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Purpose Real-life use of nonsteroidal anti-inflammatory drugs (NSAIDs) is dynamic. This study aimed to characterize the temporal association between time-varying NSAID exposure and acute myocardial infarction (MI). Methods Nested case-control analyses were conducted on a Quebec administrative health cohort. NSAID dose, confounders, and outcome status were determined for each day of follow-up. To better account for dose and timing of past exposures, flexible weighted cumulative exposure models were also fitted. Results The cohort consisted of 233 816 older adults including 21 256 acute MI cases. Dose-related increased risks of MI were found with current use of all NSAIDs. In models not accounting for duration of use, ORs (95%CI) for the most common current daily dose vs. no current exposure were: celecoxib 200 mg: 1.16 (1.10, 1.22), diclofenac 150 mg: 1.59 (1.38, 1.84), ibuprofen 1200 mg: 1.42 (1.17, 1.74), naproxen 750 mg: 1.38 (1.21, 1.58), and rofecoxib 25 mg: 1.54 (1.43, 1.66). Weighted cumulative exposure models confirmed that all NSAIDs—including naproxen—are associated with an increased risk of MI and suggested that doses taken up to 3 weeks ago for rofecoxib, ibuprofen, and naproxen and up to 75 days ago for diclofenac and celecoxib may contribute to the current MI risk. However, the celecoxib risk seems to require continuous use for more than 30 days, whereas for other NSAIDs, a heightened MI risk occurs within 7 days. Conclusions Weighted cumulative exposure analysis uncovered NSAID-specific differences in the immediate MI risk and how this risk seems to accumulate. KEY POINTS Accurate assessment of drug safety requires an etiologically correct model encompassing all relevant aspects of exposure. Weighted cumulative exposure models suggest that the relative importance of past doses on the risk of MI differs among NSAIDs. All common NSAIDs are associated with an increased MI risk. Celecoxib MI risk seems to depend on continuously using the drug for more than 30 days, whereas for ibuprofen, rofecoxib, diclofenac, and naproxen, a heightened MI risk occurs within 7 days of use.

Purpose Interaction and subgroup analyses remain controversial topics in epidemiology. A recent theoretical paper suggested that a combination of no overall treatment-outcome association and treatment effect limited to a single subgroup would imply a clinically implausible interaction, with opposite treatment effects in the two subgroups. However, this argument was based entirely on point estimates and ignored sampling error and statistical inference. Methods We simulated hypothetical studies in which treatment truly affected the outcome in only one subgroup, with no effect in the other subgroup. We generated 1000 random samples for three study designs (small clinical study, case–control, and large cohort), and different values of total sample size (N), relative size of the affected subgroup, and treatment effect. We estimated the frequency of significant results for tests of overall and subgroup-specific treatment effects, and treatment-by-subgroup interaction. Results Combination of statistically non-significant overall treatment effect and significant treatment-by-subgroup interaction occurred frequently, especially if the affected subgroup was proportionally smaller, even in studies with high power to detect the overall effect (e.g. in 37.1% of samples with N = 20 000, with 600 outcomes, and an effect (odds ratio of 1.5) limited to 30% of subjects). Furthermore, in most samples with a significant interaction, subgroup analyses correctly indicated that the significant effect was limited to one subgroup. Conclusion In studies where the treatment truly affects the risks in only one subgroup, a non-significant overall effect will often coincide with a statistically significant treatment-by-subgroup interaction. Thus, a non-significant overall effect should not prevent testing plausible interactions. Copyright © 2013 John Wiley & Sons, Ltd.

Interaction and subgroup analyses remain controversial topics in epidemiology. A recent theoretical paper suggested that a combination of no overall treatment-outcome association and treatment effect limited to a single subgroup would imply a clinically implausible interaction, with opposite treatment effects in the two subgroups. However, this argument was based entirely on point estimates and ignored sampling error and statistical inference.We simulated hypothetical studies in which treatment truly affected the outcome in only one subgroup, with no effect in the other subgroup. We generated 1000 random samples for three study designs (small clinical study, case-control, and large cohort), and different values of total sample size (N), relative size of the affected subgroup, and treatment effect. We estimated the frequency of significant results for tests of overall and subgroup-specific treatment effects, and treatment-by-subgroup interaction.Combination of statistically non-significant overall treatment effect and significant treatment-by-subgroup interaction occurred frequently, especially if the affected subgroup was proportionally smaller, even in studies with high power to detect the overall effect (e.g. in 37.1% of samples with N = 20 000, with 600 outcomes, and an effect (odds ratio of 1.5) limited to 30% of subjects). Furthermore, in most samples with a significant interaction, subgroup analyses correctly indicated that the significant effect was limited to one subgroup.In studies where the treatment truly affects the risks in only one subgroup, a non-significant overall effect will often coincide with a statistically significant treatment-by-subgroup interaction. Thus, a non-significant overall effect should not prevent testing plausible interactions.