title: Dynamic prediction in breast cancer: proving feasibility in clinical practice using the TEAM trial.
abstract: BACKGROUND
  Predictive models are an integral part of current clinical practice and help determine optimal treatment strategies for individual patients. A drawback is that covariates are assumed to have constant effects on overall survival (OS), when in fact, these effects may change during follow-up (FU). Furthermore, breast cancer (BC) patients may experience events that alter their prognosis from that time onwards. We investigated the 'dynamic' effects of different covariates on OS and developed a nomogram to calculate 5-year dynamic OS (DOS) probability at different prediction timepoints (tP) during FU.
  
  
  METHODS
  Dutch and Belgian postmenopausal, endocrine-sensitive, early BC patients enrolled in the TEAM trial were included. We assessed time-varying effects of specific covariates and obtained 5-year DOS predictions using a proportional baselines landmark supermodel. Covariates included age, histological grade, hormone receptor and HER2 status, T- and N-stage, locoregional recurrence (LRR), distant recurrence, and treatment compliance. A nomogram was designed to calculate 5-year DOS based on individual characteristics.
  
  
  RESULTS
  A total of 2602 patients were included (mean FU 6.2 years). N-stage, LRR, and HER2 status demonstrated time-varying effects on 5-year DOS. Hazard ratio (HR) functions for LRR, high-risk N-stage (N2/3), and HER2 positivity were HR = (8.427 × 0.583[Formula: see text], HR = (3.621 × 0.816[Formula: see text], and HR = (1.235 × 0.851[Formula: see text], respectively. Treatment discontinuation was associated with a higher mortality risk, but without a time-varying effect [HR 1.263 (0.867-1.841)]. All other covariates were time-constant.
  
  
  CONCLUSIONS
  The current nomogram accounts for elapsed time since starting adjuvant endocrine treatment and optimizes prediction of individual 5-year DOS during FU for postmenopausal, endocrine-sensitive BC patients. The nomogram can facilitate in determining whether further therapy will benefit an individual patient, although validation in an independent dataset is still needed.
authors: Department of Surgery.
authors: D B Y
authors: DBY
authors: Fontein
authors: Department of Medical Statistics.
authors: M
authors: M
authors: Klinten Grand
authors: Department of Medical Oncology, Leiden University Medical Center, Leiden.
authors: J W R
authors: JWR
authors: Nortier
authors: Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
authors: C
authors: C
authors: Seynaeve
authors: Department of Surgery.
authors: E
authors: E
authors: Meershoek-Klein Kranenbarg
authors: Department of Medical Oncology, Academisch Ziekenhuis Sint-Augustinus Antwerp, Antwerp, Belgium.
authors: L Y
authors: LY
authors: Dirix
authors: Department of Surgery.
authors: C J H
authors: CJH
authors: van de Velde
authors: Department of Medical Statistics. Electronic address: h.putter@lumc.nl.
authors: H
authors: H
authors: Putter
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018931:Antineoplastic Agents, Hormonal / Q000009:adverse effects / Q000627:therapeutic use*; D001530:Belgium; D014408:Biomarkers, Tumor / Q000032:analysis; D001943:Breast Neoplasms / Q000737:chemistry / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D017024:Chemotherapy, Adjuvant; D003661:Decision Support Techniques; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008408:Mastectomy / Q000009:adverse effects / Q000401:mortality; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local; D009367:Neoplasm Staging; D009426:Netherlands; D049451:Nomograms; D018579:Patient Selection; D011237:Predictive Value of Tests; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000032:analysis; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D018931:Antineoplastic Agents, Hormonal; D014408:Biomarkers, Tumor; D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human
keywords: breast cancer; dynamic prediction; landmark analysis; personalized therapy; survival probability