Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan-Reference-Cited by-同舟云学术

Comments on “sensitivity of estimands in clinical trials with imperfect compliance” by Chen and Heitjan

Published:2024-07-29 Issue: Volume: Page:
ISSN:2194-573X
Container-title:The International Journal of Biostatistics
language:en
Short-container-title:

Author:

Baker Stuart G.¹,Lindeman Karen S.²

Affiliation:

1. National Cancer Institute , Bethesda , MD , 20892-9789 , USA

2. Department of Anesthesiology , Johns Hopkins Medical Institutions , Baltimore , USA

Abstract

Abstract Chen and Heitjan (Sensitivity of estimands in clinical trials with imperfect compliance. Int J Biostat. 2023) used linear extrapolation to estimate the population average causal effect (PACE) from the complier average causal effect (CACE) in multiple randomized trials with all-or-none compliance. For extrapolating from CACE to PACE in this setting and in the paired availability design involving different availabilities of treatment among before-and-after studies, we recommend the sensitivity analysis in Baker and Lindeman (J Causal Inference, 2013) because it is not restricted to a linear model, as it involves various random effect and trend models.

Funder

Division of Cancer Prevention, National Cancer Institute

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/ijb-2023-0127/pdf

Reference12 articles.

1. Baker, SG, Lindeman, KS. The paired availability design: a proposal for evaluating epidural analgesia during labor. Stat Med 1994;13:2269–78. https://doi.org/10.1002/sim.4780132108.

2. Imbens, GW, Angrist, JD. Identification and estimation of local average treatment effects. Econometrica 1994;62:467–75. https://doi.org/10.2307/2951620.

3. Baker, SG, Lindeman, KS. Multiple discoveries in causal inference: LATE for the party. Chance 2024;2:21–5. https://doi.org/10.1080/09332480.2024.2348956.

4. Baker, SG, Kramer, BS, Lindeman, KL. Latent class instrumental variables. A clinical and biostatistical perspective. Stat Med 2016;35:147–60. Correction 2019; 38: 901. https://doi.org/10.1002/sim.6612.

5. Lee, K, Lorch, SA, Small, DS. Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models. Stat Med 2019;38:2303–16. https://doi.org/10.1002/sim.8117.