Multi-state Model for the Estimation of Overdiagnosisin the Breast Cancer Screening Program

Wendy Yi-Ying Wu

Department of Radiation Sciences, Oncology

Screening detects a breast cancer that would not have presented clinically in the woman's lifetime in the absence of screening

Overdiagnosis

Screening

Prolong lifeTreatmentPictures from Duivenvoorden HM et al., (2018) PlosONE, 13(7):e0201370Tabar L et al., (2005). Breast Cancer, Thieme

Qantification is difficult!Estimates varies from 0-56% (EUROSCREEN)

4

Control group?

Evaluation method?

Lead time adjustment?

Useful method for service-screening? Definition?

Denominators?

Illustration

5

Can

cer

prog

ress

ion

(Siz

e) Subject 1

Became detectable

Symptomatic

Time

Subject 2

BC: Breast cancer, PCDP: Preclinical screen-detected phase, CP: Clinical phase, SD: Screen-detected case

PCDP

Free of BC

CP

Non-progressive PCDP

screening

SD

Sojourn time for subject 1

Multi-state model

6

S1 1S1-S 1-S

: transitionrate from state to state at time t, S: test sensitivityBC: breast cancer, PCDP: preclinical screen-detectable phase, CP: clinical phase

Observed state Y(t)

Normal finding(Observed state 1)

Screen-detected cases

(Observed state 2)

Clinically detected cases

(Observed state 3)

Free of BC(State 1)

Progressive PCDP(State 2)

Non-progressive PCDP(State 4)

CP(State 3)Invisible

latent state X(t)

Screening registry

• PID• Date of

invitation• Date of screening• Screening results• Follow-up

(recall) results

• PID• Date of

diagnosis• Biopsy • Pathological

result

Cancer registry

Observed states{Y(t)}

Likelihood function

Estimate parameters

Calculate the expected number of

detected non-progressive BC

Data sources and work process

• Panel data (longitudinal follow-up data)o State only observed at a finite number of times (interval

censoring)o Don’t know the state between times

Screening Data

• Left truncationo Only the asymptomatic subjects will be invited

• Time-inhomogeneous modelo The incidence rate depends on the age

• Measurement erroro Sensitivity ≠ 100%

• Informative censoring ??o The subjects with shorter time to clinical phase tend to be

clinically detected if the participation rate is low

Screening DataConditional probability

Piece-wise rate/ parametric

Hidden Markov model

EM algorithm??

11Conclusion: Both methods can provide a proper estimate

True Cumulative incidence method

Multi-state model

Randomized controlled trial

1: 1 ratio screened and control, 100% participation rate

Control group provided information to stabilize the model. (help to deal with the identifiability problem)

Control group in the service screening program?

• Never-attenders (who were invited but never participated in the screening program) can provide the similar information under certain conditions, i.e. similar incidence rate

o Group A: those who will never attend any round of screeningo Group B: those who might attend the screening but does not

participate in the current/previous rounds Shorter time to CP few rounds of invitationmore clinical

cases (observation process and disease process are not independent= informative censoring??)

Population

Screened cohort

Controls / never-

attenders

PNA

1-PNA

1st screening

attenders

Not attenders

Women Invited

PPART

1- PPART

Women Invited

SD IN

NP

2nd screening

…

Women without

BC

NPSD: screen-detected, IN: interval cancer, NP: non-participant

Simulation results14

12

( )( )trt

Non-homogeneous model Homogeneous model

A naive method (removing all the never-attenders)

Given on PNA=0.1

Non-homogeneous model Homogeneous model

Future studiesMethod

• Develop a flexible multi-state model

• Age, period or cohort effect

• Informative censoring

Sweden

• Hormone replacement therapy

• Digital mammography

• Different evaluation methods

Collaboration

• Norway

• Finland

• …

THANKS FOR YOUR ATTENTION

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• Acknowledgemento Umeå University

Håkan Jonsson Lennarth Nyström

o Stockholm-Gotland Regional Cancer Centre Sven Törnberg Klara Miriam Elfström Anna Stoltenberg

Contact information:wendy.wu@umu.se