The Current State of Screening PLCO And The Current State of Screening Andrew W. Swartz, MD Family Physician Emergency Medicine Physician Flight Surgeon (Alaska ANG)

Conflicts of Interest Biases Am a member of a profession (and specialty) which profits from cancer screening and treatment No financial interest in any medical patents or products I do have a small software company, no product relation to topic today Part of my income is derived from CRC Screening Not trying to get any votes Biases Practice in fear of the legal liability associated with “missing something” May soon be graded by how many of my patients get screened Am a member of a society whose health care system is in financial jeopardy Like most rational humans, I strongly desire prevention and screening to work so that my loved ones and I may live longer and suffer less

Hypothesis NOT disproved Incorporate into Theory, Scientific Method Observe the World (for apparent associations, etc) [Re] Form Hypothesis Test Hypothesis with Experiment Hypothesis disproved Hypothesis NOT disproved Test further, Incorporate into Theory, etc.

Hierarchy of Evidence RCT’s Experiments Cohort Studies Reviews + Meta-analyses of RCT’s RCT’s Experiments Non-randomized Controlled Trials Cohort Studies Less Bias + Confounding Case-Control Studies Observations These are NOT ADDITIVE: when an expert quotes a cohort study, that does NOT equal an RCT !!! Case-Series and Reports Expert Opinion

Questionnaire

Symptomatic Diagnosis Screening Theory Cancerous Mutation Symptomatic Diagnosis Death Unscreened Holy Grail Early Diagnosis Early Treatment Lifespan Gain Screened

Background To be effective, screening must satisfy two criteria: We must be able to diagnose early. Early treatment must be better than late treatment. Upon reflection, these two criteria seem obvious. One major misunderstanding is that most persons seem to believe that we conduct clinical trials to determine if we can diagnose early. THAT IS FLAT WRONG. It is very easy to determine if a modality can diagnose early. Just mammogram 1000 women and compare staging to the general population. You can compare this to estrogen therapy. You don’t need an RCT to prove that taking estrogen raises the serum estrogen level. But you do need an RCT to prove that an increased serum estrogen level is beneficial. We conduct clinical trials to determine if early treatment is better than late treatment. That is a far more difficult task. That is why we conduct clinical trials. 1. Monographs in Epidemiology and Biostatistics, Volume 19. Morrison A. Screening in Chronic Disease, 2nd Ed. 1992. Oxford University Press. New York.

Background “The evaluation of screening must be based on measures of disease occurrence that will not be affected by early diagnosis except to the extent that early treatment is beneficial.1” 1. Monographs in Epidemiology and Biostatistics, Volume 19. Morrison A. Screening in Chronic Disease, 2nd Ed. 1992. Oxford University Press. New York. p16.

Symptomatic Diagnosis Lead-Time Bias NO TREATMENT Cancerous Mutation Symptomatic Diagnosis Death Unscreened Survival Lead-time Survival Screened Screening Diagnosis

Treatment Comparisons Cancerous Mutation Group-A Rx-A Survival Group-B Rx-B Survival

Symptomatic Diagnosis Lead-Time Bias Cancerous Mutation Symptomatic Diagnosis Death Unscreened Survival THIS is NOT a valid evaluation of screening benefit. Period. Specifically, you CANNOT compare the survival of a screened and an unscreened group. Period. Any questions????? Survival Screened Screening Diagnosis

Symptomatic Diagnosis Lead-Time Bias Cancerous Mutation Symptomatic Diagnosis Death Unscreened Survival THIS is NOT a valid evaluation of screening benefit. Period. Specifically, you CANNOT compare the survival of a screened and an unscreened group. Period. Any questions????? Survival Screened Screening Diagnosis

Background “The evaluation of screening must be based on measures of disease occurrence that will not be affected by early diagnosis except to the extent that early treatment is beneficial.1” Now… does everyone understand what Morrison meant in this statement??? 1. Monographs in Epidemiology and Biostatistics, Volume 19. Morrison A. Screening in Chronic Disease, 2nd Ed. 1992. Oxford University Press. New York. p16.

Background Mortality Morbidity “The ultimate gains derived from a screening program are reductions of serious illness and death among the people screened.1” Mortality Morbidity These two things are the ONLY valid primary outcomes. Does anyone here have ANY objections to this assertion???? 1. Monographs in Epidemiology and Biostatistics, Volume 19. Morrison A. Screening in Chronic Disease, 2nd Ed. 1992. Oxford University Press. New York. p16.

Ground Rules Valid Outcomes in Screening Trials: Mortality Incidence Survival Staging

Cumulative Case Plots The technical statistics term for these is “Ogive”, or “Cumulative Incidence Plot”.

Time since Randomization Cumulative Case Plots No Difference Time since Randomization Screened Control Screening Period Follow-up Period Number of Cases We will start by analyzing some common patterns in some idealized plots. All of these will have the same background, which reflects the screening period versus the follow-up period. All will have dashed lines for the screen group versus solid lines for the control group. X axis is time (from randomization) Y axis is the cumulative number of cases of the end-point. The SLOPE of the line is the RATE of occurrence. In General: Studies must monitor well beyond the intervention period to accurately assess outcomes. These graphs are similar to EKG's: they contain very distinctive patterns which convey a lot of information to those who know how to read them. I would argue that they can be more convincing that the statistical snapshot for a specific point in time – which is what most articles focus upon. After studying and reading enough of these plots, you can even identify an incidence plot from a mortality plot without any labeling. Also, the shape of the incidence plot tells you a lot about the experimental design. Trials which perform a prevalence screen prior to randomization look very different from those which do not. Incidence plots generally start displaying outcomes from T=0, whereas mortality plots tend to not have any data for 1 to 2 years from randomization. In this first plot, nothing is done to the screen group, which is reflected by identical outcomes.

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Cases This is just an example to illustrate different slopes. Whatever the outcome is, in this mythical example, it is happening at twice the rate in the screen group throughout both the intervention and follow-up periods. This would make no sense. It is just to illustrate slope.

Mortality Plots Cumulative Case Plots Now we are going to look at mortality plots.

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Deaths And that is what happens here. This is the same graph as the last, except a while after cessation of intervention, the screen group line deflects upward with the same slope as the control group, i.e. after the screening effect wore off, the screenees are dying at the same rate as the controls, which is what we would expect. But notice how this one is different. There is never a catch-up increase in the screen group, so all those missing deaths NEVER appear. All the deaths that were prevented during the intervention (and immediately thereafter) are maintained indefinitely. THEY WERE CURED!!! This in the Holy-Grail of cancer screening. Control Screen

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Deaths Mortality plots usually display changes that lag behind the start and cessation of intervention by a 1-2 years, or more for very slow growing cancers. Notice that there is no data for quite a while after randomization. This is because they will not include anyone who has an end-stage case of anything, much less the cancer being studied, into either group. To die of the cancer in question a month after randomization, you would likely have been quite symptomatic and probably quite ill appearing. Most studies exclude anyone with a know life-expectancy less than the anticipated follow-up period of the study. So in this chart, once some mortality starts occurring, the slope of the screen line is half that of the control line, so the screenees are dying have half the rate of the controls. This effect continues until a little while after the intervention period, then there is an abrupt increase until the two lines come together, and then the are the same. This represents delayed mortality. The screen group was dying at a lower rate. But after some follow-up time, all those missing death occurred and the totals deaths equalized in the two groups. This is a benefit. But we would prefer deaths being prevented instead of just delayed. Control Screen

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Deaths This is an in-between of the last two. Half of the missing deaths were postponed, and the jump in the screenee mortalily represents them eventually dying. But the other half of the missing deaths never appear, and thus that half was cured. Control Screen

Incidence Plots Cumulative Case Plots This next series of ideal plots will all illustrate INCIDENCE plots.

Time since Randomization Cumulative Case Plots Ideal Time since Randomization Screened Control Screening Period Follow-up Period Number of Cancers This is obviously incidence because data starts to appear immediately after T=0. The initial rapid rise in incidence in the screen group tells us that there was no prevalence screen prior to randomization, so the prevalent cases were identified during the first study screen. So the incidence plot for the screen group is shifted compared to the control group. The million dollar question is “is it shifted left (indicating early diagnosis) or is it shifted up (indicating extra diagnosis)?” The answer lies in what the curve does after cessation of screening. If cessation of screening results in a flat line for the screen group, this means that no new cancers are clinically diagnosed for a period of time after screening. If the two lines come together and then begin up at the slope of the control group, then this indicates that the plot is shifted left and we have diagnosed early. After screening, the control group is cancer free until enough time passes for them to develop symptomatic cancers, at which time they start being diagnosed at the same rate as the control group. This is the ideal pattern for true early diagnosis: an initial jump from picking up the prevalent cases, then the screen slope is the same as the control during the remaining period (i.e. new cancers are being diagnosed at the same RATE in both groups), then a flat period after screening ends, when no new symptomatic cancers are diagnosed, which the control line comes up to meet the screen line. The shift of the screen plot is to the left, not up, and in the long run there are the same number of cancers in each group. Screen Control

Time since Randomization Cumulative Case Plots Worst-Case Time since Randomization Screened Control Screening Period Follow-up Period Number of Cancers Here we have an initial jump for identification of prevalent cases. But now during the remainder of screening the slope of the screen group is twice that of the control group. This means that cancer is being diagnosed at a higher RATE in the screen group than the control group. This should not happen! Also note that once screening ends, the slope of the screen group changes to become parallel with the control group. There flat period where the screen group goes without any symptomatically diagnosed cases. So the lines never come together. All the extra cancers diagnosed during the screening period are maintained indefinitely in follow-up. This indicates that the screen plot was shifted up, not left, and that this represents EXTRA cancers, not early cancers. I want to point out something that happens here that is not obvious: There were twice as many cancers diagnosed during the screening period, representing overdiagnosis of 100%. These extra cases were maintained indefinitely. But as a percentage, the amount of overdiagnosis decreases the longer you follow it out because as both groups go on to develop more symptomatic cancers, those extra cases become a smaller percentage of the whole. THIS IS VERY IMPORTANT. The magnitude of overdiagnosis (in terms of percentages) can be covered up by reporting extended follow- up results. This type of reporting completely obscures the fact that there were never any “catch-up” cases in the control group. Overdiagnosis !!! Screen Control

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Cancers This is a pattern common in early screening trials: Since there is no initial jump, there must have been a prevalence screen done prior to randomization, and everyone with an abnormal was included. Note that the SLOPE of the screen group is twice that of the control group, meaning that new cancers are being diagnosed at twice the rate in the screen group. This is suspicious for shifting up – not left. After screening, the slopes are the same. But there is a jump in the incidence in the control group. This is because they used to think that they could shorten the time required for adequate data by screening the control group at the end. But we now know that this is dubious. In a graph like this one, they just added some overdiagnosis to the control group. We now know that a control-group screen at the end of the intervention period is invalid, and it is pretty rare to see it in any modern large trials. This graph represents the exact same results as the last graph, except the control-group screen at the end of intervention. Screen Control

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Cancers Look what happens here: As expected, the control group has a long, straight line starting near T=0. But the screen group has a jump representing finding the prevalent cases, but then the screen group line goes flat until a short period of time after the cessation of intervention. Then the slope goes up at the same rate as the control group. This represents a screen with effective prevention. Prevalent cases are diagnosed (early). But then no new cases are diagnosed in the screen group until well after screening ends. This is the ideal outcome for both cervical and colorectal screening programs, which both aim to remove pre-maligmant lesions and thus PREVENT cancer. But note: this is ideal. Do not expect to see a true flat line for the screen group. But recognized that as an increased slope indicates an increase in the cancer development rate, a decreased slope represents a decrease in the cancer development rate. This is literally “bending the curve” as the saying goes. Control Screen

Time since Randomization Cumulative Case Plots Time since Randomization Screened Control Screening Period Follow-up Period Number of Cancers ? This is an ideal plot shape for effective early diagnosis. But why do the two lines go flat at the end??? Two reasons: 1) Studies cannot start by screening everyone on the same day. The screenees are recruited and randomized over a couple years, so some might not have reached the full length of follow-up displayed on the chart. The authors should not be displaying a chart with staggered results. They should only display data out to the point that everyone has gotten to. 2) They have followed both groups so long that they are dying of other causes. Once they all die, there can be no more new diagnoses of anything. :) Screen Control

Prostate, Lung, Colorectal, and Ovarian Screening RCT PLCO Prostate, Lung, Colorectal, and Ovarian Screening RCT The Good, The Bad, And the Ugly.

One of the most massive undertakings in the history of medicine. PLCO One of the most massive undertakings in the history of medicine. 5-year design phase (1989-1994) by the National Cancer Institute Patient recruitment 1994-2001 Multi-center w/ competition for participation: University of Colorado Health Sciences Center, Denver, CO Georgetown University Medical Center, , Washington, DC Lombardi Cancer Research Center, Washington, DC Pacific Health Research Institute, Honolulu, HI Henry Ford Health System, Detroit, MI University of Minnesota, Minneapolis, MN Washington University School of Medicine, St. Louis, MO Cancer Institute of Brooklyn at Maimonides, Brooklyn, NY (discontinued 1997) University of Pittsburgh Cancer Institute, Pittsburgh, PA Satellites: Latrobe Area Hospital, Latrobe, PA Jameson Health System, New Castle, PA Trinity Health System, Steubenville, OH University of Utah School of Medicine, Salt Lake City, UT Satellite: St. Lukes Meridian Medical Center, Boise, ID Marshfield Medical Research and Education Foundation, Marshfield, WI University of Alabama at Birmingham, Birmingham, AL (added 1997)

PLCO 307 Publications 4/17/2013

PLCO Design 154,900 Screen Control 77,445 77,455 Gender: Men + Women Age: 55-74 Exclusions: PLCO Cancers Treatment for any cancer Recently screened 154,900 Randomization 77,445 Screen 77,455 Control Yearly DRE x 4y + PSA x 6y Yearly CXR x 4y Flex-Sig at 0 and 3-5y Yearly TVU x 4y + Ca125 x 6y Usual Care Framingham = 15,447 Women’s Health Initiative HT CT = 160,000 E-Alone / placebo = 10,739 (5310 / 5429) E+P / placebo = 16,608 (8506 / 8102) Men Women Prostate X   38,340 Lung 77,445 Colorectal Ovarian 39,105

Prostate Lung Colorectal Ovarian Andriole GL, Crawford DE, et al. Prostate Cancer Screening in the Randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: Mortality Results after 13 Years of Follow-up. J Natl Cancer Inst. 2012;104:125–132. Oken MM, Hocking WG, Kvale PA, et al; PLCO Project Team. Screening by chest radiograph and lung cancer mortality: the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial. JAMA. 2011; 306(17):1865-1873. Prostate Lung Schoen R.E., Pinsky P.F., Weissfeld J.L., et al. Colorectal-Cancer Incidence and Mortality with Screening Flexible Sigmoidoscopy. N Engl J Med. 2012 Jun 21;366(25):2345-57. Buys SS, Partridge E, Black A, et al. Effect of Screening on Ovarian Cancer Mortality: The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA. 2011;305(22):2295-2303. The technical statistics term for these is “Ogive”, or “Cumulative Incidence Plot”. Colorectal Ovarian

Disease-Specific Mortality Prostate Lung Screen Control DRE PSA Screen Control CXR Colorectal Ovarian Control Screen Control FS TVU Ca125

Cancer Incidence Prostate Lung Colorectal Ovarian 35 Cancer Incidence Prostate Lung Screen Control DRE PSA Screen Control CXR 1886 1274 Colorectal Ovarian Control Screen Control FS TVU Ca125 113 83

Graph Familiarization RR = x.xx NNI = y.yy NS RR = x.xx NNI = y.yy

PLCO Trial Results (at 10-13 years)

PLCO Trial Results (at 10-13 years)

PLCO-CRC: Comparison with other RCT’s 1999 – 13y 400 / 399 2014 – 11.5y 13,563 / 41,092 2010 – M15y, I10y 57,254 / 113,178 2011 – 10.5 17,136 / 17,136 2012 – 11y 77,445 / 77,455

3.2 : 1

* * 100% 77,450

CRC Mortality CRC Incidence Control CRC Mortality * Control CRC Incidence

PLCO Trial Results (at 10-13 years)

PLCO: Screening for Ovarian Cancer 44 PLCO: Screening for Ovarian Cancer Screen Control Mortality Control Incidence TVU 113 83 Screen Ca125 Screenees (39,105) NNH False-positives: 3,285 12 Abdominal surgeries: 1,080 36 Major Surg. Comps.: 222 176

Background To be effective, screening must satisfy two criteria: We must be able to diagnose early. Early treatment must be better than late treatment. Upon reflection, these two criteria seem obvious. One major misunderstanding is that most persons seem to believe that we conduct clinical trials to determine if we can diagnose early. THAT IS FLAT WRONG. It is very easy to determine if a modality can diagnose early. Just mammogram 1000 women and compare staging to the general population. You can compare this to estrogen therapy. You don’t need an RCT to prove that taking estrogen raises the serum estrogen level. But you do need an RCT to prove that an increased serum estrogen level is beneficial. We conduct clinical trials to determine if early treatment is better than late treatment. That is a far more difficult task. That is why we conduct clinical trials. 1. Monographs in Epidemiology and Biostatistics, Volume 19. Morrison A. Screening in Chronic Disease, 2nd Ed. 1985. Oxford University Press. New York.

Upon reflection, these two criteria seem obvious. One major misunderstanding is that most persons seem to believe that we conduct clinical trials to determine if we can diagnose early. THAT IS FLAT WRONG. It is very easy to determine if a modality can diagnose early. Just mammogram 1000 women and compare staging to the general population. You can compare this to estrogen therapy. You don’t need an RCT to prove that taking estrogen raises the serum estrogen level. But you do need an RCT to prove that an increased serum estrogen level is beneficial. We conduct clinical trials to determine if early treatment is better than late treatment. That is a far more difficult task. That is why we conduct clinical trials.

SCREENING

PLCO References Prostate Andriole GL, Crawford DE, et al. Prostate Cancer Screening in the Randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: Mortality Results after 13 Years of Follow-up. J Natl Cancer Inst. 2012;104:125–132. Lung Oken MM, Hocking WG, Kvale PA, et al; PLCO Project Team. Screening by chest radiograph and lung cancer mortality: the Prostate, Lung, Colorectal, and Ovarian (PLCO) randomized trial. JAMA. 2011; 306(17):1865- 1873. Colorectal Schoen R.E., Pinsky P.F., Weissfeld J.L., et al. Colorectal-Cancer Incidence and Mortality with Screening Flexible Sigmoidoscopy. N Engl J Med. 2012 Jun 21;366(25):2345-57. Ovarian Buys SS, et al. Effect of Screening on Ovarian Cancer Mortality: The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA. 2011;305(22):2295-2303.

Flex-Sig RCT References Telemark Thiis-Evensen E, Hoff GS, Sauar J, et al. Population-based surveillance by colonoscopy_ effect on the incidence of colorectal cancer. Telemark Polyp Study I. Scand J Gastroenterol 1999; 34(4):414-20. NORCCAP Holme Ø, Løberg M et al. Effect of Flexible Sigmoidoscopy Screening on Colorectal Cancer Incidence and Mortality: A Randomized Clinical Trial. JAMA. 2014;312(6):606-615. doi:10.1001/jama.2014.8266. UK Atkin WS, Edwards R, Kralj-Hans I, et al. Once-only flexible sigmoidoscopy screening in prevention of colorectal cancer: a multicentre randomised controlled trial. Lancet 375 (9726):1624-33.. SCORE Segnan N, et al. Once-Only Sigmoidoscopy in Colorectal Cancer Screening: Follow-up Findings of the Italian Randomized Controlled Trial—SCORE. J Natl Cancer Inst 2011;103:1310–1322.

Thank You Questions ?