Constructing Inverse Probability Weights for Marginal Structural Models
Open accessAbstract
The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection bias under the four assumptions of consistency, exchangeability, positivity, and no misspecification of the model used to estimate weights. In recent years, several published estimates of the effect of time-varying exposures have been based on weighted estimation of the parameters of marginal structural models because, unlike standard statistical methods, weighting can appropriately adjust for measured time-varying confounders affected by prior exposure. As an example, the authors describe the last three assumptions using the change in viral load due to initiation of antiretroviral therapy among 918 human immunodeficiency virus-infected US men and women followed for a median of 5.8 years between 1996 and 2005. The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences. For instance, a tradeoff between bias and precision is illustrated as a function of the extent to which confounding is controlled. Weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs. Inverse probability weighting provides a powerful methodological tool that may uncover causal effects of exposures that are otherwise obscured. However, as with all methods, diagnostics and sensitivity analyses are essential for proper use.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Allocation to antifibrotic treatment was not randomly assigned, and a propensity score with inverse probability of treatment weighting was used so that all statistical comparisons between treated and untreated patients were weighted to balance the two groups on clinical characteristics at treatment start.
“INSIGHTS-IPF is an observational study and thus allocation to treatment was not randomly assigned.”
From Survival and course of lung function in the presence or absence of antifibrotic treatment in patients with idiopathic pulmonary fibrosis: long-term results of the INSIGHTS-IPF registry (2020) · cites this paper as [17]
Split into 2 atoms — 1 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
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 3× · see citing works
“, liver disease), then restriction or censoring may lead to bias, whether one uses weighting or other methods (30).”
BackgroundSelf-citationHernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,772 · see citing works
Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445 · see citing works
Methotrexate and mortality in patients with rheumatoid arthritis: a prospective study
Choi, H. K., Hernán, M. Á., Seeger, J. D., Robins, J. M., & Wolfe, F. (2002) · The Lancet · doi:10.1016/s0140-6736(02)08213-2 · Cited by 1,127 · see citing works
Estimating causal effects from epidemiological data
Hernán, M. Á., & Robins, J. M. (2006) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2004.029496 · Cited by 1,029 · see citing works
Adjusted survival curves with inverse probability weights
Cole, S. R., & Hernán, M. Á. (2003) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2003.10.004 · Cited by 816 · see citing works
Self-citationQuantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810 · see citing works
D'Agostino, R. B., Lee, M., Belanger, A. J., Cupples, L. A., Anderson, K., & Kannel, W. B. (1990) · Statistics in Medicine · doi:10.1002/sim.4780091214 · Cited by 798 · see citing works
Sterne, J. A. C., Hernán, M. Á., Ledergerber, B., Tilling, K. M., Weber, R. K., Sendi, P., Rickenbach, M., Robins, J. M., & Egger, M. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67022-5 · Cited by 550 · see citing works
Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
Hernán, M. Á., Brumback, B., & Robins, J. M. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753168154 · Cited by 483 · see citing works
The Consistency Statement in Causal Inference
Cole, S. R., & Frangakis, C. (2008) · Epidemiology · doi:10.1097/ede.0b013e31818ef366 · Cited by 413 · see citing works
Self-citationAssociation, Causation, And Marginal Structural Models
Robins, J. M. (1999) · Synthese · doi:10.1023/a:1005285815569 · Cited by 404 · see citing works
Does obesity shorten life? The importance of well-defined interventions to answer causal questions
Hernán, M. A., & Taubman, S. L. (2008) · International Journal of Obesity · doi:10.1038/ijo.2008.82 · Cited by 384 · see citing works
Self-citationData, Design, and Background Knowledge in Etiologic Inference
Robins, J. M. (2001) · Epidemiology · doi:10.1097/00001648-200105000-00011 · Cited by 358 · see citing works
Hernán, M. Á., Brumback, B., & Robins, J. M. (2002) · Statistics in Medicine · doi:10.1002/sim.1144 · Cited by 335 · see citing works
Cole, S. R. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg206 · Cited by 269 · see citing works
Self-citationOn the Treatment of Grouped Observations in Life Studies
Thompson, W. A. (1977) · Biometrics · doi:10.2307/2529360 · Cited by 226 · see citing works
Brumback, B., Hernán, M. Á., Haneuse, S., & Robins, J. M. (2004) · Statistics in Medicine · doi:10.1002/sim.1657 · Cited by 212 · see citing works
Patel, K., Hernán, M. Á., Williams, P. L., et al. (2008) · Clinical Infectious Diseases · doi:10.1086/526524 · Cited by 212 · see citing works
THE ROLE OF MODEL SELECTION IN CAUSAL INFERENCE FROM NONEXPERIMENTAL DATA
Robins, J. M., & Greenland, S. (1986) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a114254 · Cited by 201 · see citing works
Hernán, M. Á., Cole, S. R., Margolick, J. B., Cohen, M. H., & Robins, J. M. (2005) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.1064 · Cited by 165 · see citing works
Self-citationA Practical Guide to Dose-Response Analyses and Risk Assessment in Occupational Epidemiology
Steenland, K., & Deddens, J. A. (2004) · Epidemiology · doi:10.1097/01.ede.0000100287.45004.e7 · Cited by 155 · see citing works
Hernán, M. Á. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi255 · Cited by 147 · see citing works
Petersen, M. L., Wang, Y., van der Laan, M. J., Guzman, D., Riley, E. D., & Bangsberg, D. R. (2007) · Clinical Infectious Diseases · doi:10.1086/521250 · Cited by 141 · see citing works
Bodnar, L. M. (2004) · American Journal of Epidemiology · doi:10.1093/aje/kwh131 · Cited by 136 · see citing works
Cole, S. R., Hernán, M. Á., Margolick, J. B., Cohen, M. H., & Robins, J. M. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi216 · Cited by 120 · see citing works
Self-citationAn Application of Model-Fitting Procedures for Marginal Structural Models
Mortimer, K. M., Neugebauer, R. S., van der Laan, M. J., & Tager, I. B. (2005) · American Journal of Epidemiology · doi:10.1093/aje/kwi208 · Cited by 108 · see citing works
Cook, N. R., Cole, S. R., & Hennekens, C. H. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.11.1045 · Cited by 99 · see citing works
Self-citationSecondhand Smoke Exposure, Pulmonary Function, and Cardiovascular Mortality
Eisner, M. D., Wang, Y., Haight, T. J., Balmes, J. R., Hammond, S. K., & Tager, I. B. (2007) · Annals of Epidemiology · doi:10.1016/j.annepidem.2006.10.008 · Cited by 90 · see citing works
Tager, I. B., Haight, T., Sternfeld, B., Yu, Z., & van Der Laan, M. (2004) · Epidemiology · doi:10.1097/01.ede.0000128401.55545.c6 · Cited by 84 · see citing works
Cole, S. R., Hernán, M. Á., Anastos, K., Jamieson, B. D., & Robins, J. M. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm047 · Cited by 58 · see citing works
Self-citationThe effect of HIV infection on overdose mortality
Wang, C., Vlahov, D., Galai, N., Cole, S. R., Bareta, J., Pollini, R. A., Mehta, S. H., Nelson, K. E., & Galea, S. (2005) · AIDS · doi:10.1097/01.aids.0000171407.30866.22 · Cited by 54 · see citing works
Self-citationWang, Y., Petersen, M. L., Bangsberg, D. R., et al. (2006) · Collection of Biostatistics Research Archive · Cited by 54 · see citing works
Assessing the Effectiveness of Antiretroviral Adherence Interventions
Petersen, M. L., Wang, Y., van der Laan, M. J., & Bangsberg, D. R. (2006) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/01.qai.0000248344.95135.8d · Cited by 53 · see citing works
A semiparametric model selection criterion with applications to the marginal structural model
Brookhart, M. A., & van der Laan, M. J. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.08.013 · Cited by 47 · see citing works
Effect of Tuberculosis on the Survival of Women Infected with Human Immunodeficiency Virus
López‐Gatell, H., Cole, S. R., Hessol, N. A., French, A. L., Greenblatt, R. M., Landesman, S. H., Preston‐Martin, S., & Anastos, K. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwk116 · Cited by 43 · see citing works
Self-citationThe effect of epoetin dose on hematocrit
Cotter, D., Zhang, Y., Thamer, M., Kaufman, J. S., & Hernán, M. Á. (2007) · Kidney International · doi:10.1038/sj.ki.5002688 · Cited by 27 · see citing works
Ko, H., Hogan, J. W., & Mayer, K. H. (2003) · Biometrics · doi:10.1111/1541-0420.00018 · Cited by 27 · see citing works
Barrón, Y., Cole, S. R., Greenblatt, R. M., Cohen, M. H., Anastos, K., DeHovitz, J. A., Delapenha, R., & Gange, S. J. (2004) · AIDS · doi:10.1097/01.aids.0000131359.37210.1f · Cited by 18 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [1]1. Robins JM. Marginal structural models. In: 1997 proceedings of the American Statistical Association, Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, 1998:1-10. (http://biosun1.harvard.edu/~robins/ msm-web.pdf).
- [38]38. Kish L. Weighting for unequal P i . J Off Stat 1992;8:183-200.
Cited by
Sorted by citations
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495 · see citing works
The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis
Riazi, K., Azhari, H., Charette, J. H., Underwood, F. E., King, J. A., Afshar, E. E., Swain, M. G., Congly, S. E., Kaplan, G. G., & Shaheen, A.-A. (2022) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(22)00165-0 · Cited by 2,246 · see citing works
Doubly robust estimation of causal effects
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq439 · Cited by 1,158 · see citing works
Lund, J. L., Richardson, D. B., & Stürmer, T. (2015) · Current Epidemiology Reports · doi:10.1007/s40471-015-0053-5 · Cited by 739 · see citing works
Addressing extreme propensity scores via the overlap weights
Li, F., & Thomas, L. E. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy201 · Cited by 694 · see citing works
Desai, R. J., & Franklin, J. M. (2019) · BMJ · doi:10.1136/bmj.l5657 · Cited by 657 · see citing works
Prevention of Diabetes With Mediterranean Diets: A Subgroup Analysis of a Randomized Trial
Salas-Salvadó, J., Bulló, M., Estruch, R., Ros, E., Covas, M.-I., Ibarrola-Jurado, N., Corella, D., Arós, F., Gómez-Gracia, E., Ruiz-Gutiérrez, V., et al. (2014) · Annals of Internal Medicine · doi:10.7326/m13-1725 · Cited by 650 · see citing works
Diagnosing and responding to violations in the positivity assumption
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548 · see citing works
The Use of Propensity Scores to Assess the Generalizability of Results from Randomized Trials
Stuart, E. A., Cole, S. R., Bradshaw, C. P., & Leaf, P. J. (2011) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00673.x · Cited by 524 · see citing works
Self-citationLong-term neurologic outcomes of COVID-19
Xu, E., Xie, Y., & Al-Aly, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-02001-z · Cited by 530 · see citing works
On the distinction between interaction and effect modification
VanderWeele, T. J. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181ba333c · Cited by 450 · see citing works
Stable weights that balance covariates for estimation with incomplete outcome data
Zubizarreta, J. R. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1023805 · Cited by 369 · see citing works
Braun, J. M., Kalkbrenner, A. E., Just, A. C., Yolton, K., Calafat, A. M., Sjödin, A., Hauser, R., Webster, G. M., Chen, A., & Lanphear, B. P. (2014) · Environmental Health Perspectives · doi:10.1289/ehp.1307261 · Cited by 328 · see citing works
Causal inference and longitudinal data: a case study of religion and mental health
VanderWeele, T. J., Jackson, J. W., & Li, S. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1281-9 · Cited by 322 · see citing works
Experimental design and primary data analysis methods for comparing adaptive interventions.
Nahum-Shani, I., Qian, M., Almirall, D., Pelham, W. E., Gnagy, B., Fabiano, G. A., Waxmonsky, J. G., Yu, J., & Murphy, S. A. (2012) · Psychological Methods · doi:10.1037/a0029372 · Cited by 298 · see citing works
Nandi, A., Glymour, M. M., & Subramanian, S. V. (2014) · Epidemiology · doi:10.1097/ede.0000000000000038 · Cited by 237 · see citing works
Ali, M. S., Prieto-Alhambra, D., Lopes, L. C., Ramos, D., Bispo, N., Ichihara, M. Y., Pescarini, J. M., Williamson, E., Fiaccone, R. L., Barreto, M. L., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00973 · Cited by 200 · see citing works
George, M. D., Baker, J. F., Winthrop, K., Hsu, J. Y., Wu, Q., Chen, L., Xie, F., Yun, H., & Curtis, J. R. (2020) · Annals of Internal Medicine · doi:10.7326/m20-1594 · Cited by 198 · see citing works
Azoulay, L., & Suissa, S. (2017) · Diabetes Care · doi:10.2337/dc16-1943 · Cited by 195 · see citing works
Behr, J., Prasse, A., Wirtz, H., Koschel, D., Pittrow, D., Held, M., Klotsche, J., Andreas, S., Claussen, M., Grohé, C., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.02279-2019 · Cited by 195 · see citing works
On the reciprocal association between loneliness and subjective well-being
VanderWeele, T. J., Hawkley, L. C., & Cacioppo, J. T. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws173 · Cited by 187 · see citing works
Mixed neuropathologies and estimated rates of clinical progression in a large autopsy sample
Brenowitz, W. D., Hubbard, R. A., Keene, C. D., Hawes, S. E., Longstreth, W. T., Woltjer, R. L., & Kukull, W. A. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.09.015 · Cited by 188 · see citing works
Messer, L. C., Oakes, J. M., & Mason, S. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwp435 · Cited by 161 · see citing works
Yeung, E. H., Zhang, C., Mumford, S. L., Ye, A., Trevisan, M., Chen, L., Browne, R. W., Wactawski-Wende, J., & Schisterman, E. F. (2010) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2010-0702 · Cited by 145 · see citing works
Wickwire, E. M., Tom, S. E., Scharf, S. M., Vadlamani, A., Bulatao, I. G., & Albrecht, J. S. (2019) · Sleep · doi:10.1093/sleep/zsz007 · Cited by 139 · see citing works
Azoulay, L. (2022) · The Oncologist · doi:10.1093/oncolo/oyac114 · Cited by 79 · see citing works
Iron status predicts malaria risk in Malawian preschool children
Jonker, F. A. M., Calis, J. C. J., van Hensbroek, M. B., Phiri, K., Geskus, R. B., Brabin, B. J., & Leenstra, T. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042670 · Cited by 77 · see citing works
Caunca, M. R., Odden, M. C., Glymour, M. M., Elfassy, T., Kershaw, K. N., Sidney, S., Yaffe, K., Launer, L., & Zeki Al Hazzouri, A. (2020) · JAMA Neurology · doi:10.1001/jamaneurol.2020.0860 · Cited by 73 · see citing works
Imputation approaches for potential outcomes in causal inference
Westreich, D., Edwards, J. K., Cole, S. R., Platt, R. W., Mumford, S. L., & Schisterman, E. F. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv135 · Cited by 64 · see citing works
Self-citationPrenatal Tdap immunization and risk of maternal and newborn adverse events.
Layton, J. B., Butler, A. M., Li, D., Boggess, K. A., Weber, D. J., McGrath, L. J., & Becker-Dreps, S. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.06.071 · Cited by 60 · see citing works
Elsaid, M. I., Li, Y., Bridges, J. F. P., Brock, G., Minacapelli, C. D., & Rustgi, V. K. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.35003 · Cited by 63 · see citing works
Murray, E. J., & Hernán, M. A. (2016) · Clinical Trials · doi:10.1177/1740774516634335 · Cited by 57 · see citing works
Self-citationEmulating a target trial of statin use and risk of dementia using cohort data
Caniglia, E. C., Rojas-Saunero, L. P., Hilal, S., Licher, S., Logan, R., Stricker, B., Ikram, M. A., & Swanson, S. A. (2020) · Neurology · doi:10.1212/wnl.0000000000010433 · Cited by 56 · see citing works
Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips
Shinozaki, T., & Suzuki, E. (2020) · Journal of Epidemiology · doi:10.2188/jea.je20200226 · Cited by 44 · see citing works
Neurodevelopmental outcomes in children in relation to placental abruption
Ananth, C., Friedman, A., Lavery, J., VanderWeele, T., Keim, S., & Williams, M. (2017) · BJOG: An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.14049 · Cited by 30 · see citing works
Mehta, S. D., Moses, S., Agot, K., Maclean, I., Odoyo-June, E., Li, H., & Bailey, R. C. (2013) · Journal of Infectious Diseases · doi:10.1093/infdis/jit371 · Cited by 26 · see citing works
Effects of prenatal bacterial infection on cognitive performance in early childhood
Lee, Y. H., Papandonatos, G. D., Savitz, D. A., Heindel, W. C., & Buka, S. L. (2020) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.12603 · Cited by 21 · see citing works
Investigating the Impact of Selection Bias in Dose-Response Analyses of Preventive Interventions
Conduct Problems Prevention Research Group*, McGowan, H. M., Nix, R. L., Murphy, S. A., & Bierman, K. L. (2010) · Prevention Science · doi:10.1007/s11121-010-0169-2 · Cited by 19 · see citing works
ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations
Dobler, D., Binder, H., Boulesteix, A., Igelmann, J., Köhler, D., Mansmann, U., Pauly, M., Scherag, A., Schmid, M., Al Tawil, A., et al. (2025) · Statistics in Medicine · doi:10.1002/sim.70263 · Cited by 20 · see citing works
Effectiveness of Empagliflozin vs Dapagliflozin for Kidney Outcomes in Type 2 Diabetes
Bonnesen, K., Heide-Jørgensen, U., Christensen, D. H., Christiansen, C. F., Lash, T. L., Hennessy, S., Matthews, A. A., Pedersen, L., Thomsen, R. W., & Schmidt, M. (2025) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2024.7381 · Cited by 17 · see citing works
Mahmoud, O., Dudbridge, F., Smith, G. D., Munafo, M., & Tilling, K. (2020) · doi:10.1101/2020.01.31.928077 · Cited by 15 · see citing works
- Authors
- Cole, S. R., & Hernan, M. A.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Cole, S. R.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-07-15
- Volume
- 168
- Issue
- 6
- Pages
- 656-664
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC2732954
- OpenAlex
- W2142495742
- Semantic Scholar
- ddd660f2a83ec98ebb7f0b96499822b8ff35d24b
- Cited by
- 2,796
- References
- 40
- Citations by year
- 2026: 285 · 2025: 236 · 2024: 211 · 2023: 217 · 2022: 254 · 2021: 240 · 2020: 227 · 2019: 208 · 2018: 153 · 2017: 150 · 2016: 133 · 2015: 126 · 2014: 92 · 2013: 83 · 2012: 66
- FWCI
- 34.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute
- National Institute of Allergy and Infectious Diseases (R03AI071763, U01 AI42590, U01-AI-31834, UO1-AI 35043, UO1 AI37613, U01AI034989, UO1-AI35040, U01AI031834, UO1‐AI‐35041, AI-34989, U01AI035040, U01AI042590, U01-AI-34993, U01AI037984, U01AI34994, U01AI034993, UO1-AI35039, U01AI035041, U01AI035043, U01AI034994, UO1 AI37984, U01AI037613, U01AI035039, UO1-AI35042, U-01-AI-35004, R01AI073127, U01AI035004, U01AI035042)
- National Center for Research Resources (5MO1 RR 00722, M01RR000722)
- NIH Clinical Center
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U01- HD-32632, U01HD032632)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Medicine › Infectious Diseases › HIV/AIDS Research and Interventions
- Keywords
- inverse probability weighting, marginal structural models, time-varying confounding, selection bias, exchangeability, positivity assumption, antiretroviral therapy, HIV viral load, causal inference, sensitivity analysis
- MeSH terms
- Acquired Immunodeficiency Syndrome, Female, Humans, Male, Probability, Multicenter Studies as Topic, HIV-1, Bias, Confounding Factors, Epidemiologic, Logistic Models, Antiretroviral Therapy, Highly Active
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
