Peter Tennant, PhD
he/him/his
Visiting Associate ProfessorDownloadHi-Res Photo
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Publications Overview
- 66 Publications
- 5,646 Citations
- 1 Yale Co-Author
Featured Publication
Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data
About
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Titles
Visiting Associate Professor
Appointments
Biostatistics
Visiting Associate ProfessorPrimary
Other Departments & Organizations
Education & Training
- PhD
- Newcastle University, Epidemiology (2016)
- MSc
- University of Leeds, Biosciences (2006)
- BSc (Hon)
- Durham University, Natural Sciences (Biology with Mathematics) (2005)
Research
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Overview
Public Health Interests
Nutrition; Maternal & Child Health; Epidemiology Methods; Reproduction; Perinatal/Prenatal Health
ORCID
0000-0003-1555-069X
Research at a Glance
Publications Timeline
A big-picture view of Peter Tennant's research output by year.
66Publications
5,646Citations
Publications
Featured Publications
Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data
Berrie L, Arnold K, Tomova G, Gilthorpe M, Tennant P. Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data. American Journal Of Epidemiology 2024, 194: 469-479. PMID: 38918044, PMCID: PMC11815499, DOI: 10.1093/aje/kwae153.Peer-Reviewed Original ResearchCitationsAltmetricHow to develop causal directed acyclic graphs for observational health research: a scoping review
Poppe L, Steen J, Loh W, Crombez G, De Block F, Jacobs N, Tennant P, Van Cauwenberg J, De Paepe A. How to develop causal directed acyclic graphs for observational health research: a scoping review. Health Psychology Review 2024, 19: 45-65. PMID: 39327907, PMCID: PMC11875439, DOI: 10.1080/17437199.2024.2402809.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetricMeSH Keywords and ConceptsAdjustment for energy intake in nutritional research: a causal inference perspective
Tomova G, Arnold K, Gilthorpe M, Tennant P. Adjustment for energy intake in nutritional research: a causal inference perspective. American Journal Of Clinical Nutrition 2021, 115: 189-198. PMID: 34313676, PMCID: PMC8755101, DOI: 10.1093/ajcn/nqab266.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCausal effectsProportion of total energyAbsence of confoundingNutrient density modelsDietary confoundersEnergy intakeEstimandsValidity of meta-analysesTotal energy intakeStandard modelLack of awarenessObscure interpretationMonte Carlo simulationsEnergy partition modelMeta-analysesConfoundingDietary componentsCarlo simulationsNutrition researchEstimationResidual modelNutritional studiesIntakeDietary causesModelAnalyses of ‘change scores’ do not estimate causal effects in observational data
Tennant P, Arnold K, Ellison G, Gilthorpe M. Analyses of ‘change scores’ do not estimate causal effects in observational data. International Journal Of Epidemiology 2021, 51: 1604-1615. PMID: 34100077, PMCID: PMC9557845, DOI: 10.1093/ije/dyab050.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCausal effect estimationChange-scoresChange scoresOutcome variablesChange-score analysisBaseline outcome variablesFollow-upCausal effectsBaseline outcomesExposure variablesObservational studyLongitudinal dataConfoundingScoresOutcomesBaselineAnalytical strategyVariablesRegression coefficientsExposureDataRegressionObservational dataUse of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations
Tennant P, Murray E, Arnold K, Berrie L, Fox M, Gadd S, Harrison W, Keeble C, Ranker L, Textor J, Tomova G, Gilthorpe M, Ellison G. Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. International Journal Of Epidemiology 2020, 50: 620-632. PMID: 33330936, PMCID: PMC8128477, DOI: 10.1093/ije/dyaa213.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetricMeSH Keywords and Concepts
2026
How can the use of different modes of survey data collection introduce bias? An introduction to mode effects using directed acyclic graphs (DAGs)
Tomova G, Silverwood R, Tennant P, Wright L. How can the use of different modes of survey data collection introduce bias? An introduction to mode effects using directed acyclic graphs (DAGs). American Journal Of Epidemiology 2026, 195: 1406-1415. PMID: 41574902, PMCID: PMC13149020, DOI: 10.1093/aje/kwag017.Peer-Reviewed Original ResearchCitationsAltmetric
2025
Estimating Discrimination in Sentencing: Distinguishing between Good and Bad Controls
Pina-Sánchez J, Hamilton M, Tennant P. Estimating Discrimination in Sentencing: Distinguishing between Good and Bad Controls. European Journal Of Empirical Legal Studies 2025, 2 DOI: 10.62355/ejels.53977.Peer-Reviewed Original ResearchConceptsLegal factorsUS federal courtsPost-treatment biasJudicial discretionBlack offendersFederal courtsHispanic offendersMagistrates' courtsRace disparitiesGender disparitiesCourtOffendersSentence discriminationDiscriminationDisparitiesRaceSentencesWalesGenderEnglandScholarsGoodsFrameworkDiscretizationComprehensive assessmentDepicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning
Franklin M, Peasgood T, Tennant P. Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning. Quality Of Life Research 2025, 34: 2175-2187. PMID: 40576738, PMCID: PMC12274227, DOI: 10.1007/s11136-025-04007-9.Commentaries, Editorials and LettersCitationsMeSH Keywords and ConceptsConceptsPatient-reported outcome measuresOutcome measuresOutcome-wide studiesCausal effectsInternal causal relationshipsOutcome constructsMultidimensional measureRelevant external variablesObservational settingMultidimensional constructOutcomesExternal variablesCausal relationshipsCausality analysisAcyclic graphConsidering minimal clinically important differences in assisted reproductive technology
Schwarze J, Broekmans F, Gupta S, Tennant P, Sunkara S, D'Hooghe T. Considering minimal clinically important differences in assisted reproductive technology. Fertility And Sterility 2025, 124: 1051-1060. PMID: 40578658, DOI: 10.1016/j.fertnstert.2025.06.026.Peer-Reviewed Original ResearchCitationsAltmetricConceptsMinimal Clinically Important DifferenceClinically Important DifferenceAssisted Reproductive TechnologyLive birthsMinimal clinically important difference thresholdAssisted reproductive technology proceduresImportant differenceReproductive technologyCumulative live birthEvidence of superiorityClinical pregnancyGroup of womenDiverse prognosisClinical significancePregnancy rateStatistical significancePregnancyBirthPublished literatureTreatmentRecommendation to improve the rigor and impact of nonrandomized studies of interventions in fertility treatment research
Schwarze J, Tennant P, Barnhart K, Platt R, Gupta S, Venetis C, D'Hooghe T, Schisterman E. Recommendation to improve the rigor and impact of nonrandomized studies of interventions in fertility treatment research. Fertility And Sterility 2025, 124: 749-758. PMID: 40467028, DOI: 10.1016/j.fertnstert.2025.05.168.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAssisted Reproductive TechnologyNonrandomized studies of interventionsRandomized Controlled TrialsNonrandomized studiesStudies of interventionsClinical practiceOvarian stimulationNon-randomized Studies of InterventionsMultidisciplinary expert consensusSelection biasAssisted reproductive technology researchReproductive technologyMethodological rigorStudy designEvidence-based clinical practiceEmbryo transferIntervention study designPatient populationTreatment outcomesDiverse patient populationsClinical decision makingGold standardControlled TrialsStudy protocolExpert consensus
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