How strong would a hidden confounder need to be? PHLT 8520's Week 9 sensitivity argument example computes an E-value of 3.0 for an illustrative risk ratio and weighs its plausibility. Searches like "phlt 8520 week 9 assignment example", "phlt8520 week 9 sample" and "phlt 8520 week 9 example" land here.
What a finished PHLT 8520 Week 9 sensitivity argument looks like
Four headed parts fill about three pages. Part one states the finding being tested, an adjusted risk ratio of 1.8 among first pregnancies, marked illustrative, and names the three distortions that could threaten it: unmeasured confounding, exposure misclassification and selection at confirmation. The second part works the confounding threat, computing an E-value of 3.0 and writing out what that means: a hidden factor tied to both night work and preterm birth by risk ratios of at least 3.0 each, beyond the measured covariates. The third part takes misclassification and argues, from the earlier measurement brief, that nondifferential error would more likely have hidden an effect than produced one. The fourth weighs selection at confirmation in words alone. A closing verdict states the size of distortion the finding survives and which threat remains most credible.
How a PHLT 8520 Week 9 example is structured
The finding is stated first and never restated with more confidence later, which fixes the argument's target. Threats are then taken in order of how directly they could overturn the result, confounding first because only it can manufacture an association from nothing. VanderWeele and Ding are credited for the E-value, which turns the question into a single threshold, and the argument adds what the number alone cannot: a judgment of whether financial strain could reach that threshold once household income is already adjusted for. Cornfield and colleagues' reasoning in the smoking debate, that a hidden cause able to explain an association must be more common among the exposed by a ratio at least as large as the association itself, is named as the older form of the idea. Lash, Fox and Fink are credited for treating bias as a quantity with stated parameters. Selection stays qualitative.
The number under pressure
An illustrative adjusted risk ratio of 1.8 among first pregnancies is stated once and never inflated later.
A threshold for a hidden cause
An E-value of 3.0 sets how strongly financial strain would need to link to both shifts and birth timing.
Is that strength believable?
With household income already adjusted, the argument judges a residual link of that size unlikely but not ruled out.
Misclassification leans the other way
Nondifferential exposure error would more plausibly have hidden an effect than created one.
Selection, judged in words
Losses before confirmation are weighed qualitatively because no data describe their exposure.
What the finding survives
A closing verdict names the distortion the result withstands and the threat still most credible.
Where marks go in PHLT 8520 Week 9
A sensitivity argument is credited for converting worry into a threshold and then judging the threshold, and this one does both. The E-value computation earns a modest share on its own; the larger block goes to the paragraph asking whether financial strain could plausibly reach that strength once income is in the model. Ordering threats by their power to manufacture an association earns reasoning credit, demonstrating awareness that confounding and nondifferential error threaten in different directions. The misclassification section is rewarded for carrying the earlier measurement conclusion forward. Keeping selection qualitative, with a reason, earns calibration credit. Arguments stall when an E-value is reported as proof of causation, when every bias is assumed to point toward the null, or when a large threshold is called impossible without saying why.
Get a PHLT 8520 Week 9 example written to your instructions
Send your Week 9 prompt and rubric, with the finding your section must test and the study it comes from. The argument computes a threshold for the most credible hidden distortion, judges whether that threshold is plausible in your setting, and states what the result withstands. First argument free; expect it in 24-48 hours.
PHLT 8520 Week 9 questions, answered
What does an E-value actually tell a reader?
The minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need with both exposure and outcome, beyond measured covariates, to fully explain away an observed association. VanderWeele and Ding introduced it to make sensitivity analysis easy to report. It says nothing about whether such a confounder exists; that judgment belongs to the author. The example makes that judgment in writing, and a custom argument does the same.
Where does the 1.8 come from?
Nowhere but this page. It was chosen as a round, moderate value that a plausible confounder might or might not explain, which makes the sensitivity argument worth having, and it carries an illustrative label wherever it appears. No study of shift work and preterm birth is summarized. If your section supplies a real estimate, a custom argument computes the threshold from it and cites the source.
Is quantitative bias analysis the same as an E-value?
The E-value is one narrow tool within a wider practice. Quantitative bias analysis, as set out by Lash, Fox and Fink, assigns explicit values to bias parameters, such as the sensitivity and specificity of an exposure record, and recalculates the estimate under them. The example uses the E-value for confounding and reasons qualitatively about the rest. A custom argument can run a simple bias analysis where the prompt asks.