| Title |
Estimation of causal effect measures with the R-package stdReg
|
|---|---|
| Published in |
European Journal of Epidemiology, March 2018
|
| DOI | 10.1007/s10654-018-0375-y |
| Pubmed ID | |
| Authors |
Arvid Sjölander |
| Abstract |
Measures of causal effects play a central role in epidemiology. A wide range of measures exist, which are designed to give relevant answers to substantive epidemiological research questions. However, due to mathematical convenience and software limitations most studies only report odds ratios for binary outcomes and hazard ratios for time-to-event outcomes. In this paper we show how logistic regression models and Cox proportional hazards regression models can be used to estimate a wide range of causal effect measures, with the R-package stdReg. For illustration we focus on the attributable fraction, the number needed to treat and the relative excess risk due to interaction. We use two publicly available data sets, so that the reader can easily replicate and elaborate on the analyses. The first dataset includes information on 487 births among 188 women, and the second dataset includes information on 2982 women diagnosed with primary breast cancer. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 43% |
| Japan | 2 | 29% |
| Spain | 1 | 14% |
| Unknown | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 58 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 16 | 28% |
| Student > Ph. D. Student | 10 | 17% |
| Other | 6 | 10% |
| Researcher | 5 | 9% |
| Student > Doctoral Student | 2 | 3% |
| Other | 10 | 17% |
| Unknown | 9 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 16 | 28% |
| Medicine and Dentistry | 13 | 22% |
| Biochemistry, Genetics and Molecular Biology | 4 | 7% |
| Mathematics | 3 | 5% |
| Agricultural and Biological Sciences | 2 | 3% |
| Other | 9 | 16% |
| Unknown | 11 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 14 December 2021.
All research outputs
#6,700,826
of 22,691,736 outputs
Outputs from European Journal of Epidemiology
#688
of 1,613 outputs
Outputs of similar age
#119,071
of 332,765 outputs
Outputs of similar age from European Journal of Epidemiology
#18
of 31 outputs
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