Smoking, diabetes and income are traced through a periodontitis and heart disease association in this PU570 Unit 5 confounding analysis example, which shrinks a crude ratio of 1.52 toward 1.0. Searches like "pu 570 unit 5 assignment example", "pu570 unit 5 sample" and "pu570 unit 5 example" land here.
What a finished PU570 Unit 5 confounding analysis looks like
The analysis runs about six pages and starts from a causal diagram. Periodontitis and heart disease sit at either end; smoking, diabetes, low income and age sit above, each with arrows into both. A crude table follows: 222 heart disease cases among 2,400 adults with periodontitis, 9.3 percent, against 340 among 5,600 without, 6.1 percent, for a crude ratio of 1.52. The table is then split by smoking. Half the exposed group smoked, against a fifth of the unexposed, and within each stratum the ratio falls to about 1.15, with a Mantel-Haenszel summary of 1.15. A supplied regression table adds diabetes and income, and the hazard ratio settles at 1.09 with an interval from 0.94 to 1.27. A closing section estimates how large the leftover confounding might be.
How a PU570 Unit 5 example is structured
The diagram comes first because it commits the analysis to its assumptions before any number is seen. Each proposed confounder is justified in a sentence with two links, one to periodontitis and one to heart disease, and one candidate is deliberately left off: systemic inflammation, which sits on the pathway from gum disease to arterial damage and would remove part of any real effect if adjusted for. The crude table then shows the starting point. Stratification by smoking is presented in full rather than summarized, since seeing the ratio fall from 1.52 to about 1.15 within both strata shows confounding more clearly than a coefficient can. The regression table follows for the remaining factors. Residual confounding is treated last and concretely: smoking was recorded as current yes or no, with no pack-years, so heavy smokers remain mixed with light ones.
A diagram before the data
Four common causes, each with arrows into both exposure and outcome, and inflammation drawn as a mediator. A mediator, the analysis explains, must stay out of the adjustment set.
Why these factors cluster
Smoking damages gum tissue and arteries; diabetes worsens both; lower income limits dental care and raises cardiovascular risk. The analysis gives each link a citation rather than an assumption.
The crude association
Risks of 9.3 and 6.1 percent over ten years, a ratio of 1.52. The analysis reports it as the starting point and makes no causal claim about it.
Split by smoking
Ratios of 1.16 among smokers and 1.15 among never-smokers, summarized at 1.15. The excess above 1.0 shrinks from 0.52 to 0.15 once a single variable is held constant.
The fuller model
Adding diabetes and income brings the hazard ratio to 1.09, interval 0.94 to 1.27. The analysis reads this as compatible with no effect and with a small one.
What a yes-or-no smoking variable leaves behind
Heavy and light smokers share one category, so residual confounding by intensity is likely. The analysis argues it would keep the adjusted figure too high, not too low.
Where marks go in PU570 Unit 5
Confounding analyses in this course are typically judged on identification, justification and handling, with a fourth row in many sections for what remains after adjustment. Identification marks go to named variables specific to this population rather than a generic list of demographics. Justification needs both links for each confounder, which is why each link carries its own citation. Handling marks reward a method shown working, here the stratified table, and a correct decision about what not to adjust for; controlling for inflammation would cost the row. The residual row goes to a concrete account of mismeasured variables and the direction they push. Deductions follow analyses that adjust for everything available, that treat a nonsignificant adjusted ratio as proof of no effect, or that omit the crude figure altogether.
Get a PU570 Unit 5 example written to your instructions
Whatever exposure and outcome the PU570 Unit 5 scenario pairs, the same sequence of diagram, strata and residual account applies. Include the dataset or article, the questions and the rubric. Within 24-48 hours a free first custom sample arrives with every confounder justified by both of its links and every adjustment decision explained.
PU570 Unit 5 questions, answered
Why leave inflammation out of the adjustment?
Because it may be how periodontitis harms arteries, which makes it a mediator rather than a confounder. Adjusting for a mediator removes part of the effect being estimated and biases the result toward no association. A causal diagram makes that distinction visible before any modeling begins, which is why many graduate sections expect one in a confounding analysis.
Does an adjusted ratio of 1.09 mean periodontitis has no effect?
No. The interval from 0.94 to 1.27 includes no effect and also a modest increase. The honest reading is that most of the crude association is explained by shared causes, and that any remaining effect is small and imprecisely estimated. A paper that reports the adjusted result as a null finding claims more certainty than the interval allows.
How should residual confounding be discussed?
Concretely. Name the variable, say how it was measured, and explain the direction the mismeasurement would push the estimate. Smoking recorded only as current yes or no leaves differences in intensity uncontrolled, and since heavier smokers have worse gums and more heart disease, the adjusted ratio probably still overstates any true effect. General statements about unmeasured factors earn little.