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Comment: Inference after covariate-adaptive randomisation: aspects of methodology and theory

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Pages 190-191 | Received 25 Mar 2021, Accepted 16 Mar 2021, Published online: 29 Mar 2021
 

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No potential conflict of interest was reported by the author(s).

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Notes on contributors

Bingkai Wang

Mr. Bingkai Wang is a PhD student of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research focuses on causal inference and clinical trials.

Ryoko Susukida

Dr. Ryoko Susukida is an assistant scientist in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. Her areas of research expertise include public health methodology and its rigorous application in mental and behavioral health research. Her current research includes data harmonization, integrative data analysis, and evidence synthesis in theareas of substance use disorder treatment and suicide prevention research.

Ramin Mojtabai

Dr. Ramin Mojtabai is a professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. His research focuses on mental health services research including trends and correlates of treatment seeking for depression, other common mental and substance use disorders, barriers to accessing services including stigma, process and outcome of treatments of common mental and substance use disorders in usual care settings and pharmacoepidemiology of antidepressants and other psychiatric drugs.

Masoumeh Amin-Esmaeili

Dr. Masoumeh Amin-Esmaeili is a postdoctoral fellow in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health. Her research interests include psychiatric epidemiology, substance use disorders, epidemiology and service research.

Michael Rosenblum

Dr. Michael Rosenblum is an associate professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research interests include adaptive clinical trial designs, robustness to model misspecification, causal inference, and HIV/AIDS prevention and treatment.

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