| 1. |
- Van den Bulcke, Bo, et al.
(författare)
-
Ethical climate and intention to leave among critical care clinicians: an observational study in 68 intensive care units across Europe and the United States.
- 2020
-
Ingår i: Intensive care medicine. - 1432-1238. ; 46:1, s. 46-56
-
Tidskriftsartikel (refereegranskat)abstract
- Apart from organizational issues, quality of inter-professional collaboration during ethical decision-making may affect the intention to leave one's job. To determine whether ethical climate is associated with the intention to leave after adjustment for country, ICU and clinicians characteristics.Perceptions of the ethical climate among clinicians working in 68 adult ICUs in 12 European countries and the US were measured using a self-assessment questionnaire, together with job characteristics and intent to leave as a sub-analysis of the Dispropricus study. The validated ethical decision-making climate questionnaire included seven factors: not avoiding decision-making at end-of-life (EOL), mutual respect within the interdisciplinary team, open interdisciplinary reflection, ethical awareness, self-reflective physician leadership, active decision-making at end-of-life by physicians, and involvement of nurses in EOL. Hierarchical mixed effect models were used to assess associations between these factors, and the intent to leave in clinicians within ICUs, within the different countries.Of 3610 nurses and 1137 physicians providing ICU bedside care, 63.1% and 62.9% participated, respectively. Of 2992 participating clinicians, 782 (26.1%) had intent to leave, of which 27% nurses, 24% junior and 22.7% senior physicians. After adjustment for country, ICU and clinicians characteristics, mutual respect OR 0.77 (95% CI 0.66- 0.90), open interdisciplinary reflection (OR 0.73 [95% CI 0.62-0.86]) and not avoiding EOL decisions (OR 0.87 [95% CI 0.77-0.98]) were all associated with a lower intent to leave.This is the first large multicenter study showing an independent association between clinicians' intent to leave and the quality of the ethical climate in the ICU. Interventions to reduce intent to leave may be most effective when they focus on improving mutual respect, interdisciplinary reflection and active decision-making at EOL.
|
|
| 2. |
- Druwé, Patrick, et al.
(författare)
-
Perception of inappropriate cardiopulmonary resuscitation by clinicians working in emergency departments and ambulance services : The REAPPROPRIATE international, multi-centre, cross sectional survey
- 2018
-
Ingår i: Resuscitation. - : Elsevier. - 0300-9572 .- 1873-1570. ; 132, s. 112-119
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction: Cardiopulmonary resuscitation (CPR) is often started irrespective of comorbidity or cause of arrest. We aimed to determine the prevalence of perception of inappropriate CPR of the last cardiac arrest encountered by clinicians working in emergency departments and out-of-hospital, factors associated with perception, and its relation to patient outcome.Methods: A cross-sectional survey was conducted in 288 centres in 24 countries. Factors associated with perception of CPR and outcome were analyzed by Cochran-Mantel-Haenszel tests and conditional logistic models.Results: Of the 4018 participating clinicians, 3150 (78.4%) perceived their last CPR attempt as appropriate, 548 (13.6%) were uncertain about its appropriateness and 320 (8.0%) perceived inappropriateness; survival to hospital discharge was 370/2412 (15.3%), 8/481 (1.7%) and 8/294 (2.7%) respectively. After adjusting for country, team and clinician's characteristics, the prevalence of perception of inappropriate CPR was higher for a non-shockable initial rhythm (OR 3.76 [2.13-6.64]; P < .0001), a non-witnessed arrest (2.68 [1.89-3.79]; P < .0001), in older patients (2.94 [2.18-3.96]; P < .0001, for patients > 79 years) and in case of a "poor" first physical impression of the patient (3.45 [2.36-5.05]; P < .0001). In accordance, non-shockable and non-witnessed arrests were both associated with lower survival to hospital discharge (0.33 [0.26 - 0.41]; P < 0.0001 and 0.25 [0.15 - 0.41]; P < 0.0001, respectively), as were older patient age (0.25 [0.14 - 0.44]; P < 0.0001 for patients > 79 years) and a "poor" first physical impression (0.26 [0.19-0.35]; P < 0.0001).Conclusions: The perception of inappropriate CPR increased when objective indicators of poor prognosis were present and was associated with a low survival to hospital discharge. Factoring clinical judgment into the decision to (not) attempt CPR may reduce harm inflicted by excessive resuscitation attempts.
|
|
| 3. |
- Gabriel, Erin E., et al.
(författare)
-
Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
- 2024
-
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:3, s. 534-547
-
Tidskriftsartikel (refereegranskat)abstract
- There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains ‘unbalanced’ even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.
|
|
| 4. |
- Gabriel, Erin E., et al.
(författare)
-
Propensity weighting plus adjustment in proportional hazards model is not doubly robust
- 2024
-
Ingår i: Biometrics. - : Oxford University Press. - 0006-341X .- 1541-0420. ; 80:3
-
Tidskriftsartikel (refereegranskat)abstract
- Recently, it has become common for applied works to combine commonly used survival analysis modelingmethods, such as the multivariable Cox model and propensity score weighting, with the intention of forming a doublyrobust estimator of an exposure effect hazard ratio that is unbiased in large samples when either the Cox model orthe propensity score model is correctly specified. This combination does not, in general, produce a doubly robustestimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulationthis lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and asimple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. Weprovide a novel proof that the combination of propensity score weighting and a proportional hazards survival model,fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcomeunder particular censoring mechanisms if either the propensity score or the outcome model is correctly specified andcontains all confounders. Given our results suggesting that double robustness only exists under the null, we outlinetwo simple alternative estimators that are doubly robust for the survival difference at a given time point (in the abovesense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimationfor the full survival curve. We provide R code to use these estimators for estimation and inference in the supporting information.
|
|
| 5. |
- Persson, Emma, 1981-
(författare)
-
Causal inference and case-control studies with applications related to childhood diabetes
- 2014
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored.An important part of estimating causal effects in an observational study is to control for covariates. The first paper of this thesis concerns the selection of minimal covariate sets sufficient for unconfoundedness of the treatment assignment. A data-driven implementation of two covariate selection algorithms is proposed and evaluated.A common sampling scheme in epidemiology, and when investigating rare events, is the case-control design. In the second paper we study estimators of the marginal causal odds ratio in matched and independent case-control designs. Estimators that, under a logistic regression model, utilize information about the known prevalence of being a case is examined and compared through simulations.The third paper investigates the particular situation where case-control sampled data is reused to estimate the effect of the case-defining event on an outcome of interest. The consequence of ignoring the design when estimating the average causal effect is discussed and a design-weighted matching estimator is proposed. The performance of the estimator is evaluated with simulation experiments, when matching on the covariates directly and when matching on the propensity score.The last paper studies the effect of type 1 diabetes mellitus (T1DM) on school achievements using data from the Swedish Childhood Diabetes Register, a population-based incidence register. We apply theoretical results from the second and third papers in the estimation of the average causal effect within the T1DM population. A matching estimator that accounts for the matched case-control design is used.
|
|
| 6. |
- Sjolander, Arvid, et al.
(författare)
-
Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease
- 2009
-
Ingår i: Biometrics. - : Wiley. - 0006-341X .- 1541-0420. ; 65:2, s. 514-520
-
Tidskriftsartikel (refereegranskat)abstract
- In many studies, the aim is to learn about the direct exposure effect, that is, the effect not mediated through an intermediate variable. For example, in circulation disease studies it may be of interest to assess whether a suitable level of physical activity can prevent disease, even if it fails to prevent obesity. It is well known that stratification on the intermediate may introduce a so-called posttreatment selection bias. To handle this problem, we use the framework of principal stratification (Frangakis and Rubin, 2002, Biometrics 58, 21-29) to define a causally relevant estimand-the principal stratum direct effect (PSDE). The PSDE is not identified in our setting. We propose a method of sensitivity analysis that yields a range of plausible values for the causal estimand. We compare our work to similar methods proposed in the literature for handling the related problem of ""truncation by death."".
|
|
| 7. |
- Varewyck, Machteld, et al.
(författare)
-
On shrinkage and model extrapolation in the evaluation of clinical center performance
- 2014
-
Ingår i: Biostatistics. - : Oxford University Press. - 1465-4644 .- 1468-4357. ; 15:4, s. 651-664
-
Tidskriftsartikel (refereegranskat)abstract
- We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.
|
|
