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Peter WG Tennant

Publications and source records attributed to Peter WG Tennant.

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Meta-analyses of dietary exposures must consider energy adjustment: recommendations from a meta-scientific review

In observational studies of dietary exposures, the energy adjustment strategy has a critical impact on the effect being estimated. Adjusting for total energy intake or expressing the exposure as a percentage of total energy, leads to a substitution effect being estimated. This impacts the interpretation of primary studies and meta-analyses. Unless energy adjustment strategies are considered, meta-analyses may end up pooling estimates for incomparable effects. This meta-scientific review aimed to investigate the extent to which meta-analyses of dietary exposures may be pooling incomparable effects by reviewing the energy adjustment strategies. We identified all meta-analyses examining the relationship between saturated fat and fish and cardiovascular disease. The two most recent and two most cited reviews for each exposure were examined, along with all primary studies. Information on the study aims, targeted effects, and interpretations were summarized. The eight meta-analyses summarised results from 82 primary studies including 144 unique models. Only one meta-analysis explicitly considered the energy adjustment strategy of the primary studies to determine eligibility for a substitution subgroup analysis. None of the meta-analyses acknowledged that they were pooling estimates for different effects. 82% of the models from the primary studies were implicitly estimating substitution effects but this was not explicitly stated in most study aims, interpretation or conclusions. Our meta-scientific review found little evidence that the energy adjustment strategies of the primary studies were being considered in the synthesis or interpretation of evidence. Consequently, the pooled estimates reflect ill-defined quantities with unclear interpretations. We offer recommendations to improve the conduct of future meta-analyses and the quality of evidence that informs nutritional recommendations.

stat.AP

How can the use of different modes of survey data collection introduce bias? A simple introduction to mode effects using directed acyclic graphs (DAGs)

Survey data are self-reported data collected directly from respondents by a questionnaire or an interview and are commonly used in epidemiology. Such data are traditionally collected via a single mode (e.g. face-to-face interview alone), but use of mixed-mode designs (e.g. offering face-to-face interview or online survey) has become more common. This introduces two key challenges. First, individuals may respond differently to the same question depending on the mode; these differences due to measurement are known as 'mode effects'. Second, different individuals may participate via different modes; these differences in sample composition between modes are known as 'mode selection'. Where recognised, mode effects are often handled by straightforward approaches such as conditioning on survey mode. However, while reducing mode effects, this and other equivalent approaches may introduce collider bias in the presence of mode selection. The existence of mode effects and the consequences of naïve conditioning may be underappreciated in epidemiology. This paper offers a simple introduction to these challenges using directed acyclic graphs by exploring a range of possible data structures. We discuss the potential implications of using conditioning- or imputation-based approaches and outline the advantages of quantitative bias analyses for dealing with mode effects.

stat.ME