scholarly journals Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

2019 ◽  
Vol 5 (2) ◽  
pp. 21-35
Author(s):  
Carlos Carvalho ◽  
Avi Feller ◽  
Jared Murray ◽  
Spencer Woody ◽  
David Yeager
Keyword(s):  
Treatment Effect ◽  
Observational Studies

Inference for treatment effect parameters in potentially misspecified high-dimensional models

Biometrika ◽  
2020 ◽  
Author(s):  
Oliver Dukes ◽  
Stijn Vansteelandt
Keyword(s):  
Sample Size ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Observational Studies ◽  
Treatment Effects ◽  
Treatment Selection ◽  
High Dimensional ◽  
Selection Mechanism ◽  
Dimensional Models ◽  
Effect Parameter

Summary Eliminating the effect of confounding in observational studies typically involves fitting a model for an outcome adjusted for covariates. When, as often, these covariates are high-dimensional, this necessitates the use of sparse estimators, such as the lasso, or other regularization approaches. Naïve use of such estimators yields confidence intervals for the conditional treatment effect parameter that are not uniformly valid. Moreover, as the number of covariates grows with the sample size, correctly specifying a model for the outcome is nontrivial. In this article we deal with both of these concerns simultaneously, obtaining confidence intervals for conditional treatment effects that are uniformly valid, regardless of whether the outcome model is correct. This is done by incorporating an additional model for the treatment selection mechanism. When both models are correctly specified, we can weaken the standard conditions on model sparsity. Our procedure extends to multivariate treatment effect parameters and complex longitudinal settings.

Trial designs, including observational to interventional

2020 ◽  
pp. 57-64
Author(s):  
Edo Richard
Keyword(s):  
Study Design ◽  
Treatment Effect ◽  
Clinical Relevance ◽  
Observational Studies ◽  
Research Question ◽  
Sample Size Calculation ◽  
Target Population ◽  
Ethical Considerations ◽  
Neuropsychiatric Diseases ◽  
The Right

Observational studies have taught us a lot about the origin of neurological and neuropsychiatric diseases. This chapter describes how we can translate this knowledge into action. Before engaging in a large RCT, several steps have to be taken. First, the potential for a treatment effect has to be compelling. The target population in the RCT has to resemble the population in which observational studies described an association. Second, the outcome of an RCT has to be chosen, and has to have clinical relevance or at least have the potential of clinical relevance in the future. Third, the right study design has to be decided on. Each research question will require a specific study design with accompanying sample size calculation. Lastly, specific ethical considerations have to be taken into account when designing and executing an intervention study. This chapter presents an overview of these issues.

scholarly journals Bias-adjusted Kaplan–Meier survival curves for marginal treatment effect in observational studies

2019 ◽  
Vol 29 (4) ◽  
pp. 592-605
Author(s):  
Xiaofei Wang ◽  
Fangfang Bai ◽  
Herbert Pang ◽  
Stephen L George
Keyword(s):  
Treatment Effect ◽  
Observational Studies ◽  
Survival Curves ◽  
Kaplan Meier
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