Overdiagnosis in. Outline

Transcription

1 Overdiagnosis in Mammography Screening Jean Ching-Yuan Fann, Huei-Shian Tsau, Chen-Yang Hsu, King-Jen Chang, Amy Ming-Fang Yen, Cheng-Ping Yu, Sam Li-Sheng Chen, Wen-Hung Kuo, László Tabár, Sherry Chiu, Hsiu-Hsi Chen -Sep-28 Outline Mammographic Screening for Breast Cancer Fallacy on Overdiagnosis Overdiagnosis in Taiwanese Randomized Controlled Trial Methodology for Estimating Overdiagnosis Personalized Probabilistic Cost-Effectiveness Analysis 2

2 Meta-analyses: UK Independent 22 Lancet Average effect: 2% mortality reduction 3 4 2

3 Cumulative Incidence (per ) Fallacy in BC mass screening. Short follow-up time: without lead-time consideration 2. Breast Cancer mixed: diagnosed before screening program, but died after program implementation With an average follow-up of 2.2 years BMJ, 2 Mixed up lead-time and over-detection Jørgensen et al., 29 Lead-time period (lead-time bias) 5 Overdiagnosis with mammography in Taiwan based on the Taiwanese randomized controlled trial for young women Eligible Population Randomization N=2,4 N=2,87 N=39,563 M U M U U M U M Control Arm M: Mammography U: Ultrasound Control Group Screen Group (M-U + U-M) RR=.3 ( ) Time since randomization (month) 6 3

4 Overdiagnosis with mammography in Taiwan based on the Taiwanese Population-based service screening 26 JAMA Oncology Total Incidence of breast cancer Mammography vs CBE: RR=.3 (95% CI:.8-.8) Over-detection: 3% 7 4

5 Methodology for Estimating Overdiagnosis. Graphic method 2. Zero-inflated model 3. Coxian Phase-Type Markov Process 9. Graphic method Curved method by comparing cumulative incidence of cancer Non-advanced cancer cancer Upper limit All over-detection arise from Non-advanced cancer (Long follow up time) advanced cancer Chen et al.,27 Screen Works! Lower limit All detected cancer became advanced cancer(no over-detection) 5

6 Survival probabiltiy Assessing overdetection in breast cancer screening using data on randomized controlled trial Chen et al.,27 Medicine Follow-up time 2. Zero-inflated model Survival of Breast Cancer, Darlana, Sweden Without consideration of over-diagnosis.9 9% Survival adjusted for prognostic factors Year since diagnosis 6

7 Survival probability Zero-inflated Poisson regression model and overdiagnosis rate Variable Count part RR/OR (95% CI) RR P-value Intercept Size, mm.5-4 vs (.76-8.) 5-9 vs ( ) 2-29 vs ( ) 3+ vs ( ) Node (+) vs. (-) 2.4( ).5 Grade 3 vs /2.62( ).8 Surgery MA vs. BCS.92( ).7 Triple Negative Yes vs No 2.49( ).3 Chemotherapy Yes vs. No.79( ).456 Radiotherapy Yes vs. No.23( ).568 Tamoxifen Yes vs. No.95( ).847 Zero part OR Intercept Detection mode.4 SD vs. RF 2.38( ) IC vs. RF.23( ) = 56.4% SD: 66.4% Overdiagnosis, 8.9% IC: 5.5% Awareness, 2.9% RE: 45.4% Treatment effect Pr(Y = y; μ, π) π + π e μ when y = = π e μ μ y y! when y =,2,3 Count Part: Poisson regression for μ Zero Part: Logistic regression for π Survival adjusted for prognostic factors Survival considering Overdiagnosis 9% 75% Year since diagnosis 3. Coxian Phase-Type Markov Process Applying the concept of cured model: S t = S P t π + S NP t π For exponentially distributed random variable exp α t = π exp α P t + π π = exp α t exp α P t exp α P t 4 7

8 Estimated natural history of breast cancer with and without consideration of over-detection, Swedish Two-County Trial (Kopparberg) Transition rate π = 2.6% MST: mean sojourn time 5 Probabilistic CEA of Personalized Breast Cancer Screening Population risk stratification for trade-off between harm and benefit Time preference for screening policy and outcome 6 8

9 Risk stratification: The recommend age to begin screening and inter-screening interval for screening by percentiles of risk score Age to begin screening 75 Inter-screening Interval Intensive (Percentile) Early commencing Low Risk High Risk Economic Evaluation Acceptability curve of primary and secondary breast cancer prevention for non-brca Carrier.9 2 GDP Annual mammography Biennial mammography Triennial mammography Risk-based screening interval

10 Acceptability curve of primary and secondary prevention of breast cancer for BRCA-carrier women GDP 2 GDP 3 GDP Annual Mammography Annual MRI+ mammography Preventive Surgery, Annual mammography Preventive Surgery, Annual MRI+ mammography Chemoprevention, annual mammography Chemoprevention, annual MRI +mammography Conclusion The estimated proportion of over-diagnosis cases is affected by lead-time, sensitivity, and follow-up time, which causes the large disparity of over-detection across studies. - Methodological flaws Use high-quality design-based study and model-based approach Probabilistic CEA for personalized screening policy is strongly recommended 2

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