Tim M Govers1, Maroeska M Rovers2, Marieke T Brands3, Emilie A C Dronkers4, Robert J Baatenburg de Jong4, Matthias A W Merkx3, Robert P Takes5, Janneke P C Grutters2. 1. Department of Operating Rooms, Radboud University Medical Center, Nijmegen, The Netherlands. Electronic address: Tim.Govers@radboudumc.nl. 2. Department of Operating Rooms, Radboud University Medical Center, Nijmegen, The Netherlands; Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands. 3. Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, The Netherlands. 4. Department of Otorhinolaryngology and Head and Neck Surgery, Erasmus University Medical Center, Rotterdam, The Netherlands. 5. Department of Otorhinolaryngology and Head and Neck Surgery, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract
OBJECTIVES: To show how prediction models can be incorporated into decision models, to allow for personalized decisions, and to assess the value of this approach using the management of the neck in early-stage oral cavity squamous cell carcinoma as an example. STUDY DESIGN AND SETTING: In a decision model, three approaches were compared: a "population-based" approach in which patients undergo the strategy that is optimal for the population; a "perfectly predicted" approach, in which each patient receives the optimal strategy for that specific patient; and a "prediction model" approach in which each patient receives the strategy that is optimal based on prediction models. The average differences in costs and quality-adjusted life years (QALYs) for the population between these approaches were studied. RESULTS: The population-based approach resulted on average in 4.9158 QALYs with €8,675 in costs, per patient. The perfectly predicted approach yielded 0.21 more QALYs and saved €1,024 per patient. The prediction model approach yielded 0.0014 more QALYs and saved €152 per patient compared with the population-based approach. CONCLUSION: The perfectly predicted approach shows that personalized care is worthwhile. However, current prediction models in the field of oral cavity squamous cell carcinoma have limited value. Incorporating prediction models into decision models appears to be a valuable method to assess the value of personalized decision making.
OBJECTIVES: To show how prediction models can be incorporated into decision models, to allow for personalized decisions, and to assess the value of this approach using the management of the neck in early-stage oral cavity squamous cell carcinoma as an example. STUDY DESIGN AND SETTING: In a decision model, three approaches were compared: a "population-based" approach in which patients undergo the strategy that is optimal for the population; a "perfectly predicted" approach, in which each patient receives the optimal strategy for that specific patient; and a "prediction model" approach in which each patient receives the strategy that is optimal based on prediction models. The average differences in costs and quality-adjusted life years (QALYs) for the population between these approaches were studied. RESULTS: The population-based approach resulted on average in 4.9158 QALYs with €8,675 in costs, per patient. The perfectly predicted approach yielded 0.21 more QALYs and saved €1,024 per patient. The prediction model approach yielded 0.0014 more QALYs and saved €152 per patient compared with the population-based approach. CONCLUSION: The perfectly predicted approach shows that personalized care is worthwhile. However, current prediction models in the field of oral cavity squamous cell carcinoma have limited value. Incorporating prediction models into decision models appears to be a valuable method to assess the value of personalized decision making.
Authors: Daphne A J J Driessen; Tim Dijkema; Willem L J Weijs; Robert P Takes; Sjoert A H Pegge; Patrik Zámecnik; Adriana C H van Engen-van Grunsven; Tom W J Scheenen; Johannes H A M Kaanders Journal: Front Oncol Date: 2021-02-05 Impact factor: 6.244
Authors: Mirre Scholte; Kas Woudstra; Janneke P C Grutters; Gerjon Hannink; Marcia Tummers; Rob P B Reuzel; Maroeska M Rovers Journal: BMJ Surg Interv Health Technol Date: 2022-09-02
Authors: Robert P Takes; Gyorgy B Halmos; John A Ridge; Paolo Bossi; Matthias A W Merkx; Alessandra Rinaldo; Alvaro Sanabria; Ludi E Smeele; Antti A Mäkitie; Alfio Ferlito Journal: Curr Oncol Rep Date: 2020-07-10 Impact factor: 5.075