Our study included the same limitations concerning the sample size as those in the Nissen and Wolski study.1
Those authors recommended an analysis of a larger population with a longer-follow-up time to provide more definitive answers about CV mortality rates and the use of rosiglitazone. Our study confirmed that rosiglitazone resulted in an increased risk of MI and stroke by 20% at the 5% significance level. This rate was comparable to the findings in a population-based study of older patients by Lipscombe et al.18
However, we did not find sufficient evidence that TZDs as a drug class increased the risk of a CV event in this high-risk population. One possible explanation is that our study population tended to be younger and was not typical of patients seen in randomized clinical trials. As stated earlier, our population consisted mostly of females and an African-American study sample, in which approximately one-third were from the city of Baltimore.
In a separate regression, our study also demonstrated that rosiglitazone use was associated with an increase in CV events, whereas pioglitazone was not. Our study supported previous meta-analysis trials and further suggested that female sex, age, and pre-existing heart conditions might be strong predictors of CV events. Interestingly enough, obesity was a CV protective factor. Because our study already controlled for preconditions like hypertension, hyperlipidemia, or a pre-existing heart condition, this finding suggested that obesity itself (not its related comorbidities) might not be a predictor of CV events in this case.
TZD doses were not significantly associated with an increased risk of CV events. Although Nissen and Wolski1
and Lincoff et al.19
suggested that pioglitazone lowered the risk of death, MI, or stroke among a diverse population of patients with diabetes, our study showed no evidence that pioglitazone reduced the likelihood of a CV event (MI or stroke) in the Medicaid population. Therefore, it is difficult for us to suggest that the insufficient evidence of an increased risk of TZD drug class effects resulted from two opposite drug effects in the same class. When rosiglitazone increases the risk, as pioglitazone decreases the risk, the offsetting of these opposite drug effects would be anticipated. Instead, we did not establish evidence that pioglitazone lowered the risk of a CV event in a high-risk population.
Another study appeared to provide a possible explanation for our findings. Türkemen et al. found that in patients with type-2 diabetes, “TZD treatment might have slight adverse effects on ventricular contractility and fluid dynamics at the beginning of the therapy. However, these changes seem to stabilize in the long term.”20
To further test and confirm this explanation, it might be useful to analyze the comparative or compatible samples using Medicare and Medicaid data sets from other states with a similar demographic distribution of patient populations.
Several limitations were inherent in our study.
First, although our propensity score–matching method significantly reduced bias, there could still be residual bias.21
Further, we used five strata in propensity scoring, and the difference in mean of covariates was not statistically significant to maximize the possibility that the distribution of covariates could be practically considered random between the two treatment groups. However, important clinical differences might still exist, even though statistical significance was not reached. We might be missing some critical, unobserved variables or risk factors for CV outcomes (e.g., unreported smoking).
Second, we used inclusion and exclusion criteria to define exposure to drugs and to identify the appropriate sample for our research. This process can help us better characterize our study sample, but it is also possible that we proceeded with certain types of selection bias with sampling when applying the exclusion and inclusion criteria of our study. We believe that propensity score methods and the discussions of sensitivity tests included in the study address that issue.
Because of the nature of common claims data, we do not have accurate information to control for severity of disease. It is not clear whether our findings represent only drug effects or the severity of disease effects. Also, ICD-9 codes were used to identify diseases; therefore, our study is subject to possible misclassification bias.
We did not explain the change in prescribing patterns. If doctors were aware of the research findings regarding rosiglitazone’s adverse effect during our observation period, this could have changed the prescription patterns and the study results. Although it is unlikely that a major shift in the prescription pattern occurred before July 2006 (the date of the first publication was May 2007), it is possible that physicians became aware of the risks of TZD prescribing for diabetic patients. This awareness could have contributed to the change in prescribing patterns.
Our study did not include a placebo group as a reference group. We did not distinguish specific TZD drug effects from those in a placebo-treated control group. Interpretations of studies involving active and multiple drug comparator groups differ from those related to placebo-controlled analyses.
Finally, we might have introduced confounding by comedication bias into the data set. We did not control for this factor because our study used an active multiple-drug comparator group as a reference group. TZDs are frequently used in combination with other antidiabetic drugs.