| Course | NU 602 Epidemiology, Research, and Theory |
|---|---|
| Unit | Unit 6 |
| Paper type | Screening test evaluation |
| Length | About 970 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MSN |
| Updated | October 2026 |
Free sample paper for NU 602 Unit 6
The Fecal Immunochemical Test for Colorectal Cancer: One Test, Two Populations, and What Happens to the People It Misclassifies
Student Name
Department of Nursing, Herzing University
NU 602: Epidemiology, Research, and Theory
Instructor Name
February 25, 2026
The Fecal Immunochemical Test for Colorectal Cancer: One Test, Two Populations, and What Happens to the People It Misclassifies
The Test and the Condition
The fecal immunochemical test (FIT) detects human hemoglobin in a stool sample, which can come from bleeding colorectal cancers and some large polyps. It is done at home, requires no bowel preparation or dietary restriction, and is recommended as one option for colorectal cancer screening in average-risk adults aged 45 to 75, repeated every year, with a positive result followed by colonoscopy (U.S. Preventive Services Task Force [USPSTF], 2021). A meta-analysis of FIT accuracy for colorectal cancer in asymptomatic adults reported a pooled sensitivity of about 79% and specificity of about 94% (Lee et al., 2014). Those figures are used for the illustration below; actual performance varies with the brand of test and the cutoff for a positive result.
An Illustrative Two-by-Two Table: Average-Risk Screening
Suppose 20,000 average-risk adults complete a FIT, and 0.5% of them, 100 people, have colorectal cancer. With sensitivity of 79% and specificity of 94%, the results are:
| Has cancer | Does not have cancer | Total | |
|---|---|---|---|
| FIT positive | 79 (true positives: cancer correctly flagged) | 1,194 (false positives: no cancer, flagged anyway) | 1,273 |
| FIT negative | 21 (false negatives: cancer missed) | 18,706 (true negatives: no cancer, correctly cleared) | 18,727 |
| Total | 100 | 19,900 | 20,000 |
Reading Down the Columns: Sensitivity and Specificity
Sensitivity is calculated down the column of people who have cancer: 79 of 100, or 79%. Specificity is calculated down the column of people without cancer: 18,706 of 19,900, or 94%. These are properties of the test in people whose disease status is known, and they do not change with how common the disease is in the population tested (Trevethan, 2017). They answer the laboratory's question: how well does the test separate people with and without the disease?
Reading Across the Rows: Predictive Values
The person holding a result asks a different question: given my result, how likely is it that I have cancer? That is answered across the rows. The positive predictive value is 79 of 1,273 positive results, or 6.2%. The negative predictive value is 18,706 of 18,727 negative results, or 99.9%. In other words, about 94 of every 100 people with a positive FIT in this population do not have cancer, while a negative result is very reassuring.
This is the number patients and clinicians most often misunderstand. A test that is 94% specific sounds as if a positive result means a 94% chance of disease. It does not; in a population where cancer is uncommon, most positive results come from the much larger group without cancer.
The Same Test in a Higher-Prevalence Population
Now suppose the same FIT is used in 20,000 people referred from primary care with symptoms such as a change in bowel habit, among whom 5% have colorectal cancer. Sensitivity and specificity are unchanged. The results are 790 true positives, 210 false negatives, 17,860 true negatives, and 1,140 false positives. The positive predictive value rises to 790 of 1,930, or 40.9%, and the negative predictive value falls slightly to 98.8%.
The test has not changed; the population has. Where disease is ten times as common, a positive result is almost seven times as likely to mean cancer. Where it is rare, even a specific test produces mostly false positives. This is why the same FIT result is interpreted differently in average-risk screening and in a symptomatic patient, and why screening programs are aimed at defined populations.
Changing the Cutoff: A Trade-Off, Not a Fix
A FIT is positive when the hemoglobin concentration in the sample exceeds a set threshold, and programs can choose that threshold. Lowering it catches more cancers but also flags more people without cancer. To illustrate, suppose a lower cutoff raised sensitivity to 85% and lowered specificity to 90% in the same average-risk population of 20,000. True positives would rise from 79 to 85, six more cancers found. False positives would rise from 1,194 to 1,990, nearly 800 more colonoscopies in people without cancer, and the positive predictive value would fall from 6.2% to 4.1%. Whether six additional cancers justify roughly 800 additional colonoscopies depends on colonoscopy capacity, cost, and how many people with a positive result actually complete follow-up. If a program already struggles to get people with positive results to colonoscopy, a lower cutoff may add burden without adding detected cancers, because the extra positives wait in the same queue.
What Happens to the People Misclassified
The 1,194 false positives in the screening population each face a colonoscopy, which requires bowel preparation and sedation, usually a day off work, and a small risk of bleeding or perforation, particularly if a polyp is removed. Many will also experience days or weeks of anxiety between the positive result and the colonoscopy. Some will have polyps found and removed, which is a benefit, since FIT also detects some advanced adenomas, but the program must have colonoscopy capacity for about 6 of every 100 people screened.
The 21 false negatives are the more serious concern. They are reassured by a negative result while a cancer grows. Two features of FIT screening reduce the harm. Some cancers that do not bleed at the time of one test will bleed at the next, so annual repetition catches many of them; and patients are taught that a negative screening test does not rule out cancer if symptoms such as rectal bleeding or a persistent change in bowel habit appear, which should prompt evaluation regardless of the last result.
Judgment
For average-risk adults, FIT is a reasonable screening test: inexpensive, non-invasive, acceptable to many people who decline colonoscopy, and very reassuring when negative. Its limits are a low positive predictive value in screening, which makes colonoscopy follow-up essential and makes timely follow-up the weak point of most FIT programs, and a sensitivity that depends on repeating the test every year. A screening program built on FIT is only as effective as its annual return rate and its colonoscopy follow-up rate, and nurses who run such programs should track both.
References
Lee, J. K., Liles, E. G., Bent, S., Levin, T. R., & Corley, D. A. (2014). Accuracy of fecal immunochemical tests for colorectal cancer: Systematic review and meta-analysis. Annals of Internal Medicine, 160(3), 171-181. https://doi.org/10.7326/M13-1484
Trevethan, R. (2017). Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Frontiers in Public Health, 5, Article 307. https://doi.org/10.3389/fpubh.2017.00307
U.S. Preventive Services Task Force. (2021). Screening for colorectal cancer: US Preventive Services Task Force recommendation statement. JAMA, 325(19), 1965-1977. https://doi.org/10.1001/jama.2021.6238
NU 602 Unit 6 instructions, in plain terms
In Unit 6 you evaluate a screening test using measures such as sensitivity, specificity and predictive values. Expect to describe the test and condition, build a two-by-two table, calculate the measures, explain how prevalence affects predictive values, discuss the consequences of false positives and false negatives and reach a judgment about the test's use. Many versions ask you to compare two populations or two cutoffs. Check whether your section expects published performance figures or allows illustrative ones; either works if sourced or labeled. Show your table and calculations. The human consequences of misclassification matter, so describe what happens to a person with a false result, not only the percentage, and connect the judgment to a guideline.
How this NU 602 Unit 6 example is built
The paper begins with the test and what it detects, then builds a two-by-two table with performance figures drawn from a meta-analysis. Sensitivity and specificity are calculated down the columns with the numbers shown. Predictive values are calculated across the rows, and the paper explains that this is the question the patient asks. The second population uses the same test with higher prevalence, and a second table shows how positive predictive value changes. The cutoff section explains that lowering the threshold catches more cancers but creates more false positives. The consequences section turns counts into experiences: bowel preparation, sedation and small risks for false positives, false reassurance for false negatives. The judgment weighs these for average-risk screening.
Where the points sit in the NU 602 Unit 6 rubric
The Herzing rubric for a screening evaluation tends to reward the description of the test, the two-by-two table and calculations, the interpretation of predictive values, the discussion of consequences and the overall judgment. Calculation criteria require correct sensitivity, specificity and predictive values with working shown. Interpretation points go to a clear explanation of why predictive values change with prevalence. Consequences often separate strong papers from adequate ones, because the rubric rewards attention to what a false result means for the person holding it. Counting false positives is a start; describing the colonoscopy each one triggers finishes the thought. The judgment earns marks when it is supported by guidelines and the calculations. Writing covers readable tables and APA 7 references, including Trevethan's explanation of the measures and the Task Force's 2021 screening statement.
NU 602 Unit 6 help before you submit
The commonest Unit 6 error is calculating predictive values down the columns instead of across the rows. Another is treating sensitivity and specificity as if they change with prevalence. Students often present percentages without the table, which hides errors. The effect of prevalence on predictive values is frequently stated but not shown with numbers. Consequences are reduced to a sentence about anxiety. Cutoff changes are presented as improvements rather than trade-offs. Finally, source your performance figures or label them illustrative, because graders need to know whether the 79 percent sensitivity reflects published data or an example built for the exercise.
Get a NU 602 Unit 6 example written to your instructions
Send the Unit 6 instructions and the rubric from your NU 602 classroom, plus the test, the condition and any table your section provides. We write a custom example with the two-by-two labeled and computed, the two pairs of measures separated, prevalence varied to move predictive value and the harms costed per error, returned in 24 to 48 hours. The first custom sample is free.
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NU 602 Unit 6 questions, answered
Why does prevalence change the answer?
Because predictive value depends on how much disease is present, while sensitivity and specificity do not. Run an excellent test through a population where the condition is rare and most positive results will belong to healthy people, simply because there are so many more of them to misclassify. That is not a flaw in the test; it is why screening is targeted at groups with elevated risk.
Which error is worse, a false positive or a false negative?
It depends on the condition and on what follows the result, and your paper should argue the case rather than assert a rule. A missed aggressive cancer costs more than an unnecessary repeat image, while a false positive leading to an invasive procedure with real complications is not a trivial event either. Name the downstream pathway for each and let that decide.
Do I need real performance figures for the test?
Use published figures where your instructions ask for a real test, and cite them, since values differ by population and by the cutoff a study used. Where the section supplies a table or asks for a constructed one, label the numbers as illustrative. Either way, never mix invented cell counts into a paragraph discussing a genuine screening program.
Where can I find a free NU 602 Unit 6 sample paper?
The whole NU 602 Unit 6 FIT evaluation is posted here for free, both two-by-two tables, the cutoff trade-off and the consequences. A custom evaluation can examine another screening test or population your section names.
Why does disease prevalence change a test's positive predictive value?
With low prevalence, most positive results come from the large group without disease, so false positives outnumber true ones. This sample shows the same FIT has a much higher positive predictive value in symptomatic patients than in average-risk screening.