Case Crossover paper and time trends

There was a new paper e-published recently in Pharmacoepidmeiology and Drug safety that used the case-crossover study design:

"Purpose
Elevated levels of phosphorus (P) and calcium (Ca) have been shown in observational studies to be associated with an increased risk of adverse clinical outcomes including mortality. Vitamin D sterols have been shown to increase the risk of hypercalcemia and hyperphosphatemia in clinical trials. We sought to explore these risks in real-world clinical practice.
Methods
We employed a case–crossover design, which eliminates confounding by non-time-varying patient characteristics by comparing, within each patient, vitamin D doses before the event with those at an earlier period. Using this method, we estimated the risk of hypercalcemic (Ca ≥ 11 g/dL) and hyperphosphatemic (P ≥ 8 g/dL) events for patients at different dose quartiles of vitamin D relative to patients not on a vitamin D sterol.
Results
There was a dose-dependent association between vitamin D dose quartile and risk of hypercalcemia or hyperphosphatemia. In adjusted analyses, each increase in vitamin D quartile was associated with a multiple of hypercalcemia risk between 1.7 and 19 times compared with those not on vitamin D and a multiple of hyperphosphatemia risk between 1.8 and 4.
Conclusion
Use of vitamin D sterols is associated with an increased risk of hypercalcemic and hyperphosphatemic events in real-world clinical practice. Other potential predictors of these events, such as phosphate binder use and dialysate Ca levels, were not examined in this analysis."

It seems to be an interesting paper but I have one concern. If you look at the discussion section of the paper, the authors note that:

In our sensitivity analysis, we used 1-month periods to assess vitamin D exposure. In this analysis, estimates of the association between vitamin D dose and risk of events were smaller than those in the primary analysis, particularly for hypercalcemia. One possible explanation for this finding is that the average 2-month exposure measure is a superior indicator, compared with the 1-month assessment, of both the dose and duration of vitamin D exposure. As well, it could be that some dose changes in the month prior to the event had already occurred in response to increasing Ca levels and that, for this reason, the dose 2 months prior to the event is a more accurate reflection of the dose that gave rise to the hypercalcemic or hyperphosphatemic event.

Another explanation that I did not see addressed is the possibility that there is a time trend occuring. If the frequency of vitamin D administration (or the dose) increased with time then you would expect to see smaller estimates in the sensitivity analysis as well. But it would be an artefact of changing exposure over time.

That being said, it was a real pleasure to read a well justified use of the case-crossover design in a medication paper. Hopefully this is a sign that there will be more use of with-in person study designs in the future in epidemiology. The ability to handle time invariant confounders is a serious advantage of this approach.

Why restrict the types of NSAIDs used as active comparators?

I am always surprised when people do not include negative controls when there is a very obvious candidate to be used as such. Consider this article:

Chronic analgesic use with either COT or COX-2 was associated with an increased risk of cardiovascular outcomes. These findings suggest either a selection of high-risk patients to chronic analgesic treatment, coupled with unmeasured or residual confounding, or a potential cardiovascular effect of these medications. Further research is warranted to evaluate causes for this association.

Why did the researchers not use an active comparator that is known to be null (a negative control)? After all, the participants who are tkaing pain medication may be systematically different from those who do not. It is prescription claims data so it is unclear whether or not you can adjust for these kinds of differences.

So why would you not at least look at Naproxen and Ibuprofen users?

Yes, the categories were: Opioids, Rofecoxib, Celecoxib,Valdecoxib, and General population. Covariates were:

We derived variables representing demographics, medical history of angina, coronary heart disease (CHD), congestive heart failure, arrhythmias, ischemic stroke, transient cerebral ischemia, peripheral vascular disease, diabetes, hypertension, hyperlipidemia, hypercholesterolemia, smoking, and obesity; and dispensing of nitrates, anti-platelet agents, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, beta-blockers, calcium channel blockers, and diuretics. Baseline history of chronic diseases of the musculoskeletal system, diseases of the esophagus, hyperthyroidism, medical care required for general ill-defined symptoms and respiratory or chest symptoms, including dyspnea and upper respiratory symptoms, were also included.

Now I have done a paper on the misclassification of ibuprofen and naproxen in claims data but the issue there was sensitivity and not specificity. There is no reason that naproxen or ibuprofen could not be negative controls (or that aspirin could not be a positive control). It would certainly make the unexpected results of this analysis easier to interpret!