Cancer Survival Programme and Early Diagnosis of Cancer

Date of Approval
Application Number
16_011
Technical Summary

The main research objective is to produce population-based evidence for improving diagnosis and survival for cancer patients. Focusing on the pre-diagnostic phase and, on adult bowel cancer and brain tumours in children and young adults, we aim to evaluate the roles played by primary-care symptomatic presentations on (i) first, the risk of being diagnosed with the tumour during emergency admission, then, on (ii) the extend of the tumour and on survival from the tumour.

We will use an up-to-date version of linked datasets combining the national cancer data repository and CPRD. We will extract from that the relevant cancer patients with at least one year of CPRD records prior to cancer diagnosis.

We will primarily model the association between emergency diagnosis and symptoms/clusters of symptoms using logistic regression analysis, and the association between emergency diagnosis and consultation rates for relevant symptoms, using Poisson regression. We will account for patients’ socio-demographic and clinical characteristics. Random effect will be considered to account for patient-level clustering of repeated symptoms. We will then estimate the associations of the symptoms patterns identified at this step with the extent of the disease and the survival from the disease, using multinomial/logistic regression and excess hazard model, respectively.

Collaborators

Bernard Rachet - Chief Investigator - London School of Hygiene & Tropical Medicine ( LSHTM )
Cristina Renzi - Collaborator - University College London ( UCL )
Krishnan Bhaskaran - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Michel Coleman - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Rachael Williams - Collaborator - CPRD
Thomas Chu - Collaborator - London School of Hygiene & Tropical Medicine ( LSHTM )
Timothy Card - Collaborator - University of Nottingham

Linkages

NCRAS Cancer Registration Data;Other