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Research Articles

Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models

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References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
  • Allison, P. D., Williams, R., & Moral-Benito, E. (2017). Maximum likelihood for cross-lagged panel models with fixed effects. Socius: Sociological Research for a Dynamic World, 3, 237802311771057. https://doi.org/10.1177/2378023117710578
  • Amin, S., Korhonen, M., & Huikari, S. (2023). Unemployment and mental health: An instrumental variable analysis using municipal-level data for Finland for 2002–2019. Social Indicators Research, 166(3), 627–643. https://doi.org/10.1007/s11205-023-03081-1
  • Andersen, H. K. (2022). Equivalent approaches to dealing with unobserved heterogeneity in cross-lagged panel models? Investigating the benefits and drawbacks of the latent curve model with structured residuals and the random intercept cross-lagged panel model. Psychological Methods, 27(5), 730–751. https://doi.org/10.1037/met0000285
  • Audrain-McGovern, J., Leventhal, A. M., & Strong, D. R. (2015). The role of depression in the uptake and maintenance of cigarette smoking. In M. De Biasi (Ed.), International review of neurobiology (Vol. 124, pp. 209–243). Academic Press. https://doi.org/10.1016/bs.irn.2015.07.004
  • Auton, A., Abecasis, G. R., Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., Clark, A. G., Donnelly, P., Eichler, E. E., Flicek, P., Gabriel, S. B., Gibbs, R. A., Green, E. D., Hurles, M. E., Knoppers, B. M., Korbel, J. O., Lander, E. S., Lee, C., … Abecasis, G. R. (2015). A global reference for human genetic variation. Nature, 526(7571), 68–74. https://doi.org/10.1038/nature15393
  • Becker, S. J., Nargiso, J. E., Wolff, J. C., Uhl, K. M., Simon, V. A., Spirito, A., & Prinstein, M. J. (2012). Temporal relationship between substance use and delinquent behavior among young psychiatrically hospitalized adolescents. Journal of Substance Abuse Treatment, 43(2), 251–259. https://doi.org/10.1016/j.jsat.2011.11.005
  • Bollen, K. A. (2012). Instrumental variables in sociology and the social sciences. Annual Review of Sociology, 38(1), 37–72. https://doi.org/10.1146/annurev-soc-081309-150141
  • Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., Mcnally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., Van Borkulo, C. D., Van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), 58. https://doi.org/10.1038/s43586-021-00055-w
  • Cambini, C., & Rondi, L. (2010). Incentive regulation and investment: Evidence from European energy utilities. Journal of Regulatory Economics, 38(1), 1–26. https://doi.org/10.1007/s11149-009-9111-6
  • Campbell, M. L., Bozec, L. J., McGrath, D., & Barrett, S. P. (2012). Alcohol and tobacco co-use in nondaily smokers: An inevitable phenomenon? Drug and Alcohol Review, 31(4), 447–450. https://doi.org/10.1111/j.1465-3362.2011.00328.x
  • Castro-de-Araujo, L. F. S., Singh, M., Zhou, Y., Vinh, P., Verhulst, B., Dolan, C. V., & Neale, M. C. (2023). MR-DoC2: Bidirectional causal modeling with instrumental variables and data from relatives. Behavior Genetics, 53(1), 63–73. https://doi.org/10.1007/s10519-022-10122-x
  • Davey Smith, G., & Ebrahim, S. (2003). ‘Mendelian randomization’: Can genetic epidemiology contribute to understanding environmental determinants of disease? International Journal of Epidemiology, 32(1), 1–22. https://doi.org/10.1093/ije/dyg070
  • de Vries, L. P., Baselmans, B. M. L., Luykx, J. J., de Zeeuw, E. L., Minică, C. C., de Geus, E. J. C., Vinkers, C. H., & Bartels, M. (2021). Genetic evidence for a large overlap and potential bidirectional causal effects between resilience and well-being. Neurobiology of Stress, 14, 100315. https://doi.org/10.1016/j.ynstr.2021.100315
  • Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods, 23(4), 774–799. https://doi.org/10.1037/met0000168
  • Epstein, A. M., Sher, T. G., Young, M. A., & King, A. C. (2007). Tobacco chippers show robust increases in smoking urge after alcohol consumption. Psychopharmacology, 190(3), 321–329. https://doi.org/10.1007/s00213-006-0438-8
  • Falk, D. E., Yi, H. Y., & Hiller-Sturmhöfel, S. (2006). An epidemiologic analysis of co-occurring alcohol and tobacco use and disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Alcohol Research & Health, 29(3), 162–171.
  • Gagniuc, P. A. (2017). Markov chains: From theory to implementation and experimentation. John Wiley & Sons, Incorporated. Retrieved from http://ebookcentral.proquest.com/lib/vcu/detail.action?docID=4908192
  • Gakidou, E., Afshin, A., Abajobir, A. A., Abate, K. H., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abdulle, A. M., Abera, S. F., Aboyans, V., Abu-Raddad, L. J., Abu-Rmeileh, N. M. E., Abyu, G. Y., Adedeji, I. A., Adetokunboh, O., Afarideh, M., Agrawal, A., Agrawal, S., Ahmadieh, H., … Murray, C. J. L. (2017). Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet, 390(10100), 1345–1422. https://doi.org/10.1016/s0140-6736(17)32366-8
  • Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018). Recruiting the ABCD sample: Design considerations and procedures. Developmental Cognitive Neuroscience, 32, 16–22. https://doi.org/10.1016/j.dcn.2018.04.004
  • George, M., Hsu, J., Hennessy, S., Chen, L., Xie, F., Curtis, J., & Baker, J. (2022). Risk of serious infection with low-dose glucocorticoids in patients with rheumatoid arthritis. Epidemiology, 33(1), 65–74. https://doi.org/10.1097/EDE.0000000000001422
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424. https://doi.org/10.2307/1912791
  • Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
  • Hamer, M., Chastin, S., Viner, R. M., & Stamatakis, E. (2021). Childhood obesity and device‐measured sedentary behavior: An instrumental variable analysis of 3,864 mother–offspring Pairs. Obesity, 29(1), 220–225. https://doi.org/10.1002/oby.23025
  • Harris, K. M. (2013). Add health study: Design and accomplishments paper (Waves I-IV). The University of North Carolina. https://doi.org/10.17615/C6TW87
  • Harrison, E. L. R., & Mckee, S. A. (2011). Non-daily smoking predicts hazardous drinking and alcohol use disorders in young adults in a longitudinal U.S. sample. Drug and Alcohol Dependence, 118(1), 78–82. https://doi.org/10.1016/j.drugalcdep.2011.02.022
  • Hassan, M., Oueslati, W., & Rousselière, D. (2020). Exploring the link between energy based taxes and economic growth. Environmental Economics and Policy Studies, 22(1), 67–87. https://doi.org/10.1007/s10018-019-00247-5
  • Hawkley, L. C., Thisted, R. A., Masi, C. M., & Cacioppo, J. T. (2010). Loneliness predicts increased blood pressure: 5-Year cross-lagged analyses in middle-aged and older adults. Psychology and Aging, 25(1), 132–141. https://doi.org/10.1037/a0017805
  • Hume, D. ([1739] 2009). A treatise of human nature: Being an attempt to introduce the experimental method of reasoning into moral subjects. The Floating Press.
  • Hunter, M. D., Garrison, S. M., Burt, S. A., & Rodgers, J. L. (2021). The analytic identification of variance component models common to behavior genetics. Behavior Genetics, 51(4), 425–437. https://doi.org/10.1007/s10519-021-10055-x
  • Hurley, L. L., Taylor, R. E., & Tizabi, Y. (2012). Positive and negative effects of alcohol and nicotine and their interactions: A mechanistic review. Neurotoxicity Research, 21(1), 57–69. https://doi.org/10.1007/s12640-011-9275-6
  • Kuiper, R. M., & Ryan, O. (2018). Drawing conclusions from cross-lagged relationships: Re-considering the role of the time-interval. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 809–823. https://doi.org/10.1080/10705511.2018.1431046
  • Labrecque, J., & Swanson, S. A. (2018). Understanding the assumptions underlying instrumental variable analyses: A brief review of falsification strategies and related tools. Current Epidemiology Reports, 5(3), 214–220. https://doi.org/10.1007/s40471-018-0152-1
  • Lac, A., & Donaldson, C. D. (2021). Sensation seeking versus alcohol use: Evaluating temporal precedence using cross-lagged panel models. Drug and Alcohol Dependence, 219, 108430. https://doi.org/10.1016/j.drugalcdep.2020.108430
  • Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N., & Davey Smith, G. (2008). Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine, 27(8), 1133–1163. https://doi.org/10.1002/sim.3034
  • Levey, D. F., Gelernter, J., Polimanti, R., Zhou, H., Cheng, Z., Aslan, M., Quaden, R., Concato, J., Radhakrishnan, K., Bryois, J., Sullivan, P. F., & Stein, M. B. (2020). Reproducible genetic risk loci for anxiety: Results from ∼200,000 participants in the million veteran program. The American Journal of Psychiatry, 177(3), 223–232. https://doi.org/10.1176/appi.ajp.2019.19030256
  • Ligthart, L., van Beijsterveldt, C. E. M., Kevenaar, S. T., de Zeeuw, E., van Bergen, E., Bruins, S., Pool, R., Helmer, Q., van Dongen, J., Hottenga, J.-J., Van’t Ent, D., Dolan, C. V., Davies, G. E., Ehli, E. A., Bartels, M., Willemsen, G., de Geus, E. J. C., & Boomsma, D. I. (2019). The Netherlands Twin Register: Longitudinal research based on twin and twin-family designs. Twin Research and Human Genetics, 22(6), 623–636. https://doi.org/10.1017/thg.2019.93
  • Lim, K. X., Oginni, O. A., Rimfeld, K., Pingault, J.-B., & Rijsdijk, F. (2022). Investigating the causal risk factors for self-harm by integrating Mendelian randomisation within twin modelling. Behavior Genetics, 52(6), 324–337. https://doi.org/10.1007/s10519-022-10114-x
  • Liu, M., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., Datta, G., Davila-Velderrain, J., McGuire, D., Tian, C., Zhan, X., Choquet, H., Docherty, A. R., Faul, J. D., Foerster, J. R., Fritsche, L. G., Gabrielsen, M. E., Gordon, S. D., Haessler, J., … Vrieze, S. (2019). Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nature Genetics, 51(2), 237–244. https://doi.org/10.1038/s41588-018-0307-5
  • Luke, H. D. (1999). The origins of the sampling theorem. IEEE Communications Magazine, 37(4), 106–108. https://doi.org/10.1109/35.755459
  • Ma, D., Serbin, L. A., & Stack, D. M. (2019). How children’s anxiety symptoms impact the functioning of the hypothalamus–pituitary–adrenal axis over time: A cross-lagged panel approach using hierarchical linear modeling. Development and Psychopathology, 31(1), 309–323. https://doi.org/10.1017/s0954579417001870
  • Mahajan, S. D., Homish, G. G., & Quisenberry, A. (2021). Multifactorial etiology of adolescent nicotine addiction: A review of the neurobiology of nicotine addiction and its implications for smoking cessation pharmacotherapy. Frontiers in Public Health, 9, 664748. https://doi.org/10.3389/fpubh.2021.664748
  • Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46(5), 816–841. https://doi.org/10.1080/00273171.2011.606716
  • Maydeu-Olivares, A., Shi, D., & Fairchild, A. J. (2020). Estimating causal effects in linear regression models with observational data: The instrumental variables regression model. Psychological Methods, 25(2), 243–258. https://doi.org/10.1037/met0000226
  • Maydeu-Olivares, A., Shi, D., & Rosseel, Y. (2019). Instrumental variables two-stage least squares (2SLS) vs. maximum likelihood structural equation modeling of causal effects in linear regression models. Structural Equation Modeling: A Multidisciplinary Journal, 26(6), 876–892. https://doi.org/10.1080/10705511.2019.1607740
  • McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model for moment structures. The British Journal of Mathematical and Statistical Psychology, 37(Pt 2), 234–251. https://doi.org/10.1111/j.2044-8317.1984.tb00802.x
  • McDowell, B., Chapman, C., Smith, B., Button, A., Chrischilles, E., & Mezhir, J. (2015). Pancreatectomy predicts improved survival for pancreatic adenocarcinoma: Results of an instrumental variable analysis. Annals of Surgery, 261(4), 740–745. https://doi.org/10.1097/SLA.0000000000000796
  • Mcelroy, E., Belsky, J., Carragher, N., Fearon, P., & Patalay, P. (2018). Developmental stability of general and specific factors of psychopathology from early childhood to adolescence: Dynamic mutualism or p-differentiation? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 59(6), 667–675. https://doi.org/10.1111/jcpp.12849
  • Mckee, S. A., & Weinberger, A. H. (2013). How can we use our knowledge of alcohol-tobacco interactions to reduce alcohol use? Annual Review of Clinical Psychology, 9(1), 649–674. https://doi.org/10.1146/annurev-clinpsy-050212-185549
  • Mehta, P. D., Neale, M. C., & Flay, B. R. (2004). Squeezing interval change from ordinal panel data: Latent growth curves with ordinal outcomes. Psychological Methods, 9(3), 301–333. https://doi.org/10.1037/1082-989x.9.3.301
  • Minică, C. C., Dolan, C. V., Boomsma, D. I., De Geus, E., & Neale, M. C. (2018). Extending causality tests with genetic instruments: An integration of mendelian randomization with the classical twin design. Behavior Genetics, 48(4), 337–349. https://doi.org/10.1007/s10519-018-9904-4
  • Murayama, K., & Gfrörer, T. (2022). Thinking clearly about time-invariant confounders in cross-lagged panel models: A guide for choosing a statistical model from a causal inference perspective. PsyArXiv. https://doi.org/10.31234/osf.io/bt9xr
  • Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., Estabrook, R., Bates, T. C., Maes, H. H., & Boker, S. M. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81(2), 535–549. https://doi.org/10.1007/s11336-014-9435-8
  • Oginni, O. A., Lim, K. X., Rahman, Q., Jern, P., Eley, T. C., & Rijsdijk, F. V. (2023). Bidirectional causal associations between same-sex attraction and psychological distress: Testing moderation and mediation effects. Behavior Genetics, 53(2), 118–131. https://doi.org/10.1007/s10519-022-10130-x
  • Oud, J. H. L., & Jansen, R. A. R. G. (2000). Continuous time state space modeling of panel data by means of sem. Psychometrika, 65(2), 199–215. https://doi.org/10.1007/BF02294374
  • Patalay, P., Sharpe, H., & Wolpert, M. (2015). Internalising symptoms and body dissatisfaction: Untangling temporal precedence using cross-lagged models in two cohorts. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 56(11), 1223–1230. https://doi.org/10.1111/jcpp.12415
  • Piasecki, T. M., Jahng, S., Wood, P. K., Robertson, B. M., Epler, A. J., Cronk, N. J., Rohrbaugh, J. W., Heath, A. C., Shiffman, S., & Sher, K. J. (2011). The subjective effects of alcohol–tobacco co-use: An ecological momentary assessment investigation. Journal of Abnormal Psychology, 120(3), 557–571. https://doi.org/10.1037/a0023033
  • R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
  • Rakowski, D., & Yamani, E. (2021). Endogeneity in the mutual fund flow–performance relationship: An instrumental variables solution. Journal of Empirical Finance, 64, 247–271. https://doi.org/10.1016/j.jempfin.2021.09.003
  • Reed, Z. E., Wootton, R. E., & Munafò, M. R. (2022). Using Mendelian randomization to explore the gateway hypothesis: Possible causal effects of smoking initiation and alcohol consumption on substance use outcomes. Addiction, 117(3), 741–750. https://doi.org/10.1111/add.15673
  • Richmond, R. C., & Davey Smith, G. (2022). Mendelian randomization: Concepts and scope. Cold Spring Harbor Perspectives in Medicine, 12(1), a040501. https://doi.org/10.1101/cshperspect.a040501
  • Rimfeld, K., Malanchini, M., Spargo, T., Spickernell, G., Selzam, S., Mcmillan, A., Dale, P. S., Eley, T. C., & Plomin, R. (2019). Twins early development study: A genetically sensitive investigation into behavioral and cognitive development from infancy to emerging adulthood. Twin Research and Human Genetics, 22(6), 508–513. https://doi.org/10.1017/thg.2019.56
  • Ryabko, D. (2019). Introduction. In D. Ryabko (Ed.), Asymptotic nonparametric statistical analysis of stationary time series (pp. 1–8). Springer International Publishing. https://doi.org/10.1007/978-3-030-12564-6_1
  • Saunders, G. R. B., Wang, X., Chen, F., Jang, S.-K., Liu, M., Wang, C., Gao, S., Jiang, Y., Khunsriraksakul, C., Otto, J. M., Addison, C., Akiyama, M., Albert, C. M., Aliev, F., Alonso, A., Arnett, D. K., Ashley-Koch, A. E., Ashrani, A. A., Barnes, K. C., … Vrieze, S. (2022). Genetic diversity fuels gene discovery for tobacco and alcohol use. Nature, 612(7941), 720–724. https://doi.org/10.1038/s41586-022-05477-4
  • Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021). Genome-wide association studies. Nature Reviews Methods Primers, 1(1), 59. https://doi.org/10.1038/s43586-021-00056-9
  • van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842–861. https://doi.org/10.1037/0033-295X.113.4.842
  • van der Sluis, S., Dolan, C. V., Neale, M. C., & Posthuma, D. (2008). Power calculations using exact data simulation: A useful tool for genetic study designs. Behavior Genetics, 38(2), 202–211. https://doi.org/10.1007/s10519-007-9184-x
  • van Ouytsel, J., Lu, Y., Ponnet, K., Walrave, M., & Temple, J. R. (2019). Longitudinal associations between sexting, cyberbullying, and bullying among adolescents: Cross-lagged panel analysis. Journal of Adolescence, 73(1), 36–41. https://doi.org/10.1016/j.adolescence.2019.03.008
  • Venables, W., Ripley, B. D. (2002). Statistics complements to modern applied statistics with S (4th ed.). Springer. Retrieved from https://www.stats.ox.ac.uk/pub/MASS4/
  • Verhulst, B., & Neale, M. C. (2021). Best practices for binary and ordinal data analyses. Behavior Genetics, 51(3), 204–214. https://doi.org/10.1007/s10519-020-10031-x
  • Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P.-R., Bhatia, G., Do, R., Hayeck, T., Won, H.-H., Kathiresan, S., Pato, M., Pato, C., Tamimi, R., Stahl, E., Zaitlen, N., Pasaniuc, B., … Price, A. L. (2015). Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics, 97(4), 576–592. https://doi.org/10.1016/j.ajhg.2015.09.001
  • Voelkle, M. C., Oud, J. H., Davidov, E., & Schmidt, P. (2012). An SEM approach to continuous time modeling of panel data: Relating authoritarianism and anomia. Psychological Methods, 17(2), 176–192. https://doi.org/10.1037/a0027543
  • Voelkle, M. C., & Oud, J. H. L. (2013). Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes. The British Journal of Mathematical and Statistical Psychology, 66(1), 103–126. https://doi.org/10.1111/j.2044-8317.2012.02043.x
  • Vrieze, S. I. (2012). Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods, 17(2), 228–243. https://doi.org/10.1037/a0027127
  • Wang, D. G., Fan, J. B., Siao, C. J., Berno, A., Young, P., Sapolsky, R., Ghandour, G., Perkins, N., Winchester, E., Spencer, J., Kruglyak, L., Stein, L., Hsie, L., Topaloglou, T., Hubbell, E., Robinson, E., Mittmann, M., Morris, M. S., Shen, N., … Lander, E. S. (1998). Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science, 280(5366), 1077–1082. https://doi.org/10.1126/science.280.5366.1077
  • Weinberger, A. H., Pilver, C. E., Hoff, R. A., Mazure, C. M., & McKee, S. A. (2013). Changes in smoking for adults with and without alcohol and drug use disorders: Longitudinal evaluation in the US population. The American Journal of Drug and Alcohol Abuse, 39(3), 186–193. https://doi.org/10.3109/00952990.2013.785557
  • West, R. (2017). Tobacco smoking: Health impact, prevalence, correlates and interventions. Psychology & Health, 32(8), 1018–1036. https://doi.org/10.1080/08870446.2017.1325890
  • Wilks, S. S. (1938). The large-sample distribution of the likelihood ratio for testing composite hypotheses. The Annals of Mathematical Statistics, 9(1), 60–62. https://doi.org/10.1214/aoms/1177732360
  • Wray, N. R., Lin, T., Austin, J., Mcgrath, J. J., Hickie, I. B., Murray, G. K., & Visscher, P. M. (2021). From basic science to clinical application of polygenic risk scores. JAMA Psychiatry, 78(1), 101–109. https://doi.org/10.1001/jamapsychiatry.2020.3049