The number of new cases and daily breast cancer-related deaths are projected to increase significantly, even with new treatments. The common way to evaluate treatments is through clinical trials. However, these take about 6 years, cost about £2 million, and often struggle to get participants. Observing globally accepted information in trials therefore becomes challenging. This study seeks to assess the feasibility of using demographic characteristics, diagnoses and symptoms and referral data, linked with cancer registration and treatment data from CPRD to examine measurements which can predict how cancers are progressing and the effect of any treatment.
Specifically, in this study we will examine the patient demographic characteristics, proportion of patients with a breast cancer diagnosis, how often measurements on tumour sizes, quality of life and morbidity-related measurements are taken during follow-up visits for different breast cancer type patients
We will examine anonymized data from patients were over 18 years old and received a breast cancer diagnosis between 2005 and 2018, and are based in England.
The findings from this study will provide insights into what variables and methods will be used for my NIHR Doctoral fellowship application that seeks to improve the efficiency of clinical trials through alternative measurements found in female breast cancer data in registries.
The number of new cases and daily breast cancer-related deaths are projected to increase significantly, even with new treatments. The standard way to evaluate treatments is through clinical trials. However, these take long (average 6 years), are costly (average £2 billion), and face recruitment challenges. This makes observation of globally accepted endpoints in such trials challenging. Emphasis is shifting to alternative measures that provide more information on disease progression and can predict patients’ survival throughout a study. These measures can be found in demographic characteristics, diagnoses and symptoms and referral data, which can be linked to tumours and is available in registries.
Extensive reviews of evidence shows that the use of registry data in trials depends on data structure and completeness. This study will examine the Cancer Registration and Treatment data that is linked with demographic characteristics, diagnoses and symptoms and referral data for possible measurements that can be used as proxies to patient survival, tumour progression and quality of life for different breast cancer patients.
Specifically, we will examine
• Proportion of patients with a diagnosis of the different breast cancer types and their demographic characteristics
• Proportion of patients with at least 2 years of follow-up following diagnosis
• Time between tumor, quality of life and morbidity-related measurements for each breast cancer type
• Size of the tumors for each breast cancer type at diagnosis and at follow-up
Anonymized data from patients who were over 18 years old and received a breast cancer diagnosis between 2005 and 2018, and are based in England will be analysed.
The findings from this study will provide insights into what variables and methods will be used for my NIHR Doctoral fellowship application that seeks to improve the efficiency of clinical trials through alternative measurements found in female breast cancer data in registries.
Type of tumour; tumour size; quality of life; comorbidity score; tumour screening; tumour treatment; cancer stage; demographic characteristics
Dorcas Kareithi - Chief Investigator - Newcastle University
Dorcas Kareithi - Corresponding Applicant - Newcastle University
James Wason - Collaborator - Newcastle University
Jingky Lozano-Kuehne - Collaborator - Newcastle University
NCRAS Tumour / Treatment data