Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response

Xiaoqing Tan, Judah Abberbock, Priya Rastogi, Gong Tang
Proceedings of the First Conference on Causal Learning and Reasoning, PMLR 177:734-753, 2022.

Abstract

In neoadjuvant trials on early-stage breast cancer, patients are usually randomized into a control group and a treatment group with an additional target therapy. Early efficacy of the new regimen is assessed via the binary pathological complete response (pCR) and the eventual efficacy is assessed via long-term clinical outcomes such as survival. Although pCR is strongly associated with survival, it has not been confirmed as a surrogate endpoint. To fully understand its clinical implication, it is important to establish causal estimands such as the causal effect in survival for patients who would achieve pCR under the new regimen. Under the principal stratification framework, previous studies focus on sensitivity analyses by varying model parameters in an imposed model on counterfactual outcomes. Under mild assumptions, we propose an approach to estimate those model parameters using empirical data and subsequently the causal estimand of interest. We also extend our approach to address censored outcome data. The proposed method is applied to a recent clinical trial and its performance is evaluated via simulation studies.

Cite this Paper

BibTeX
@InProceedings{pmlr-v177-tan22a, title = {Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response}, author = {Tan, Xiaoqing and Abberbock, Judah and Rastogi, Priya and Tang, Gong}, booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning}, pages = {734--753}, year = {2022}, editor = {Schölkopf, Bernhard and Uhler, Caroline and Zhang, Kun}, volume = {177}, series = {Proceedings of Machine Learning Research}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v177/tan22a/tan22a.pdf}, url = {https://proceedings.mlr.press/v177/tan22a.html}, abstract = {In neoadjuvant trials on early-stage breast cancer, patients are usually randomized into a control group and a treatment group with an additional target therapy. Early efficacy of the new regimen is assessed via the binary pathological complete response (pCR) and the eventual efficacy is assessed via long-term clinical outcomes such as survival. Although pCR is strongly associated with survival, it has not been confirmed as a surrogate endpoint. To fully understand its clinical implication, it is important to establish causal estimands such as the causal effect in survival for patients who would achieve pCR under the new regimen. Under the principal stratification framework, previous studies focus on sensitivity analyses by varying model parameters in an imposed model on counterfactual outcomes. Under mild assumptions, we propose an approach to estimate those model parameters using empirical data and subsequently the causal estimand of interest. We also extend our approach to address censored outcome data. The proposed method is applied to a recent clinical trial and its performance is evaluated via simulation studies. } }
Endnote
%0 Conference Paper %T Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response %A Xiaoqing Tan %A Judah Abberbock %A Priya Rastogi %A Gong Tang %B Proceedings of the First Conference on Causal Learning and Reasoning %C Proceedings of Machine Learning Research %D 2022 %E Bernhard Schölkopf %E Caroline Uhler %E Kun Zhang %F pmlr-v177-tan22a %I PMLR %P 734--753 %U https://proceedings.mlr.press/v177/tan22a.html %V 177 %X In neoadjuvant trials on early-stage breast cancer, patients are usually randomized into a control group and a treatment group with an additional target therapy. Early efficacy of the new regimen is assessed via the binary pathological complete response (pCR) and the eventual efficacy is assessed via long-term clinical outcomes such as survival. Although pCR is strongly associated with survival, it has not been confirmed as a surrogate endpoint. To fully understand its clinical implication, it is important to establish causal estimands such as the causal effect in survival for patients who would achieve pCR under the new regimen. Under the principal stratification framework, previous studies focus on sensitivity analyses by varying model parameters in an imposed model on counterfactual outcomes. Under mild assumptions, we propose an approach to estimate those model parameters using empirical data and subsequently the causal estimand of interest. We also extend our approach to address censored outcome data. The proposed method is applied to a recent clinical trial and its performance is evaluated via simulation studies.
APA
Tan, X., Abberbock, J., Rastogi, P. & Tang, G.. (2022). Identifying Principal Stratum Causal Effects Conditional on a Post-treatment Intermediate Response. Proceedings of the First Conference on Causal Learning and Reasoning, in Proceedings of Machine Learning Research 177:734-753 Available from https://proceedings.mlr.press/v177/tan22a.html.

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