Assisted Reproductive Technology and Cardiovascular Outcomes in Parents and Offspring

Wei, Shu Qin; Paradis, Gilles; Ayoub, Aimina; Lewin, Antoine; Auger, Nathalie

doi:10.1016/j.cjca.2023.09.013

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…One example of where this research/policy interaction occurs is in the study of the relationship between MAR and later health outcomes. For example, observational studies have linked MAR to cardiovascular disorders, diabetes, thyroid disease, obesity, and cancer ( Dayan et al , 2017 ; Vassard et al , 2018 ; Murugappan et al , 2021 ; Cullinane et al , 2022 ; Yiallourou et al , 2022 ; Magnus et al , 2023 ; Sachdev et al , 2023 ; Wei et al , 2023 ). Given that more than 1.9 million IVF cycles are performed each year globally, with a similar number of ovulation induction cycles estimated ( Chambers et al , 2021 ), even a slight increase in the risk of poor health outcomes following such treatments may represent a significant burden of disease and concern for patients and clinicians.…”

Section: Case Study: Health Outcomes Following Assisted Reproduction ...mentioning

confidence: 99%

Estimating the impact of bias in causal epidemiological studies: the case of health outcomes following assisted reproduction

Walker,

Venetis,

Opdahl

et al. 2024

Human Reproduction

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Researchers interested in causal questions must deal with two sources of error: random error (random deviation from the true mean value of a distribution), and bias (systematic deviance from the true mean value due to extraneous factors). For some causal questions, randomization is not feasible, and observational studies are necessary. Bias poses a substantial threat to the validity of observational research and can have important consequences for health policy developed from the findings. The current piece describes bias and its sources, outlines proposed methods to estimate its impacts in an observational study, and demonstrates how these methods may be used to inform debate on the causal relationship between medically assisted reproduction (MAR) and health outcomes, using cancer as an example. In doing so, we aim to enlighten researchers who work with observational data, especially regarding the health effects of MAR and infertility, on the pitfalls of bias, and how to address them. We hope that, in combination with the provided example, we can convince readers that estimating the impact of bias in causal epidemiologic research is not only important but necessary to inform the development of robust health policy and clinical practice recommendations.

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