This is the last of a series of 3 columns on uncertainty. A response to uncertainty can be radicalization, It is argued that confirmation bias plays a role in this initial “radicalization”: the tendency to prefer information that confirms one's belief as “facts” to contradictory information (“fake news”). People who have just switched to something new, vegetarianism for example, or who have just started a new study, can be very fanatical at first and want to lecture everyone. But no one has the energy to compete again and again on the cutting edge of the “game” (infatuation, new beliefs, etc.), so luckily the nuance returns with time. After the radical "infatuation" (outshout/ignore uncertainty), uncertainty regains its place and space is once again created for the human dimension, for solidarity and nuance. The couple in love who give themselves completely to each other eventually regains an eye for the rest of the world. I have been a vegetarian for over 50 years, so I never really talk about that anymore. Bias fades with time.
MULTIFILE
With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.
BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
MULTIFILE
![Causes of reporting bias: a theoretical framework [version 2; peer review: 2 approved]](https://publinova-harvester-content-prod.s3.amazonaws.com/thumbnails/files/previews/pdf/20240419021423483168.68f_a863_4e94_84cd_cf2a63540cea_18310_jenny_van_der_stee-thumbnail-400x300.png)
