ASSESSMENT OF LEAD-TIME BIAS IN ESTIMATES OF RELATIVE SURVIVAL FOR BREAST CANCER

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1 ASSESSMENT OF LEAD-TIME BIAS IN ESTIMATES OF RELATIVE SURVIVAL FOR BREAST CANCER Therese M-L Andersson 1, Mark Rutherford 2, Keith Humphreys 1 1 Karolinska Institutet, Stockholm, Sweden 2 University of Leicester, Leicester, Great Britain

2 Cancer patient survival Population-based cancer patient survival is an important measure for the evaluation of cancer patient care Often measured using relative survival rrrrrrrrrrrrrrrr ssssssssssssssss = oooooooooooooooo aaaaaa cccccccccc ssssssssssssssss eeeeeeeeeeeeeeee ssssssssssssssss Interpreted as the net survival, the proportion of patients alive if there would be no other causes of death Often summarised by the 1-, 5- or 10-year relative survival ratio (RSR) Therese M-L Andersson

3 Cancer patient survival Temporal trends in RSR can be useful for evaluating the impact of changes in cancer care on the prognosis of cancer patients Problematic for cancer sites where screening has been introduced due to the potential of lead-time bias Even so, RSRs are often presented for breast cancer, a site where screening has led to early diagnosis, with the assumption that the lead-time bias is small We have performed a simulation study based on a natural history model of breast cancer tumor growth to evaluate the effect of lead-time bias due to mammography screening on RSR Therese M-L Andersson

4 Lead time Interpreting trends in cancer patient survival. Dickman PW, Adami H-O. Journal of Internal Medicine Therese M-L Andersson

5 Why use a simulation approach If we would compare the RSR in a cohort without screening to a cohort with screening, we would not be able to know how much of the difference is due to lead-time By simulating data we know the truth within those data, and can therefore draw conclusions about lead-time bias By assuming that the earlier diagnosis due to screening does not change the prognosis, we know that the only difference in our simulated data is due to lead-time Since we assume no change in prognosis, we are not drawing any conclusions about the effectiveness of screening Therese M-L Andersson

6 Simulation strategy Detectable Screen detection Symptomatic detection Death due to breast cancer Birth Death due to other causes Therese M-L Andersson

7 Simulation strategy Birth cohorts of size , from 1870 to 1965 About 1/5 of the average female birth cohort in Sweden Death due to causes other than breast cancer was simulated based on death rates in Swedish women, by age and year Incidence rates in 5-year age groups in year 1973 used as basis for age-dependent probability of breast cancer onset, with 10 year lag Times between cancer onset and diagnosis varied between individuals Therese M-L Andersson

8 Simulation strategy Time to symptomatic detection (age at diagnosis), and tumor size at detection simulated from a tumor growth model Exponential growth with gamma distributed inverse growth rate: VV tt = VV 0 exp tt rr, rr ~ GGGGGGGGGG(τ 1, τ 2 ) Time to symptomatic detection follows a hazard funtion that depends on size: PP TT dddddd tt, tt + dddd TT dddddd > tt = ηvv tt dddd + oo(dddd) Data from a Swedish regional quality register (years ) used for estimating the parameters (η, τ 1 = τ 2 ) Therese M-L Andersson

9 Simulation strategy Survival from breast cancer from Weibull model including linear effect of age, with the hazard as: h tt = γαtt α 1 ee ρ aaaaaa The parameters for the Weibull model (γ, α, ρ) were estimated using data from the Swedish Cancer Registry (years ) 3 different scenarios of breast cancer survival was used (low, moderate and high), the point estimate and 95% CI of γ & α The final age at death for each individual is the minimum of age at death due to breast cancer and death due to other causes Therese M-L Andersson

10 Simulation strategy Screening every 2 nd year, for ages 40-74, with 100% participation Screening sensitivity (where dd is size of tumor): exp(β 1 + β 2 dd) 1 + exp(β 1 + β 2 dd) Screening sensitivity parameters (β 1, β 2 ) from a paper by Abrahamsson and Humphreys 3 different scenarios of screening sensitivity (low, moderate and high), the point estimate and 95% CI of β 1 & β 2 Therese M-L Andersson

11 Methods Simulated 9 settings, with 3 levels of breast cancer survival and 3 different screening sensitivities (low, moderate and high) 200 simulations for each setting 1-, 5- and 10-year age-standardised RSRs estimated for each of the 9 simulated settings, both in the absence and presence of screening, for diagnoses in the calendar period Since screening only changes the time of diagnosis, and not death, the difference is only due to lead-time Calculated absolute and relative bias Therese M-L Andersson

12 Results Breast Cancer Survival Screening sensitivity # diagnosed cases % screen detected Mean leadtime (years) Median leadtime (years) Moderate Low Moderate Moderate Moderate High Therese M-L Andersson

13 Results Breast Cancer Survival Screening sensitivity 1-year RSR no screening 1-year RSR screening Absolute bias Relative bias % Low Low Low Moderate Low High Moderate Low Moderate Moderate Moderate High High Low High Moderate High High Therese M-L Andersson

14 Results Breast Cancer Survival Screening sensitivity 5-year RSR no screening 5-year RSR screening Absolute bias Relative bias % Low Low Low Moderate Low High Moderate Low Moderate Moderate Moderate High High Low High Moderate High High Therese M-L Andersson

15 Results Breast Cancer Survival Screening sensitivity 10-year RSR no screening 10-year RSR screening Absolute bias Relative bias % Low Low Low Moderate Low High Moderate Low Moderate Moderate Moderate High High Low High Moderate High High Therese M-L Andersson

16 Conclusion The largest bias, both absolute and relative, was observed for the 5-year RSR The largest absolute bias was 5.7 percentage points and the largest relative bias was 8.4% The possibility of lead-time bias should not be neglected when interpreting trends in breast cancer survival or differences between groups or countries However, it is also not likely to have a great impact on survival estimates. Therese M-L Andersson

17 References Assessment of lead-time bias in estimates of relative survival for breast cancer. TM-L Andersson, MJ Rutherford, K Humphreys. Cancer Epidemiology A statistical model of breast cancer tumour growth with estimation of screening sensitivity as a function of mammographic density. L Abrahamsson, K Humphreys. Statistical methods in medical research A natural history model of stage progression applied to breast cancer. SK Plevritis, P Salzman, BM Sigal, PW Glynn. Statistics in Medicine Therese M-L Andersson