Disentangling Treatment and Placebo Effects in Randomized Experiments Using Principal Stratification—An Introduction

2018 ◽  
pp. 11-23 ◽  
Author(s):  
Reagan Mozer ◽  
Rob Kessels ◽  
Donald B. Rubin
Keyword(s):  
Principal Stratification ◽  
Randomized Experiments ◽  
Placebo Effects

Estimation of Causal Effects via Principal Stratification When Some Outcomes are Truncated by “Death”

2003 ◽  
Vol 28 (4) ◽  
pp. 353-368 ◽  
Author(s):  
Junni L. Zhang ◽  
Donald B. Rubin
Keyword(s):  
Sample Space ◽  
Causal Effects ◽  
Outcome Variable ◽  
Principal Stratification ◽  
Randomized Experiments ◽  
Large Sample ◽  
Traditional Approaches

The topic of “truncation by death” in randomized experiments arises in many fields, such as medicine, economics and education. Traditional approaches addressing this issue ignore the fact that the outcome after the truncation is neither “censored” nor “missing,” but should be treated as being defined on an extended sample space. Using an educational example to illustrate, we will outline here a formulation for tackling this issue, where we call the outcome “truncated by death” because there is no hidden value of the outcome variable masked by the truncating event. We first formulate the principal stratification ( Frangakis & Rubin, 2002 ) approach, and we then derive large sample bounds for causal effects within the principal strata, with or without various identification assumptions. Extensions are then briefly discussed.

Principal Stratification Approach to Broken Randomized Experiments

2003 ◽  
Vol 98 (462) ◽  
pp. 299-323 ◽  
Author(s):  
John Barnard ◽  
Constantine E Frangakis ◽  
Jennifer L Hill ◽  
Donald B Rubin
Keyword(s):  
Principal Stratification ◽  
Randomized Experiments

A Modified General Location Model for Noncompliance With Missing Data

2010 ◽  
Vol 35 (2) ◽  
pp. 154-173 ◽  
Author(s):  
Hui Jin ◽  
John Barnard ◽  
Donald B. Rubin
Keyword(s):  
New York ◽  
New York City ◽  
Missing Data ◽  
School Choice ◽  
Location Model ◽  
Principal Stratification ◽  
Randomized Experiments ◽  
Pattern Mixture Model ◽  
Scholarship Program ◽  
Missing Data Techniques

Missing data, especially when coupled with noncompliance, are a challenge even in the setting of randomized experiments. Although some existing methods can address each complication, it can be difficult to handle both of them simultaneously. This is true in the example of the New York City School Choice Scholarship Program, where both the covariates and the outcomes were sometimes missing, and there was complicated noncompliance. The authors propose a modified general location model to integrate the ideas of missing data techniques and principal stratification and then analyze the same data as in Barnard, Frangakis, Hill, and Rubin (2003) , where a pattern-mixture model was used. Their results are presented and compared with those in Barnard et al.

The Effects of Questionnaire Length and Behavioral Consequences on Careless Responding

2020 ◽  
Vol 36 (2) ◽  
pp. 410-420 ◽  
Author(s):  
Anthony M. Gibson ◽  
Nathan A. Bowling
Keyword(s):  
Self Report ◽  
Randomized Experiments ◽  
High Quality ◽  
Buffering Effect ◽  
Careless Responding ◽  
Main Effect ◽  
Limited Support ◽  
Report Data ◽  
Self Report Data ◽  
Consistent Support

Abstract. The current paper reports the results of two randomized experiments designed to test the effects of questionnaire length on careless responding (CR). Both experiments also examined whether the presence of a behavioral consequence (i.e., a reward or a punishment) designed to encourage careful responding buffers the effects of questionnaire length on CR. Collectively, our two studies found (a) some support for the main effect of questionnaire length, (b) consistent support for the main effect of the consequence manipulations, and (c) very limited support for the buffering effect of the consequence manipulations. Because the advancement of many subfields of psychology rests on the availability of high-quality self-report data, further research should examine the causes and prevention of CR.

Learned Placebo Effects in the Immune System

2014 ◽  
Vol 222 (3) ◽  
pp. 148-153 ◽  
Author(s):  
Sabine Vits ◽  
Manfred Schedlowski
Keyword(s):  
Nervous System ◽  
Immune System ◽  
Associative Learning ◽  
Placebo Response ◽  
Immunosuppressive Drug ◽  
Immune Functions ◽  
Placebo Effects ◽  
New Therapeutic Approaches ◽  
Biological Variables ◽  
Experimental Approaches

Associative learning processes are one of the major neuropsychological mechanisms steering the placebo response in different physiological systems and end organ functions. Learned placebo effects on immune functions are based on the bidirectional communication between the central nervous system (CNS) and the peripheral immune system. Based on this “hardware,” experimental evidence in animals and humans showed that humoral and cellular immune functions can be affected by behavioral conditioning processes. We will first highlight and summarize data documenting the variety of experimental approaches conditioning protocols employed, affecting different immunological functions by associative learning. Taking a well-established paradigm employing a conditioned taste aversion model in rats with the immunosuppressive drug cyclosporine A (CsA) as an unconditioned stimulus (US) as an example, we will then summarize the efferent and afferent communication pathways as well as central processes activated during a learned immunosuppression. In addition, the potential clinical relevance of learned placebo effects on the outcome of immune-related diseases has been demonstrated in a number of different clinical conditions in rodents. More importantly, the learned immunosuppression is not restricted to experimental animals but can be also induced in humans. These data so far show that (i) behavioral conditioned immunosuppression is not limited to a single event but can be reproduced over time, (ii) immunosuppression cannot be induced by mere expectation, (iii) psychological and biological variables can be identified as predictors for this learned immunosuppression. Together with experimental approaches employing a placebo-controlled dose reduction these data provide a basis for new therapeutic approaches to the treatment of diseases where a suppression of immune functions is required via modulation of nervous system-immune system communication by learned placebo effects.

Optimal Experimental Design With Nesting of Persons in Organizations

2013 ◽  
Vol 221 (3) ◽  
pp. 145-159 ◽  
Author(s):  
Gerard J. P. van Breukelen
Keyword(s):  
Repeated Measures ◽  
Optimal Number ◽  
Health Centers ◽  
Randomized Experiments ◽  
Optimal Sample ◽  
Treatment Conditions ◽  
Sampling Cost ◽  
Nested Designs ◽  
Simple Equations ◽  
Number Of Individuals

This paper introduces optimal design of randomized experiments where individuals are nested within organizations, such as schools, health centers, or companies. The focus is on nested designs with two levels (organization, individual) and two treatment conditions (treated, control), with treatment assignment to organizations, or to individuals within organizations. For each type of assignment, a multilevel model is first presented for the analysis of a quantitative dependent variable or outcome. Simple equations are then given for the optimal sample size per level (number of organizations, number of individuals) as a function of the sampling cost and outcome variance at each level, with realistic examples. Next, it is explained how the equations can be applied if the dependent variable is dichotomous, or if there are covariates in the model, or if the effects of two treatment factors are studied in a factorial nested design, or if the dependent variable is repeatedly measured. Designs with three levels of nesting and the optimal number of repeated measures are briefly discussed, and the paper ends with a short discussion of robust design.

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