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1. Empirically assessing the plausibility of unconfoundedness in observational studies

Author

Hartwig, Fernando Pires, Tilling, Kate, and Smith, George Davey

Subjects
Statistics - Methodology
Abstract

The possibility of unmeasured confounding is one of the main limitations for causal inference from observational studies. There are different methods for partially empirically assessing the plausibility of unconfoundedness. However, most currently available methods require (at least partial) assumptions about the confounding structure, which may be difficult to know in practice. In this paper we describe a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate set of covariates suffices for confounding adjustment) which does not require any assumptions about the confounding structure, requiring instead assumptions related to temporal ordering between covariates, exposure and outcome (which can be guaranteed by design), measurement error and selection into the study. The proposed method essentially relies on testing the association between a subset of covariates (those associated with the exposure given all other covariates) and the outcome conditional on the remaining covariates and the exposure. We describe the assumptions underlying the method, provide proofs, use simulations to corroborate the theory and illustrate the method with an applied example assessing the causal effect of length-for-age measured in childhood and intelligence quotient measured in adulthood using data from the 1982 Pelotas (Brazil) birth cohort. We also discuss the implications of measurement error and some important limitations.

Published
2024

7. Relationship between Collider Bias and Interactions on the Log-Additive Scale

Author

Gkatzionis, Apostolos, Seaman, Shaun R., Hughes, Rachael A., and Tilling, Kate

Subjects
Statistics - Methodology
Abstract

Collider bias occurs when conditioning on a common effect (collider) of two variables $X, Y$. In this manuscript, we quantify the collider bias in the estimated association between exposure $X$ and outcome $Y$ induced by selecting on one value of a binary collider $S$ of the exposure and the outcome. In the case of logistic regression, it is known that the magnitude of the collider bias in the exposure-outcome regression coefficient is proportional to the strength of interaction $\delta_3$ between $X$ and $Y$ in a log-additive model for the collider: $\mathbb{P} (S = 1 | X, Y) = \exp \left\{ \delta_0 + \delta_1 X + \delta_2 Y + \delta_3 X Y \right\}$. We show that this result also holds under a linear or Poisson regression model for the exposure-outcome association. We then illustrate by simulation that even if a log-additive model with interactions is not the true model for the collider, the interaction term in such a model is still informative about the magnitude of collider bias. Finally, we discuss the implications of these findings for methods that attempt to adjust for collider bias, such as inverse probability weighting which is often implemented without including interactions between variables in the weighting model., Comment: Main Part: 19 pages, 5 figures, 3 tables. Supplement: 16 pages, 3 figures, 5 tables

Published
2023

9. Using Instruments for Selection to Adjust for Selection Bias in Mendelian Randomization

Author

Gkatzionis, Apostolos, Tchetgen, Eric J. Tchetgen, Heron, Jon, Northstone, Kate, and Tilling, Kate

Subjects
Statistics - Methodology, Quantitative Biology - Quantitative Methods
Abstract

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children., Comment: Main part: 28 pages, 3 figures, 4 tables. Supplement: 24 pages, 8 figures, 10 tables. Paper currently under review

Published
2022

12. Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people

Author

Al Arab, Marwa, Almaghrabi, Fatima, Andrews, Colm, Badrick, Ellena, Baz, Sarah, Beckford, Chelsea, Berman, Samantha, Bolton, Tom, Booth, Charlotte, Bowyer, Ruth, Boyd, Andy, Bridger-Staatz, Charis, Brophy, Sinead, Campbell, Archie, Campbell, Kirsteen C, Carnemolla, Alisia, Carpentieri, Jd, Cezard, Genevieve, Chaturvedi, Nishi, Cheetham, Nathan, Costello, Ruth, Cowling, Thomas, Crane, Matthew, Cuitun Coronado, Jose Ignacio, Curtis, Helen, Denaxas, Spiros, Denholm, Rachel, Di Gessa, Giorgio, Dobson, Richard, Douglas, Ian, Evans, Katharine M, Fang, Chao, Ferreira, Vanessa, Finnigan, Lucy, Fisher, Louis, Flaig, Robin, Folarin, Amos, Forbes, Harriet, Foster, Diane, Fox, Laura, Freydin, Maxim, Garcia, Paz, Gibson, Andy, Glen, Fiona, Goldacre, Ben, Goncalves Soares, Ana, Greaves, Felix, Green, Amelia, Green, Mark, Green, Michael, Griffith, Gareth, Hamill Howes, Lee, Hamilton, Olivia, Herbet, Annie, Herrett, Emily, Hopcroft, Lisa, Horne, Elsie, Hou, Bo, Hughes, Alun, Hulme, William, Huntley, Lizzie, Ip, Samantha, Jacques, Wels, Jezzard, Peter, Jones, Louise, Kanagaratnam, Arun, Karthikeyan Suseeladevi, Arun, Katikireddi, Vittal, Kellas, John, Kennedy, Jonathan I, Kibble, Milla, Knight, Rochelle, Knueppel, Anika, Kopasker, Daniel, Kromydas, Theocharis, Kwong, Alex, Langan, Sinead, Lemanska, Agnieszka, Lukaschuk, Elena, Mackenna, Brain, Macleod, John, Maddock, Jane, Mahalingasivam, Viyaasan, Mansfield, Kathryn, McArdle, Fintan, McCartney, Daniel, McEachan, Rosie, McElroy, Eoin, McLachlan, Stela, Mitchell, Ruth, Moltrecht, Bettina, Morley, Jess, Nab, Linda, Neubauer, Stefan, Nigrelli, Lidia, North, Teri, Northstone, Kate, Oakley, Jacqui, Palmer, Tom, Park, Chloe, Parker, Michael, Parsons, Sam, Patalay, Praveetha, Patel, Kishan, Perez-Reche, Francisco, Piechnik, Stefan, Piehlmaier, Dominik, Ploubidis, George, Rafeti, Elena, Raman, Betty, Ranjan, Yatharth, Rapala, Alicja, Rhead, Rebecca, Roberts, Amy, Sampri, Alexia, Sanders, Zeena-Britt, Santorelli, Gillian, Saunders, Laura C, Shah, Anoop, Shah, Syed Ahmar, Sharp, Steve, Shaw, Richard, Sheard, Laura, Sheikh, Aziz, Silverwood, Richard, Smeeth, Liam, Smith, Stephen, Stafford, Jean, Steptoe, Andrew, Sterne, Jonathan, Steves, Claire, Stewart, Callum, Taylor, Kurt, Tazare, John, Teece, Lucy, Thomas, Richard, Thompson, Ellen, Tilling, Kate, Timpson, Nicholas, Tomlinson, Laurie, Toms, Renin, Tunnicliffe, Elizabeth, Turner, Emma L, Walker, Alex, Walker, Venexia, Walter, Scott, Wang, Kevin, Wei, Yinghui, Whitehorn, Rebecca, Wielgoszewska, Bozena, Wild, James M, Willan, Kathryn, Willans, Robert, Williams, Dylan, Wong, Andrew, Wood, Angela, Woodward, Hannah, Wright, John, Yang, Tiffany, Zaninotto, Paola, Zheng, Bang, Zhu, Jingmin, Eastwood, Sophie, Horne, Elsie M F, Massey, Jon, Hopcroft, Lisa E M, Cuitun Coronado, Jose, Davy, Simon, Dillingham, Iain, Morton, Caroline, and Sterne, Jonathan A C

Published
2024
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13. Mixed-effects location scale models for joint modelling school value-added effects on the mean and variance of student achievement

Author

Leckie, George, Parker, Richard, Goldstein, Harvey, and Tilling, Kate

Subjects
Statistics - Applications
Abstract

School value-added models are widely applied to study, monitor, and hold schools to account for school differences in student learning. The traditional model is a mixed-effects linear regression of student current achievement on student prior achievement, background characteristics, and a school random intercept effect. The latter is referred to as the school value-added score and measures the mean student covariate-adjusted achievement in each school. In this article, we argue that further insights may be gained by additionally studying the variance in this quantity in each school. These include the ability to identify both individual schools and school types that exhibit unusually high or low variability in student achievement, even after accounting for differences in student intakes. We explore and illustrate how this can be done via fitting mixed-effects location scale versions of the traditional school value-added model. We discuss the implications of our work for research and school accountability systems.

Published
2021

15. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E)

Author

Higgins, Julian P.T., Morgan, Rebecca L., Rooney, Andrew A., Taylor, Kyla W., Thayer, Kristina A., Silva, Raquel A., Lemeris, Courtney, Akl, Elie A., Bateson, Thomas F., Berkman, Nancy D., Glenn, Barbara S., Hróbjartsson, Asbjørn, LaKind, Judy S., McAleenan, Alexandra, Meerpohl, Joerg J., Nachman, Rebecca M., Obbagy, Julie E., O'Connor, Annette, Radke, Elizabeth G., Savović, Jelena, Schünemann, Holger J., Shea, Beverley, Tilling, Kate, Verbeek, Jos, Viswanathan, Meera, and Sterne, Jonathan A.C.

Published
2024
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18. Conceptualising Natural and Quasi Experiments in Public Health

Author

de Vocht, Frank, Katikireddi, Srinivasa Vittal, McQuire, Cheryl, Tilling, Kate, Hickman, Matthew, and Craig, Peter

Subjects
Statistics - Methodology
Abstract

Background: Natural or quasi experiments are appealing for public health research because they enable the evaluation of events or interventions that are difficult or impossible to manipulate experimentally, such as many policy and health system reforms. However, there remains ambiguity in the literature about their definition and how they differ from randomised controlled experiments and from other observational designs. Methods: We conceptualise natural experiments in in the context of public health evaluations, align the study design to the Target Trial Framework, and provide recommendation for improvement of their design and reporting. Results: Natural experiment studies combine features of experiments and non-experiments. They differ from RCTs in that exposure allocation is not controlled by researchers while they differ from other observational designs in that they evaluate the impact of event or exposure changes. As a result they are, in theory, less susceptible to bias than other observational study designs. Importantly, the strength of causal inferences relies on the plausibility that the exposure allocation can be considered "as-if randomised". The target trial framework provides a systematic basis for assessing the plausibility of such claims, and enables a structured method for assessing other design elements. Conclusions: Natural experiment studies should be considered a distinct study design rather than a set of tools for analyses of non-randomised interventions. Alignment of natural experiments to the Target Trial framework will clarify the strength of evidence underpinning claims about the effectiveness of public health interventions., Comment: 28 pages, 1 figure, 1 table, 1 supplementary table

Published
2020
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19. Framework for the Treatment And Reporting of Missing data in Observational Studies: The TARMOS framework

Author

Lee, Katherine J, Tilling, Kate, Cornish, Rosie P, Little, Roderick JA, Bell, Melanie L, Goetghebeur, Els, Hogan, Joseph W, and Carpenter, James R

Subjects
Statistics - Methodology, 6207
Abstract

Missing data are ubiquitous in medical research. Although there is increasing guidance on how to handle missing data, practice is changing slowly and misapprehensions abound, particularly in observational research. We present a practical framework for handling and reporting the analysis of incomplete data in observational studies, which we illustrate using a case study from the Avon Longitudinal Study of Parents and Children. The framework consists of three steps: 1) Develop an analysis plan specifying the analysis model and how missing data are going to be addressed. An important consideration is whether a complete records analysis is likely to be valid, whether multiple imputation or an alternative approach is likely to offer benefits, and whether a sensitivity analysis regarding the missingness mechanism is required. 2) Explore the data, checking the methods outlined in the analysis plan are appropriate, and conduct the pre-planned analysis. 3) Report the results, including a description of the missing data, details on how the missing data were addressed, and the results from all analyses, interpreted in light of the missing data and the clinical relevance. This framework seeks to support researchers in thinking systematically about missing data, and transparently reporting the potential effect on the study results., Comment: 37 pages, including 3 Figures, 1 table and supplementary material

Published
2020

24. Ethnic differences in the indirect effects of the COVID-19 pandemic on clinical monitoring and hospitalisations for non-COVID conditions in England: a population-based, observational cohort study using the OpenSAFELY platform

Author

Chaturvedi, Nishi, Park, Chloe, Carnemolla, Alisia, Williams, Dylan, Knueppel, Anika, Boyd, Andy, Turner, Emma L., Evans, Katharine M., Thomas, Richard, Berman, Samantha, McLachlan, Stela, Crane, Matthew, Whitehorn, Rebecca, Oakley, Jacqui, Foster, Diane, Woodward, Hannah, Campbell, Kirsteen C., Timpson, Nicholas, Kwong, Alex, Soares, Ana Goncalves, Griffith, Gareth, Toms, Renin, Jones, Louise, Annie, Herbert, Mitchell, Ruth, Palmer, Tom, Sterne, Jonathan, Walker, Venexia, Huntley, Lizzie, Fox, Laura, Denholm, Rachel, Knight, Rochelle, Northstone, Kate, Kanagaratnam, Arun, Horne, Elsie, Forbes, Harriet, North, Teri, Taylor, Kurt, Arab, Marwa A.L., Walker, Scott, Coronado, Jose I.C., Karthikeyan, Arun S., Ploubidis, George, Moltrecht, Bettina, Booth, Charlotte, Parsons, Sam, Wielgoszewska, Bozena, Bridger-Staatz, Charis, Steves, Claire, Thompson, Ellen, Garcia, Paz, Cheetham, Nathan, Bowyer, Ruth, Freydin, Maxim, Roberts, Amy, Goldacre, Ben, Walker, Alex, Morley, Jess, Hulme, William, Nab, Linda, Fisher, Louis, MacKenna, Brian, Andrews, Colm, Curtis, Helen, Hopcroft, Lisa, Green, Amelia, Patalay, Praveetha, Maddock, Jane, Patel, Kishan, Stafford, Jean, Jacques, Wels, Tilling, Kate, Macleod, John, McElroy, Eoin, Shah, Anoop, Silverwood, Richard, Denaxas, Spiros, Flaig, Robin, McCartney, Daniel, Campbell, Archie, Tomlinson, Laurie, Tazare, John, Zheng, Bang, Smeeth, Liam, Herrett, Emily, Cowling, Thomas, Mansfield, Kate, Costello, Ruth E., Wang, Kevin, Mansfield, Kathryn, Mahalingasivam, Viyaasan, Douglas, Ian, Langan, Sinead, Brophy, Sinead, Parker, Michael, Kennedy, Jonathan, McEachan, Rosie, Wright, John, Willan, Kathryn, Badrick, Ellena, Santorelli, Gillian, Yang, Tiffany, Hou, Bo, Steptoe, Andrew, Giorgio, Di Gessa, Zhu, Jingmin, Zaninotto, Paola, Wood, Angela, Cezard, Genevieve, Ip, Samantha, Bolton, Tom, Sampri, Alexia, Rafeti, Elena, Almaghrabi, Fatima, Sheikh, Aziz, Shah, Syed A., Katikireddi, Vittal, Shaw, Richard, Hamilton, Olivia, Green, Michael, Kromydas, Theocharis, Kopasker, Daniel, Greaves, Felix, Willans, Robert, Glen, Fiona, Sharp, Steve, Hughes, Alun, Wong, Andrew, Howes, Lee Hamill, Rapala, Alicja, Nigrelli, Lidia, McArdle, Fintan, Beckford, Chelsea, Raman, Betty, Dobson, Richard, Folarin, Amos, Stewart, Callum, Ranjan, Yatharth, Carpentieri, Jd, Sheard, Laura, Fang, Chao, Baz, Sarah, Gibson, Andy, Kellas, John, Neubauer, Stefan, Piechnik, Stefan, Lukaschuk, Elena, Saunders, Laura C., Wild, James M., Smith, Stephen, Jezzard, Peter, Tunnicliffe, Elizabeth, Sanders, Zeena-Britt, Finnigan, Lucy, Ferreira, Vanessa, Green, Mark, Rhead, Rebecca, Kibble, Milla, Wei, Yinghui, Lemanska, Agnieszka, Perez-Reche, Francisco, Piehlmaier, Dominik, Teece, Lucy, Parker, Edward, Walker, Alex J., Inglesby, Peter, Curtis, Helen J., Morton, Caroline E., Morley, Jessica, Mehrkar, Amir, Bacon, Sebastian C.J., Hickman, George, Croker, Richard, Evans, David, Ward, Tom, DeVito, Nicholas J., Green, Amelia C.A., Massey, Jon, Smith, Rebecca M., Hulme, William J., Davy, Simon, Andrews, Colm D., Hopcroft, Lisa E.M., Drysdale, Henry, Dillingham, Iain, Park, Robin Y., Higgins, Rose, Cunningham, Christine, Wiedemann, Milan, Maude, Steven, Macdonald, Orla, Butler-Cole, Ben F.C., O'Dwyer, Thomas, Stables, Catherine L., Wood, Christopher, Brown, Andrew D., Speed, Victoria, Bridges, Lucy, Schaffer, Andrea L., Walters, Caroline E., Rentsch, Christopher T., Bhaskaran, Krishnan, Schultze, Anna, Williamson, Elizabeth J., McDonald, Helen I., Tomlinson, Laurie A., Mathur, Rohini, Eggo, Rosalind M., Wing, Kevin, Wong, Angel Y.S., Grieve, Richard, Grint, Daniel J., Mansfield, Kathryn E., Douglas, Ian J., Evans, Stephen J.W., Walker, Jemma L., Cowling, Thomas E., Herrett, Emily L., Parker, Edward P.K., Bates, Christopher, Cockburn, Jonathan, Parry, John, Hester, Frank, Harper, Sam, O'Hanlon, Shaun, Eavis, Alex, Jarvis, Richard, Avramov, Dima, Griffiths, Paul, Fowles, Aaron, Parkes, Nasreen, Nicholson, Brian, Perera, Rafael, Harrison, David, Khunti, Kamlesh, Sterne, Jonathan AC., Quint, Jennifer, Henderson, Alasdair D., Carreira, Helena, Bidulka, Patrick, Warren-Gash, Charlotte, Hayes, Joseph F., Quint, Jennifer K., Katikireddi, Srinivasa Vittal, and Langan, Sinéad M.

Published
2023
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25. Sample-constrained partial identification with application to selection bias

Author

Tudball, Matthew, Hughes, Rachael, Tilling, Kate, Bowden, Jack, and Zhao, Qingyuan

Subjects
Statistics - Methodology
Abstract

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure and conclude with a substantive motivating example on the causal effect of education on income in the highly-selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the R package selectioninterval., Comment: 23 pages, 6 figures

Published
2019
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33. Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study

Author

Kawabata, Emily, primary, Major-Smith, Daniel, additional, Clayton, Gemma L, additional, Shapland, Chin Yang, additional, Morris, Tim P, additional, Carter, Alice R, additional, Fernández-Sanlés, Alba, additional, Borges, Maria Carolina, additional, Tilling, Kate, additional, Griffith, Gareth J, additional, Millard, Louise AC, additional, Smith, George Davey, additional, Lawlor, Deborah A, additional, and Hughes, Rachael A, additional

Published
2024
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34. A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts

Author

Burrows, Kimberley, primary, Heiskala, Anni, additional, Bradfield, Jonathan P, additional, Balkhiyarova, Zhanna, additional, Ning, Lijiao, additional, Boissel, Mathilde, additional, Chan, Yee-Ming, additional, Froguel, Philippe, additional, Bonnefond, Amelie, additional, Hakonarson, Hakon, additional, Couto Alves, Alessander, additional, Lawlor, Deborah A, additional, Kaakinen, Marika, additional, Jarvelin, Marjo-Riitta, additional, Grant, Struan F.A., additional, Tilling, Kate, additional, Prokopenko, Inga, additional, Sebert, Sylvain, additional, Canouil, Mickael, additional, and Warrington, Nicole M, additional

Published
2024
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37. Long COVID burden and risk factors in 10 UK longitudinal studies and electronic health records

Author

Thompson, Ellen J., Williams, Dylan M., Walker, Alex J., Mitchell, Ruth E., Niedzwiedz, Claire L., Yang, Tiffany C., Huggins, Charlotte F., Kwong, Alex S. F., Silverwood, Richard J., Di Gessa, Giorgio, Bowyer, Ruth C. E., Northstone, Kate, Hou, Bo, Green, Michael J., Dodgeon, Brian, Doores, Katie J., Duncan, Emma L., Williams, Frances M. K., Steptoe, Andrew, Porteous, David J., McEachan, Rosemary R. C., Tomlinson, Laurie, Goldacre, Ben, Patalay, Praveetha, Ploubidis, George B., Katikireddi, Srinivasa Vittal, Tilling, Kate, Rentsch, Christopher T., Timpson, Nicholas J., Chaturvedi, Nishi, and Steves, Claire J.

Published
2022
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42. Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Author

Wade, Kaitlin H., Yarmolinsky, James, Giovannucci, Edward, Lewis, Sarah J., Millwood, Iona Y., Munafò, Marcus R., Meddens, Fleur, Burrows, Kimberley, Bell, Joshua A., Davies, Neil M., Mariosa, Daniela, Kanerva, Noora, Vincent, Emma E., Smith-Byrne, Karl, Guida, Florence, Gunter, Marc J., Sanderson, Eleanor, Dudbridge, Frank, Burgess, Stephen, Cornelis, Marilyn C., Richardson, Tom G., Borges, Maria Carolina, Bowden, Jack, Hemani, Gibran, Cho, Yoonsu, Spiller, Wes, Richmond, Rebecca C., Carter, Alice R., Langdon, Ryan, Lawlor, Deborah A., Walters, Robin G., Vimaleswaran, Karani Santhanakrishnan, Anderson, Annie, Sandu, Meda R., Tilling, Kate, Davey Smith, George, Martin, Richard M., and Relton, Caroline L.

Published
2022
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44. Psychological distress, depression, anxiety, and life satisfaction following COVID-19 infection: evidence from 11 UK longitudinal population studies

Author

Thompson, Ellen J, Stafford, Jean, Moltrecht, Bettina, Huggins, Charlotte F, Kwong, Alex S F, Shaw, Richard J, Zaninotto, Paola, Patel, Kishan, Silverwood, Richard J, McElroy, Eoin, Pierce, Matthias, Green, Michael J, Bowyer, Ruth C E, Maddock, Jane, Tilling, Kate, Katikireddi, S Vittal, Ploubidis, George B, Porteous, David J, Timpson, Nic, Chaturvedi, Nish, Steves, Claire J, and Patalay, Praveetha

Published
2022
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47. Counterfactual Mediation Analysis with a Latent Class Exposure.

Author

Hammerton, Gemma, Heron, Jon, Lewis, Katie, Tilling, Kate, and Vansteelandt, Stijn

Subjects
COUNTERFACTUALS (Logic), LATENT class analysis (Statistics), LATENT variables, TEST methods
Abstract

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank

Author

Elmore, Andrew R., primary, Adhikari, Nimish, additional, Hartley, April E, additional, Aparicio, Hugo Javier, additional, Posner, Daniel, additional, Hemani, Gibran, additional, Tilling, Kate, additional, Gaunt, Tom R., additional, Wilson, Peter W.F., additional, Casas, JP, additional, Gaziano, J. Michael, additional, Davey Smith, George, additional, Paternoster, Lavinia, additional, Cho, Kelly, additional, and Peloso, Gina M, additional

Published
2024
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50. Epigenetic aging & embodying injustice: US My Body My Story and Multi-Ethnic Atherosclerosis Study

Author

Krieger, Nancy, primary, Testa, Christian, additional, Chen, Jarvis T., additional, Johnson, Nykesha, additional, Watkins, Sarah H., additional, Suderman, Matthew, additional, Simpkin, Andrew J., additional, Tilling, Kate, additional, Waterman, Pamela D., additional, Coull, Brent A., additional, Vivo, Immaculata De, additional, Smith, George Davey, additional, Roux, Ana V. Diez, additional, and Relton, Caroline, additional

Published
2023
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