Article thumbnail

Benefits of ICU admission in critically ill patients: Whether instrumental variable methods or propensity scores should be used

By Romain Pirracchio, Charles Sprung, Didier Payen and Sylvie Chevret
Topics: Research Article
Publisher: BioMed Central
OAI identifier:
Provided by: PubMed Central

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles


  1. (1996). A critical appraisal of propensity-score matching in the medical literature between
  2. (1994). A: Rationing critical care – what happens to patients who are not admitted? Theor Surg
  3. (1996). Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology
  4. (1994). Angrist JD: Identification and estimation of local average treatment effects. Econometrica
  5. (1974). Assessment of coma and impaired consciousness. A practical scale. Lancet
  6. (1997). Bruining H: Organization of intensive care units in Europe: lessons from the EPIC study. Intensive Care Med
  7. (2004). De Boer A: Methods to assess intended effects of drug treatment in observational studies are reviewed.
  8. (2003). Econometric Analysis. 5 edition. Upper Saddle River,
  9. (1998). Econometrics in outcomes research: the use of instrumental variables. Annu Rev Public Health
  10. (2000). Elbourne DR: Randomized trials or observational tribulations?
  11. (2001). Estimations of limited dependent variable models with dummy endogenous regressors: simple strategies for empirical pratice.
  12. (2001). et al: Compliance with triage to intensive care recommendations. Crit Care Med
  13. (1993). F: A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. Jama
  14. (2008). Grootendorst P: Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research. Stat Med
  15. (2000). Horwitz RI: Randomized, controlled trials, observational studies, and the hierarchy of research designs.
  16. (1996). Identification of causal effects using instrumental variables.
  17. (1997). Instrumental variable estimation of count data models: application to models of cigarette smoking behaviour. Review of Economics and Statistics
  18. (1997). Instrumental variables for logistic regression: an illustration. Soc Sci Res
  19. (2009). Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships.
  20. (2009). Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance.
  21. (1998). Intensive care training and specialty status in Europe: international comparisons. Task Force on Educational issues of the European Society of Intensive Care Medicine. Intensive Care Med
  22. (2001). JC: Effectiveness of chemotherapy for advanced lung cancer in the elderly: instrumental variable and propensity analysis.
  23. (1994). JP: Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. Jama
  24. (1984). Karnofsky performance status revisited: reliability, validity, and guidelines.
  25. (2006). Klungel OH: Instrumental variables: application and limitations. Epidemiology
  26. (2009). MA: Instrumental variable analysis for estimation of treatment effects with dichotomous outcomes.
  27. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology
  28. (1995). Measuring effects without randomized trials? Options, problems, challenges. Med Care
  29. (2007). Mendelian randomization as an instrumental variable approach to causal inference.
  30. (2007). MJ: Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. Jama
  31. (2004). Model selection, confounder control, and marginal structural models: review and new applications. The American Statistician
  32. (1997). Mortality among appropriately referred patients refused admission to intensive-care units. Lancet
  33. (2002). Odds ratio, relative risk, absolute risk reduction, and the number needed to treat–which of these should we use? Value Health
  34. (2007). Preference-based instrumental variable methods for the estimation of treatment effects: assessing validity and interpreting results.
  35. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak.
  36. Propensity scores in intensive care and anaesthesiology literature: a systematic review. Intensive Care Med .
  37. (2010). Reasons for refusal of admission to intensive care and impact on mortality. Intensive Care Med
  38. (2006). Robins JM: Instruments for causal inference: an epidemiologist’s dream? Epidemiology
  39. (2006). Schneeweiss S: Instrumental variable analysis of secondary pharmacoepidemiologic data. Epidemiology
  40. (2010). Schneeweiss S: Instrumental variable methods in comparative safety and effectiveness research. Pharmacoepidemiol Drug Saf
  41. (2006). Sepsis in European intensive care units: results of the SOAP study. Crit Care Med
  42. (1997). Silva JMCS: Endogeneity in count data models:an application to demand for health care.
  43. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika
  44. (2007). The performance of different propensity score methods for estimating marginal odds ratios. Stat Med
  45. (2010). The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Stat Med
  46. (2009). The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med Decis Making
  47. (1980). The Theory and Pratice of Econometrics.
  48. (1998). The unpredictability paradox: review of empirical comparisons of randomised and non-randomised clinical trials. Bmj
  49. (1996). Thijs LG: The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med
  50. Tibshirani R: An Introduction to the Bootstrap. Boca
  51. (2003). Triaging patients to the ICU: a pilot study of factors influencing admission decisions and patient outcomes. Intensive Care Med