Framing Causal Questions in Life Course Epidemiology

Literature Cited

1. Acad. Med. Sci 2018. Multimorbidity: a priority for global health research Int. Policy Rep. Acad. Med. Sci. London: https://acmedsci.ac.uk/policy/policy-projects/multimorbidity
2. Ackerman B, Lesko CR, Siddique J, Susukida R, Stuart EA. 2021. Generalizing randomized trial findings to a target population using complex survey population data. Stat. Med. 40:1101–20
   [Google Scholar]
3. Baiocchi M, Small DS, Lorch S, Rosenbaum PR 2010. Building a stronger instrument in an observational study of perinatal care for premature infants. J. Am. Stat. Assoc. 105:1285–96
   [Google Scholar]
4. Barker DJ, Osmond C. 1986. Infant mortality, childhood nutrition, and ischaemic heart disease in England and Wales. Lancet 1:1077–81
   [Google Scholar]
5. Ben-Shlomo Y, Mishra G, Kuh D 2014. Life course epidemiology. Handbook of Epidemiology W Ahrens, I Pigeot 1521–49 New York: Springer
   [Google Scholar]
6. Bianco-Miotto T, Craig JM, Gasser YP, van Dijk SJ, Ozanne SE. 2017. Epigenetics and DOHaD: from basics to birth and beyond. J. Dev. Orig. Health Dis. 8:513–19
   [Google Scholar]
7. Bogin B. 1999. Patterns of Human Growth. Cambridge, UK: Cambridge Univ. Press. , 2nd ed..
8. Bowden J, Davey Smith G, Burgess S 2015. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44:512–25
   [Google Scholar]
9. Breuer J, Freud S. 2004. Studies on Hysteria transl. N Luckhurst London: Penguin
10. Burgess S, Swanson SA, Labrecque JA. 2021. Are Mendelian randomization investigations immune from bias due to reverse causation?. Eur. J. Epidemiol. 36:253–57
    [Google Scholar]
11. Carlin JB, Gurrin LC, Sterne JAC, Morley R, Dwyer T 2005. Regression models for twin studies: a critical review. Int. J. Epidemiol. 34:1089–99
    [Google Scholar]
12. Cheesman R, Eilertsen EM, Ahmadzadeh YI, Gjerde LC, Hannigan LJ et al. 2020. How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa). BMC Med. 18:284
    [Google Scholar]
13. Daniel RM, De Stavola B. 2019. Mediation analysis for life course studies. Lifecourse Pathways to Health GB Ploubidis 1–40 New York: Springer
    [Google Scholar]
14. Didelez V, Dawid AP, Geneletti S 2006. Direct and indirect effects of sequential treatments. Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence R Dechter, T Richardson 138–46 Arlington, VA: AUAI
    [Google Scholar]
15. Drummond C. 2009. Replicability is not reproducibility: nor is it good science Paper presented at the Evaluation Methods for Machine Learning Workshop at the 26th ICML Montreal, Quebec, Canada:
16. Dugoff EH, Schuler M, Stuart EA. 2014. Generalizing observational study results: applying propensity score methods to complex surveys. Health Serv. Res. 49:284–303
    [Google Scholar]
17. Elango S, Garcia JL, Heckman J, Hojman A. 2015. Early childhood education NBER Work. Pap. 21766
18. Emsley R, Dunn G, White IR. 2010. Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Stat. Methods Med. Res. 19:237–70
    [Google Scholar]
19. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J et al. 2013. Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int. J. Epidemiol. 42:97–110
    [Google Scholar]
20. Gillman MW. 2002. Epidemiological challenges in studying the fetal origins of adult chronic disease. Int. J. Epidemiol. 31:294–99
    [Google Scholar]
21. Goetghebeur E, le Cessie S, De Stavola B, Moodie EE, Waernbaum I et al. 2020. Formulating causal questions and principled statistical answers. Stat. Med. 39:4922–48
    [Google Scholar]
22. Green MJ, Popham F. 2017. Life course models: improving interpretation by consideration of total effects. Int. J. Epidemiol. 46:1057–62
    [Google Scholar]
23. Heckman J, Pinto R, Savelyev P. 2013. Understanding the mechanisms through which an influential early childhood program boosted adult outcomes. Am. Econ. Rev. 103:62052–86
    [Google Scholar]
24. Hernán MA, Robins JM. 2006. Instruments for causal inference: an epidemiologist's dream?. Epidemiology 17:360–72
    [Google Scholar]
25. Hernán MA, Robins JM. 2020. Causal Inference: What If Boca Raton, FL: Chapman & Hall/CRC Press
26. Hewitt JK, Silberg JL, Rutter M, Simonoff E, Meyer JM et al. 1997. Genetics and developmental psychopathology: 1. Phenotypic assessment in the Virginia Twin Study of Adolescent Behavioral Development. J. Child Psychol. Psychiatry 38:943–63
    [Google Scholar]
27. Hill AB. 1965. The environment and disease: association or causation?. Proc. R Soc. Med. 58:295–300
    [Google Scholar]
28. Keogh RH, Shaw PA, Gustafson P, Carroll RJ, Deffner V et al. 2020. STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology. Part 1: Basic theory and simple methods of adjustment. Stat. Med. 39:2197–231
    [Google Scholar]
29. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. 2008. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat. Med. 27:1133–63
    [Google Scholar]
30. Lawlor DA, Tilling K, Davey Smith G. 2016. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45:1866–86
    [Google Scholar]
31. le Cessie S, Debeij J, Rosendaal FR, Cannegieter SC, Vandenbroucke JP. 2012. Quantification of bias in direct effects estimates due to different types of measurement error in the mediator. Epidemiology 23:551–60
    [Google Scholar]
32. Liu S, Jones RN, Glymour MM. 2010. Implications of lifecourse epidemiology for research on determinants of adult disease. Public Health Rev 32:489–511
    [Google Scholar]
33. Loeys T, Moerkerke B, Raes A, Rosseel Y, Vansteelandt S. 2014. Estimation of controlled direct effects in the presence of exposure-induced confounding and latent variables. Struct. Equation Model. 21:396–407
    [Google Scholar]
34. Loh WW, Moerkerke B, Loeys T, Poppe L, Crombez G, Vansteelandt S 2020. Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomized studies. Multivariate Behav. Res. 55:763–85
    [Google Scholar]
35. MacKinnon DP. 2008. Introduction to Statistical Mediation Analysis New York: Routledge
36. Marott SC, Nordestgaard BG, Zacho J, Friberg J, Jensen GB et al. 2010. Does elevated C-reactive protein increase atrial fibrillation risk? A Mendelian randomization of 47,000 individuals from the general population. J. Am. Coll. Cardiol. 56:789–95
    [Google Scholar]
37. Mathur MB, VanderWeele TJ. 2019. Challenges and suggestions for defining replication “success” when effects may be heterogeneous: comment on Hedges and Schauer 2019. Psychol. Methods 24:571–75
    [Google Scholar]
38. Mayer SE, Surachman A, Prather AA, Puterman E, Delucchi KL et al. 2021. The long shadow of childhood trauma for depression in midlife: examining daily psychological stress processes as a persistent risk pathway. Psychol. Med. In press
    [Google Scholar]
39. McAdams TA, Rijsdijk FV, Zavos HMS, Pingault JB. 2021. Twins and causal inference: leveraging nature's experiment. Cold Spring Harb. . Perspect. Med. 11:6a039552
    [Google Scholar]
40. Med. Res. Counc 2000. A Framework for the Development and Evaluation of RCTs for Complex Interventions to Improve Health London: MRC
41. Micali N, Daniel RM, Ploubidis GB, De Stavola BL. 2018. Maternal prepregnancy weight status and adolescent eating disorder behaviors: a longitudinal study of risk pathways. Epidemiology 29:579–89
    [Google Scholar]
42. Mishra G, Nitsch D, Black S, De Stavola B, Kuh D, Hardy R 2009. A structured approach to modelling the effects of binary exposure variables over the life course. Int. J. Epidemiol. 38:528–37
    [Google Scholar]
43. Moreno-Betancur M, Carlin JB. 2018. Understanding interventional effects: a more natural approach to mediation analysis?. Epidemiology 29:614–17
    [Google Scholar]
44. Naimi AI, Kaufman JS. 2015. Counterfactual theory in social epidemiology: reconciling analysis and action for the social determinants of health. Curr. Epidemiol. Rep. 2:52–60
    [Google Scholar]
45. Nicholls DE, Viner RM. 2009. Childhood risk factors for lifetime anorexia nervosa by age 30 years in a national birth cohort. J. Am. Acad. Child Adolesc. Psychiatry 48:791–99
    [Google Scholar]
46. Ogburn EL, VanderWeele TJ. 2012. Analytic results on the bias due to nondifferential misclassification of a binary mediator. Am. J. Epidemiol. 176:555–61
    [Google Scholar]
47. Open Sci. Collab 2015. Estimating the reproducibility of psychological science. Science 349:aac4716
    [Google Scholar]
48. Pearl J. 1995. Causal diagrams for empirical research. Biometrika 82:669–88
    [Google Scholar]
49. Pearl J. 2001. Direct and indirect effects. Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence411–20 Seattle: Morgan Kaufmann
    [Google Scholar]
50. Robins JM. 1986. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Math. Model. 7:1393–1512
    [Google Scholar]
51. Robins JM, Greenland S. 1992. Identifiability and exchangeability for direct and indirect effects. Epidemiology 3:143–55
    [Google Scholar]
52. Robins JM, Hernán MA, Brumback B. 2000. Marginal structural models and causal inference in epidemiology. Epidemiology 11:550–60
    [Google Scholar]
53. Rommel AS, Rijsdijk F, Greven CU, Asherson P, Kuntsi J 2015. A longitudinal twin study of the direction of effects between ADHD symptoms and IQ. PLOS ONE 10:e0124357
    [Google Scholar]
54. Sjölander A, Frisell T, Öberg S. 2012. Causal interpretation of between-within models for twin research. Epidemiol. Methods 1:110
    [Google Scholar]
55. Sjölander A, Frisell T, Öberg S. 2021. Sibling comparison studies. Annu Rev. Stat. Appl. 9:71–94
    [Google Scholar]
56. Skrondal A, Laake P. 2001. Regression among factor scores. Psychometrika 66:563–75
    [Google Scholar]
57. Smith AD, Heron J, Mishra G, Gilthorpe MS, Ben-Shlomo Y, Tilling K. 2015. Model selection of the effect of binary exposures over the life course. Epidemiology 26:719–26
    [Google Scholar]
58. Sullivan AJ, Gunzler DD, Morris N, VanderWeele TJ 2021. Longitudinal mediation analysis with latent growth curves. arXiv:2103.05765 [stat.ME]
59. Swanson SA, Hernán MA. 2013. Commentary: how to report instrumental variable analyses. Epidemiology 24:370–74
    [Google Scholar]
60. Swanson SA, Hernán MA. 2018. The challenging interpretation of instrumental variable estimates under monotonicity. Int. J. Epidemiol. 47:1289–97
    [Google Scholar]
61. van der Laan MJ, Gruber S. 2012. Targeted minimum loss based estimation of causal effects of multiple time point interventions. Int. J. Biostat 8:19
    [Google Scholar]
62. VanderWeele TJ, Hernán M 2012. Causal effects and natural laws: towards a conceptualization of causal counterfactuals for nonmanipulable exposures, with application to the effects of race and sex. Statistical Perspectives and Applications C Berzuini, AP Dawid, L Bernardinelli 101–13 New York: Wiley
    [Google Scholar]
63. VanderWeele TJ, Valeri L, Ogburn EL 2012. The role of measurement error and misclassification in mediation analysis: mediation and measurement error. Epidemiology 23:561–64
    [Google Scholar]
64. VanderWeele TJ, Vansteelandt S. 2009. Conceptual issues concerning mediation, interventions and composition. Stat. Interface 2:457–68
    [Google Scholar]
65. VanderWeele TJ, Vansteelandt S, Robins JM. 2014. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology 25:300–6
    [Google Scholar]
66. Vansteelandt S, Daniel RM. 2017. Interventional effects for mediation analysis with multiple mediators. Epidemiology 28:258–65
    [Google Scholar]
67. Wardle J, von Wagner C, Kralj-Hans I, Halloran SP, Smith SG et al. 2016. Effects of evidence-based strategies to reduce the socioeconomic gradient of uptake in the English NHS Bowel Cancer Screening Programme (ASCEND): four cluster-randomised controlled trials. Lancet 387:751–59
    [Google Scholar]
68. Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA 2019. Target validity and the hierarchy of study designs. Am. J. Epidemiol. 188:438–43
    [Google Scholar]
69. Zehr JL, Culbert KM, Sisk CL, Klump KL. 2007. An association of early puberty with disordered eating and anxiety in a population of undergraduate women and men. Horm. Behav. 52:427–35
    [Google Scholar]
70. Zheng J, Baird D, Borges MC, Bowden J, Hemani G et al. 2017. Recent developments in Mendelian randomization studies. Curr. Epidemiol. Rep. 4:330–45
    [Google Scholar]