Peter W. G. Tennant
University of Leeds
StatisticsEpidemiologyInfant mortalityObstetricsOdds ratioCausal inferencePediatricsCohort studyAnomaly (natural sciences)PregnancyBody mass indexOverweightPopulationCausal effectGestationAbnormalityDiabetes mellitusBirth weightMedicineCohortConfounding
61Publications
21H-index
2,338Citations
Publications 60
Newest
#1Noah Haber (Stanford University)H-Index: 9
#2Sarah Wieten (Stanford University)H-Index: 5
view all 49 authors...
Abstract null Background null Avoiding “causal” language with observational study designs is common publication practice, often justified as being a more cautious approach to interpretation. null Objectives null We aimed to i) estimate the degree to which causality was implied by both the language linking exposures to outcomes and by action recommendations in the high-profile health literature, ii) examine disconnects between language and recommendations, iii) identify which linking phrases were...
Source
#1Peter W. G. Tennant (The Turing Institute)H-Index: 21
#2Eleanor J Murray (BU: Boston University)H-Index: 15
Last. George T. H. Ellison (University of Leeds)H-Index: 27
view all 13 authors...
Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Methods Original health research articles published during 1999-2017 mentioning 'directed acyclic graphs' (or similar) or citing DAGitty were identified from Scopus, Web ...
23 CitationsSource
#1Georgia D Tomova (University of Leeds)H-Index: 2
#2Kellyn F Arnold (University of Leeds)H-Index: 6
Last. Peter W G Tennant (University of Leeds)H-Index: 4
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Background: Four models are commonly used to adjust for energy intake when estimating the causal effect of a dietary component on an outcome; (1) the 9standard model9 adjusts for total energy intake, (2) the 9energy partition model9 adjusts for remaining energy intake, (3) the 9nutrient density model9 rescales the exposure as a proportion of total energy, and (4) the 9residual model9 indirectly adjusts for total energy by using a residual. It remains underappreciated that each approach evaluates...
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#1Kellyn F Arnold (University of Leeds)H-Index: 6
#2Vinny Davies (Glas.: University of Glasgow)H-Index: 4
Last. Mark S. Gilthorpe (The Turing Institute)H-Index: 42
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Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Nevertheless, these two concepts are often conflated in practice. We use the framework of generalized linear models (GLMs) to illustrate that predictive and causal queries require distinct processes for their application and subsequent interpretation o...
11 CitationsSource
#1Kellyn F Arnold (University of Leeds)H-Index: 6
#2L Berrie (University of Leeds)H-Index: 4
Last. Mark S. Gilthorpe (The Turing Institute)H-Index: 42
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BACKGROUND: Compositional data comprise the parts of some whole, for which all parts sum to that whole. They are prevalent in many epidemiological contexts. Although many of the challenges associated with analysing compositional data have been discussed previously, we do so within a formal causal framework by utilizing directed acyclic graphs (DAGs). METHODS: We depict compositional data using DAGs and identify two distinct effect estimands in the generic case: (i) the total effect, and (ii) the...
11 CitationsSource
The UK Government asserts that its response to the coronavirus disease 2019 (COVID-19) pandemic is based on evidence and expert modelling. However, different scientists can reach different conclusions based on the same evidence, and small differences in assumptions can lead to large differences in model predictions.
32 CitationsSource
#1Peter W. G. Tennant (University of Leeds)H-Index: 21
#2Wendy J Harrison (University of Leeds)H-Index: 9
Last. George T. H. Ellison (University of Leeds)H-Index: 27
view all 13 authors...
BACKGROUND: Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require adjustment when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. METHODS: Original health research articles published during 1999-2017 mentioning "directed acyclic graphs" or similar or citing DAGitty were identified from Scopus, Web of...
22 CitationsSource
#1Sarah C Gadd (University of Leeds)H-Index: 4
#2Peter W. G. Tennant (The Turing Institute)H-Index: 21
Last. Mark S. Gilthorpe (The Turing Institute)H-Index: 42
view all 5 authors...
Longitudinal data is commonly analysed to inform prevention policies for diseases that may develop throughout life. Commonly methods interpret the longitudinal data as a series of discrete measurements or as continuous patterns. Some of the latter methods condition on the outcome, aiming to capture ‘average’ patterns within outcome groups, while others capture individual-level pattern features before relating these to the outcome. Conditioning on the outcome may prevent meaningful interpretation...
3 CitationsSource
#1Tomasina Stacey (University of Huddersfield)H-Index: 16
#2Peter W. G. Tennant (University of Leeds)H-Index: 21
6 CitationsSource
#1Peter W. G. Tennant (University of Leeds)H-Index: 21
#2Kellyn F ArnoldH-Index: 6
Last. Mark S. GilthorpeH-Index: 42
view all 4 authors...
Background: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the resulting 'change' as the outcome variable. In observational data, this approach can produce misleading causal effect estimates. The present article uses directed acyclic graphs (DAGs) and simple simulations to provide an accessible explanation of why change scores do not estimate causal effects in observational data. Method...
1 Citations