How to Develop Statistical Predictive Risk Models in Oncology Nursing to Enhance Psychosocial and Supportive Care

TY - JOUR

T1 - How to Develop Statistical Predictive Risk Models in Oncology Nursing to Enhance Psychosocial and Supportive Care

AU - Harris, Jenny

AU - Purssell, Edward

AU - Ream, Emma

AU - Jones, Anne

AU - Armes, Jo

AU - Cornelius, Victoria

PY - 2020/12

Y1 - 2020/12

N2 - Objectives: Predictive risk models are advocated in psychosocial oncology practice to provide timely and appropriate support to those likely to experience the emotional and psychological consequences of cancer and its treatments. New digital technologies mean that large scale and routine data collection are becoming part of everyday clinical practice. Using these data to try to identify those at greatest risk for late psychosocial effects of cancer is an attractive proposition in a climate of unmet need and limited resource. In this paper, we present a framework to support the development of high-quality predictive risk models in psychosocial and supportive oncology. The aim is to provide awareness and increase accessibility of best practice literature to support researchers in psychosocial and supportive care to undertake a structured evidence-based approach. Data Sources: Statistical prediction risk model publications. Conclusion: In statistical modeling and data science different approaches are needed if the goal is to predict rather than explain. The deployment of a poorly developed and tested predictive risk model has the potential to do great harm. Recommendations for best practice to develop predictive risk models have been developed but there appears to be little application within psychosocial and supportive oncology care. Implications for Nursing Practice: Use of best practice evidence will ensure the development and validation of predictive models that are robust as these are currently lacking. These models have the potential to enhance supportive oncology care through harnessing routine digital collection of patient-reported outcomes and the targeting of interventions according to risk characteristics.

AB - Objectives: Predictive risk models are advocated in psychosocial oncology practice to provide timely and appropriate support to those likely to experience the emotional and psychological consequences of cancer and its treatments. New digital technologies mean that large scale and routine data collection are becoming part of everyday clinical practice. Using these data to try to identify those at greatest risk for late psychosocial effects of cancer is an attractive proposition in a climate of unmet need and limited resource. In this paper, we present a framework to support the development of high-quality predictive risk models in psychosocial and supportive oncology. The aim is to provide awareness and increase accessibility of best practice literature to support researchers in psychosocial and supportive care to undertake a structured evidence-based approach. Data Sources: Statistical prediction risk model publications. Conclusion: In statistical modeling and data science different approaches are needed if the goal is to predict rather than explain. The deployment of a poorly developed and tested predictive risk model has the potential to do great harm. Recommendations for best practice to develop predictive risk models have been developed but there appears to be little application within psychosocial and supportive oncology care. Implications for Nursing Practice: Use of best practice evidence will ensure the development and validation of predictive models that are robust as these are currently lacking. These models have the potential to enhance supportive oncology care through harnessing routine digital collection of patient-reported outcomes and the targeting of interventions according to risk characteristics.

KW - Cancer

KW - Distress

KW - Predictive risk models

KW - Psychological

KW - Psychosocial

KW - Regression models

KW - Supportive care

UR - http://www.scopus.com/inward/record.url?scp=85096541965&partnerID=8YFLogxK

U2 - 10.1016/j.soncn.2020.151089

DO - 10.1016/j.soncn.2020.151089

M3 - Review article

AN - SCOPUS:85096541965

SN - 0749-2081

VL - 36

JO - Seminars in Oncology Nursing

JF - Seminars in Oncology Nursing

IS - 6

M1 - 151089

ER -