title: Clarifying the debate on population-based screening for breast cancer with mammography: A systematic review of randomized controlled trials on mammography with Bayesian meta-analysis and causal model.
abstract: BACKGROUND
  The recent controversy about using mammography to screen for breast cancer based on randomized controlled trials over 3 decades in Western countries has not only eclipsed the paradigm of evidence-based medicine, but also puts health decision-makers in countries where breast cancer screening is still being considered in a dilemma to adopt or abandon such a well-established screening modality.
  
  
  METHODS
  We reanalyzed the empirical data from the Health Insurance Plan trial in 1963 to the UK age trial in 1991 and their follow-up data published until 2015. We first performed Bayesian conjugated meta-analyses on the heterogeneity of attendance rate, sensitivity, and over-detection and their impacts on advanced stage breast cancer and death from breast cancer across trials using Bayesian Poisson fixed- and random-effect regression model. Bayesian meta-analysis of causal model was then developed to assess a cascade of causal relationships regarding the impact of both attendance and sensitivity on 2 main outcomes.
  
  
  RESULTS
  The causes of heterogeneity responsible for the disparities across the trials were clearly manifested in 3 components. The attendance rate ranged from 61.3% to 90.4%. The sensitivity estimates show substantial variation from 57.26% to 87.97% but improved with time from 64% in 1963 to 82% in 1980 when Bayesian conjugated meta-analysis was conducted in chronological order. The percentage of over-detection shows a wide range from 0% to 28%, adjusting for long lead-time. The impacts of the attendance rate and sensitivity on the 2 main outcomes were statistically significant. Causal inference made by linking these causal relationships with emphasis on the heterogeneity of the attendance rate and sensitivity accounted for the variation in the reduction of advanced breast cancer (none-30%) and of mortality (none-31%). We estimated a 33% (95% CI: 24-42%) and 13% (95% CI: 6-20%) breast cancer mortality reduction for the best scenario (90% attendance rate and 95% sensitivity) and the poor scenario (30% attendance rate and 55% sensitivity), respectively.
  
  
  CONCLUSIONS
  Elucidating the scenarios from high to low performance and learning from the experiences of these trials helps screening policy-makers contemplate on how to avoid errors made in ineffective studies and emulate the effective studies to save women lives.
authors.affiliation: Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei Department of Health Industry Management, School of Healthcare Management, Kainan University, Tao-Yuan, Taiwan BC Women's Hospital, Vancouver, British Columbia Department of Health Care Management, College of Management, Chang Gung University, Tao-Yuan Cheng Ching General Hospital, Taichung, Taiwan Department of Preventive Medicine, College of Medicine, Catholic University of Korea, Seoul, Korea Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore Screening Assessment & Management Division, Research Center for Cancer Prevention and Screening, National Cancer Center, Tokyo, Japan Department of Epidemiology, Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand Cancer Screening Assessment and Management Division, Research Center for Cancer Prevention and Screening, National Cancer Center, Tokyo, Japan School of Public Health, Makassar University, Makassar, Indonesia Community Treatment Centre, Universiti Malaysia Sabah, Sabah, Malaysia Department of Epidemiology, Tianjin Colorectal and Anal Disease Research Institute, Tianjin, China.
authors.forename: Tony Hsiu-Hsi
authors.initials: TH
authors.lastname: Chen
authors.forename: Amy Ming-Fang
authors.initials: AM
authors.lastname: Yen
authors.forename: Jean Ching-Yuan
authors.initials: JC
authors.lastname: Fann
authors.forename: Paula
authors.initials: P
authors.lastname: Gordon
authors.forename: Sam Li-Sheng
authors.initials: SL
authors.lastname: Chen
authors.forename: Sherry Yueh-Hsia
authors.initials: SY
authors.lastname: Chiu
authors.forename: Chen-Yang
authors.initials: CY
authors.lastname: Hsu
authors.forename: King-Jen
authors.initials: KJ
authors.lastname: Chang
authors.forename: Won-Chul
authors.initials: WC
authors.lastname: Lee
authors.forename: Khay Guan
authors.initials: KG
authors.lastname: Yeoh
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Saito
authors.forename: Supannee
authors.initials: S
authors.lastname: Promthet
authors.forename: Chisato
authors.initials: C
authors.lastname: Hamashima
authors.forename: Alimin
authors.initials: A
authors.lastname: Maidin
authors.forename: Fredie
authors.initials: F
authors.lastname: Robinson
authors.forename: Li-Zhong
authors.initials: LZ
authors.lastname: Zhao
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis* / Q000401:mortality; D006801:Humans; D008327:Mammography; D008403:Mass Screening; D000069342:Medical Overuse; D010342:Patient Acceptance of Health Care; D016032:Randomized Controlled Trials as Topic; D012680:Sensitivity and Specificity