The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Methods for dealing with time‐dependent confounding
Abstract
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in medical research. When estimating the effect of a time‐varying treatment or exposure on an outcome of interest measured at a later time, standard methods fail to give consistent estimators in the presence of time‐varying confounders if those confounders are themselves affected by the treatment. Robins and colleagues have proposed several alternative methods that, provided certain assumptions hold, avoid the problems associated with standard approaches. They include the g‐computation formula, inverse probability weighted estimation of marginal structural models and g‐estimation of structural nested models. In this tutorial, we give a description of each of these methods, exploring the links and differences between them and the reasons for choosing one over the others in different settings. Copyright © 2012 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
A structural approach to selection bias
Hernán, M. A., Hernández-Díaz, S., & Robins, J. M. (2004) · Epidemiology · doi:10.1097/01.ede.0000135174.63482.43 · Cited by 2,844 · cited 2×
“[14] call collider-stratification bias.”
BackgroundRobins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451
The interpretation of interaction in contingency tables
Simpson, E. H. (1951) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1951.tb00088.x · Cited by 2,301 · cited 1×
“Because of the non-collapsibility of the odds ratio [24,25], it is not possible for P.”
BackgroundConfounding and Collapsibility in Causal Inference
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Statistical Science · doi:10.1214/ss/1009211805 · Cited by 933 · cited 1×
“Because of the non-collapsibility of the odds ratio [24,25], it is not possible for P.”
BackgroundOrellana, L., Rotnitzky, A., & Robins, J. M. (2010) · The International Journal of Biostatistics · doi:10.2202/1557-4679.1200 · Cited by 202
On model selection and model misspecification in causal inference.
Vansteelandt, S., Bekaert, M., & Claeskens, G. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210387717 · Cited by 172
A review of causal estimation of effects in mediation analyses
Ten Have, T. R., & Joffe, M. M. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210391076 · Cited by 103
Cited by
A conceptual framework for the neurobiological study of resilience
Kalisch, R., Müller, M. B., & Tüscher, O. (2015) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1400082x · Cited by 669
Naimi, A. I., Cole, S. R., & Kennedy, E. H. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw323 · Cited by 332
Weuve, J., Proust‐Lima, C., Power, M. C., Gross, A. L., Hofer, S. M., Thiébaut, R., Chêne, G., Glymour, M. M., Dufouil, C., & Initiative, M. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.06.1885 · Cited by 243
Analysis of randomised trials with long-term follow-up
Herbert, R. D., Kasza, J., & Bø, K. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0499-5 · Cited by 80
Analyzing Patient Trajectories With Artificial Intelligence
Allam, A., Feuerriegel, S., Rebhan, M., & Krauthammer, M. (2021) · Journal of Medical Internet Research · doi:10.2196/29812 · Cited by 77
Metadata
- Authors
- Daniel, R. M., Cousens, S. N., De Stavola, B. L., Kenward, M. G., & Sterne, J. A. C.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2012-12-03
- Volume
- 32
- Issue
- 9
- Pages
- 1584-1618
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2159198921
- Cited by
- 376
- References
- 10
- FWCI
- 11.09
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
