To assess the relationship between lifetime ovulatory years (LOY) and Epithelial ovarian cancer (EOC) risk and survival.
A systematic review was performed in accordance with PRISMA guidelines. Relevant studies were identified from PubMed, MEDLINE, and Embase through December 31, 2021 combining the following search: [("ovulation" or "ovulation cycles" or "ovulatory age" or "ovulatory cycles") and ("ovarian cancer" or "ovarian neoplasms") and ("humans" and "female")]. Reference lists of identified articles were searched for additional studies. Studies were excluded from consideration if they were not a published, peer-review article; not in English; lacked data on effect sizes; had data included in another publication; or were a review article, cross-sectional study, or case report. Two independent investigators screened abstracts and full texts for eligibility, extracted study-level data, and assigned study quality. Disagreements between abstractors were discussed and resolved by consensus.
Thirty-one reports were included in the qualitative review of LOY and EOC risk, inclusive of 24 studies with sufficient data to be included in the meta-analysis. Women with the highest level of LOY had 2.26 times higher odds of EOC than women with the lowest level of LOY (95% CI 1.94-2.83). LOY was associated with risk of serous (pooled OR 2.31, 95% CI 1.60-3.33) and endometrioid tumors (pooled OR 3.05, 95% CI 2.08-4.45) but not mucinous disease (pooled OR 1.52, 95% CI 0.87-2.64). There were only four studies examining the LOY-survival association, which precluded a quantitative assessment; however, three of the published studies reported worse outcome with greater LOY.
LOY is a risk factor for specific EOC histotypes and may also influences EOC survival. Standard definitions of LOY, participant-level data, and larger sample size will enable more precise quantitation of the LOY-EOC association, which can inform EOC risk assessment models.

Lifetime ovulations and epithelial ovarian cancer risk and survival: A systematic review and meta-analysis.

Analysis of rare binary events is an important problem for biomedical researchers. Due to the sparsity of events in such problems, meta-analysis that integrates information across multiple studies can be applied to increase the efficiency of statistical inference. Although it is critical to examine whether the effect sizes are homogeneous across all studies, a comprehensive review of homogeneity tests has been lacking, and in particular, no attention has been paid to infrequent dichotomous outcomes. We systematically review statistical methods for homogeneity testing. By conducting an extensive simulation analysis and two case studies, we examine the performance of 30 tests in meta-analysis of rare binary outcomes. When using log-odds ratio as the association measure, our simulation results suggest that there is no uniform winner. However, we recommend the test proposed by Kulinskaya and Dollinger (BMC Med Res Methodol, 2015, 15), which uses a gamma distribution to approximate the null distribution, for its generally good performance; for very rare events coupled with small within-study sample sizes, in addition to the Kulinskaya-Dollinger test, we further recommend the conditional score test based on the random-effects hypergeometric model proposed by Liang and Self (Biometrika, 1985, 72:353-358). One should be cautious about the use of the Wald tests, the Lipsitz tests (Biometrics, 1998, 54:148-160), and tests proposed by Bhaumik et al (J Am Stat Assoc, 2012, 107:555-567).

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