Ben-Shlomo Y, Cooper R, Kuh D. The last two decades of life course epidemiology, and its relevance for research on ageing. Int J Epidemiol. 2016;45(4):973–88.
Article
PubMed
PubMed Central
Google Scholar
Grimm KJ, Ram N, Hamagami F. Nonlinear growth curves in developmental research. Child Dev. 2011;82(5):1357–71.
Article
PubMed
PubMed Central
Google Scholar
Howe LD, Tilling K, Matijasevich A, Petherick ES, Santos AC, Fairley L, et al. Linear spline multilevel models for summarising childhood growth trajectories: a guide to their application using examples from five birth cohorts. Stat Methods Med Res. 2016;25(5):1854–74.
Article
PubMed
Google Scholar
Lourenço BH, Villamor E, Augusto RA, Cardoso MA. Influence of early life factors on body mass index trajectory during childhood: a population-based longitudinal analysis in the Western Brazilian Amazon. Matern Child Nutr. 2015;11(2):240–52.
Article
PubMed
Google Scholar
Cole TJ, Donaldson MD, Ben-Shlomo Y. SITAR--a useful instrument for growth curve analysis. Int J Epidemiol. 2010;39(6):1558–66.
Article
PubMed
PubMed Central
Google Scholar
Herle M, Micali N, Abdulkadir M, Loos R, Bryant-Waugh R, Hübel C, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol. 2020;35(3):205–22.
Article
PubMed
PubMed Central
Google Scholar
Sauerbrei W, Abrahamowicz M, Altman DG, le Cessie S, Carpenter J, initiative obotS. STRengthening analytical thinking for observational studies: the STRATOS initiative. Stat Med. 2014;33(30):5413–32.
Article
PubMed
PubMed Central
Google Scholar
Tu YK, Tilling K, Sterne JA, Gilthorpe MS. A critical evaluation of statistical approaches to examining the role of growth trajectories in the developmental origins of health and disease. Int J Epidemiol. 2013;42(5):1327–39.
Article
PubMed
Google Scholar
Curran PJ, Obeidat K, Losardo D. Twelve frequently asked questions about growth curve modeling. J Cogn Dev. 2010;11(2):121–36.
Article
PubMed
PubMed Central
Google Scholar
Macdonald-Wallis C, Lawlor DA, Palmer T, Tilling K. Multivariate multilevel spline models for parallel growth processes: application to weight and mean arterial pressure in pregnancy. Stat Med. 2012;31(26):3147–64.
Article
PubMed
PubMed Central
Google Scholar
Twisk JW. Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis. Eur J Epidemiol. 2004;19(8):769–76.
Article
PubMed
Google Scholar
Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G. Longitudinal data analysis. USA: Chapman & Hall/CRC; 2009.
Google Scholar
Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38(4):963–74.
Article
CAS
PubMed
Google Scholar
Goldstein H, De Stavola B. Statistical modelling of repeated measurement data. Longitud Life Course Stud. 2010;1(2):170–85.
Google Scholar
Cole TJ: sitar: Super Imposition by Translation and Rotation growth curve analysis. R package version 1.2.0. 2021. https://cran.r-project.org/web/packages/sitar/index.html.
Google Scholar
Perperoglou A, Sauerbrei W, Abrahamowicz M, Schmid M. A review of spline function procedures in R. BMC Med Res Methodol. 2019;19(1):46.
Article
PubMed
PubMed Central
Google Scholar
Suk HW, West SG, Fine KL, Grimm KJ. Nonlinear growth curve modeling using penalized spline models: a gentle introduction. Psychol Methods. 2019;24(3):269–90.
Article
PubMed
Google Scholar
Aris IM, Bernard JY, Chen LW, Tint MT, Pang WW, Lim WY, et al. Infant body mass index peak and early childhood cardio-metabolic risk markers in a multi-ethnic Asian birth cohort. Int J Epidemiol. 2017;46(2):513–25.
PubMed
Google Scholar
Fonseca MJ, Moreira C, Santos AC. Adiposity rebound and cardiometabolic health in childhood: results from the generation XXI birth cohort. Int J Epidemiol. 2021;50(4):1260-71.
Harrell F. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. 1st ed. New York: Springer; 2001.
Book
Google Scholar
Desquilbet L, Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29(9):1037–57.
PubMed
Google Scholar
Mackenzie ML, Donovan CR, McArdle BH. Regression spline mixed models: A forestry example. J Agric Biol Environ Stat. 2005;10(4):394.
Article
Google Scholar
James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning with applications in R. New York: Springer; 2017.
Google Scholar
Naumova EN, Must A, Laird NM. Tutorial in biostatistics: evaluating the impact of ‘critical periods’ in longitudinal studies of growth using piecewise mixed effects models. Int J Epidemiol. 2001;30(6):1332–41.
Article
CAS
PubMed
Google Scholar
Beath KJ. Infant growth modelling using a shape invariant model with random effects. Stat Med. 2007;26(12):2547–64.
Article
PubMed
Google Scholar
Cole TJ, Kuh D, Johnson W, Ward KA, Howe LD, Adams JE, et al. Using super-imposition by translation and rotation (SITAR) to relate pubertal growth to bone health in later life: the Medical Research Council (MRC) National Survey of health and development. Int J Epidemiol. 2016.
Berlin KS, Parra GR, Williams NA. An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models. J Pediatr Psychol. 2013;39(2):188–203.
Article
PubMed
Google Scholar
van de Schoot R, Sijbrandij M, Winter SD, Depaoli S, Vermunt JK. The GRoLTS-checklist: guidelines for reporting on latent trajectory studies. Struct Equ Model Multidiscip J. 2017;24(3):451–67.
Article
Google Scholar
Lennon H, Kelly S, Sperrin M, Buchan I, Cross AJ, Leitzmann M, et al. Framework to construct and interpret latent class trajectory modelling. BMJ Open. 2018;8(7):e020683.
Article
PubMed
PubMed Central
Google Scholar
Proust-Lima C, Philipps V, Liquet B. Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. J Stat Softw. 2017;1(Issue 2):2017.
Google Scholar
Harvey N, Dennison E, Cooper C. Osteoporosis: a lifecourse approach. J Bone Miner Res. 2014;29(9):1917–25.
Article
PubMed
Google Scholar
Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, et al. Cohort profile: the Avon longitudinal study of parents and children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42(1):97–110.
Article
PubMed
Google Scholar
Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, et al. Cohort profile: the ‘children of the 90s’-the index offspring of the Avon longitudinal study of parents and children. Int J Epidemiol. 2013;42(1):111–27.
Article
PubMed
Google Scholar
McCormack SE, Cousminer DL, Chesi A, Mitchell JA, Roy SM, Kalkwarf HJ, et al. Association between linear growth and bone accrual in a diverse cohort of children and adolescents. JAMA Pediatr. 2017;171(9):e171769.
Article
PubMed
PubMed Central
Google Scholar
Baxter-Jones AD, Faulkner RA, Forwood MR, Mirwald RL, Bailey DA. Bone mineral accrual from 8 to 30 years of age: an estimation of peak bone mass. J Bone Miner Res. 2011;26(8):1729–39.
Article
PubMed
Google Scholar
Nowok B, Raab GM, Dibben C. synthpop: bespoke creation of synthetic data in R. J Stat Softw. 2016;74(11):26.
Article
Google Scholar
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Software. 2015;67(1):1–48. https://doi.org/10.18637/jss.v067.i01.
Elhakeem A, Frysz M, Tilling K, Tobias JH, Lawlor DA. Association between age at puberty and bone accrual from 10 to 25 years of age. JAMA Netw Open. 2019;2(8):e198918.
Article
PubMed
PubMed Central
Google Scholar
Jackowski SA, Erlandson MC, Mirwald RL, Faulkner RA, Bailey DA, Kontulainen SA, et al. Effect of maturational timing on bone mineral content accrual from childhood to adulthood: evidence from 15 years of longitudinal data. Bone. 2011;48(5):1178–85.
Article
PubMed
Google Scholar
Cousminer DL, Mitchell JA, Chesi A, Roy SM, Kalkwarf HJ, Lappe JM, et al. Genetically determined later puberty impacts lowered bone mineral density in childhood and adulthood. J Bone Miner Res. 2018;33(3):430–6.
Article
PubMed
Google Scholar
Khera AV, Chaffin M, Wade KH, Zahid S, Brancale J, Xia R, et al. Polygenic prediction of weight and obesity trajectories from birth to adulthood. Cell. 2019;177(3):587–596.e589.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jensen SM, Ritz C, Ejlerskov KT, Mølgaard C, Michaelsen KF. Infant BMI peak, breastfeeding, and body composition at age 3 y. Am J Clin Nutr. 2014;101(2):319–25.
Article
PubMed
CAS
Google Scholar
Cousminer DL, Wagley Y, Pippin JA, Elhakeem A, Way GP, Pahl MC, et al. Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrual. Genome Biol. 2021;22(1):1.
Article
CAS
PubMed
PubMed Central
Google Scholar
O'Keeffe LM, Simpkin AJ, Tilling K, Anderson EL, Hughes AD, Lawlor DA, et al. Sex-specific trajectories of measures of cardiovascular health during childhood and adolescence: a prospective cohort study. Atherosclerosis. 2018;278:190–6.
Article
CAS
PubMed
Google Scholar
Lambert PC, Abrams KR, Jones DR, Halligan AW, Shennan A. Analysis of ambulatory blood pressure monitor data using a hierarchical model incorporating restricted cubic splines and heterogeneous within-subject variances. Stat Med. 2001;20(24):3789–805.
Article
CAS
PubMed
Google Scholar
Snijders T. Power and sample size in multilevel modeling. In: Everitt BS, Howell DC, editors. Encyclopedia of Statistics in Behavioral Science. Chicester: Wiley; 2005.
Google Scholar
Guo Y, Logan HL, Glueck DH, Muller KE. Selecting a sample size for studies with repeated measures. BMC Med Res Methodol. 2013;13(1):100.
Article
PubMed
PubMed Central
Google Scholar
Simpkin AJ, Sayers A, Gilthorpe MS, Heron J, Tilling K. Modelling height in adolescence: a comparison of methods for estimating the age at peak height velocity. Ann Hum Biol. 2017;44(8):715–22.
Article
PubMed
PubMed Central
Google Scholar
Tilling K, Macdonald-Wallis C, Lawlor DA, Hughes RA, Howe LD. Modelling childhood growth using fractional polynomials and linear splines. Ann Nutr Metab. 2014;65(2–3):129–38.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kwong ASF, Manley D, Timpson NJ, Pearson RM, Heron J, Sallis H, et al. Identifying critical points of trajectories of depressive symptoms from childhood to young adulthood. J Youth Adolesc. 2019;48(4):815–27.
Article
PubMed
PubMed Central
Google Scholar
Cole TJ. Optimal design for longitudinal studies to estimate pubertal height growth in individuals. Ann Hum Biol. 2018;45(4):314–20.
Article
PubMed
PubMed Central
Google Scholar
Wood SN. Generalized additive models an introduction with R. 2nd ed: Chapman & Hall/CRC; USA: 2017.
Book
Google Scholar
Pedersen EJ, Miller DL, Simpson GL, Ross N. Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ. 2019;7:e6876.
Article
PubMed
PubMed Central
Google Scholar
Wood SN. mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation. 2021. p. 1.8-136 https://cran.r-project.org/web/packages/mgcv/index.html.
Google Scholar
Wood SN, Scheipl F. gamm4: generalized additive mixed models using ‘mgcv’and ‘lme4’; 2017. p. 0.2–5. http://cran.nexr.com/web/packages/gamm4/index.html
Book
Google Scholar
Kohli N, Harring JR, Zopluoglu C. A finite mixture of nonlinear random coefficient models for continuous repeated measures data. Psychometrika. 2016;81(3):851–80.
Article
PubMed
Google Scholar
Lock EF, Kohli N, Bose M. Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika. 2018;83(3):733–50.
Article
PubMed
Google Scholar
Ding M, Chavarro JE, Fitzmaurice GM. Development of a mixture model allowing for smoothing functions of longitudinal trajectories. Stat Methods Med Res. 2021;30(2):549–62.
Article
PubMed
Google Scholar
Buscot M-J, Thomson RJ, Juonala M, Sabin MA, Burgner DP, Lehtimäki T, et al. Distinct child-to-adult body mass index trajectories are associated with different levels of adult cardiometabolic risk. Eur Heart J. 2018;39(24):2263–70.
Article
PubMed
Google Scholar
Kwong ASF, Lopez-Lopez JA, Hammerton G, Manley D, Timpson NJ, Leckie G, et al. Genetic and environmental risk factors associated with trajectories of depression symptoms from adolescence to young adulthood. JAMA Netw Open. 2019;2(6):e196587.
Article
PubMed
PubMed Central
Google Scholar
Elhakeem A, Heron J, Tobias JH, Lawlor DA. Physical activity throughout adolescence and peak hip strength in young adults. JAMA Netw Open. 2020;3(8):e2013463.
Article
PubMed
PubMed Central
Google Scholar
Hulman A, Witte DR, Vistisen D, Balkau B, Dekker JM, Herder C, et al. Pathophysiological characteristics underlying different glucose response curves: a latent class trajectory analysis from the prospective EGIR-RISC study. Diabetes Care. 2018;41(8):1740–8.
Article
CAS
PubMed
Google Scholar
Lévêque E, Lacourt A, Philipps V, Luce D, Guénel P, Stücker I, et al. A new trajectory approach for investigating the association between an environmental or occupational exposure over lifetime and the risk of chronic disease: application to smoking, asbestos, and lung cancer. Plos One. 2020;15(8):e0236736.
Article
PubMed
PubMed Central
CAS
Google Scholar
Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiological epidemiology. Int J Epidemiol. 2016;45(6):1866–86.
PubMed
Google Scholar
Madden JM, Li X, Kearney PM, Tilling K, Fitzgerald AP. Exploring diurnal variation using piecewise linear splines: an example using blood pressure. Emerg Themes Epidemiol. 2017;14:1–1.
Article
PubMed
PubMed Central
Google Scholar
Brilleman SL, Howe LD, Wolfe R, Tilling K. Bayesian piecewise linear mixed models with a random change point: an application to BMI rebound in childhood. Epidemiology. 2017;28(6):827–33.
Article
PubMed
PubMed Central
Google Scholar
Crozier SR, Johnson W, Cole TJ, Macdonald-Wallis C, Muniz-Terrera G, Inskip HM, et al. A discussion of statistical methods to characterise early growth and its impact on bone mineral content later in childhood. Ann Hum Biol. 2019;46(1):17–26.
Article
PubMed
PubMed Central
Google Scholar
Sayers A, Heron J, Smith A, Macdonald-Wallis C, Gilthorpe MS, Steele F, et al. Joint modelling compared with two stage methods for analysing longitudinal data and prospective outcomes: a simulation study of childhood growth and BP. Stat Methods Med Res. 2017;26(1):437–52.
Article
CAS
PubMed
Google Scholar
Parker RMA, Leckie G, Goldstein H, Howe LD, Heron J, Hughes AD, et al. Joint modeling of individual trajectories, within-individual variability, and a later outcome: systolic blood pressure through childhood and left ventricular mass in early adulthood. Am J Epidemiol. 2021;190(4):652-62.
Smith AD, Hardy R, Heron J, Joinson CJ, Lawlor DA, Macdonald-Wallis C, et al. A structured approach to hypotheses involving continuous exposures over the life course. Int J Epidemiol. 2016;45(4):1271–9.
PubMed
PubMed Central
Google Scholar
Lee KJ, Tilling K, Cornish RP, Little RJ, Bell ML, Goetghebeur E, et al. Framework for the treatment and reporting of missing data in observational studies: the TARMOS framework. J Clin Epidemiol. 2021;134:79-88
van Buuren S. Flexible imputation of missing data. 2nd ed. Chapman & Hall/CRC. USA; 2018.
Matteo Quartagno SG, Carpenter J. jomo: a flexible package for two-level joint modelling multiple imputation. R J. 2019;11(2):205–28.
Article
Google Scholar
Hughes RA, Heron J, Sterne JAC, Tilling K. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int J Epidemiol. 2019;48(4):1294–304.
Article
PubMed
PubMed Central
Google Scholar
Twisk J, de Boer M, de Vente W, Heymans M. Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. J Clin Epidemiol. 2013;66(9):1022–8.
Article
PubMed
Google Scholar
Huque MH, Carlin JB, Simpson JA, Lee KJ. A comparison of multiple imputation methods for missing data in longitudinal studies. BMC Med Res Methodol. 2018;18(1):168.
Article
PubMed
PubMed Central
Google Scholar
Huque MH, Moreno-Betancur M, Quartagno M, Simpson JA, Carlin JB, Lee KJ. Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model. Biom J. 2020;62(2):444–66.
Article
PubMed
Google Scholar
VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34(3):211–9.
Article
PubMed
PubMed Central
Google Scholar
Groenwold RHH, Palmer TM, Tilling K. To Adjust or Not to Adjust? When a "Confounder" Is Only Measured After Exposure. Epidemiology. 2021;32(2):194-201. https://doi.org/10.1097/EDE.0000000000001312.
Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21(3):383–8.
Article
PubMed
PubMed Central
Google Scholar
Taylor K, Elhakeem A, Nader JLT, Yang T, Isaevska E, Richiardi L, et al. Effect of maternal prepregnancy/early-pregnancy BMI and pregnancy smoking and alcohol on congenital heart diseases: a parental negative control study. J Am Heart Assoc. 2021;10(11):e020051
Brion MJ, Lawlor DA, Matijasevich A, Horta B, Anselmi L, Araújo CL, et al. What are the causal effects of breastfeeding on IQ, obesity and blood pressure? Evidence from comparing high-income with middle-income cohorts. Int J Epidemiol. 2011;40(3):670–80.
Article
PubMed
PubMed Central
Google Scholar
Wills AK, Lawlor DA, Matthews FE, Aihie Sayer A, Bakra E, Ben-Shlomo Y, et al. Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts. PLoS Med. 2011;8(6):e1000440.
Article
PubMed
PubMed Central
Google Scholar
Jaddoe VWV, Felix JF, Andersen AN, Charles MA, Chatzi L, Corpeleijn E, et al. The LifeCycle project-EU child cohort network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents. Eur J Epidemiol. 2020;35(7):709–24.
Article
PubMed
PubMed Central
Google Scholar
Ronkainen J, Nedelec R, Atehortua A, Balkhiyarova Z, Zhanna A, Dang V, et al. LongITools: dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases. Environ Epidemiol. 2021;6(1):e184. https://doi.org/10.1097/EE9.0000000000000184.
Hughes RA, Tilling K, Lawlor DA. Combining longitudinal data from different cohorts to examine the life-course trajectory. Am J Epidemiol. 2021;190(12):2680-9.
Pinot de Moira A, Haakma S, Strandberg-Larsen K, van Enckevort E, Kooijman M, Cadman T, et al. The EU Child Cohort Network’s core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables. Eur J Epidemiol. 2021;36(5):565–80.
Article
PubMed
PubMed Central
Google Scholar
Nader JL, López M, Julvez J, Guxens M, Cadman T, Elhakeem A, et al. Cohort description: measures of early-life behaviour and later psychopathology in the LifeCycle project - EU child cohort network. J Epidemiol. 2021. (Epub ahead of print). https://doi.org/10.2188/jea.JE20210241.
Baxter-Jones AD, Burrows M, Bachrach LK, Lloyd T, Petit M, Macdonald H, et al. International longitudinal pediatric reference standards for bone mineral content. Bone. 2010;46(1):208–16.
Article
PubMed
Google Scholar