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Published on: May 20, 2014
Do nonsteroidal anti-inflammatory drugs affect the risk of developing ovarian cancer? A meta-analysis
Stefanos Bonovas1, Kalitsa Filioussi, Nikolaos M Sitaras
1Department of Pharmacology, School of Medicine, University of Athens, Athens, Greece. sbonovas@med.uoa.gr
Aim:
The relationship between the use of nonsteroidal anti-inflammatory drugs (NSAIDs), including aspirin, and the risk of ovarian cancer has been controversial. This study examines the strength of this association by conducting a detailed meta-analysis of the studies published in peer-reviewed literature on the subject.
Methods:
A comprehensive search for articles published up to April 2004 was performed, reviews of each study were conducted and data were abstracted. Prior to meta-analysis, the studies were evaluated for publication bias and heterogeneity. Pooled relative risk estimates (RR) and 95% confidence intervals (CIs) were calculated.
Results:
Ten reports (six case-control and four cohort studies), published between 1998 and 2004, were identified. There was no evidence of an association between aspirin use and ovarian cancer risk either assuming a random-effects model (RR = 0.92, 95% CI 0.80, 1.06), or a fixed-effects model (RR = 0.93, 95% CI 0.81, 1.06). Similarly, we did not find evidence of an association between non-aspirin NSAID use and ovarian cancer, both on the basis of a random-effects model (RR = 0.86, 95% CI 0.68, 1.08), and on the basis of a fixed-effects model (RR = 0.88, 95% CI 0.76, 1.01). When the analyses were stratified into subgroups, there was no evidence that study design substantially influenced the estimate of effects. Furthermore, our analysis did not show decreasing risks with increasing frequency or duration of use, features often associated with causal relationships.
Conclusions:
Our meta-analysis findings do not support that NSAID use plays a role in the chemoprevention of ovarian cancer. Future research should examine potential relationships between specific NSAIDs and ovarian cancer, taking into account the possible biases that may have affected this meta-analysis.
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