scholarly journals Nonparametric Bounds and Sensitivity Analysis of Treatment Effects

2014 ◽  
Vol 29 (4) ◽  
pp. 596-618 ◽  
Author(s):  
Amy Richardson ◽  
Michael G. Hudgens ◽  
Peter B. Gilbert ◽  
Jason P. Fine
Keyword(s):  
Sensitivity Analysis ◽  
Treatment Effects ◽  
Nonparametric Bounds

scholarly journals Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects

2017 ◽  
Vol 11 (1) ◽  
pp. 225-247 ◽  
Author(s):  
Trang Quynh Nguyen ◽  
Cyrus Ebnesajjad ◽  
Stephen R. Cole ◽  
Elizabeth A. Stuart
Keyword(s):  
Sensitivity Analysis ◽  
Treatment Effects ◽  
Target Population

Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data

2013 ◽  
Vol 21 (2) ◽  
pp. 233-251 ◽  
Author(s):  
Walter R. Mebane ◽  
Paul Poast
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Treatment Effect ◽  
Bayesian Method ◽  
Treatment Effects ◽  
Applied Research ◽  
Average Treatment Effect ◽  
Potential Importance ◽  
Treatment Data

How a treatment causes a particular outcome is a focus of inquiry in political science. When treatment data are either nonrandomly assigned or missing, the analyst will often invoke ignorability assumptions: that is, both the treatment and missingness are assumed to be as if randomly assigned, perhaps conditional on a set of observed covariates. But what if these assumptions are wrong? What if the analyst does not know why—or even if—a particular subject received a treatment? Building on Manski, Molinari offers an approach for calculating nonparametric identification bounds for the average treatment effect of a binary treatment under general missingness or nonrandom assignment. To make these bounds substantively more informative, Molinari's technique permits adding monotonicity assumptions (e.g., assuming that treatment effects are weakly positive). Given the potential importance of these assumptions, we develop a new Bayesian method for performing sensitivity analysis regarding them. This sensitivity analysis allows analysts to interpret the assumptions' consequences quantitatively and visually. We apply this method to two problems in political science, highlighting the method's utility for applied research.

Other Issues in Statistics II

2018 ◽  
pp. 284-303
Author(s):  
Jorge Leite ◽  
Munir Boodhwani ◽  
Felipe Fregni
Keyword(s):  
Sensitivity Analysis ◽  
Clinical Practice ◽  
Subgroup Analysis ◽  
Treatment Effects ◽  
Meta Analysis ◽  
False Positives ◽  
Random Model ◽  
Data Search ◽  
Baseline Characteristics

This chapter focuses on two important concepts: subgroup analysis and meta-analysis. Subgroup analysis is especially concerned about variability and how treatment effects can differ due to specific characteristics of the population. Important issues, however, arise when planning for subgroup analysis, such as dealing with false positives and methods for dealing with multiple statistical comparisons. The chapter also points out that the analysis of data from subgroups of patients with certain baseline characteristics is typically insufficient to change general clinical practice. The second section of this chapter focuses on meta-analysis, which is a method of pooling data from several studies in order to quantify the overall effect of an intervention or exposure, and thus potentially change clinical practice. This section discusses data search and synthesis, quantification of heterogeneity, choice of fixed or random model, as well as sensitivity analysis.

scholarly journals Bias and Sensitivity Analysis When Estimating Treatment Effects from the Cox Model with Omitted Covariates

Biometrics ◽  
2013 ◽  
Vol 69 (4) ◽  
pp. 850-860 ◽  
Author(s):  
Nan Xuan Lin ◽  
Stuart Logan ◽  
William Edward Henley
Keyword(s):  
Sensitivity Analysis ◽  
Treatment Effects ◽  
Cox Model
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