| Title |
The meaning of confounding adjustment in the presence of multiple versions of treatment: an application to organ transplantation
|
|---|---|
| Published in |
European Journal of Epidemiology, January 2019
|
| DOI | 10.1007/s10654-019-00484-8 |
| Pubmed ID | |
| Authors |
Kerollos Nashat Wanis, Arin L. Madenci, Mary Katherine Dokus, Mark S. Orloff, Mark A. Levstik, Roberto Hernandez-Alejandro, Miguel A. Hernán |
| Abstract |
Causal inference for treatments with many versions requires a careful specification of the versions of treatment. Specifically, the existence of multiple relevant versions of treatment has implications for the selection of confounders. To illustrate this, we estimate the effect of organ transplantation using grafts from donors who died due to anoxic drug overdose, on recipient graft survival in the US. We describe how explicitly outlining the target trial (i.e. the hypothetical randomized trial which would answer the causal question of interest) to be emulated by an observational study analysis helps conceptualize treatment versions, guides selection of appropriate adjustment variables, and helps clarify the settings in which causal effects of compound treatments will be of value to decision-makers. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 15% |
| Researcher | 4 | 12% |
| Student > Bachelor | 4 | 12% |
| Other | 3 | 9% |
| Student > Postgraduate | 3 | 9% |
| Other | 9 | 26% |
| Unknown | 6 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 32% |
| Mathematics | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Engineering | 2 | 6% |
| Social Sciences | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 13 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 April 2019.
All research outputs
#13,119,167
of 23,124,001 outputs
Outputs from European Journal of Epidemiology
#1,176
of 1,644 outputs
Outputs of similar age
#203,150
of 437,851 outputs
Outputs of similar age from European Journal of Epidemiology
#16
of 30 outputs
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We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.