Causal inference in longitudinal comparative effectiveness studies with repeated measures of a continuous intermediate variable

Statistics in Medicine

Volumn 33, Issue 20, 2014, Pages 3509-3527

  Wang, Chen Pin ^a   Jo, Booil ^b   Hendricks Brown, C ^c  

^a UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

^b STANFORD UNIVERSITY   (United States)

^c NORTHWESTERN UNIVERSITY   (United States)

Author keywords

Causal inference; Comparative effectiveness studies; Growth mixture model; Principal stratification; Propensity score

Indexed keywords

ANTIDIABETIC AGENT; GLUCOSE;

ARTICLE; CALCULATION; CARDIOVASCULAR DISEASE; CAUSAL INFERENCE; COMPARATIVE EFFECTIVENESS; COVARIANCE; HAMILTON SCALE; HOSPITALIZATION; LONGITUDINAL STUDY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MORTALITY; PROBABILITY; PROPENSITY SCORE; SIMULATION; STATISTICAL PARAMETERS; STRATIFIED SAMPLE; AGED; COMPARATIVE STUDY; COMPUTER SIMULATION; DIABETES MELLITUS; EPIDEMIOLOGY; HUMAN; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; STATISTICAL MODEL; TREATMENT OUTCOME;

AGED; CARDIOVASCULAR DISEASES; CAUSALITY; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DIABETES MELLITUS; HUMANS; HYPOGLYCEMIC AGENTS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; REGRESSION ANALYSIS; TREATMENT OUTCOME;

EID: 84905442305     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6120     Document Type: Article

References (43)

1. Institute of Medicine Committee on Comparative Effectiveness Research. On Initial National Priorities for Comparative Effectiveness Research. National Academy of Sciences Press: Washington, D.C., 2009.
2. Rosenbaum P, Rubin D. The causal role of the propensity score in observational studies for causal effects. Biometrika 1983; 70(1):41-55.
3. Joffe M, Colditz G. Restriction as a method for reducing bias in the estimation of direct effects. Statistics in Medicine 1998; 17(19):2233-2249.
4. Achy-Brou A, Frangakis C, Griswold M. Estimating treatment effects of longitudinal designs using regression models on propensity scores. Biometrics 2010; 66(3):824-833.
5. Wang C, Hazuda H. Better glycemic control is associated with maintenance of lower-extremity function over time in mexican american and European American older adults with diabetes. Diabetes Care 2011; 34(2):268-273.
6. Rubin D. On the limitations of comparative effectiveness research. Statistics in Medicine 2010; 29(19):1991-1995.
7. Angrist J, Imbens G, Rubin D. Identification of causal effects using instrumental variables. Journal of the American Statistical Association 1996; 91(434):444-455.
8. Frangakis C, Rubin D. Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika 1999; 86(2):365-379.
9. Frangakis C, Rubin D. Principal stratification in causal inference. Biometrics 2002; 58(1):21-29.
10. Lin J, Ten Have T, Elliott M. Longitudinal nested compliance class model in the presence of time-varying noncompliance. Journal of the American Statistical Association 2008; 103(482):462-473.
11. Lin J, Ten Have T, Elliott M. Nested Markov compliance class model in the presence of time-varying noncompliance. Biometrics 2009; 65(2):505-513.
12. Muthén B, Brown H. Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling. Statistics in Medicine 2009; 28(27):3363-3385.
13. Jo B, Wang C, Ialongo N. Using latent outcome trajectory classes in causal inference. Statistics and Its Interface 2009; 2(4):403-412.
14. Muthén B, Brown C, Masyn K, Jo B, Khoo S, Yang C, Wang C, Kellam S, Carlin J, Liao J. General growth mixture modeling for randomized preventive interventions. Biostatistics 2002; 3(4):459-475.
15. Splawa-Neyman J, Dabrowska D, Speed T. On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science 1990; 5(4):465-472.
16. Rubin D. Bayesian inference for causal effects: The role of randomization. Annals of Statistics 1978; 6(1):34-58.
17. Bandeen-Roche K, Miglioretti D, Zeger S, Rathouz P. Latent variable regression for multiple discrete outcomes. Journal of the American Statistical Association 1997; 92(440):1375-1386.
18. Wang C, Brown C, Bandeen-Roche K. Residual diagnostics for growth mixture models: examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association 2005; 100(471):1054-1076.
19. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 1997; 20(7):1183-1197.
20. Dawid A. Conditional independence in statistical theory (with discussion). Journal of the Royal Statistical Society Series B 1979; 41(1):1-31.
21. Rubin D. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Statistics in Medicine 2007; 26(1):20-36.
22. Tan M, Baksi A, Krahulec B, Kubalski P, Stankiewicz A, Urquhart R, Edwards G, Johns D, GLAL Study Group. Comparison of pioglitazone and gliclazide in sustaining glycemic control over 2 years in patients with type 2 diabetes. Diabetes Care 2005; 28(3):544-550.
23. UKPDS Group. UKPDS 33: intensive blood glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes. Lancet 1998; 352(9131):837-853.
24. Akaike H. A new look at the statistical identification model. IEEE Transactions on Automatic Control 1974; 19(6):716-723.
25. Schwarz G. Estimating the dimension of a model. The Annals of Statistics 1978; 6(2):239-472.
26. Mclachlan G, Krishnan T. The EM Algorithm and Extensions. Wiley: New York, 1997.
27. Muthén L, Muthén B. Mplus User's Guide. Muthén & Muthén: Los Angeles, Unknown Month 1998.
28. Diamond G, Bax L, Kaul S. Uncertain effects of rosiglitazone on the risk for myocardial infarction and cardiovascular death. Annals of Internal Medicine 2007; 147(8):578-581.
29. http://www.healthquality.va.gov/diabetes/DM2010-FUL-v4e.pdf.
30. Schwartz K, Monsur J, Bartoces M, West P, Neale A. Correlation of same-visit HbA1c test with laboratory-based measurements: a MetroNet study. BioMed Central Family Practice 2005; 6: 28.
31. Tseng C, Brimacombe M, Xie M, Rajan M, Wang H, Kolassa J, Crystal S, Chen TC, Pogach L, Safford M. Seasonal patterns in monthly hemoglobin A1c values. American Journal of Epidemiology 2005; 161(6):565-574.
32. http://www.pbm.va.gov/CriteriaForUse.aspx.
33. http://www.fda.gov/Drugs/DrugSafety/ucm255005htm.
34. Stafylas P, Sarafidis P, Lasaridis A. The controversial effects of thiazolidinediones on cardiovascular morbidity and mortality. International Journal of Cardiology 2009; 131(3):298-304.
35. Sharma A, Review S. Peroxisome proliferator-activated receptor gamma and adipose tissue-understanding obesity-related changes in regulation of lipid and glucose metabolism. Journal of Clinical Endocrinology Metabolism 2007; 92(2):386-395.
36. Lu M, Sarruf D, Talukdar S, Sharma S, Li P, Bandyopadhyay G, Nalbandian S, Fan W, Gayen J, Mahata S, Webster N, Schwartz M, Olefsky J. Brain PPAR- γ promotes obesity and is required for the insulin-sensitizing effect of thiazolidinediones. Nature Medicine 2011; 17(5):618-622.
37. Fisher R. Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 1915; 10(4):507-521.
38. Jorgenson B, Knudsen S. Parameter orthogonality and bias adjustment for estimating functions. Scandinavian Journal of Statistics 2004; 31(1):93-114.
39. Imai K, Keele L, Yamamoto T. Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science 2010; 25(1):51-71.
40. Hancock G, Samuelsen K. Advances in Latent Variable Mixture Models. Information Age Publishing: Greenwich CT, 2007, pp. 317-341.
41. Nylund K, Asparouhov T, Muthn B. Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal 2007; 14(4):535-569.
42. Jo B. Causal inference in randomized experiments with mediational processes. Psychological Methods 2008; 13(4):314-336.
43. Gallop R, Small D, Lin J, Elliott M, Joffe M, Ten Have T. Mediation analysis with principal stratification. Statistics in Medicine 2009; 28(7):1108-1130.