Background: Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a... Show moreBackground: Instrumental variable (IV) analysis holds the potential to estimate treatment effects from observational data. IV analysis potentially circumvents unmeasured confounding but makes a number of assumptions, such as that the IV shares no common cause with the outcome. When using treatment preference as an instrument, a common cause, such as a preference regarding related treatments, may exist. We aimed to explore the validity and precision of a variant of IV analysis where we additionally adjust for the provider: adjusted IV analysis. Methods: A treatment effect on an ordinal outcome was simulated (beta - 0.5 in logistic regression) for 15.000 patients, based on a large data set (the IMPACT data, n = 8799) using different scenarios including measured and unmeasured confounders, and a common cause of IV and outcome. We compared estimated treatment effects with patient-level adjustment for confounders, IV with treatment preference as the instrument, and adjusted IV, with hospital added as a fixed effect in the regression models. Results: The use of patient-level adjustment resulted in biased estimates for all the analyses that included unmeasured confounders, IV analysis was less confounded, but also less reliable. With correlation between treatment preference and hospital characteristics (a common cause) estimates were skewed for regular IV analysis, but not for adjusted IV analysis. Conclusion: When using IV analysis for comparing hospitals, some limitations of regular IV analysis can be overcome by adjusting for a common cause. Show less
Amini, M.; Leeuwen, N. van; Eijkenaar, F.; Graaf, R. van de; Samuels, N.; Oostenbrugge, R. van; ... ; MR CLEAN Registry Investigators 2022
Introduction: Various statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study... Show moreIntroduction: Various statistical approaches can be used to deal with unmeasured confounding when estimating treatment effects in observational studies, each with its own pros and cons. This study aimed to compare treatment effects as estimated by different statistical approaches for two interventions in observational stroke care data. Patients and methods: We used prospectively collected data from the MR CLEAN registry including all patients (n = 3279) with ischemic stroke who underwent endovascular treatment (EVT) from 2014 to 2017 in 17 Dutch hospitals. Treatment effects of two interventions - i.e., receiving an intravenous thrombolytic (IVT) and undergoing general anesthesia (GA) before EVT- on good functional outcome (modified Rankin Scale <= 2) were estimated. We used three statistical regression-based approaches that vary in assumptions regarding the source of unmeasured confounding: individual-level (two subtypes), ecological, and instrumental variable analyses. In the latter, the preference for using the interventions in each hospital was used as an instrument. Results: Use of IVT (range 66-87%) and GA (range 0-93%) varied substantially between hospitals. For IVT, the individual-level (OR similar to 1.33) resulted in significant positive effect estimates whereas in instrumental variable analysis no significant treatment effect was found (OR 1.11; 95% CI 0.58-1.56). The ecological analysis indicated no statistically significant different likelihood (beta = - 0.002%; P=0.99) of good functional outcome at hospitals using IVT 1% more frequently. For GA, we found non-significant opposite directions of points estimates the treatment effect in the individual-level (ORs similar to 0.60) versus the instrumental variable approach (OR =1.04).The ecological analysis also resulted in a non-significant negative association (0.03% lower probability). Discussion and conclusion: Both magnitude and direction of the estimated treatment effects for both interventions depend strongly on the statistical approach and thus on the source of (unmeasured) confounding.These issues should be understood concerning the specific characteristics of data, before applying an approach and interpreting the results. Instrumental variable analysis might be considered when unobserved confounding and practice variation is expected in observational multicenter studies. Show less
Glas, N.A. de; Kiderlen, M.; Craen, A.J.M. de; Hamaker, M.E.; Portielje, J.E.A.; Velde, C.J.H. van de; ... ; Bastiaannet, E. 2015
Transfusion-related acute lung injury (TRALI) is the most common serious side effect of blood transfusion. TRALI could be caused by donor leukocyte antibodies, present primarily in female and... Show moreTransfusion-related acute lung injury (TRALI) is the most common serious side effect of blood transfusion. TRALI could be caused by donor leukocyte antibodies, present primarily in female and transfused donors (Chapters 1 and 2). In The Netherlands this led to the exclusion of female and transfused donors from the donation of plasma for transfusion from 1st October 2006. In this thesis we aimed to quantitatively estimate the expected effect of the implementation of this measure. Chapters 5 through 7 suggest nearly all TRALI caused by plasma rich products to be preventable by the deferral of female or allo-exposed donors, while there is no such effect on TRALI caused by plasma poor products. Further, we evaluate the effectiveness of the plasma measure and other potential donor deferral strategies at keeping leukocyte antibodies out of the blood supply (Chapter 8), address several common methodological problems in research of side effects of blood transfusions (Chapters 3 and 4) and discuss all our findings and some more general issues concerning the use of the population attributable risk, as opposed to the relative risk, and correction of the population attributable risk for confounding (Chapter 9). Show less
