A Meta-Epidemiological Assessment of the Role of Pilot Studies in the Design of Well-Powered Trials - the Pilot Project

试点干预研究（又称可行性或初步研究）在预防和控制艾滋病毒/艾滋病方面发挥着不可或缺的基本作用。 发展、完善和传播社会科学/公共卫生干预措施。在试点研究中， 收集有关重要过程和干预措施潜在功效的初步证据。尽管 他们在开发和资助几乎每一个强大的随机干预，试点， 关于如何设计这些研究， 将提供信息，为进一步测试和改进干预措施的决策提供信息。其他 指南，如CONSORT，TREND，SPIRIT或TIDeR，关注与内部效度相关的因素， 透明度/复制，未能解决干预者需要考虑的实质性问题， 测试干预措施的早期阶段，例如交付的内容、交付对象以及 对交付的支持，以及这些是否可以在更大的，功能良好的试验中扩展。根据我们初步的 研究结果，这些问题，我们称之为“普遍性偏见”，是一些假设的新兴 导致“假阳性或夸大的早期发现”和随后失败的有力试验的因素。 识别和避免概括性偏差可以指导早期干预决策 测试，以便根据NIH的说法，试点研究的结果可以“......提高获得 在随后的有力试验中获得有意义的结果。”在建议的研究中，我们会进行全面的 对试点研究在测试和扩展中发挥的作用进行系统回顾和元流行病学评估 的干预措施。这些信息将用于告知科学实践领域如何设计更多 信息丰富的试点研究，可以增加在效能良好的试验中干预成功的可能性。为 在这项研究中，我们将使用一种创新的多阶段交叉验证方法来开发一套工作集， 干预者在对干预措施进行初步测试时应避免普遍性偏见。在 第一阶段，我们将在一个大型试点机构中识别、定义和编目候选的可推广性偏差。 干预研究（N = 740）的主题儿童肥胖。在第二阶段，我们将交叉验证偏差， 他们，并确定新的样本中的试点干预研究的主题成人肥胖（预计 800篇研究报告）。在第3阶段，我们将在一个新的样本中对偏差进行双重交叉验证， 关于烟草和艾滋病毒/艾滋病专题的试点干预措施（预计各有400项研究样本）。在第四阶段， 原则的工作草案将通过与主要期刊编辑的德尔菲调查加以完善。本研究 这是一项重要的工作，因为它将是第一个系统地制定干预试点研究指南的机构。这 这项研究具有创新性，因为它将探讨一个基本上被忽视的专题（试点研究）， 综合定量和定性数据，以了解如何改进试点研究， 这是制定高质量干预措施的一个重要但被忽视的方面。

Pilot intervention studies (aka feasibility or preliminary studies) play an indispensable, fundamental role in the development, refinement, and dissemination of social science/public health interventions. In pilot studies, preliminary evidence on important processes and potential efficacy of an intervention are collected. Despite their prominence in the development and funding of almost every well-powered randomized intervention, pilot studies have received very little attention regarding how they should be designed so that the study’s findings will provide information to inform decisions about further testing and refining of an intervention. Other guidelines, such as CONSORT, TREND, SPIRIT, or TIDeR, focus on factors associated with internal validity or transparency/replication, and fail to address substantive issues interventionists need to consider during the early stages of testing an intervention, such as what is delivered, who its delivered to, and the intensity of support for delivery and whether these can be scaled in a larger, well-powered trial. Based on our preliminary findings, these issues, which we refer to as “generalizability biases,” are a few of the hypothesized emerging factors that lead to “false-positive or exaggerated early discoveries” and subsequent failed well-powered trials. The identification and avoidance of generalizability biases can guide intervention decisions during the early testing so that, according to the NIH, a pilot study’s results can “…enhance the probability of obtaining meaningful results in subsequent well-powered trials.” In the proposed study, we will conduct a comprehensive systematic review and meta-epidemiological assessment of the role pilot studies play in the testing and scaling of interventions. This information will be used to inform the field of scientific practice on how to design more informative pilot studies that can increase the probability of success of interventions in well-powered trials. For this study, we will use an innovative multi-phase, cross-validation approach to develop a working set of generalizability biases interventionists should avoid when conducting preliminary tests of an intervention. In Phase 1 we will identify, define, and catalogue candidate generalizability biases within a large body of pilot intervention studies (N = 740) on the topic of child obesity. In Phase 2 we will cross-validate the biases, refine them, and identify new ones in a sample of pilot intervention studies on the topic of adult obesity (anticipated sample of 800 studies). In Phase 3 we will perform a double cross-validation of the biases in a new sample of pilot interventions on the topics of tobacco and HIV/AIDS (anticipated sample of 400 studies each). In Phase 4, a working draft of the principles will be refined via Delphi survey with leading Journal Editors. This study is significant because it will be the first to systematically develop guidance for intervention pilot studies. This study is innovative because it will address a topic that has largely been ignored (pilot studies) and will synthesize both quantitative and qualitative data to understand how pilot studies can be improved – an essential, yet overlooked aspect of developing high-quality interventions.