The Bias Dynamics Model
Correcting for Meta-biases in Therapeutic Prediction
Bibliographic Data
| ID | 10707601 |
|---|---|
| Authors | Adrian Erasmus (0000-0001-8944-1598, University of Alabama, corresponding author) |
| Year | 2023 |
| Volume | 90 |
| Issue | 5 |
| Pages | 1204-1213 |
| Publication date | 2023-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0031-8248 • E-ISSN: 1539-767X |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psa.2023.66 |
| OpenAlex | W4364365443 |
| Language | EN |
| Citations received | 1 |
| References cited | 15 |
Inferences from clinical research results to estimates of therapeutic effectiveness suffer due to various biases. I argue that predictions of medical effectiveness are prone to failure because current medical research overlooks the impacts of a particularly detrimental set of biases: meta-biases. Meta-biases are linked to higher-level characteristics of medical research and their effects are only observed when comparing sets of studies that share certain meta-level properties. I offer a model for correcting research results based on meta-research evidence, the bias dynamics model, which employs regularly updated empirical bias coefficients to attenuate estimates of therapeutic effectiveness
Econometrics · Economics · Meta-analysis · Publication bias · Set (abstract data type) · Computer Science · Health Systems, Economic Evaluations, Quality of Life · Medicine · Meta-analysis and systematic reviews · Statistical Methods in Clinical Trials
| Citations per year | 0.5 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |