Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies

Smeden, Maarten van; Vries, Bas B. L. Penning de; Nab, Linda

doi:10.1016/j.jclinepi.2020.11.006

Cited by 21 publications

(12 citation statements)

References 40 publications

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“…Most commonly, these approaches are applied in isolation, or sequentially to account for a combination of bias due to confounding, selection and measurement. However, other methods also exist that use models to simultaneously address all three types of bias – van Smeden and colleagues (van Smeden, Penning de Vries, Nab, & Groenwold, 2020) provide a review on these types of biases. The first step in causal inference with observational data is to identify and measure the important confounders and include them correctly in the statistical model.…”

Section: Statistical Approaches To Causal Inferencementioning

confidence: 99%

Causal inference with observational data: the need for triangulation of evidence

2021

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The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.

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Section: Statistical Approaches To Causal Inferencementioning

confidence: 99%

Causal inference with observational data: the need for triangulation of evidence

2021

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“…Another frequent problem with biomedical data is the usually high proportion of missing data. While simply excluding patients with missing data before training is an option in some cases, selection bias can arise when other factors influence missing data 147 , and it is often more appropriate to address these gaps with statistical tools, such as multiple imputation 148 . As a result, imputation is a pervasive preprocessing step in many biomedical scientific fields, ranging from genomics to clinical data.…”

Section: Masked (And Shifted) Targetmentioning

confidence: 99%

Multimodal biomedical AI

Acosta

Falcone

Rajpurkar

et al. 2022

Nat Med

459

162

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“…Examples include multilevel data with different data definitions per cluster [91,92] or insurance claim data with only limited granularity in defining exposures and outcomes [93]. To reduce the effect of these biases, epidemiologists and statisticians have developed frameworks that are readily available in most statistical software [89,[94][95][96]. Probably because ML was developed in a more deterministic environment, implementations of epidemiological frameworks to ML techniques are still lacking.…”

Section: Biasmentioning

confidence: 99%

Modern Learning from Big Data in Critical Care: Primum Non Nocere

Gravesteijn

Lingsma

2022

Neurocrit Care

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Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.

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Approaches to addressing missing values, measurement error, and confounding in epidemiologic studies

Cited by 21 publications

References 40 publications

Causal inference with observational data: the need for triangulation of evidence

Causal inference with observational data: the need for triangulation of evidence

Multimodal biomedical AI

Modern Learning from Big Data in Critical Care: Primum Non Nocere

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