A drug is administered sequentially to incoming patients. A response Y to treatment and a covariate X is measured (X might be a side effect). The experiment is stopped when the covariate falls outside some acceptable region. We study the effect that this optional stopping has on the significance level of the test and we found that this effect is surprisingly small in the examples considered. An approximation to the problem is found. This approximation does not depend on the distribution of the variable X but only on the correlation coefficient between X and Y.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29874/1/0000223.pd
The principal subject of this thesis is hypothesis testing and related problems of estimation for ...
Estimation of average treatment effects under unconfoundedness or selection on observ-ables is often...
This article develops methods of statistical monitoring of clinical trials with multiple co-primary ...
The problems addressed in this thesis are motivated by the following problem from clinical trials. S...
Sequential designs of Randomized Clinical Trials (RCT) allow repeated significance testing based on...
Sequential regression approaches can be used to analyze processes in which covariates are revealed i...
This study demonstrates the existence of a testable condition for the identification of the causal e...
When a clinical trial is subject to a series of interim analyses as a result of which the study may ...
In two-arm randomized controlled trials (RCTs) with baseline covariates that are prognostic for the ...
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are o...
In this paper we consider a method for monitoring a clinical trial whose patients are sequentially e...
The credibility of standard instrumental variables assumptions is often under dispute. This paper im...
A common problem when conducting an experiment or observational study for the purpose of causal infe...
In this paper, we argue that causal effect models for realistic individualized treatment rules repre...
In sequential experiments, subjects become available for the study over a period of time, and covari...
The principal subject of this thesis is hypothesis testing and related problems of estimation for ...
Estimation of average treatment effects under unconfoundedness or selection on observ-ables is often...
This article develops methods of statistical monitoring of clinical trials with multiple co-primary ...
The problems addressed in this thesis are motivated by the following problem from clinical trials. S...
Sequential designs of Randomized Clinical Trials (RCT) allow repeated significance testing based on...
Sequential regression approaches can be used to analyze processes in which covariates are revealed i...
This study demonstrates the existence of a testable condition for the identification of the causal e...
When a clinical trial is subject to a series of interim analyses as a result of which the study may ...
In two-arm randomized controlled trials (RCTs) with baseline covariates that are prognostic for the ...
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are o...
In this paper we consider a method for monitoring a clinical trial whose patients are sequentially e...
The credibility of standard instrumental variables assumptions is often under dispute. This paper im...
A common problem when conducting an experiment or observational study for the purpose of causal infe...
In this paper, we argue that causal effect models for realistic individualized treatment rules repre...
In sequential experiments, subjects become available for the study over a period of time, and covari...
The principal subject of this thesis is hypothesis testing and related problems of estimation for ...
Estimation of average treatment effects under unconfoundedness or selection on observ-ables is often...
This article develops methods of statistical monitoring of clinical trials with multiple co-primary ...