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Evaluating bias caused by screening in observational risk-factor studies of lung cancer nested in the PLCO randomized screening trial.

机译：在PLCO随机筛查试验中嵌套的肺癌的观察性危险因素研究中，评价由筛查引起的偏倚。

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摘要

It is well-known that bias such as lead-time and length distort studies of screening efficacy whether survival or incidence is of interest. A third bias, usually called overdiagnosis bias, occurs when an individual is only diagnosed with disease before death from a different cause because he/she is screened. These forms of bias can also arise in observational studies where the proportion screened and screening rates vary by risk-factor strata. This difference in screening behaviors influences corresponding case ascertainment or case enrollment probabilities which can lead to erroneous conclusions about the size of the risk-factor effect on the disease. It has been suggested that classic confounding occurs in such risk-factor studies when screening is efficacious; therefore, it can be addressed by conventional analyses such as stratification or confounder adjustment in regression models. However, even if the test is not efficacious, screening creates changes in case ascertainment probabilities which must be addressed using alternative methods. Recurrence-time models, long used for screening programs, can be adapted to model the affect screening use has on risk-factor studies. These models can be used to study the magnitude of potential bias, but may also be adapted to provide an analytic approach to correct estimates for such bias. The risk-factor studies nested in the PLCO trial are potentially affected by such bias, and this randomized study also provides a structure within which models of screening bias may be tested and validated. To validate our model, a variety of nested case-control studies will be developed that measure the effect smoking has on lung cancer and the degree to which the bias affecting those estimates change based on the study design will be determined. This process will include (a) expanding a previously developed lead-time bias model to incorporate length and overdiagnosis; (b) incorporating a more flexible and realistic model of screening that can incorporate the patterns documented in the PLCO trial; (c) exploring if the mathematical model is valid using varied nested study designs within PLCO and comparing resulting logistic regression estimates to simulated results; and (d) using the validated models to produce correction factors for use in other nested risk-factor studies. Results indicate that the mathematical model is highly sensitive to overdiagnosis as increasing rates increase expected bias, but relatively insensitive to using different screening test sensitivities. Increasing screening behavior differential during the study, preclinical duration length, and selecting from the intervention group are associated with increasing expected screening bias. Increasing screening behavior before the study and selecting from the usual-care group are associated with a decreasing expected screening bias. Although the mathematical model couldn't be validated as a correction factor here, the results suggest using a shorter preclinical duration distribution for the model may produce more accurate screening bias values. The focus of this work was to identify if chest x-ray screening could modify the estimated risk of smoking on lung cancer diagnosis. An additional goal was to develop a usable method for adjusting observational studies of lung cancer for the bias arising from differential chest x-ray screening between ever and never smoking groups. In a boarder sense, this work has provided an explanation of the effect screening use may have on an observational risk-factor study and an example of how to implement the mathematical technique. Additionally, this project has provided a more general method for doing sensitivity analyses on the screening related assumptions involved with these studies, whether nested in a randomized trial or sampled from the population at large.
机译：众所周知，诸如生存期或发病率等筛查效率的偏差（例如提前期和长度扭曲研究）都会引起人们的关注。第三种偏见，通常称为过度诊断偏见，发生在因筛查而仅在其死于其他原因之前被诊断出患有某种疾病的患者。这些形式的偏见也可能出现在观察性研究中，在这些研究中，筛查的比例和筛查率因风险因素分层而异。筛查行为的这种差异会影响相应的病例确定或病例招募概率，这可能导致关于该疾病的危险因素影响的大小的错误结论。有人建议，在有效筛查中，经典的混淆因素会发生在此类危险因素研究中。因此，可以通过常规分析，例如回归模型中的分层或混杂因素调整来解决。但是，即使测试无效，筛查也会导致案例确定概率发生变化，必须使用其他方法来解决。长期用于筛查程序的复发时间模型可用于模拟筛查对风险因素研究的影响。这些模型可用于研究潜在偏差的大小，但也可进行调整以提供一种分析方法来校正此类偏差的估计值。嵌套在PLCO试验中的风险因素研究可能会受到这种偏倚的影响，这项随机研究还提供了一种结构，可以在其中测试和验证筛选偏倚的模型。为了验证我们的模型，将开展一系列嵌套的病例对照研究，以研究吸烟对肺癌的影响，并根据研究设计确定影响这些估计值的偏倚程度。该过程将包括：（a）扩展先前开发的提前期偏差模型，以结合长度和过度诊断； （b）纳入更灵活，更现实的筛查模型，该模型可以纳入PLCO试验中记录的模式； （c）使用PLCO内的各种嵌套研究设计来探索数学模型是否有效，并将逻辑回归估计结果与模拟结果进行比较； （d）使用经过验证的模型来产生校正因子，以用于其他嵌套风险因子研究。结果表明，数学模型对过度诊断高度敏感，因为增加的比率会增加预期的偏倚，但对使用不同的筛查测试敏感性相对不敏感。研究期间筛查行为差异，临床前持续时间长度以及从干预组中选择的增加与预期筛查偏倚的增加有关。在研究前增加筛查行为并从常规护理组中进行选择会降低预期的筛查偏倚。尽管此处无法将数学模型验证为校正因子，但结果表明，对于模型使用较短的临床前持续时间分布可能会产生更准确的筛查偏倚值。这项工作的重点是确定胸部X光筛查是否可以改变估计吸烟对肺癌诊断的风险。另一个目标是开发一种可用于调整肺癌观察性研究的有用方法，以解决以往和从未吸烟组之间因胸部X射线筛查差异而引起的偏倚。在寄予厚望的意义上，这项工作提供了筛查用途对观察性危险因素研究可能产生的影响的解释，并提供了如何实施数学技术的示例。此外，该项目还提供了一种更通用的方法，可以对涉及这些研究的筛查相关假设进行敏感性分析，无论是嵌套在随机试验中还是从总体人群中进行抽样。

著录项

  • 作者

    Jansen, Rick Jeffrey.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Biology Biostatistics.;Environmental Health.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 154 p.
  • 总页数 154
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
  • 关键词

  • 入库时间 2022-08-17 11:37:56

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