Statistical Analysis With Missing Data (2nd ed.) (Book)

Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage. Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.

/pdf/collider-bias-undermines-our-understanding-of-covid-19-31399ms7i6.pdf

Collider bias undermines our understanding of COVID-19 disease risk and severity

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

https://www.medrxiv.org/content/medrxiv/early/2020/05/20/2020.05.04.20090506.full.pdf

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Cross Cultural Survey Methods

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Strings Attached: Untangling the Ethics of Incentives

The past decade has seen a rise in the use of online panels for conducting survey research. However, the popularity of online panels, largely driven by relatively low implementation costs and high rates of Internet penetration, has been met with criticisms regarding their ability to accurately represent their intended target populations. This criticism largely stems from the fact that (1) non-Internet (or offline) households, despite their relatively small size, constitute a highly selective group unaccounted for in Internet panels, and (2) the preeminent use of nonprobability-based recruitment methods likely contributes a self-selection bias that further compromises the representativeness of online panels. In response to these criticisms, some online panel studies have taken steps to recruit probability-based samples of individuals and providing them with the means to participate online. Using data from one such study, the German Internet Panel, this article investigates the impact of including offline households in the sample on the representativeness of the panel. Consistent with studies in other countries, we find that the exclusion of offline households produces significant coverage biases in online panel surveys, and the inclusion of these households in the sample improves the representativeness of the survey despite their lower propensity to respond.

Does the Recruitment of Offline Households Increase the Sample Representativeness of Probability-Based Online Panels? Evidence From the German Internet Panel

Research has shown that the non-Internet population is hesitant to respond to online survey requests. However, also subgroups in the Internet population with low digital affinity may hesitate to re...

The Influence of a Person’s Digital Affinity on Unit Nonresponse and Attrition in an Online Panel:

This FORS guide discusses the effect of incentives on survey completion/response rate, sample composition, and response quality. The guide particularly emphasizes the effects of different types of incentives – conditional vs. unconditional, monetary vs. non-monetary – for encouraging survey participation and reducing panel survey attrition in the Swiss context.

https://serval.unil.ch/resource/serval:BIB_61CADA0E4DA6.P002/REF.pdf

Incentives in surveys

This guide is for survey practitioners who want to collect information on national minorities. The guide addresses the main concerns with surveying national minorities, points out particularities in Switzerland, and gives some practical recommendations for survey practitioners.

https://serval.unil.ch/resource/serval:BIB_600505B8B6A1.P001/REF.pdf

Surveying national minorities

This guide is for survey practitioners who want to conduct web surveys considering mobile devices or a mobile-only web survey. The guide points out different strategies on how to handle multiple devices and specifically mobile devices in web surveys with regard to web survey design and data quality. Furthermore, it addresses issues of questionnaire design and raises questions that can help to decide for one or the other survey software.

https://serval.unil.ch/resource/serval:BIB_F23F3A529600.P001/REF.pdf

Jessica M. E. Herzing

Papers

Does the Recruitment of Offline Households Increase the Sample Representativeness of Probability-Based Online Panels? Evidence From the German Internet Panel

The Influence of a Person’s Digital Affinity on Unit Nonresponse and Attrition in an Online Panel:

Incentives in surveys

Surveying national minorities

Mobile web surveys