Biological Variability in Ovarian Cancer Incidence and Severity
Principal Investigator
Name
Mariah Malak Bilalaga
Degrees
MBBS
Institution
MedStar Union Memorial Hospital
Position Title
Resident Physician
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1630
Initial CDAS Request Approval
Jul 22, 2024
Title
Biological Variability in Ovarian Cancer Incidence and Severity
Summary
We would like to use the PLCO dataset to explore the incidence and severity of ovarian cancer across different demographic variables including: age, sex, race, ethnicity, BMI, smoking status, medication use, and diabetes.
We will include female adults aged between 55-75 years without history of ovarian cancer and who had ovaries at baseline. Ovarian cancer will be defined based on positive diagnostic/confirmatory test results post-baseline. Severity will be defined based on TNM staging. Covariates include age, sex, race, ethnicity, BMI, smoking status, medication use, and diabetes. Competing risk regression models will be employed to explore the longitudinal associations, accounting for the risk of death. “Don’t know” and “Refuse” responses were treated as missing values and excluded from the regressions. All P values were two-sided but not adjusted for multiple analyses. Cutoff for statistical significance was set at .05. All analyses will be conducted using R software for statistical computing.
We hypothesize that older females, minority groups, and those with higher BMI will have delayed diagnosis, and greater ovarian cancer severity.
We will include female adults aged between 55-75 years without history of ovarian cancer and who had ovaries at baseline. Ovarian cancer will be defined based on positive diagnostic/confirmatory test results post-baseline. Severity will be defined based on TNM staging. Covariates include age, sex, race, ethnicity, BMI, smoking status, medication use, and diabetes. Competing risk regression models will be employed to explore the longitudinal associations, accounting for the risk of death. “Don’t know” and “Refuse” responses were treated as missing values and excluded from the regressions. All P values were two-sided but not adjusted for multiple analyses. Cutoff for statistical significance was set at .05. All analyses will be conducted using R software for statistical computing.
We hypothesize that older females, minority groups, and those with higher BMI will have delayed diagnosis, and greater ovarian cancer severity.
Aims
1- Explore the relationship between baseline demographic variables and incidence of ovarian cancer.
2- Explore the relationship between baseline demographic variables and severity of ovarian cancer.
Collaborators
Greeshma Gaddipati - MEDSTAR UNION MEMORIAL Hospital
Ramya Vasireddy - MEDSTAR UNION MEMORIAL Hospital
Joseph Atarere - MEDSTAR UNION MEMORIAL Hospital
Louay Almidani - JOHNS HOPKINS UNIVERSITY