outcomes of patients with prostate cancer treated in clinical trials versus those treated in the typical clinical setting.
Study Design: This is a retrospective cohort study using electronic medical record data from patients diagnosed with prostate cancer. Patients will be divided into two groups: those treated in clinical trials and those treated in the typical clinical setting. Demographic variables including age, race, BMI, PSA at diagnosis, Charlson comorbidity index, volume of disease (based on imaging), hemoglobin, albumin, creatinine, and bilirubin will be compared between the two groups. Outcome variables of interest include duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
Participants: The study will include all patients diagnosed with prostate cancer who received treatment.
Primary and Secondary Outcome Measure(s): The primary outcome of this study is to understand the differences in characteristics of patients in trials with those in the real world. The secondary outcomes are to compare the duration of treatment and survival.
Statistical Analysis: Descriptive statistics will be used to summarize the demographic characteristics of patients in each group. Multivariable regression models will be used to adjust for potential confounding factors and to determine the association between treatment setting and outcomes of interest. Propensity score matching will also be conducted to account for differences in baseline characteristics between the two groups.

Brief Project Background and Statement of Project Significance: Prostate cancer is one of the most common cancers among men worldwide. Clinical trials are often conducted to test new treatments, and their results can guide clinical decision-making. However, patients in clinical trials may not be representative of the broader patient population, potentially limiting the generalizability of trial results. Understanding the differences in demographics and outcomes between clinical trial patients and real-world patients is crucial to improve the external validity of clinical trial results and inform clinical practice. This study aims to explore and compare the demographics and outcomes of patients with prostate cancer treated in clinical trials versus those treated in the real-world setting. The findings of this study can help clinicians and researchers better understand the limitations of clinical trial results and guide the development of more representative and effective treatments for prostate cancer patients.The specific aims of the project are:
1. To compare the demographic and clinical characteristics of patients with prostate cancer treated in clinical trials versus real-world settings.
2. To evaluate the impact of demographic and clinical characteristics on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
3. To compare the differences in treatment results between the two settings and how patients respond to different treatments for prostate cancer.
4. To investigate the differences in adverse events, progression-free survival, treatment duration, and overall survival between different kinds of treatments for prostate cancer.
5. If adequate numbers of patients are available, to employ a propensity score matching algorithm to match patients based on baseline characteristics and compare outcomes.
The specific hypotheses to be evaluated are:
1. Patients with prostate cancer treated in clinical trials will have different demographic and clinical characteristics compared to those treated in real-world settings.
2. Certain demographic and clinical characteristics will be associated with better treatment outcomes, including longer progression-free survival, overall survival, and better response to treatment.
3. Patients treated in clinical trials will have different treatment outcomes compared to those treated in real-world settings, potentially due to differences in patient characteristics or treatment protocols.
4. Different types of treatments for prostate cancer will have different rates of adverse events, progression-free survival, treatment duration, and overall survival.
Propensity score matching will help account for potential confounding variables and provide a more accurate comparison of treatment outcomes between patients treated in clinical trials and real-world settings.

Specific Aims of the Project: The specific aims of the project are:
1. To compare the demographic and clinical characteristics of patients with prostate cancer treated in clinical trials versus real-world settings.
2. To evaluate the impact of demographic and clinical characteristics on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.
3. To compare the differences in treatment results between the two settings and how patients respond to different treatments for prostate cancer.
4. To investigate the differences in adverse events, progression-free survival, treatment duration, and overall survival between different kinds of treatments for prostate cancer.
5. If adequate numbers of patients are available, to employ a propensity score matching algorithm to match patients based on baseline characteristics and compare outcomes.
The specific hypotheses to be evaluated are:
1. Patients with prostate cancer treated in clinical trials will have different demographic and clinical characteristics compared to those treated in real-world settings.
2. Certain demographic and clinical characteristics will be associated with better treatment outcomes, including longer progression-free survival, overall survival

Study Design: Meta-analysis (analysis of multiple trials together)

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: We will use the dataset from studies to compare data relating to the subject.
There will be no specific inclusion/exclusion criteria, the study will include all patients who are diagnosed with prostate cancer and received treatment.
The data selection process may still involve certain criteria to ensure data quality and availability of required variables(Age, Race, BMI, PSA, setting of treatment)

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: The primary outcome of our study is to understand the differences in baseline characteristics of patients in clinical trials compared to real-world patients. This will allow us to identify potential factors that may influence the outcomes of prostate cancer treatment and determine whether clinical trial patients are representative of the general population of prostate cancer patients.
The secondary outcomes focus on treatment outcomes and include duration of treatment, progression-free survival, overall survival, rate of change of PSA, other available assessments of tumor response (imaging), and subsequent treatments. These outcomes will help us understand whether there are differences in treatment response and outcomes between clinical trial patients and real-world patients. We will also be able to compare the effectiveness of different treatments for prostate cancer and identify potential differences in adverse events, progression-free survival, treatment duration, and overall survival between different types of treatments.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: The main independent variables for this study are the setting of treatment (clinical trial vs. real-world setting) and the type of treatment for prostate cancer. Other independent variables of interest include age, race, BMI, PSA at diagnosis, Charlson comorbidity index (or equivalent), volume of disease (based on imaging), hemoglobin, albumin, creatinine, and bilirubin. These independent variables will be used to compare the demographic and clinical characteristics of patients treated in clinical trials and real-world settings and to assess the impact of these variables on treatment outcomes, including duration of treatment, progression-free survival, overall survival, and rate of change of PSA to assess tumor response.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: The independent variables of interest for this study include:
1. Age: a continuous variable representing the age of the patient at diagnosis.
2. Race: a categorical variable representing the patient's self-reported race/ethnicity.
3. BMI: a continuous variable representing the patient's body mass index at diagnosis.
4. PSA at diagnosis: a continuous variable representing the initial prostate-specific antigen level at diagnosis.
5. Charlson comorbidity index (or equivalent): a continuous variable representing the severity of comorbidities at diagnosis.
6. Volume of disease (based on imaging): a continuous variable representing the extent of disease at diagnosis.
7. Hemoglobin: a continuous variable representing the patient's hemoglobin level at diagnosis.
8. Albumin: a continuous variable representing the patient's albumin level at diagnosis.
9. Creatinine: a continuous variable representing the patient's creatinine level at diagnosis.
10. Bilirubin: a continuous variable representing the patient's bilirubin level at diagnosis.
11. Setting of Treatment: a categorical variable representing whether the patients were treated in a clinical trial or in a real-world setting.

Statistical Analysis Plan: Descriptive statistics will be used to summarize the demographics and clinical characteristics of patients treated in clinical trials and real-world settings. Chi-square tests and t-tests will be used to compare differences in demographic and clinical characteristics between the two groups. Cox proportional hazards regression models will be used to compare differences in treatment outcomes, including progression-free survival and overall survival, between the two groups. Logistic regression models will be used to compare differences in adverse events between treatment types. Propensity score matching will be employed to match patients based on their baseline characteristics, and sensitivity analyses will be conducted to assess the robustness of the results.

Narrative Summary: We are looking to explore and understand the differences between patients treated in clinical trials compared to real-world patients treated in the typical clinical setting. We have access the entire US VA database of patients treated for prostate cancer and would like to compared baseline characteristics and outcomes with treatment between clinical trials and routine care.
Exposure variables of interest:
Age, race, BMI, PSA at diagnosis, charlson comorbidities/ index (or equivalent), volume of disease (based on imaging), hemoglobin, albumin, creatinine, bilirubin.
Outcome variables of interests:
Duration of treatment, progression free survival, overall survival, rate of change

Project Timeline: 1 year, we plan to start the project after we receive the data and analysis completion date 6 months after and 1 year for the manuscript and to report back to YODA.

Dissemination Plan: Physicians specialized in Oncology, we plan to submit the data to Frontier and Clinical GU, European Oncology Urology.

Bibliography:

Schoen, Martin & Carson, Kenneth & Eisen, Seth & Bennett, Charles & Luo, Suhong & Reimers, Melissa & Knoche, Eric & Whitmer, Alison & Yan, Yan & Drake, Bettina & Sanfilippo, Kristen. (2022). Survival of veterans treated with enzalutamide and abiraterone for metastatic castrate resistant prostate cancer based on comorbid diseases. Prostate Cancer and Prostatic Diseases. 1-8. 10.1038/s41391-022-00588-5.
Schoen, Martin & Carson, Kenneth & Eisen, Seth & Bennett, Charles & Luo, Suhong & Reimers, Melissa & Knoche, Eric & Whitmer, Alison & Yan, Yan & Drake, Bettina & Sanfilippo, Kristen. (2022). Survival of Veterans Treated with Enzalutamide and Abiraterone for Metastatic Castrate Resistant Prostate Cancer based on Comorbid Diseases. 10.21203/rs.3.rs-1624365/v1.
Yoon, Harrison & Luo, Suhong & Sanfilippo, Kristen & Linneman, Travis & Whitmer, Alison & Schoen, Martin. (2022). Statin type and survival of patients with metastatic castrate-resistant prostate cancer receiving abiraterone and enzalutamide: A nationwide retrospective cohort study.. Journal of Clinical Oncology. 40. 50-50. 10.1200/JCO.2022.40.6_suppl.050.
Cheranda, Nina & Luo, Suhong & Riekhof, Forest & Govindan, Srinivas & Sanfilippo, Kristen & Schoen, Martin. (2022). Survival of patients with metastatic prostate cancer and comorbid obesity.. Journal of Clinical Oncology. 40. 116-116. 10.1200/JCO.2022.40.6_suppl.116.
Govindan, Srinivas & Luo, Suhong & Cheranda, Nina & Riekhof, Forest & Schoen, Martin. (2022). Treatment outcomes of patients with metastatic prostate cancer and co-morbid diabetes mellitus.. Journal of Clinical Oncology. 40. 113-113. 10.1200/JCO.2022.40.6_suppl.113.
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