“The View From Space”

Understanding the methods and context of a new major paper in the cancer screening literature

Ben Larson (MS-3) December 2012

Screening criteria: a reminder

Two necessary, though not sufficient, criteria for any screening program which are particularly relevant to breast mammography in average risk patients:

1. There should be scientific evidence of screening program effectiveness

2. The overall benefits of screening should outweigh the harm

“Effectiveness”

In the context of cancer screening, effectiveness means:

1. Deadly cancers are diagnosed earlier

2. Earlier diagnosis leads to decreased mortality

November 22, 2012

N Engl J Med 2012;367:1998-2005

The Question:

There has been a decrease in the incidence of breast cancer mortality over the past 30 years.

How much of this decrease is due to screening mammography, which began around that time?

The Problem:

Many things have changed over the past 30 years, especially the surgical, medical, and radiotherapy for breast carcinomata.

How can the effect of screening itself upon mortality be isolated?

Possible Solution:

Screening attempts to detect pre-clinical disease, i.e., increase its lead time.

Putting aside whether and how much increasing the lead time improves mortality, one could use stage at initial presentation as a meaningful evaluation of screening’s ability to diagnose deadly cancers earlier.

Their Conclusion

Even with “very extreme” assumptions biasing in favor of screening, the authors estimate there are about a million women who underwent treatment for breast cancer who would not otherwise have presented clinically with advanced disease.

Seriously?

A million women?

Is that even possible?

Where exactly did that number come from?

The Nitty Gritty

To determine this with 30 years’ data, Drs. Bleyer and Welch needed to:• Determine a reasonable estimate for the baseline

incidence of breast cancer• Determine if and how much incidence has changed

over 30 years of screening• Control for any major external drivers of changing

incidence• Present data in multiple ways, preferably biasing

toward screening

Estimate for the baseline incidence

• Data on incidence was first collected in 1973• The years 1976-1978 were chosen to estimate

true baseline incidence• This interval was a few years after an artificial

uptick in breast cancer incidence (due to the so-called “Betty Ford blip”), but also just before screening mammography became truly widespread

Adjustments

1. Adjust for increasing incidence

2. Adjust for hormone replacement therapy

1. Increasing incidence

Must attempt to separate true increasing baseline incidence from lead-time bias “increased incidence”

Women under 40 are not screened, and their incidence increased about 0.25% / year (95% [CI] 0.04% – 0.47%) for the past 30 years

This increasing incidence was hypothesized to apply to women over 40, though this cannot be proven

2. HRT

A strong link exists between therapeutic post-menopausal sex steroids and diagnosed cancers.

The study authors proposed “capping” the incidence of these early cancers between 1990 and 2005 (where the bulk of HRT’s effect was felt), by using today’s incidence and not counting incidence during those years above that cap.

Confined to the breast

Nodal involvement or direct extension

Ductal carcinoma in situ

Distant metastases

These areas under the curve represent proposed “excess incidence” due to HRT

DEFINITIONS

This line represents a zoomed in view of the red, “Late-stage” line on the previous slide (note the y-axis change)

These lines represent the two components of late-stage disease

Inference?

Almost all the variability in late-stage disease is mirrored by changes in regional disease, whereas the number of women presenting with distant metastases over the past 30 years is remarkably constant.

We would not expect this if screening mammography were able to detect small lesions destined to spread widely.

“Overdiagnosis”

The authors use this term to mean the calculated number of women who underwent treatment for breast carcinomata, who otherwise would not have presented with “late stage” clinical disease.

Late-stage disease, in this case, refers to the sum of regional disease and distant metastases.

“Best guess” represents the estimated baseline rate of incidence increase, assuming the increase among women under 40 holds for those over 40. “Extreme assumption” doubles that rate; or, if you will, uses a value just outside the 95% confidence interval (95% [CI] 0.04% – 0.47%).

The “very extreme assumption” not only uses this larger value (0.5%) for increasing baseline incidence, but also “assumes the highest rate of baseline disease ever observed” (113 cases per 100,000 women, observed in 1985).

How exactly do these assumptions bias in favor or screening?

The “excess detection” column is synonymous with “overdiagnosis.” It is the difference between the extra diagnoses of early-stage disease yielded by screening and the reduction in diagnoses of late stage disease for each set of assumptions. “Surplus” and “reduction” imply a known baseline incidence; that baseline rate is what changes between the Base Case, Best Guess, and Extreme Assumption, in a manner outlined on the previous slide.

The Very Extreme Assumption combines the generous assumption of increasing underlying incidence with a generous assumption of late stage disease presentation. Remember, the difference between surplus of early disease and reduction in late disease equals “overdiagnosis.” There are multiple arithmetic operations going into these numbers, which are detailed in the paper’s appendix and explained on the next slide.

This column is the incidence of late-stage breast cancer presentation per 100,000 women capped for HRT. You can see how the Very Extreme Assumption was generated, using 1985’s highest recorded incidence and then starting with that number in 1979, increasing that base rate with the 0.5%/yr derived from the Extreme Assumption.

We can now see that the Very Extreme Assumption favors screening by maximizing the base rate of late-stage diagnoses, and therefore also maximizes the calculated reduction in late-stage disease. The actual number of observed cases, shown in Table 1 above, is simply a measured value.

“Breast cancer overdiagnosis is a complex and sometimes contentious issue...Our investigation takes a different view, which might be considered the view from space. It does not involve a selected group of patients, a specific protocol, or a single point in time. Instead, it considers national data over a period of three decades and details what has actually happened since the introduction of screening mammography. There has been plenty of time for the surplus of diagnoses of early-stage cancer to translate into a reduction in diagnoses of late-stage cancer — thus eliminating concern about lead time. This broad view is the major strength of our study.”

N Engl J Med 2012;367:1998-2005

A bit of context

What Barron Lerner, a physician and historian, has written about a debate over mammography in 1997 still applies:

“Experts on opposing sides of the screening debate had not really disagreed about what the data showed. Rather, they had interpreted and then presented the statistics differently.”

Barron H. Lerner, “To See With the Eyes of Tomorrow: A History of Screening Mammography,” Background Paper for the Institute of Medicine report: Mammography and Beyond: Developing Technologies for the Early Detection of Breast Cancer, March 2001.

A single example among many

A 2002 Swedish paper purports to demonstrate to its critics that the mortality benefit from invitation to mammography improves mortality.

What’s interesting is not how they arrived at their conclusion. Rather, it’s how excited they are about what their data show:

Lancet 2002; 359: 909–19

Mortality RR = 0.98 (95% [CI] 0.96 – 1.00)

Lancet 2002; 359: 909–19

A parting thought from Dr. Lerner

“The production of better data alone cannot eliminate the role that economics, authority and ideology play in the assessment of mammography and other early detection technologies.

Sociocultural factors not only influence the answers to questions about cancer screening, but also the questions themselves.”