Long-term Impact of Fertility Treatment Study

生育治疗的使用正在增加，多达10%的婴儿是在使用辅助生殖后出生的 技术(ART)或非体外受精生育治疗(NIFT)。这些治疗通常需要大量的社会责任 以及个人对资源和时间的投资，但很少有关于长期健康或 治疗后出生的儿童的发育结果。事实上，现有的大多数文学作品都围绕着 发生在出生或婴儿期的事件，关于后来生活中的事件的研究结果有限且喜忧参半。这是有限的 文献还反映了重大的方法学挑战，包括：(1)没有足够的后续时间来评估 青春期以后和成年后的医疗状况发展；(2)许多潜在的混杂因素， 包括需要治疗(不孕不育)的原始原因，以及获得和使用治疗的差异， 例如，按收入或教育差别获得机会；以及(3)使用分析方法，这些方法既没有解决 相关的偏见来源，或依赖定义不明确的假设来评估治疗效果。要解决这两个问题 需要更多的证据并缓解方法学上的担忧，我们建议比较 使用多个基于人口的、全国范围内的丰富生育治疗与不同生育治疗相关的结果 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(根据不孕症的类型分层)基于两种类型的结果：1)临床事件；2)教育 事件。我们将对1980-2020年间生育治疗后出生的近40万名儿童进行四项研究 人口(在丹麦、荷兰、瑞典和马萨诸塞州)。在这些人群中，我们有 关于我们的结果和潜在的联合创始人的全面的、个人层面的信息，例如，不孕不育的类型， 治疗、教育、收入和临床特征。我们将执行预测性分析和因果分析，以便 告知临床医生和潜在父母，他们首先考虑使用生育治疗，然后在 可用的治疗方法。我们的统计方法包括一系列方法，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，使用 工具变量和g公式。简而言之，这项研究提供了评估长期 在生育治疗后出生的孩子的结局，具有最长的随访时间，最广泛的一套 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.