Abstract
Background and Objective
Due to relatively low incidence, bladder cancer screening might have a better ratio of benefits and harms if restricted to a high-risk population. We used data from the PLCO study and applied simple decision analytic techniques to compare different eligibility criteria for a screening trial.
Methods
For a variety of possible eligibility criteria, we calculated the percentage of the population aged 55 – 74 classified as being at high risk for developing invasive or high-grade carcinoma and therefore likely to benefit from screening. We used regression models to calculate a risk score based on age, sex, smoking history and family history of bladder cancer. We then calculated the reduction in cases given hypothetical risk reductions associated with screening. The trade-off between people screened and tumors avoided was calculated as a net benefit.
Results
The five-year probability of being diagnosed with invasive bladder cancer was 0.24%. Using a risk score > 6 or >8 as the eligibility criterion for a trial was generally superior to including all older adults. In a typical scenario, a risk score > 6 would result in ~25% of the population being screened to prevent 57 invasive or high grade bladder cancers per 100,000; screening the entire population would prevent only an additional 38 cases.
Conclusions
Screening for bladder cancer can be optimized by restricting to a sub-group at elevated risk. Different eligibility criteria for a screening trial can be compared rationally using decision-analytic techniques.
Introduction
Population-based trends have demonstrated that major reductions in cancer mortality have occurred most commonly under only one of two circumstances: reduced exposure to a carcinogen (stomach, liver and lung cancer) or screening (breast, cervix and colon cancer). Bladder cancer would seem a natural candidate for screening due to the availability of an inexpensive, non-invasive test (urinalysis) and a lead time between screen detection and progression to advanced disease that is sufficiently long to allow for intervention. The majority of bladder cancers are of urothelial cell origin and arise from the bladder epithelium. The natural pathway of progression of invasive bladder tumors is thought to proceed from cancer development within the epithelium to subsequent invasion of the detrusor muscle.
Outcome of bladder cancer is closely associated with the stage at presentation. Between 25% - 35% of tumors present as muscle invasive lesions. These tumors are associated with a high mortality rate whereas lower stage lesions are frequently cured with less intensive treatments. Early detection of tumors that are destined to progress may allow for earlier curative intervention and potentially could obviate the need for radical cystectomy or chemotherapy.
Although bladder cancer is the fourth most common tumor among US males, gender-specific incidence in the general population is 80 - 90% lower than that of other cancers, such as breast or prostate, that are typically subject to screening[1]. This has caused inevitable problems for screening studies. For example, Messing et al identified only 10 high grade or invasive bladder tumors in 1575 participants in a bladder cancer screening study[2, 3] and Lotan et al found only 1 high grade lesion in a series of 1502 patients considered at high risk for bladder cancer development due to either a smoking history or chemical exposure3.
The natural response to such studies is that bladder cancer screening should be restricted to those at highest risk. Yet this immediately requires an answer as to what constitutes high risk. In the case of bladder cancer, for example, it is known that smoking is a risk factor, but a decision has to be taken as to how many pack years is enough to place someone in a high risk category. We have previously argued[4] that choice of risk cut-points varies between trials. For instance, the National Lung Screening Trial included only smokers, or recent quitters, with a smoking history of 30 pack-years or more[5] whereas the Prostate, Lung, Colon, Ovary (PLCO) trial included older individuals irrespective of smoking history. It is often unclear how trials determined cut-points, such as smoking history, for eligibility criteria. We have shown in both a methodologic paper[4] and a practical application[6], that risk cut-points can be chosen rationally on the basis of a simple, decision-analytic methodology. The overall aim is to find a risk cut-off that would capture a sufficiently large number of invasive cancers, without subjecting an unnecessarily high proportion of the population to unnecessary screening. In this paper, we apply this methodology to a bladder cancer data set in order to address the question of the optimal risk group for a trial of bladder cancer screening. We assume that, were the trial to demonstrate a benefit, the criteria used to select patients for bladder cancer screening in practice would be close to those used in the trial. As such, the trial is designed to reflect what would be the optimum strategy at the population level.
Methods
Data for this investigation was obtained from the PLCO study on January 14, 2010, which included participant follow-up to December 31st 2008. The PLCO trial has been described previously[7]. In brief, men and women aged 55-74 who reported no history of prostate, lung, colorectal or ovarian cancer were enrolled during 1993–2001 at 10 centers around the United States in a randomized trial designed to evaluate the effect of screening for those four cancers. PLCO did not screen for bladder cancer. At baseline, participants completed a self-report questionnaire that collected information on exposures including demographics, smoking habits, personal medical history and family history of cancer. Cancer cases were ascertained through routine follow-up of positive trial screening exams and through the use of a mailed annual study update questionnaire. All cancers were confirmed by retrieving medical records, which were then abstracted by Certified Tumor Registrars at each of the screening centers.
For this analysis, the data sets were separated into training (one-third; N=49873) and validation (two-thirds; n=99746) sets by the PLCO data management group. We used the training data set to evaluate a variety of screening strategies; the four strategies with the best performance characteristics were then evaluated in the validation data set. We used both risk group and risk score approaches. To develop risk scores we used a Cox proportional hazards regression to calculate model coefficients; these were divided by the smallest coefficient and rounded to the nearest integer to derive ‘points’ for each variable that were summed for the total risk score. The variables contributing to the risk score are age, sex, smoking history and family history of bladder cancer. Risk scores were calculated by assigning 2 points for age≥65, 2 points for 10 - 19 pack years, 4 points for 20+ pack years, 4 points for male, 1 point for family history. We compared several potential screening strategies that were developed using simple decision rules (e.g. screen if age≥65 and smoker with 10+ pack years).
Our aim was to identify a group of participants with an increased probability of developing invasive bladder cancer, as these patients have the potential to benefit the most from a screening program. We assumed that finding a bladder cancer when in situ would lower the likelihood that the patient would develop more advanced stage disease, which increases the risk of radical surgery and cancer-specific death. High grade in situ tumors, although rare, are generally considered aggressive, and so we included grade III or IV carcinoma in situ in our definition of an event. We also assumed that invasive cancers diagnosed in the PLCO were detected clinically, rather than by screening, on the grounds that screening for bladder cancer remains very rare in the US.
We estimated the screening rate in the population under different criteria for trial eligibility by counting the proportion of patients in our data set meeting each criterion. To calculate the sensitivity and specificity of the various screening strategies for survival time data we used a previously published methodology[6]. In brief, we used Kaplan-Meier estimates of survival at a landmark time, predefined to be 5 years, and converted these to relevant conditional probabilities using Bayes’ theorem.
As we are treating incidence as a binary event, relative risk is defined as the risk of invasive or high grade disease at five years with screening, divided by five-year risk without screening. As before, we assumed a constant relative risk reduction. For example, given a relative risk of 0.7, a patient with a 10% probability of invasive bladder cancer without screening would have a 7% risk with screening; a patient with a 1% baseline risk would have a 0.7% risk if screened.
To identify the optimal eligibility criteria for a screening trial, we needed to account for the fact that some approaches would reduce the event rate more than others, but would involve a greater proportion of the population being screened. To calculate whether the reduction in malignant disease offsets the increase in the proportion of the population screened, we need to consider the maximum number of patients a clinician would consider screening to prevent one invasive or high grade cancer. This is known as the “number-needed-to-screen threshold” or “number-willing-to-screen” (NWS) and is a judgment that can vary from clinician to clinician. This number tells us how a physician weights the benefits of detecting a potentially invasive bladder cancer case whilst still in situ against the harms of screening, which include inconvenience, anxiety and harm from work-up and over-treatment, as well as financial costs of follow-up interventions such as imaging and cytoscopy. NWS for other cancers have been reported in the range 1000 – 2000 to prevent one death[8]. We used a lower range to reflect the change of endpoint from mortality to invasive cancer.
The reduction in event rate is estimated by assuming various relative risk reductions in the proportion of the population that is subject to screening, such that:
The clinical net benefit is then calculated using the screening rate, defined as the number of individuals in the population recommended for screening divided by the total population.
In other words, the screening rate is weighted by a factor related to the degree of harm or cost associated with screening. Because net benefit includes both screening and event rates, the optimal screening strategy is the one with the highest net benefit, irrespective of the absolute size of differences in net benefit. To illustrate the calculation of net benefit, imagine that a trialist would not subject more than 1000 individuals to yearly screening to find one invasive bladder cancer while still in situ. Further imagine that the trialist wished to evaluate a screening strategy that recommended screening for 50% of the population, and that, without screening, there would be 200 invasive tumors per 100,000, 150 of which would be found in the 50% who are screened. If the anticipated relative risk of screening is 60%, implementation of the screening strategy would lead to a total of 50 invasive tumors in those not screened plus 150 × 0.6 in those subject to screening, a total of 140 invasive tumors. This means that the incidence of invasive tumors will be reduced by 200 – 140 = 60 while screening 50% of 100,000 = 50,000. Given an NWS of 1000, the net benefit is 60 – (50,000 ÷ 1000) = 10.
To calculate sample size for trials using different eligibility criteria, we used standard formula for a binary comparison of proportions[9], with a power of 80% and an alpha of 5%. Event rates with and without screening were calculated for different relative risks and eligibility criteria as described above; the number needing to be assessed for eligibility was calculated by multiplying the sample size by the reciprocal of the proportion of the population meeting the eligibility criteria. All analyses were conducted using Stata 11.0 (Stata Corp., College Station, TX).
Results
Table 1 shows the baseline characteristics of the training and validation data sets. The median age at the time of randomization was 62 years (IQR 58, 67) and approximately half (51%) were female. Slightly more than half the participants reported a history of smoking (53%), while 25% reported smoking for more than 30 pack years. High grade in situ cancer was rare, equivalent to only 2% of invasive cases.
Table 1.
Summary of patient characteristics. All values are median (IQR) or frequency (proportion).
| Training set N=49,873 | Validation set N=99,746 | |
|---|---|---|
| Age at randomization | 62 (58, 67) | 62 (58, 67) |
| Female | 25,348 (51%) | 50,666 (51%) |
| Married | 37,717 (76%) | 75,193 (75%) |
| White race | 44,139 (89%) | 88,104 (88%) |
| Number of comorbidities | ||
| 0 | 14,752 (30%) | 29,645 (30%) |
| 1 | 16,262 (33%) | 32,162 (32%) |
| 2 | 10,794 (22%) | 21,438 (21%) |
| 3+ | 8,065 (16%) | 16,501 (17%) |
| Smoking history (pack years) | ||
| 0 (non-smoker) | 23,664 (47%) | 47,356 (47%) |
| 1-10 | 4,766 (10%) | 9,491 (10%) |
| 10-20 | 4,995 (10%) | 9,846 (10%) |
| 20-30 | 3,915 (8%) | 7,711 (8%) |
| 30+ | 12,533 (25%) | 25,342 (25%) |
| Family history of bladder cancer | 901 (2%) | 1,769 (2%) |
| Invasive or high grade bladder cancer* | 264 | 506. |
Total number of events during entire period of follow-up
Over the entire period of follow-up, 770 patients developed invasive or high grade bladder cancer (264 in the training cohort and 506 in the validation cohort). The five-year probability for the entire validation cohort was 0.24% (95% CI: 0.21%, 0.27%).
Five-year risk of bladder cancer in the validation set was 0.238% overall; 0.341% vs. 0.181% for participants aged ≥65 compared to <65; 0.121%, 0.239% and 0.434%for those with <10 pack years, 10 – 19 pack years and 20+ pack year smoking history; 0.405% vs. 0.078% in males vs. females and 0.230% vs. 0.238% in those with and without a family history. It can be seen that family history did not distinguish in the validation set. This may because of the relative rarity of a reported family history (<2%) and the consequent imprecision of statistical estimates of the association between family history and outcome.
After evaluating a variety of risk criteria on the training set, four were chosen for evaluation on the validation set: two approaches based on risk scores and, as comparison groups, the strategies of screening all or none. Table 2 shows the number of participants screened under each of these four strategies and the expected number of diagnoses of invasive or high grade bladder cancer within 5 years given various levels of screening effectiveness. Compared to the strategy of screening no one, screening those with a risk score > 8 requires screening only 8.4% of population, while reducing the rate of invasive or high grade cancer by 6-14% (14-34 events per 100,000 participants), depending on the assumed relative risk reduction of screening. Using the less strict threshold for screening of a risk score > 6 results in a greater number of population screened (23%), but a larger reduction in the event rate (12-30%; 48-119 events within 5 years per 100,000 participants).
Table 2. Percentage of patients screening under each screening strategy, and the associated reduction in 5-year risk of invasive or high grade bladder cancer for various relative risks.
Sensitivity is the proportion of patients who are diagnosed within five years who would be screened by a particular screening strategy; specificity is the proportion of patients free of disease by five years who would be spared screening by a particular screening strategy. The number of events is standardized to 100,000 of the population.
| Screening strategy | Percentage of patients screened | Sensitivity | Specificity | Number of patients per 100,000 with event within 5 years (reduction in event rate from screening none) | |||
|---|---|---|---|---|---|---|---|
| Relative Risk | |||||||
| 0.50 | 0.60 | 0.70 | 0.80 | ||||
| None | 0.0% | 0.0% | 100.0% | 238 (0%) | 238 (0%) | 238 (0%) | 238 (0%) |
| Risk Score* > 8 | 8.4% | 28.8% | 91.7% | 204 (14%) | 211 (12%) | 218 (9%) | 224 (6%) |
| Risk Score*> 6 | 23.4% | 59.8% | 76.7% | 167 (30%) | 181 (24%) | 195 (18%) | 210 (12%) |
| All | 100.0% | 100.0% | 0.0% | 119 (50%) | 143 (40%) | 167 (30%) | 190 (20%) |
Risk score: 2 points for age>65, 2 points for 10+ pack years, 4 points for 20+ pack years, 4 points for male, 1 point for family history
Table 3 shows the net benefit for each screening strategy across a range of NWS and relative risks of invasive disease. The optimal screening strategy is the one with the highest net benefit. When screening is assumed to be more effective (smaller relative risk) and tolerable (higher NWS), approaches that screen more participants are optimal (risk score>6). Where screening is thought less effective, the optimal strategy is to screen fewer participants (risk score>8 or none). One would only opt not to screen anyone if the NWS was <=500 and the assumed risk reduction was ≤20% or if the NWS was ≤250 and the assumed risk reduction was ≤40%. Of note, there was no combination of screening effectiveness and tolerability for which the optimal strategy was to screen everyone in the population. In other words, the risk score is important to identify population groups at sufficiently high risk for the development of invasive bladder cancer to benefit from screening.
Table 3. Net benefit associated with various screening strategies and relative risks for number-willing-to-screen of 250, 500, 750, 1000 and 1500 people.
The optimal screening strategy for each threshold for screening, and relative risk reduction associated with screening, is the one with the highest net benefit (bolded). The net benefit is in terms of invasive and high grade cancers prevented per 100,000 patients. For example, a relative risk of 0.60 and a clinician willing to screen 750 people to prevent one invasive or high grade bladder cancer, the net benefit for risk score > 8 is 16.2. This means that screening only patients with a risk score > 8 is equivalent to preventing 16 events per 100,000 without screening anyone not destined to be diagnosed with invasive or high grdae cancer. Most importantly, for a relative risk of 0.60 and NWS of 750, the net benefit for risk score >6 is 25.7 and the net benefit for a “screen all” strategy is negative; because the highest net benefit is for risk score >6, this would be the appropriate eligibility criterion for a trial if clinicians thought that screening would reduced risk by 40%, and felt it worth screening 750 patients to prevent one invasive or high grade cancer.
| Screening strategy and number-willing-to-screen (NWS) | Relative risk of invasive or high grade cancer in screening versus no screening | |||
|---|---|---|---|---|
| 0.50 | 0.60 | 0.70 | 0.80 | |
| None | 0.00 | |||
| Risk Score*> 8 | ||||
| 250 | 0.8 | −6.1 | −12.9 | −19.8 |
| 500 | 17.5 | 10.7 | 3.8 | −3.0 |
| 750 | 23.1 | 16.2 | 9.4 | 2.5 |
| 1000 | 25.9 | 19 | 12.2 | 5.3 |
| 1500 | 28.7 | 21.8 | 15 | 8.1 |
| Risk Score*> 6 | ||||
| 250 | −22.6 | −36.8 | −51 | −65.3 |
| 500 | 24.3 | 10.1 | −4.2 | −18.4 |
| 750 | 39.9 | 25.7 | 11.5 | −2.8 |
| 1000 | 47.7 | 33.5 | 19.3 | 5 |
| 1500 | 55.5 | 41.3 | 27.1 | 12.8 |
| All | ||||
| 250 | −280.9 | −304.8 | −328.6 | −352.4 |
| 500 | −80.9 | −104.8 | −128.6 | −152.4 |
| 750 | −14.3 | −38.1 | −61.9 | −85.7 |
| 1000 | 19.1 | −4.8 | −28.6 | −52.4 |
| 1500 | 52.4 | 28.6 | 4.8 | −19 |
Risk score: 2 points for age>65, 2 points for 10+ pack years, 4 points for 20+ pack years, 4 points for male, 1 point for family history
Some power calculations are given in table 4. It is clear that a randomized trial of screening for bladder cancer would only be feasible if restricted to a high risk group: a trial including all older adults would require approximately three times as many patients. It is also clear that conducting a trial on a sub-group of patients at increased risk would require large numbers to be assessed for eligibility. However, this would likely prove to be only a minor practical challenge given that the criteria would be so simple and may be available in electronic medical records. Family history of bladder cancer may not be routinely recorded but reclassifies only a small minority of patients. For example, excluding family history from the risk score would reclassify less than ~ 250 per 100,000 of participants as high risk, only one of whom would be found to have invasive bladder cancer within five years. Therefore, family history might be dropped from eligibility assessment if obtaining these data caused practical difficulties.
Table 4.
Power calculations for various trials scenarios. The columns give the sample size requirements for detecting different relative risks of screening.
| Relative risk | 0.60 | 0.70 | 0.80 | |||
|---|---|---|---|---|---|---|
| N for trial with 80% power | N assessed for eligibility | N for trial with 80% power | N assessed for eligibility | N for trial with 80% power | N assessed for eligibility | |
| Risk Score* > 8 | 20,268 | 242,027 | 37,610 | 449,114 | 88,152 | 1,052,653 |
| Risk Score*> 6 | 27,338 | 116,682 | 50,734 | 216,539 | 118,930 | 507,608 |
| All | 69,934 | 69,934 | 129,812 | 129,812 | 304,368 | 304,368 |
As sensitivity analyses, we excluded any cancers occurring within the first year on the grounds that these may not be prevented by screening. This did not substantively change our results. For example, a screening strategy that used a risk score >8 would have a specificity of 92% and a sensitivity of 31% instead of 92% and 29%. We also examined all cancers occurring within 10 years, instead of 5 years. Due to the higher number of cancers detected with a longer follow up, using 10 years did shift the optimal screening strategy towards those which screened a larger proportion of the population. For example, the optimal screening strategy would be to screen all if the relative risk was 0.6 and the NWS was >=1000. Nonetheless, a risk score > 6 was the favored strategy for most scenarios.
Discussion
We have clearly demonstrated that screening for bladder cancer needs to be restricted to a sub-group at elevated risk. Under reasonable assumptions for the benefits of screening – in terms of reducing radical surgery and cancer-specific death – and harms – in terms of inconvenience, anxiety and harms of associated with work-up of false positive results, as well as financial costs - implementing screening for a high-risk sub-group in the population was clearly preferable to screening all or none of the population. Moreover, we were able to compare different definitions of high risk using decision-analytic methods to identify optimal criteria for bladder cancer screening.
Our modeling approach is not based upon a particular approach to screening, either in terms of frequency, or type of test. We assume that different tests will vary with respect to effectiveness (compare a highly sensitive test given yearly to a less sensitive test given every five years) and tolerability (compare an expensive test with poor specificity to an inexpensive, highly specific test). As such, we provide results for a variety of different scenarios for tolerability (in terms of number willing to screen) and effectiveness (in terms of relative risk).
There are several possible limitations of our paper. First, our population consists of those volunteering for a screening trial. Although this makes the cohort ideal for our primary aim – to determine the inclusion criteria for a trial of bladder cancer screening – PLCO volunteers may not be representative of the population as a whole[10]. This would appear to be an inherent limitation of randomized trials on cancer screening. Second, occupational exposure to chemicals such as azo dyes is known to increase bladder cancer risk[11], and such exposures were not recorded in the PLCO. Given that pertinent occupational exposure is relatively rare, it is difficult to justify including it formally in a risk prediction model. One might imagine that occupational exposure would be a matter of clinical judgment, for example, a doctor might choose to screen an older man who had been exposed, even if that man had never smoked. Third, it is possible that participation in the PLCO reduced the risk of invasive bladder cancer. For example, an early stage bladder cancer might be diagnosed and treated in a patient being evaluated for prostate cancer. That said, prostate and colorectal screening is widely prevalent in the community, and ovarian screening only affects women, who are known to be at lower risk of bladder cancer. Moreover, this effect would only have been seen in the 50% of our cohort randomized to screening. Hence we do not anticipate that this would have a major impact on our findings.
We did not include race in our model. Although whites have a higher incidence of bladder cancer, mortality is higher amongst African Americans[12]. It would therefore be hard to justify including race as a predictor, one that would lead to fewer African Americans undergoing screening. Nonetheless, we did repeat our analyses adjusting for race and found no important differences: for example, the relative risk for a one point increase in risk score was identical to two decimal places.
We also demonstrated that restricting screening to a high risk group would dramatically improve the feasibility of a randomized trial of bladder screening. While the maximum number of screened to prevent one invasive or high grade bladder cancer is a clinician judgment that can vary, it is clear that delineating parameters prior to the trial that enriches for a population truly at risk for the disease, dramatically improves the likelihood of determining if a novel biomarker or treatment strategy is of value.
The exact nature of the optimal screening strategy for bladder cancer has yet to be established. Microscopic blood in the urine has been found in essentially all patients with bladder cancer if serially evaluated. Simple home microscopic urine evaluation can be accomplished with chemical reagent strip for hemoglobin analyses. This has served as the screening test of choice in most previous bladder cancer screening efforts to determine who should undergo definitive work up. Microhematuria, however, is a nonspecific finding, identified in 15-20% of all patients screened[13, 14]. The poor specificity of microhematuria and the relatively low incidence of bladder cancer in the general population leads to many unnecessary investigations (cystoscopy and imaging) which greatly increases the cost of such a screening strategy and decreasing its relative benefit and patient acceptance. Other urinary biomarkers have been FDA approved for bladder cancer identification[15], however, their use as a potential secondary screening test has yet to be established
In sum, we have shown that any trial of bladder cancer screening should be restricted to a subgroup at elevated risk. Moreover, we have demonstrated that different eligibility criteria for risk can be compared rationally using decision analytic techniques.
Acknowledgments
Supported in part by funds from David H. Koch provided through the Prostate Cancer Foundation, the Sidney Kimmel Center for Prostate and Urologic Cancers and P50-CA92629 SPORE grant from the National Cancer Institute to Dr. P. T. Scardino.
Contributor Information
Andrew J. Vickers, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, NY, NY 10021
Caroline Bennette, University of Washington, Pharmaceutical Outcomes Research and Policy Program, Box 357630, H375 Health Science Building, Seattle, WA 98195-7630. cb11@uw.edu.
Adam S. Kibel, Brigham and Women's Hospital, Dana Farber Cancer Institute, 75 Francis Street, Boston MA 02215. kas7@partners.org
Amanda Black, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville MD 20892. blacka@mail.nih.gov.
Grant Izmirlian, National Cancer Institute, Division of Cancer Prevention, 6130 Executive Blvd, MSC 7354, Bethesda, MD 20892-7354. izmirlig@mail.nih.gov.
Andrew J. Stephenson, Cleveland Clinic, Mail Code Q10-1 9500 Euclid Avenue Cleveland, OH 44195. stephea2@ccf.org
Bernard Bochner, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, NY, NY 10021. bochnerb@mskcc.org.
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