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  string(91) "Individual Participant-Level Data, which includes Full CSR and all supporting documentation"
  ["property_scientific_abstract"]=>
  string(1875) "Background: Clinical trials are a critical part of the development of novel cancer treatments and patient reported quality-of-life (QOL) is an important outcome to assess. QOL can be used as a surrogate indicator for the tolerability of therapy. However, QOL is a complex construct that is related not only to treatment-related adverse events (AEs) and physical symptoms but can also be impacted by other factors. No large-scale studies have correlated AE severity and change in QOL scores.

Objectives: Among patients with cancer receiving hormonal therapy, immunotherapy, or chemotherapy as part of a phase II-III randomized clinical trial captured in the YODA collaborative, our objectives are to correlate change in overall QOL score with the occurrence of a treatment-related AE, assess which QOL domains are most significantly affected by treatment-related AEs, and to explore how the relationship between QOL and AEs is affected by disease site, gender, treatment stage, performance status, treatment response, and treatment blinding. We will also correlate AE and QOL with overall survival.

Methods: This will be a meta-analysis of individual patient data from phase II-III randomized controlled trials. Overall QOL scores will be compared by maximum grade AE. Change in QOL score will be compared based on AE grade groups using the Wilcoxon rank-sum test. Correlation between QOL score and AE will be assessed using Spearman correlational coefficient and the Bland-Altman approach. Subgroup and sensitivity analyses will be completed by primary cancer site, gender, performance status, and treatment response.

Significance: Understanding impact of AEs on overall QOL scores and specific QOL domains will allow us to better manage the effect of treatment-related AEs as well as improve patient-physician communication and the quality of patient care."
  ["project_brief_bg"]=>
  string(2233) "Clinical trials are a critical part of the development of novel cancer treatments. In addition to efficacy and survival, patient reported quality-of-life (QOL) is an important outcome. It may seem intuitive that QOL scores should be affected by adverse events (AEs) and, indeed, QOL scores can sometimes be used as a surrogate indicator for the tolerability of therapy. However, QOL is a complex construct that is related not only to treatment-related AEs and physical symptoms but can also be impacted by other factors including patient expectation, performance status, gender, as well as psychosocial determinants1-4.

During the conduct of clinical trials, collection and classification of AEs is traditionally done as part of a standardized toxicity assessment tool and presented on an ordinal scale5. The amount of distressed caused by the AE is not specifically collected. QOL score can be a potential method to gauge the impact of an AE during cancer treatment; however, the impact of an AE on QOL has been poorly characterized. No large-scale studies have correlated AE severity and change in QOL scores. 

It has been shown that regular assessment of patient QOL can independently improve survival6. Understanding impact of AEs on overall QOL scores and specific QOL domains will allow us to better manage the effect of treatment-related AEs as well as improve patient-physician communication and the quality of patient care. In the setting of advanced cancer, the attribution of the AE to anti-cancer treatment, as opposed to the disease itself, can also be a challenge. Thus, exploring the relationship between AE and QOL based on assigned treatment group is also of interest. 

Patient reported QOL and treatment related AEs are rigorously monitored during the conduct of any clinical trial. Therefore, meta-analysis of individualized patient data from well-conducted cancer clinical trials in the YODA project will enable us to explore the relationship between AE and QOL. 

Understanding impact of AEs on overall QOL scores and specific QOL domains will allow us to better manage the effect of treatment-related AEs as well as improve patient-physician communication and the quality of patient care."
  ["project_specific_aims"]=>
  string(853) "Among patients with cancer receiving hormonal therapy or immunotherapy as part of a phase II-III comparative clinical trial captured in the YODA collaborative, our objectives are to:

1.	To correlate change in overall QOL score (from baseline) with the occurrence of a treatment-related AE at 3- (primary outcome), 6-, and 12- months (secondary outcomes).

2.	To assess which QOL domains are most significantly affected by treatment-related AEs (secondary outcome).

3.	To explore how the relationship between QOL and AEs is affected by disease site, gender, treatment stage, performance status, treatment response, and treatment blinding (subgroup analysis).

4.	To explore the relationship between AEs and overall survival (OS) (exploratory analysis).

5.	To explore the relationship between QOL and OS (exploratory analysis)."
  ["project_study_design"]=>
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      ["label"]=>
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    ["label"]=>
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  ["project_research_methods"]=>
  string(423) "We will include all phase II-III randomized cancer-based clinical trials using hormonal therapy, immunotherapy, or chemotherapy. Patients with who have available and complete QOL and adverse event data on at least one follow-up assessment will be included. Patients who do not have any captured QOL data will be excluded. Incomplete QOL assessments (where calculating an overall QOL score is not possible) will be excluded."
  ["project_main_outcome_measure"]=>
  string(1128) "Overall QOL scores will be compared by maximum grade AE at 3- (primary outcome), 6-, and 12-months (secondary outcomes). Given that the most common AEs may vary depending on the disease site and treatment agent, all types of AEs will be considered, but the AE with the highest grade (using the Clavien Dindo scoring system) will be used as the primary predictor in the model. Change in QOL score will be compared based on maximal AE grade group using the Wilcoxon rank-sum test. Correlation between QOL score and AE will be assessed using Spearman correlational coefficient where low correlation, 0.10 to 0.29; moderate correlation, 0.30 to 0.49; and high correlation, > 0.50 and the Bland-Altman approach. Subgroup analysis will be completed as stated above based on disease site, gender, treatment stage, performance status, treatment response, and treatment blinding. We will assess which QOL domains are most significantly affected by AEs. We will explore the impact of experiencing any grade III-IV AE (at any time) and the impact of multiple grade III-IV AEs on OS. We will explore the impact of change in QOL score on OS."
  ["project_main_predictor_indep"]=>
  string(81) "Most severe AE at each time period (graded according to the Clavien Dindo system)"
  ["project_other_variables_interest"]=>
  string(448) "Due to potential variability of the type of AE based on patient and disease characteristics, subgroup analysis will be completed by:

-	Primary disease site

-	Type of agent received

-	Localized or metastatic disease

-	Gender

-	Age

-	Baseline comorbidities

-	Baseline performance status score (ECOG 0 vs 1 vs 2)

-	Treatment response (i.e. responders vs non-responders)

-	Open label versus blinded"
  ["project_stat_analysis_plan"]=>
  string(918) "Summary statistics will be presented to describe the baseline characteristics of included patients. Demographic variables that are continuous and normally distributed will be reported as mean with standard deviation. Demographic variables that are continuous and not normally distributed will be reported as median with interquartile range. Categorical variables will be presented as numbers with percentages. Overall QOL scores will be compared by maximum grade AE at each time interval as categorized using the Clavien Dindo system. Change in QOL score will be compared based on AE grade groups using the Wilcoxon rank-sum test. Correlation between QOL score and AE will be assessed using Spearman correlational coefficient to assess the strength of linear associations and agreement where low correlation, 0.10 to 0.29; moderate correlation, 0.30 to 0.49; and high correlation, > 0.50 and the Bland-Altman approach."
  ["project_timeline"]=>
  string(230) "Date	Objective

March ? October 2022	Project submission

November 2022 ? January 2023	Data cleaning

February ? April 2023	Data analysis

May 2023  Manuscript drafting

June 2023	Submission for publication"
  ["project_dissemination_plan"]=>
  string(165) "Our results will be presented at local, national, and international cancer meetings. The results of this project will be published in a high-impact oncology journal."
  ["project_bibliography"]=>
  string(1341) "
1.	Rodrguez AM, Mayo NE, Gagnon B. Independent contributors to overall quality of life in people with advanced cancer. Br J Cancer. May 14 2013;108(9):1790-800. doi:10.1038/bjc.2013.146

2.	Huschka MM, Mandrekar SJ, Schaefer PL, Jett JR, Sloan JA. A pooled analysis of quality of life measures and adverse events data in north central cancer treatment group lung cancer clinical trials. Cancer. Feb 15 2007;109(4):787-95. doi:10.1002/cncr.22444

3.	Atherton PJ, Watkins-Bruner DW, Gotay C, et al. The Complementary Nature of Patient-Reported Outcomes and Adverse Event Reporting in Cooperative Group Oncology Clinical Trials: A Pooled Analysis (NCCTG N0591). J Pain Symptom Manage. Oct 2015;50(4):470-9.e9. doi:10.1016/j.jpainsymman.2015.04.016

4.	Niska JR, Thorpe CS, Halyard MY, et al. Patient-reported quality-of-life outcomes in relation to provider-assessed adverse events during head and neck radiotherapy. J Patient Rep Outcomes. Jul 16 2020;4(1):60. doi:10.1186/s41687-020-00227-4

5.	Institute NC. Common Terminology Criteria for Adverse Events. Accessed January 4, 2022.

6.	Basch E, Deal AM, Dueck AC, et al. Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment. JAMA. 07 11 2017;318(2):197-198. doi:10.1001/jama.2017.7156
"
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    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(10464)
    ["id"]=>
    int(10464)
    ["title"]=>
    string(36) "yoda_project_review_-_2022-4974_site"
    ["filename"]=>
    string(40) "yoda_project_review_-_2022-4974_site.pdf"
    ["filesize"]=>
    int(1769128)
    ["url"]=>
    string(89) "https://yoda.yale.edu/wp-content/uploads/2023/08/yoda_project_review_-_2022-4974_site.pdf"
    ["link"]=>
    string(82) "https://yoda.yale.edu/data-request/2022-4974/yoda_project_review_-_2022-4974_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1363"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(36) "yoda_project_review_-_2022-4974_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(5936)
    ["date"]=>
    string(19) "2023-08-09 16:57:33"
    ["modified"]=>
    string(19) "2023-08-09 19:12:36"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["search_order"]=>
  string(5) "-8190"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(1) {
  [0]=>
  string(6) "Canada"
}


pi affil
array(1) {
  [0]=>
  string(8) "Academia"
}


products
array(5) {
  [0]=>
  string(6) "zytiga"
  [1]=>
  string(8) "yondelis"
  [2]=>
  string(8) "darzalex"
  [3]=>
  string(9) "imbruvica"
  [4]=>
  string(7) "erleada"
}


num of trials
array(1) {
  [0]=>
  string(2) "15"
}


res
array(1) {
  [0]=>
  string(1) "3"
}