Comparing g-computation, propensity score-based weighting, and targeted maximum likelihood estimation for analyzing externally controlled trials with both measured and unmeasured confounders: a simulation study

[1] Hatswell AJ(2016)Regulatory approval of pharmaceuticals without a randomised controlled study: analysis of EMA and FDA approvals 1999–2014 BMJ Open 6 e011666-750

[2] Baio G(2016)Oncologic drugs advisory committee recommendations and approval of cancer drugs by the US food and drug administration JAMA Oncol 2 744-1115

[3] Berlin JA(2020)Limitations in clinical trials leading to anticancer drug approvals by the US food and drug administration JAMA Intern Med 180 1108-118

[4] Irs A(2017)Methodological challenges for the evaluation of clinical effectiveness in the context of accelerated regulatory approval: an overview J Clin Epidemiol 90 108-633

[5] Freemantle N(2020)A framework for methodological choice and evidence assessment for studies using external comparators from real-world data Drug Saf 43 623-762

[6] Tibau A(2017)An introduction to g methods Int J Epidemiol 46 756-73

[7] Ocana A(2017)Targeted maximum likelihood estimation for causal inference in observational studies Am J Epidemiol 185 65-1377

[8] Anguera G(2020)G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study Sci Rep 10 9219-1121

[9] Hilal T(2020)A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) Orphanet J Rare Dis 15 69-1512

[10] Gonzalez-Velez M(2019)Propensity score weighting using overlap weights: a new method applied to Regorafenib clinical data and a cost-effectiveness analysis Value Health 22 1370-686

[1] Hatswell AJ(2016)Regulatory approval of pharmaceuticals without a randomised controlled study: analysis of EMA and FDA approvals 1999–2014 BMJ Open 6 e011666-750

[2] Baio G(2016)Oncologic drugs advisory committee recommendations and approval of cancer drugs by the US food and drug administration JAMA Oncol 2 744-1115

[3] Berlin JA(2020)Limitations in clinical trials leading to anticancer drug approvals by the US food and drug administration JAMA Intern Med 180 1108-118

[4] Irs A(2017)Methodological challenges for the evaluation of clinical effectiveness in the context of accelerated regulatory approval: an overview J Clin Epidemiol 90 108-633

[5] Freemantle N(2020)A framework for methodological choice and evidence assessment for studies using external comparators from real-world data Drug Saf 43 623-762

[6] Tibau A(2017)An introduction to g methods Int J Epidemiol 46 756-73

[7] Ocana A(2017)Targeted maximum likelihood estimation for causal inference in observational studies Am J Epidemiol 185 65-1377

[8] Anguera G(2020)G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study Sci Rep 10 9219-1121

[9] Hilal T(2020)A roadmap to using historical controls in clinical trials - by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG) Orphanet J Rare Dis 15 69-1512

[10] Gonzalez-Velez M(2019)Propensity score weighting using overlap weights: a new method applied to Regorafenib clinical data and a cost-effectiveness analysis Value Health 22 1370-686