Long-term Impact of Fertility Treatment Study

生育治疗的使用正在增长，多达10%的分娩发生在使用辅助生殖技术后。 技术（ART）或非IVF生育治疗（NIFT）。这些治疗往往需要大量的社会 以及个人对资源和时间的投资，但关于长期健康或 治疗后出生的儿童的发展结果。事实上，大多数现有的文献都围绕着 出生或婴儿期发生的事件，对以后生活中发生的事件的发现有限且混合。本有限 文献还反映了主要的方法学挑战，包括（i）没有足够的后续时间来评估 青春期后和成年期的医学状况发展;（ii）许多潜在的混杂因素， 包括需要治疗的原始原因（不孕症），以及获得和使用治疗的变化， 例如，在一个实施例中，按收入或受教育程度的差别获取;以及（iii）使用分析方法， 相关的偏倚来源，或依赖于定义不清的假设来评估治疗效果。既要解决 需要更多的证据，并减轻方法上的问题，我们建议比较的风险， 使用多个基于人群的、全国范围内丰富的 数据集与现代因果推理方法相结合。我们的目标是研究生育率的影响 治疗（按不孕类型分层）对两种类型的结果：1）临床事件; 2）教育 事件我们将研究1980年至2020年期间生育治疗后出生的近40万名儿童， 人口（在丹麦，荷兰，瑞典和马萨诸塞州）。在这些人群中， 关于我们的成果和潜在共同创始人的全面的个人信息，例如，不孕症的类型， 治疗、教育、收入和临床特征。我们将进行预测和因果分析，以便 告知临床医生和潜在的父母，因为他们首先考虑使用生育治疗，然后选择 可用的治疗。我们的统计方法包括一系列的方法，从传统的 生存和重复测量分析，以及最近开发的因果分析技术， 观测数据，例如，逆概率加权我们还将探索其他方法， 工具变量和g公式。简而言之，这项研究提供了评估长期 生育治疗后出生的儿童的结局，随访时间最长， 人口统计学、临床和治疗特征，以及最先进的分析方法的使用。

Use of fertility treatments is growing with as many as 10% of births occurring after use of assisted reproductive technologies (ART) or non-IVF fertility treatments (NIFT). These treatments often require substantial societal and individual investments of resources and time, yet there is little information on the long-term health or development outcomes for children born after treatment. Indeed, most of the existing literature centers around events occurring during birth or infancy, with limited and mixed findings about events later in life. This limited literature also reflects major methodological challenges, including (i) insufficient follow-up time to assess the development of medical conditions past adolescence and into adulthood; (ii) numerous potential confounders, including the original reason for needing treatment (infertility), and variation in access to and use of treatments, e.g., differential access by income or education; and (iii) use of analytic approaches that either do not address relevant sources of bias, or rely on ill-defined assumptions to assess treatment effects. To both address the need for more evidence and mitigate the methodological concerns, we propose to compare the risk of outcomes associated with different fertility treatments using multiple population-based, nationwide rich datasets combined with modern causal inference approaches. Our aims are to examine the impact of fertility treatments (stratified by the type of infertility) on two types of outcomes: 1) clinical events; and 2) educational events. We will study nearly 400,000 children born after fertility treatments during 1980-2020 in four populations (in Denmark, the Netherlands, Sweden, and Massachusetts). Across these populations, we have comprehensive, individual-level information on our outcomes and potential cofounders, e.g., types of infertility, treatments, education, income, and clinic traits. We will perform both predictive and causal analyses in order to inform clinicians and potential parents as they first consider use of fertility treatments, then choose between available treatments. Our statistical methods encompass a series of approaches starting with traditional survival and repeated measures analyses, and more recently developed techniques for causal analysis with observational data, e.g., inverse-probability weighting. We also will explore alternative approaches using instrumental variables and the g-formula. In short, this study provides the best opportunity to assess long-term outcomes in children born after fertility treatments, with the longest follow-up time, most extensive set of demographic, clinical, and treatment characteristics, and use of state-of-the-art analytic approaches.