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Article ; Online: Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction assumption.

Millard, Louise A C / Davey Smith, George / Tilling, Kate

2024

Abstract: Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification ... ...

Abstract	Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r
Language	English
Publishing date	2024-02-29
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	632614-6
ISSN	1573-7284 ; 0393-2990
ISSN (online)	1573-7284
ISSN	0393-2990
DOI	10.1007/s10654-024-01097-6
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Article: Measurement and modelling of air pollution and atmospheric chemistry in the U.K. West Midlands conurbation: overview of the PUMA Consortium project.

Harrison, R M / Yin, J / Tilling, R M / Cai, X / Seakins, P W / Hopkins, J R / Lansley, D L / Lewis, A C / Hunter, M C / Heard, D E / Carpenter, L J / Creasey, D J / Lee, J D / Pilling, M J / Carslaw, N / Emmerson, K M / Redington, A / Derwent, R G / Ryall, D /

Mills, G / Penkett, S A

The Science of the total environment

2006 Volume 360, Issue 1-3, Page(s) 5–25

Abstract: The PUMA (Pollution of the Urban Midlands Atmosphere) Consortium project involved intensive measurement campaigns in the Summer of 1999 and Winter of 1999/2000, respectively, in which a wide variety of air pollutants were measured in the UK West Midlands ...

Abstract	The PUMA (Pollution of the Urban Midlands Atmosphere) Consortium project involved intensive measurement campaigns in the Summer of 1999 and Winter of 1999/2000, respectively, in which a wide variety of air pollutants were measured in the UK West Midlands conurbation including detailed speciation of VOCs and major component analysis of aerosol. Measurements of the OH and HO2 free radicals by the FAGE technique demonstrated that winter concentrations of OH were approximately half of those measured during the summer despite a factor of 15 reduction in production through the photolysis of ozone. Detailed box modelling of the fast reaction chemistry revealed the decomposition of Criegee intermediates formed from ozone-alkene reactions to be responsible for the majority of the formation of hydroxyl in both the summer and winter campaigns, in contrast to earlier rural measurements in which ozone photolysis was predominant. The main sinks for hydroxyl are reactions with NO2, alkenes and oxygenates. Concentrations of the more stable hydrocarbons were found to be relatively invariant across the conurbation, but the impacts of photochemistry were evident through analyses of formaldehyde which showed the majority to be photochemical in origin as opposed to emitted from road traffic. Measurements on the upwind and downwind boundaries of the conurbation revealed substantial enhancements in NOx as a result of emissions within the conurbation, especially during westerly winds which carried relatively clean air. Using calcium as a tracer for crustal particles, it proved possible to reconstruct aerosol mass from the major chemical components with a fairly high degree of success. The organic to elemental carbon ratios showed a far greater influence of photochemistry in summer than winter, presumably resulting mainly from the greater availability of biogenic precursors during the summer campaign. Two urban airshed models were developed and applied to the conurbation, one Eulerian, the other Lagrangian. Both were able to give a good simulation of concentrations of both primary and secondary pollutants at urban background locations.
MeSH term(s)	Acetone/analysis ; Aerosols/analysis ; Air Pollutants/analysis ; Air Pollution/analysis ; Carbon Monoxide/analysis ; Environmental Monitoring ; Formaldehyde/analysis ; Free Radicals/analysis ; Hydrocarbons/analysis ; Models, Theoretical ; Nitrogen Oxides/analysis ; Ozone/analysis ; Particle Size ; Peracetic Acid/analogs & derivatives ; Peracetic Acid/analysis ; Photochemistry ; Reproducibility of Results ; United Kingdom
Chemical Substances	Aerosols ; Air Pollutants ; Free Radicals ; Hydrocarbons ; Nitrogen Oxides ; Acetone (1364PS73AF) ; Formaldehyde (1HG84L3525) ; Ozone (66H7ZZK23N) ; Carbon Monoxide (7U1EE4V452) ; Peracetic Acid (I6KPI2E1HD) ; peroxyacetyl nitrate (SQ8V0P4N89)
Language	English
Publishing date	2006-05-01
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2005.08.053
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Article ; Online: Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing.

Parker, Richard M A / Tilling, Kate / Terrera, Graciela Muniz / Barrett, Jessica K

American journal of epidemiology

2023 Volume 193, Issue 1, Page(s) 159–169

Abstract: Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of ... ...

Abstract	Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of the mean value. Investigating risk factors associated with IIV has typically involved deriving a summary statistic for each person from residual error around a fitted mean. However, this ignores uncertainty in the estimates, prohibits exploring associations with time-varying factors, and is biased by floor/ceiling effects. To address this, we propose a mixed-effects location scale beta-binomial model for estimating average probability and IIV in a word recall test in the English Longitudinal Study of Ageing. After adjusting for mean performance, an analysis of 9,873 individuals across 7 (mean = 3.4) waves (2002-2015) found IIV to be greater at older ages, with lower education, in females, with more difficulties in activities of daily living, in later birth cohorts, and when interviewers recorded issues potentially affecting test performance. Our study introduces a novel method for identifying groups with greater IIV in bounded discrete outcomes. Our findings have implications for daily functioning and care, and further work is needed to identify the impact for future health outcomes.
MeSH term(s)	Aged ; Female ; Humans ; Activities of Daily Living ; Aging/psychology ; Cognition ; Longitudinal Studies ; Models, Statistical ; Quality of Life ; Risk Factors ; Male
Language	English
Publishing date	2023-08-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	2937-3
ISSN	1476-6256 ; 0002-9262
ISSN (online)	1476-6256
ISSN	0002-9262
DOI	10.1093/aje/kwad169
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Article ; Online: Multiple imputation strategies for missing event times in a multi-state model analysis.

Curnow, Elinor / Hughes, Rachael A / Birnie, Kate / Tilling, Kate / Crowther, Michael J

Statistics in medicine

2024 Volume 43, Issue 6, Page(s) 1238–1255

Abstract: In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times ...

Abstract	In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.
MeSH term(s)	Humans ; Data Interpretation, Statistical ; Computer Simulation ; Probability ; Research Design ; Bias
Language	English
Publishing date	2024-01-22
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.10011
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Article ; Online: Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression.

Lawton, Michael / Ben-Shlomo, Yoav / Gkatzionis, Apostolos / Hu, Michele T / Grosset, Donald / Tilling, Kate

European journal of epidemiology

2024

Abstract: Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an ... ...

Abstract	Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.
Language	English
Publishing date	2024-01-28
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	632614-6
ISSN	1573-7284 ; 0393-2990
ISSN (online)	1573-7284
ISSN	0393-2990
DOI	10.1007/s10654-023-01093-2
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Article ; Online: Quantitative bias analysis in practice: review of software for regression with unmeasured confounding.

Kawabata, Emily / Tilling, Kate / Groenwold, Rolf H H / Hughes, Rachael A

BMC medical research methodology

2023 Volume 23, Issue 1, Page(s) 111

Abstract: Background: Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would ... ...

Abstract	Background: Failure to appropriately account for unmeasured confounding may lead to erroneous conclusions. Quantitative bias analysis (QBA) can be used to quantify the potential impact of unmeasured confounding or how much unmeasured confounding would be needed to change a study's conclusions. Currently, QBA methods are not routinely implemented, partly due to a lack of knowledge about accessible software. Also, comparisons of QBA methods have focused on analyses with a binary outcome. Methods: We conducted a systematic review of the latest developments in QBA software published between 2011 and 2021. Our inclusion criteria were software that did not require adaption (i.e., code changes) before application, was still available in 2022, and accompanied by documentation. Key properties of each software tool were identified. We provide a detailed description of programs applicable for a linear regression analysis, illustrate their application using two data examples and provide code to assist researchers in future use of these programs. Results: Our review identified 21 programs with [Formula: see text] created post 2016. All are implementations of a deterministic QBA with [Formula: see text] available in the free software R. There are programs applicable when the analysis of interest is a regression of binary, continuous or survival outcomes, and for matched and mediation analyses. We identified five programs implementing differing QBAs for a continuous outcome: treatSens, causalsens, sensemakr, EValue, and konfound. When applied to one of our illustrative examples, causalsens incorrectly indicated sensitivity to unmeasured confounding whereas the other four programs indicated robustness. sensemakr performs the most detailed QBA and includes a benchmarking feature for multiple unmeasured confounders. Conclusions: Software is now available to implement a QBA for a range of different analyses. However, the diversity of methods, even for the same analysis of interest, presents challenges to their widespread uptake. Provision of detailed QBA guidelines would be highly beneficial.
MeSH term(s)	Humans ; Confounding Factors, Epidemiologic ; Software ; Bias ; Linear Models ; Regression Analysis
Language	English
Publishing date	2023-05-04
Publishing country	England
Document type	Systematic Review ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-023-01906-8
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Article ; Online: Sensitivity analyses gain relevance by fixing parameters observable during the empirical analyses.

Hemani, Gibran / Gkatzionis, Apostolos / Tilling, Kate / Davey Smith, George

Genetic epidemiology

2023 Volume 47, Issue 6, Page(s) 461–462

Language	English
Publishing date	2023-07-07
Publishing country	United States
Document type	Letter
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22530
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Article ; Online: Trial arm outcome variance difference after dropout as an indicator of missing-not-at-random bias in randomized controlled trials.

Hazewinkel, Audinga-Dea / Tilling, Kate / Wade, Kaitlin H / Palmer, Tom

Biometrical journal. Biometrische Zeitschrift

2023 Volume 65, Issue 8, Page(s) e2200116

Abstract: Randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for ... ...

Abstract	Randomized controlled trials (RCTs) are vulnerable to bias from missing data. When outcomes are missing not at random (MNAR), estimates from complete case analysis (CCA) and multiple imputation (MI) may be biased. There is no statistical test for distinguishing between outcomes missing at random (MAR) and MNAR. Current strategies rely on comparing dropout proportions and covariate distributions, and using auxiliary information to assess the likelihood of dropout being associated with the outcome. We propose using the observed variance difference across trial arms as a tool for assessing the risk of dropout being MNAR in RCTs with continuous outcomes. In an RCT, at randomization, the distributions of all covariates should be equal in the populations randomized to the intervention and control arms. Under the assumption of homogeneous treatment effects and homoskedastic outcome errors, the variance of the outcome will also be equal in the two populations over the course of follow-up. We show that under MAR dropout, the observed outcome variances, conditional on the variables included in the model, are equal across trial arms, whereas MNAR dropout may result in unequal variances. Consequently, unequal observed conditional trial arm variances are an indicator of MNAR dropout and possible bias of the estimated treatment effect. Heterogeneous treatment effects or heteroskedastic outcome errors are another potential cause of observing different outcome variances. We show that for longitudinal data, we can isolate the effect of MNAR outcome-dependent dropout by considering the variance difference at baseline in the same set of patients who are observed at final follow-up. We illustrate our method in simulation for CCA and MI, and in applications using individual-level data and summary data.
MeSH term(s)	Humans ; Randomized Controlled Trials as Topic ; Computer Simulation ; Probability ; Bias
Language	English
Publishing date	2023-09-20
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	131640-0
ISSN	1521-4036 ; 0323-3847 ; 0006-3452
ISSN (online)	1521-4036
ISSN	0323-3847 ; 0006-3452
DOI	10.1002/bimj.202200116
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Article ; Online: Relative age in the school year and risk of mental health problems in childhood, adolescence and young adulthood.

Broughton, Thomas / Langley, Kate / Tilling, Kate / Collishaw, Stephan

Journal of child psychology and psychiatry, and allied disciplines

2022 Volume 64, Issue 1, Page(s) 185–196

Abstract: Purpose: Relative age within the school year ('relative age') is associated with increased rates of symptoms and diagnoses of mental health disorders, including ADHD. We aimed to investigate how relative age influences mental health and behaviour before, ...

Abstract	Purpose: Relative age within the school year ('relative age') is associated with increased rates of symptoms and diagnoses of mental health disorders, including ADHD. We aimed to investigate how relative age influences mental health and behaviour before, during and after school (age range: 4-25 years). Method: We used a regression discontinuity design to examine the effect of relative age on risk of mental health problems using data from a large UK population-based cohort (Avon Longitudinal Study of Parents and Children (ALSPAC); N = 14,643). We compared risk of mental health problems between ages 4 and 25 years using the parent-rated Strengths and Difficulties Questionnaire (SDQ), and depression using self-rated and parent-rated Short Mood and Feelings Questionnaire (SMFQ) by relative age. Results: The youngest children in the school year have greater parent-rated risk of mental health problems, measured using parent-rated SDQ total difficulties scores. We found no evidence of differences before school entry [estimated standardised mean difference (SMD) between those born on 31 August and 1 September: .02 (-.05, .08)]. We found that estimates of effect size for a 1-year difference in relative age were greatest at 11 years [SMD: .22 (.15, .29)], but attenuated to the null at 25 years [SMD: -.02 (-.11, .07)]. We did not find consistent evidence of differences in self-rated and parent-rated depression by relative age. Conclusions: Younger relative age is associated with poorer parent-rated general mental health, but not symptoms of depression.
MeSH term(s)	Child ; Adolescent ; Humans ; Young Adult ; Adult ; Child, Preschool ; Mental Health ; Longitudinal Studies ; Schools ; Surveys and Questionnaires ; Mental Disorders/epidemiology
Language	English
Publishing date	2022-08-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	218136-8
ISSN	1469-7610 ; 0021-9630 ; 0373-8086
ISSN (online)	1469-7610
ISSN	0021-9630 ; 0373-8086
DOI	10.1111/jcpp.13684
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Article ; Online: Collider bias from selecting disease samples distorts causal inferences.

Hemani, Gibran / Tilling, Kate / Davey Smith, George

Genetic epidemiology

2022 Volume 46, Issue 3-4, Page(s) 213–215

MeSH term(s)	Bias ; Humans ; Mendelian Randomization Analysis
Language	English
Publishing date	2022-02-07
Publishing country	United States
Document type	Letter ; Research Support, Non-U.S. Gov't ; Comment
ZDB-ID	605785-8
ISSN	1098-2272 ; 0741-0395
ISSN (online)	1098-2272
ISSN	0741-0395
DOI	10.1002/gepi.22443
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