Weighted χ² tests for multiple group comparisons in observational studies

References

• McCaffrey DF, Griffin BA, Almirall D, et al. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013;32(19):3388–3414.
   PubMed Web of Science ®Google Scholar
• Yan X, Abdia Y, Datta S, et al. Estimation of average treatment effects among multiple treatment groups by using an ensemble approach. Stat Med. 2019;38:2828–2846.
   PubMed Web of Science ®Google Scholar
• Hernán M, Robins J. Causal Inference: What If . Boca Raton: Chapman & Hall/CRC; 2020. (Chapman & Hall/CRC Monographs on Statistics & Applied Probability).
   Google Scholar
• Horwitz RI. The experimental paradigm and observational studies of cause-effect relationships in clinical medicine. J Chronic Dis. 1987;40(1):91–99.
   PubMedGoogle Scholar
• Rubin DB. Teaching statistical inference for causal effects in experiments and observational studies. J Educ Behav Stat. 2004;29(3):343–367.
   Web of Science ®Google Scholar
• Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70(1):41–55.
   Web of Science ®Google Scholar
• Imbens GW. The role of the propensity score in estimating dose-response functions. Biometrika2000;87(3):706–710.
   Web of Science ®Google Scholar
• Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–346.
   PubMed Web of Science ®Google Scholar
• Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med. 2004;23(19):2937–2960.
   PubMed Web of Science ®Google Scholar
• Rosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987;82(398):387–394.
   Web of Science ®Google Scholar
• Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79(387):516–524.
   Web of Science ®Google Scholar
• Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39(1):33–38.
   Web of Science ®Google Scholar
• Yoshida K, Hernández-Díaz S, Solomon DH, et al. Matching weights to simultaneously compare three treatment groups: comparison to three-way matching. Epidemiology 2017;28(3):387.
   PubMed Web of Science ®Google Scholar
• Li F, Li F. Propensity score weighting for causal inference with multiple treatments. Ann Appl Stat. 2019;13(4):2389–2415.
   Web of Science ®Google Scholar
• Lopez MJ, Gutman R. Estimation of causal effects with multiple treatments: a review and new ideas. Stat Sci. 2017;32:432–454.
   Web of Science ®Google Scholar
• Proschan MA, Brittain EH. A primer on strong vs weak control of familywise error rate. Stat Med. 2020;39(9):1407–1413.
   PubMed Web of Science ®Google Scholar
• Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11(5):550–560.
   PubMed Web of Science ®Google Scholar
• Abdia Y, Kulasekera K, Datta S, et al. Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: a comparative study. Biom J. 2017;59(5):967–985.
   PubMed Web of Science ®Google Scholar
• Vaart AW, Wellner JA. Weak convergence and empirical processes: with applications to statistics. New York: Springer; 1996.
   Google Scholar
• Mooney CZ, Mooney CF, Mooney CL, et al. Bootstrapping: A nonparametric approach to statistical inference. Vol. 95. Thousand Oaks (CA): Sage; 1993.
   Google Scholar
• Rice TK, Schork NJ, Rao D. Methods for handling multiple testing. Adv. Genet. 2008;60:293–308.
   PubMed Web of Science ®Google Scholar
• Rubin DB, Thomas N. Matching using estimated propensity scores: relating theory to practice. Biometrics 1996;52:249–264.
   PubMed Web of Science ®Google Scholar
• Hirano K, Imbens GW, Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 2003;71(4):1161–1189.
   Web of Science ®Google Scholar
• Centers for Disease Control and Prevention, The BRFSS Data User Guide. Atlanta, GA: Department of Health and Human Services; 2013.
   Google Scholar
• Dietary Guidelines Advisory Committee. Dietary Guidelines for Americans 2015-2020. Government Printing Office; 2015.
   Google Scholar
• Moore LV, Dodd KW, Thompson FE, et al. Using behavioral risk factor surveillance system data to estimate the percentage of the population meeting us department of agriculture food patterns fruit and vegetable intake recommendations. Am. J. Epidemiol.. 2015;181(12):979–988.
   PubMed Web of Science ®Google Scholar
• Dauchet L, Amouyel P, Dallongeville J. Fruits, vegetables and coronary heart disease. Nat Rev Cardiol. 2009;6(9):599–608.
   PubMed Web of Science ®Google Scholar
• World Health Organization. Diet, Nutrition, and the Prevention of Chronic Diseases: Report of a Joint WHO/FAO Expert Consultation, Vol. 916. Geneve: World Health Organization; 2003.
   Google Scholar