Setting Limits and their Relation to Well-being in End of Life Care
Last registered on March 12, 2017

Pre-Trial

Trial Information
General Information
Title
Setting Limits and their Relation to Well-being in End of Life Care
RCT ID
AEARCTR-0002051
Initial registration date
February 28, 2017
Last updated
March 12, 2017 6:22 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
Busara Center for Behavioral Economics
Other Primary Investigator(s)
PI Affiliation
University of Chicago
PI Affiliation
University of Chicago
PI Affiliation
Middlebury College
Additional Trial Information
Status
In development
Start date
2016-03-01
End date
2018-02-28
Secondary IDs
Abstract
In 2011, $205 billion was spent on medical care for patients who were in their last year of life (Aldridge and Kelley, 2015). Medicare expenditures alone were responsible for 60% of this figure (Institute of Medicine, 2015). Given the magnitude of this expenditure and its expected increase (Lindgren, 2016) it is important to explore whether patient preferences for their own medical care at the end of life are satisfied. Though death at the end of every life is predictable with certainty, the death of each individual takes its own time, even as new medical technologies have made it possible for physicians to extend this time. Many of these technologies are high cost, and many also are emotionally, if not technically, difficult to stop once started. The result is that patients and their families are often asked to make difficult, sometimes excruciating decisions about limiting and ending intensive medical treatment as the time of death approaches. Added to these difficulties is the fact that the “end of life” is a time that is only known for certain retroactively.

To illuminate some of the difficult issues surrounding decisions concerning medical care at the end of life, our research focuses on two goals: (i) to understand patient preferences in specific medical scenarios and (ii) to understand public attitudes about government policy that could set limits on spending on care at the end of life. A common confound in the study of preferences and attitudes is that respondents may be influenced by social norms, or even the survey itself (e.g., framing and social desirability bias). Also, stated preferences do not always predict behaviors, thus respondents may not know or be able to fully articulate their underlying preferences. Thus, in contrast to most studies on patient attitudes, we augment simple questions pertaining to preferences with a randomization design intended to elicit subtle beliefs and views. Specifically, we present respondents with primes intended to highlight a certain aspect of EOLC (e.g., fear of death, opportunity cost) and observe how shifting the patient’s focus to the given topic affects their stated preferences.
External Link(s)
Registration Citation
Citation
Brauner, Daniel et al. 2017. "Setting Limits and their Relation to Well-being in End of Life Care ." AEA RCT Registry. March 12. https://www.socialscienceregistry.org/trials/2051/history/14892
Experimental Details
Interventions
Intervention(s)
We present respondents with primes intended to highlight a certain aspect of EOLC (e.g., fear of death, opportunity cost) and observe how shifting the patient’s focus to the given topic affects their stated preferences.
Intervention Start Date
2017-03-06
Intervention End Date
2017-12-31
Primary Outcomes
Primary Outcomes (end points)
1-5 scale that indicates how much a respondent agrees with the statement “It is appropriate for the government to set limits on how much it will contribute financially to medical care at the end of life.”

1-5 scale that indicates how much a respondent agrees with the statement “It is appropriate for the patient to get the medical treatment in question for this situation.”

1-5 scale how much a respondent agrees with the statement “Spending more money on end of life care leads to better care at the end of life.”



1-5 scale that indicates how much a respondent agrees with the statement “It is appropriate for the patient to get the medical treatment in question for this situation.”
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Google survey with 3 different versions (based on informational primes) varied at the individualistic level.
Experimental Design Details
Randomization Method
By computer.
Randomization Unit
Individual.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Not clustered
Sample size: planned number of observations
TBD based on response - targeting 500 general respondents and 200 medical professionals
Sample size (or number of clusters) by treatment arms
TBD based on response - aiming for 700 individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
BSD IRB The University of Chicago Biological Sciences Division/University of Chicago Medical Cent
IRB Approval Date
2017-02-15
IRB Approval Number
IRB17-0075
Analysis Plan
Analysis Plan Documents
EOLC PAP.pdf

MD5: 4d8afbede3c06afae3db522b284ca87f

SHA1: 4bc613943689494f52b5a0546f749f798dbe5212

Uploaded At: February 27, 2017

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers