IHP 340 Module 4 Confidence Interval Journal example

Reviewed by Hattie Culpepper, MA Statistics for Healthcare Professionals Southern New Hampshire University Full sample paper Free custom sample in 24 to 48h

This complete IHP 340 Module 4 journal explains confidence intervals through three results from one real trial, SPRINT, which compared intensive and standard blood pressure targets. A composite clinic quality coordinator reads the intervals for the main cardiovascular outcome, for death from any cause and for stroke, and explains what each interval says about precision, whether it rules out no effect, and why an interval that crosses 1 is not proof of no benefit. The trial and statistical sources are real.

What this page holds

This finished IHP 340 Module 4 journal entry, about 360 words, interprets three 95 percent confidence intervals from the SPRINT trial, explains the link to statistical significance and precision, and reflects on how intervals change a manager's reading of results. Searches like "ihp 340 module 4 assignment", "ihp340 module 4 confidence interval journal" and "ihp 340 module 4 example" land here.

The IHP 340 Module 4 example, in full

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Module Four Journal: Understanding Confidence Intervals

Journal: Three intervals from one trial

Before this module, when I read a study I looked for the P value and stopped. A confidence interval gives more: a range of values for the true effect that are reasonably compatible with the data, where the width shows how precise the estimate is and the position shows whether no effect is ruled out (Daniel & Cross, 2018). I practiced on the SPRINT trial, which randomized adults at high cardiovascular risk to a systolic blood pressure target below 120 or below 140.

For the main composite outcome of heart attack, stroke, heart failure and cardiovascular death, the hazard ratio was 0.75, and its 95 percent interval ran from 0.64 to 0.89 (SPRINT Research Group, 2015). A hazard ratio of 1 would mean no difference. The whole interval sits below 1, so the benefit is statistically significant, and even the least favorable value, 0.89, still represents an 11 percent lower rate of events.

For death from any cause, the hazard ratio was 0.73, with an interval of 0.60 to 0.90 (SPRINT Research Group, 2015). This interval also excludes 1, but it is slightly wider, because deaths were fewer than composite events and fewer events mean less precision.

For stroke alone, the trial reported a hazard ratio of 0.89 with an interval of 0.63 to 1.25. This interval crosses 1, so the difference is not statistically significant. But the interval runs from a 37 percent reduction to a 25 percent increase, which means the trial could not tell us much about stroke either way, not that intensive treatment has no effect on stroke. Reading it as proof of no effect would be a common and serious error (Hazra, 2017).

The exercise changed how I read the quality reports at my clinic. Our dashboard reports each provider's blood pressure control rate as a single percentage. A provider with 20 hypertensive patients who has a control rate of 60 percent has a far wider interval around that rate than one with 400 patients. I plan to ask our analyst to add intervals, so that we stop ranking providers on differences that are within the range of chance.

What this page is doingThe journal interprets three intervals, each illustrating a different point: significance, precision and the meaning of an interval that crosses the null. The highlighted sentence corrects the most common misreading of a nonsignificant result.
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References

Daniel, W. W., & Cross, C. L. (2018). Biostatistics: A foundation for analysis in the health sciences (11th ed.). Wiley.

Hazra, A. (2017). Using the confidence interval confidently. Journal of Thoracic Disease, 9(10), 4124-4129. https://doi.org/10.21037/jtd.2017.09.14

SPRINT Research Group. (2015). A randomized trial of intensive versus standard blood-pressure control. New England Journal of Medicine, 373(22), 2103-2116. https://doi.org/10.1056/NEJMoa1511939

How this IHP 340 Module 4 example is structured

A journal entry is reflective but should still teach, so this one is anchored in three numbers. The first paragraph explains what a confidence interval is in plain terms. The next three paragraphs each take one interval from the trial and explain what it shows. The last paragraph reflects on how reading intervals, rather than P values alone, changes the writer's approach to reports at work.

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Send your IHP 340 Module 4 journal prompt and the rubric. A journal entry on confidence intervals written to that prompt arrives within 24 to 48 hours, and the first sample is free. The paper above is an original model document written by our desk, not a submitted student paper and not an official Southern New Hampshire University document.

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IHP 340 Module 4 questions, answered

What does the IHP 340 Module 4 journal usually ask?

Mid-course journals in healthcare statistics often ask students to explain confidence intervals in their own words, interpret an interval from a study or dataset, and reflect on how the concept applies to decisions in healthcare. Check your prompt for any required example or length.

What does a 95 percent confidence interval mean?

If the same study were repeated many times and an interval calculated each time, about 95 percent of those intervals would contain the true value. Practically, it gives a range of values that are reasonably compatible with the data, and its width shows how precise the estimate is.

How can I tell from a confidence interval whether a result is significant?

For ratios such as hazard ratios or odds ratios, an interval that does not include 1 indicates a statistically significant difference at the matching level. For differences in means, the null value is 0. An interval that includes the null value is compatible with no effect, but it may also be compatible with a meaningful effect.