Causal inference in randomized medical trials


Holland PW. Statistics and causal inference. J Am Stat Assoc. 1986;81:945–60.


Aldrich J. Correlations real and spurious in Pearson and Yuletide. (1995). Stat Sci. 1995;10:364–76.


Pearl J. Causality: fashions, reasoning, and inference. 2nd edn. Cambridge: Cambridge College Press; 2009.


Rubin D. Estimating causal results of therapies in randomized and nonrandomized research. J Educ Psychol. 1974;66:688–701.


Rubin D. Causal inference utilizing potential outcomes. J Am Stat Assoc. 2005;100:322–31.


Chen P, Tsiatis AA. Causal inference on the distinction of the restricted imply life between two teams. Biometrics. 2001;57:1030–eight.


Uno H, Claggett B, Tian L, Inoue E, Gallo P, Miyata T, et al. Shifting past the hazard ratio in quantifying the between-group distinction in survival evaluation. J Clin Oncol. 2014;32:2380–5.


Hernan MA. The hazards of hazard ratios. Epidemiology. 2010;21:13–15.


Lin H, Li Y, Jiang L, Li G. A semiparametric linear transformation mannequin to estimate causal results for survival knowledge. Can J Stat. 2014;42:18–35.


Aalen OO, Prepare dinner RJ, Røysland Ok. Does Cox evaluation of a randomized survival examine yield a causal therapy impact? Lifetime Information Anal. 2015;21:579–93.


Vock DM, Tsiatis AA, Davidian M, Laber EB, Tsuang WM, Finlen-Copland A, et al. Assessing the causal impact of organ transplantation on the distribution of residual lifetime. Biometrics. 2013;69:820–9.


Imbens GW, Rubin DB. Causal inference for statistics, social, and biomedical sciences. Cambridge: Cambridge College Press; 2015.


Rosenbaum P. Conditional permutation checks and the propensity rating in observational research. J Am Stat Assoc. 1984;79:565–74.


Neyman J. On the 2 totally different elements of the consultant methodology: the strategy of stratified sampling and the strategy of purposive choice. J R Stat Soc. 1934;97:558–625.


Robins JM, Rotnitzky A, Zhao LP. Evaluation of semiparametric regression fashions for repeated outcomes within the presence of lacking knowledge. J Am Stat Assoc. 1995;90:106–21.


Martinussen T, Vansteelandt S. On collapsibility and confounding bias in Cox and Aalen regression fashions. Lifetime Information Anal. 2013;19:279–96.


Rosenbaum PR, Rubin DB. The central position of the propensity rating in observational research for causal results. Biometrika. 1983;70:41–55.


Rubin DB, Thomas N. Matching utilizing estimated propensity rating: relating concept to apply. Biometrics. 1996;52:249–64.


Rubin DB, Thomas N. Combining propensity rating matching with further adjustment for prognostic covariates. J Am Stat Assoc. 2000;95:573–85.


Austin PC. Using propensity rating strategies with survival or time-to-event outcomes: reporting measures of impact much like these utilized in randomized experiments. Stat Med. 2014;33:1242–58.


Cochran WG. The effectiveness of adjustment by subclassification in eradicating bias in observational research. Biometrics. 1968;24:295–313.


Gu X, Rosenbaum P. Comparability of multivariate matching strategies: constructions, distances, and algorithms. J Comput Graph Stat. 1993;2:405–20.


Austin PC. The efficiency of various propensity rating strategies for estimating marginal hazard ratios. Statistics in Medication. 2013;32: 2837–49.


Crump R, Hotz VJ, Imbens G, Mitnik O. Coping with restricted overlap in estimation of common therapy results. Biometrika. 2009;96:187–99.


Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–29.


Imbens GW, Rubin DB. Bayesian inference for causal results in randomized experiments with noncompliance. Ann Stat. 1997;25:305–27.


Cuzick J, Sasieni P, Myles J, Tyrer J. Estimating the impact of therapy in a proportional hazards mannequin within the presence of non-compliance and contamination. J R Stat Soc, Ser B. 2007;69:565–88.


Little RJ, Lengthy Q, Lin X. A comparability of strategies for estimating the causal impact of a therapy in randomized medical trials topic to noncompliance. Biometrics. 2009;65:640–9.


Balke A, Pearl J. Bounds on therapy results from research with imperfect compliance. J Am Stat Assoc. 1997;92:1171–6.


Cheng J, Small DS. Bounds on causal results in three-arm trials with non-compliance. J R Stat Soc, Ser B. 2006;68:815–36.


Heckman JJ, Vytlacil EJ. Native instrumental variables and latent variable fashions for figuring out and bounding therapy results. Proc Natl Acad Sci USA. 1999;96:4730–four.


Angrist JD, Imbens GW, Rubin DB. Identification of causal results utilizing instrumental variables (with dialogue). J Am Stat Assoc. 1996;91:444–72.


Tchetgen Tchetgen EJ, Walter S, Vansteelandt S, Martinussen T, Glymour M. Instrumental variable estimation in a survival context. Epidemiology. 2015;26:402–10.


Zheng C, Dai R, Hari PN, Zhang MJ. Instrumental variable with competing threat mannequin. Stat Med. 2017;36:1240–55.


Li J, Fantastic J, Brookhart A. Instrumental variable additive hazards fashions. Biometrics. 2015;71:122–30.


Martinussen T, Nøbro Sørensen D, Vansteelandt S. Instrumental variables estimation underneath a structural Cox mannequin. Biostatistics. 2019;20:65-79.


Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS Medical Trial with inverse likelihood of censoring weighted (IPCW) log-rank checks. Biometrics. 2000;56:779–88.


Loeys T, Goetghebeur E. A causal proportional hazards estimator for the impact of therapy truly acquired in a randomized trial with all-or-nothing compliance. Biometrics. 2003;59:100–5.


Frangakis CE, Rubin DB. Addressing problems of intention‐to‐deal with evaluation within the mixed presence of all‐or‐none therapy‐noncompliance and subsequent lacking outcomes. Biometrika. 1999;86:365–79.


Robins JM, Tsiatis AA. Correcting for noncompliance in randomized trials utilizing rank preserving structural failure time fashions. Commun Stat. 1991;20:2609–31.


VanderWeele TJ. Unmeasured confounding and hazard scales: sensitivity evaluation for whole, direct, and oblique results. Eur J Epidemiol. 2013;28:113–7.


Lin DY, Psaty BM, Kronmal RA. Assessing the sensitivity of regression outcomes to unmeasured confounders in observational research. Biometrics. 1998;54:948–63.


VanderWeele TJ. Sensitivity evaluation: distributional assumptions and confounding assumptions. Biometrics. 2008;64:645–9.


Carnegie NB. Assessing sensitivity to unmeasured confounding utilizing a simulated potential confounder. J Res Educ Eff. 2016;9:395–420.


Ding P, VanderWeele TJ. Sensitivity evaluation with out assumptions. Epidemiology. 2016;27:368–77.


Branson M, Whitehead J. Estimating a therapy impact in survival research by which sufferers swap therapy. Stat Med. 2002;21:2449–63.


Hernán MA, Brumback B, Robins JM. Marginal structural fashions to estimate the causal impact of zidovudine on the survival of HIV-positive males. Epidemiology. 2000;11:561–70.


Robins JM, Hernán MA, Brumback B. Marginal structural fashions and causal inference in epidemiology. Epidemiology. 2000;11:550–60.


Bodnar L, Davidian M, Siega-Riz AM, Tsiatis AA. Marginal structural fashions for analyzing causal results of time-dependent therapies: an software in perinatal epidemiology. Am J Epidemiol. 2004;159:926–34.


Robins JM. Structural nested failure time fashions. In: Armitage P, Colton T (eds) Encyclopedia of Biostatistics (Chichester: John Wiley & Sons., 1998) pp. 4372–89.


Vansteelandt S, Joffe M. Structural nested fashions and G-estimation: the partially realized promise. Stat Sci. 2014;29:707–31.


van der Laan MJ, Rose S. Focused studying causal inference for observational and experimental knowledge. New York: Springer; 2011.


Wager S, Du W, Taylor J, Tibshirani RJ. Excessive-dimensional regression changes in randomized experiments. Proc Natl Acad Sci USA. 2016;113:12673–eight.


Bloniarz A, Liu H, Zhang CH, Sekhon JS, Yu B. Lasso changes of therapy impact estimates in randomized experiments. Proc Natl Acad Sci USA. 2016;113:7383–90.

Supply hyperlink

wordpress autoblog

amazon autoblog

affiliate autoblog

wordpress web site

web site growth

Show More

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *