Misclassification in binary and categorical variables: development of methods and software for epidemiology

在大多数研究中，暴露、混杂因素、介质、效应调节剂或结局中的一些可能测量错误（在分类变量的情况下称为错误分类）。为了检查测量误差，子样本可能具有关于误测变量的真实值的数据（验证），仅具有随机误差的变量的测量（校准），或误测变量的重复测量（复制）。验证或校准数据通常不可用，因此我们将重点介绍使用复制数据的缓解方法。大多数流行病学分析并不试图评估测量误差或模拟其对结论的影响。敏感性分析是可用的，但许多只适用于特定或简单的情景。广泛使用的数据资源，如英国生物银行和ALSPAC复制数据收集的一个子集的样品，但这些复制数据很少用于检查或调整测量误差。此外，重复样本通常不是研究样本的随机子集。这对偏差和调整测量误差的方法的影响尚未得到研究。该项目将侧重于解决错误分类的方法，而不是连续变量的标准经典测量误差模型。一个重要的应用将是对衡量社会经济地位的分类变量的因果效应进行建模（例如，达到的最高教育水平、五分位数的地区一级贫困分数、各类家庭收入、社会地位类别等）对健康结果的影响。另一个重点领域是自我报告的变量（例如，吸烟状况、抑郁评分）。我们还可以考虑差分测量误差（例如，受教育程度较低的人报告吸烟状况的方式与受教育程度较高的人不同）。该项目将研究错误分类对因果分析的影响，酌情使用代数和模拟。将使用模拟比较仅使用复制数据检查和调整测量误差的方法（检查偏倚和把握度），并将其应用于ALSPAC和UKBB的复制数据。方法可能包括但不限于工具变量分析、回归校准和贝叶斯校正。即使邀请的样本是随机选择的，完成重复评估的样本也可能不是。我们将使用模拟来检查非随机重复样本对使用上述方法校正测量误差的影响。这些方法将适用于ALSPAC和UKBB对（非随机）子样本的重复测量。最后，我们将开发在测量误差和错误分类的情况下进行敏感性分析的软件，以使所开发的方法得到广泛应用。该项目属于EPSRC“统计和应用概率”和“软件工程”研究领域的福尔斯。它将涉及与MRC综合流行病学单位（人口健康科学，布里斯托医学院），伦敦卫生和热带医学学院，ALSPAC和英国生物银行的合作。

In most studies there is potential for some of the exposure, confounders, mediators, effect modifiers, or outcome to be measured with error (called misclassification in the case of categorical variables). To examine measurement error, subsamples may have data on the true value of the mismeasured variable (validation), a measure of the variable which only has random error (calibration), or repeat measures of the mismeasured variable (replication). Validation or calibration data are not often available, so we will focus on mitigation methods that use replication data. Most epidemiological analyses do not attempt to assess measurement error or model its influence on conclusions. Sensitivity analyses are available, but many only apply to specific or simple scenarios. Widely used data resources such as UK Biobank and ALSPAC replicated data collection on a subset of their samples, and yet these replication data are rarely used to examine or adjust for measurement error. In addition, the replication sample is often not a random subset of the study sample. The implications of this for bias and methods to adjust for measurement error have not been examined. The project will focus on methods to address misclassification rather than the standard classical measurement error model for continuous variables. One important application will be in modelling the causal effect of categorical variables measuring socioeconomic position (e.g., highest education level reached, area-level deprivation score in quintiles, family income in categories, social position category, etc) on health outcomes. Another area of focus will be self-reported variables (e.g., smoking status, depression score). We may also consider differential measurement error (e.g., where those with lower education levels report their smoking status differently to those with higher education levels). The project will examine the impact of misclassification on causal analyses, using algebra and simulation as appropriate. Methods to examine and adjust for measurement error using replication data only will be compared (examining bias and power) using simulations and applied to replication data from ALSPAC and UKBB. Methods may include but are not limited to instrumental variable analyses, regression calibration, and Bayesian correction. Even if the invited sample is randomly selected, the sample completing the repeat assessments may not be. We will use simulations to examine the impact of non-random repeated samples on correction for measurement error using the above methods. These methods will be applied to the repeated measures available within both ALSPAC and UKBB on a (non-random) subsample of respondents. Lastly, we will develop software for sensitivity analysis in the case of measurement error and misclassification, to enable widespread uptake of the methods developed. This project falls within the EPSRC "Statistics and applied probability" and "Software engineering" research areas. It will involve collaboration with the MRC Integrative Epidemiology Unit (Population Health Sciences, Bristol Medical School), the London School of Hygiene and Tropical Medicine, ALSPAC, and the UK Biobank.