Using Longitudinal Complex Survey Data

Literature Cited

1. Angrist JD, Imbens GW, Rubin DB. 1996. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91:444–55 [Google Scholar]
2. Arnold BF, Hogan DR, Colford JM, Hubbard AE. 2011. Simulation methods to estimate design power: an overview for applied research. BMC Med. Res. Methodol. 11:94 [Google Scholar]
3. Bailar BA. 1975. The effects of rotation group bias on estimates from panel surveys. J. Am. Stat. Assoc. 70:23–30 [Google Scholar]
4. Binder DA. 1992. Fitting Cox's proportional hazards model from survey data. Biometrika 79:139–47 [Google Scholar]
5. Boudreau C, Lawless JF. 2006. Survival analysis based on the proportional hazards model and survey data. Can. J. Stat. 34:203–16 [Google Scholar]
6. Bradford Hill A. 1965. The environment and disease: causation or association. Proc. R. Soc. Med. 58:295–300 [Google Scholar]
7. Carrillo IA, Chen J, Wu C. 2010. The pseudo-GEE approach to the analysis of longitudinal surveys. Can. J. Stat. 38:540–54 [Google Scholar]
8. Carrillo IA, Karr AF. 2013. Combining cohorts in longitudinal surveys. Surv. Methodol. 39:149–82 [Google Scholar]
9. Cox DR. 1992. Causality: some statistical aspects. J. R. Stat. Soc. Ser. A 155:291–301 [Google Scholar]
10. Daniels MJ, Hogan JW. 2008. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis Boca Raton, FL: Chapman & Hall
11. Deng Y, Hillygus S, Reiter JP, Si Y, Zheng S. 2013. Handling attrition in longitudinal studies: the case for refreshment samples. Stat. Sci. 28:238–56 [Google Scholar]
12. Diggle PJ, Heagerty P, Liang K-Y, Zeger SL. 2013. Analysis of Longitudinal Data Oxford, UK: Oxford Univ. Press, 2nd ed..
13. DMHDRU (Dunedin Multidisc. Health Dev. Res. Unit) 2014. The Science of Us: The 1,000 Most Studied People in the World Dunedin, NZ: DMHDRU
14. Driezen P, Thompson ME. 2011. Comparing Policy Measures Across Multiple ITC Countries: Adjusting for Time-in-Sample Tech. Rep., Dec. 13, Int. Tob. Control Policy Eval. Proj. http://www.itcproject.org/files/ITC_Technical_Report_time-in-sample-adjustment_Dec2011.pdf
15. Eideh AAH, Nathan G. 2009. Joint treatment of nonignorable dropout and informative sampling for longitudinal survey data. See Lynn 2009 251–64
16. Elliott MR, Raghunathan TE, Li Y. 2010. Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes. Biostatistics 11:353–72 [Google Scholar]
17. ESRC (Econ. Soc. Res. Counc.) 2014. Understanding Society: The UK Longitudinal Household Study. https://www.understandingsociety.ac.uk/ [Google Scholar]
18. Feder M, Nathan G, Pfeffermann D. 2000. Multilevel modelling of complex survey longitudinal data with time varying random effects. Surv. Methodol. 26:53–65 [Google Scholar]
19. Fong GT, Hyland A, Borland R, Hammond D, Hastings G. et al. 2006. Reductions in tobacco smoke pollution and increases in support for smoke-free public places following the implementation of comprehensive smoke-free workplace legislation in the Republic of Ireland: findings from the ITC Ireland/UK Survey. Tob. Control 15:Suppl. 351–58 [Google Scholar]
20. Gelman A. 2007. Struggles with survey weighting and regression modelling. Stat. Sci. 22:155–64 [Google Scholar]
21. Guttmacher AE, Hirschfeld S, Collins FS. 2013. The National Children's Study—a proposed plan. N. Engl. J. Med. 369:1873–75 [Google Scholar]
22. Hajducek DM, Lawless JF. 2013. Estimation of finite population duration distributions from longitudinal survey panels with intermittent followup. Lifetime Data Anal. 19:371–92 [Google Scholar]
23. Krosnick J. 1991. Response strategies for coping with the cognitive demands of attitude measures in surveys. Appl. Cogn. Psychol. 5:213–36 [Google Scholar]
24. Lauritzen SL. 2001. Causal inference from graphical models. Complex Stochastic Systems OE Barndorff-Nielsen, DR Cox, C Klüppelberg 63–107 Boca Raton:, FL: Chapman & Hall [Google Scholar]
25. Lawless JF. 2003. Censoring and weighting in survival estimation from survey data Presented at Stat. Soc. Can. Annu. Meet., Jun. 8–11, Halifax, Can. http://www.ssc.ca/survey/documents/SSC2003_J_Lawless.pdf
26. Lawless JF, Wild C, Kalbfleisch JD. 2009. Estimation for response-selective and missing data problems in regression. J. R. Stat. Soc. B 61:413–38 [Google Scholar]
27. Liang KY, Zeger SL. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73:13–22 [Google Scholar]
28. Lin DY. 2000. On fitting Cox's proportional hazards model to survey data. Biometrika 87:37–48 [Google Scholar]
29. Little R, Zhang G. 2009. Robust likelihood-based analysis of longitudinal survey data with missing values. See Lynn 2009 317–32
30. Little RJA. 1993. Pattern-mixture models for multivariate incomplete data. J. Am. Stat. Assoc. 88:125–34 [Google Scholar]
31. Little RJA, Rubin DB. 2002. Statistical Analysis with Missing Data New York: John Wiley & Sons, 2nd ed..
32. Lynn P. 2009. Methodology of Longitudinal Surveys Chichester, UK: John Wiley & Sons
33. Mahmood SS, Levy D, Vasan RS, Wang TJ. 2013. The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective. Lancet 383:999–1008 [Google Scholar]
34. Manrique-Vallier D. 2014. Longitudinal mixed membership trajectory models for disability survey data. arXiv:1309.2324 [stat.AP]
35. Manton KG. 1988. A longitudinal study of functional change and mortality in the United States. Gerontology 43:153–61 [Google Scholar]
36. Manton KG, Stallard E, Woodbury MA. 1991. A multivariate event history model based upon fuzzy states: estimation from longitudinal surveys with informative nonresponse. J. Off. Stat. 7:261–93 [Google Scholar]
37. Oldford RW, Waddell A. 2011. Visual clustering of high-dimensional data by navigating low-dimensional spaces. Proc. 58th World Stat. Congr. Int. Stat. Inst., Dublin, Aug. 21–26 3294–303 The Hague, Neth: ISI. http://2011.isiproceedings.org/papers/650370.pdf [Google Scholar]
38. Pantoja-Galicia N, Kovacevic M, Thompson ME. 2009. Assessing the temporal association of events using complex longitudinal surveys. See Lynn 2009 333–50
39. Patterson HD. 1950. Sampling on successive occasions with partial replacement of units. J. R. Stat. Soc. B 12:241–55 [Google Scholar]
40. Pfeffermann D, Sverchkov M. 1999. Parametric and semi-parametric estimation of regression models fitted to survey data. Sankhya Ind. J. Stat. B 61:166–86 [Google Scholar]
41. Piesse A, Judkins D, Kalton G. 2009. Using longitudinal surveys to evaluate interventions. See Lynn 2009 303–16
42. Pietrzak RH, Van Ness PH, Fried TR, Galea S, Norris FH. 2013. Trajectories of posttraumatic stress symptomatology in older persons affected by a large-magnitude disaster. J. Psychiatr. Res. 47:520–26 [Google Scholar]
43. Roberts G, Ren Q, Rao JNK. 2009. Using marginal mean models for data from longitudinal surveys with a complex design: some advances in methods. See Lynn 2009 351–66
44. Rubin-Bleuer S. 2011. The proportional hazards model for survey data from independent and clustered super-populations. J. Multivariate Anal. 102:884–95 [Google Scholar]
45. Sangalli LM, Ramsay JO, Ramsay TO. 2013. Spatial spline regression models. J. R. Stat. Soc. B 75:681–703 [Google Scholar]
46. Shadish WR, Cook TD, Campbell DT. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin
47. SHARE Proj. (Surv. Health Ageing Retire. Eur. Proj.) 2014. Survey of Health, Ageing and Retirement in Europe http://www.share-project.org
48. Skinner CJ, Holmes DJ. 2003. Random effects models for longitudinal survey data. Analysis of Survey Data R Chambers, CJ Skinner 205–19 Chichester, UK: John Wiley & Sons [Google Scholar]
49. Skinner CJ, Mason B. 2012. Weighting in the regression analysis of survey data with a cross-national application. Can. J. Stat. 40:697–711 [Google Scholar]
50. Skinner CJ, Vieira MDT. 2007. Variance estimation in the analysis of clustered longitudinal survey data. Surv. Methodol. 33:3–12 [Google Scholar]
51. Smith P, Lynn P, Elliot D. 2009. Sample design for longitudinal surveys. See Lynn 2009 21–34
52. Spencer SJ, Zanna MP, Fong GT. 2005. Establishing a causal chain: why experiments are often more effective than mediational analyses in examining psychological processes. J. Personal. Soc. Psychol. 89:845–51 [Google Scholar]
53. Sutradhar B, Kovacevic M. 2000. Analyzing ordinal longitudinal survey data: Generalized estimating equations approach. Biometrika 87:837–48 [Google Scholar]
54. Tambay JL, Catlin G. 1995. Sample design of the National Population Health Survey. Health Rep. 7:29–38 Statistics Canada, Cat. No. 82-003 [Google Scholar]
55. Thompson ME, Boudreau C, Driezen P. 2005. Incorporating time-in-sample in longitudinal survey models Presented at Stat. Can. Symp. Methodol. Chall. Future Inf. Needs, Oct.
56. Verhagen J, Fox JP. 2013. Longitudinal measurement in health-related surveys. A Bayesian joint growth model for multivariate ordinal responses. Stat. Med. 32:2988–3005 [Google Scholar]
57. VanderWeele TJ, Vansteelandt S. 2009. Conceptual issues concerning mediation, interventions and composition. Stat. Interface 2:457–68 [Google Scholar]
58. Vieira MDT, Skinner CJ. 2008. Estimating models for panel survey data under complex sampling. J. Off. Stat. 24:343–64 [Google Scholar]
59. Wang C, Little R, Nan B, Harlow SD. 2011. A hot-deck multiple imputation procedure for gaps in longitudinal recurrent event histories. Biometrics 67:1573–82 [Google Scholar]
60. Yong H-H, Borland R, Thrasher JF, Thompson ME. 2012. Stability of cigarette consumption over time among continuing smokers: a latent growth curve analysis. Nicotine Tob. Res. 14:531–39 [Google Scholar]
61. Yong H-H, Borland R, Thrasher JF, Thompson ME, Nagelhout GE. et al. 2014. Mediational pathways of the impact of cigarette warning labels on quit attempts. Health Psych. 33:1410–20 [Google Scholar]