| 1. |
- Andersson, Björn, 1984-, et al.
(författare)
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Sensitivity analysis of violations of the faithfulness assumption
- 2014
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Ingår i: Journal of Statistical Computation and Simulation. - : Taylor & Francis Group. - 0094-9655 .- 1563-5163. ; 84:7, s. 1608-1620
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We study implications of violations of the fatihfulness condition due to parameter cancellations on estimation of the DAG skeleton. Three settings are investigated: when i) faithfulness is guaranteed ii) faithfulness is not guaranteed and iii) the parameter distributions are concentrated around unfaithfulness (near-unfaithfulness). In a simulation study the effetcs of the different settings are compared using the PC and MMPC algorithms. The results show that the performance in the faithful case is almost unchanged compared to the unrestricted case whereas there is a general decrease in performance under the near-unfaithful case as compared to the unrestricted case. The response to near-unfaithful parameterisations is similar between two algorithms, with the MMPC algorithm having higher true positive rates and the PC algorithm having lower false positive rates.
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| 2. |
- Berhan, Yonas, 1970-, et al.
(författare)
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Impact of parental socioeconomic status on excess mortality in subjects with childhood onset type-1 diabetes
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Aims/Hypothesis: The aim of this study was to analyze the possible impact of parental and individual socioeconomic status (SES) on all cause mortality in a population based cohort of childhood onset T1D.Methods: Subjects recorded in the Swedish Childhood Diabetes Registry (SCDR) January 1 1978 to December 31 2008 were included (n=14 409). The SCDR was linked to the Swedish Cause of Death Register (CDR) and the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA). SES measures (education and income support) wtypeere retrieved from the LISA for the years 1990-2010. Mortality data were retrieved from the CDR as of December 31, 2010.Results: At a mean follow-up of 24.4 years (maximum 47.5), 238 deaths occurred in a total of 357 048 person-years at risk. In crude analyses, low maternal education predicted mortality for male cases only (p=0.046), while parental income support predicted mortality in both sexes (p<0.001 for both). In Cox models stratified by age at death groups and adjusted for age at onset and sex, parental income support predicted mortality among young adults ( ≥18 years of age) but not for children. Including the adult patient´s own SES in a Cox model showed that individual income support to the patient predicted mortality occurring at ≥ 24 years of age when adjusting for age at onset, sex and parental SES.Conclusions/Interpretation: Low parental SES, mirrored by the need of income support, increases mortality risk in childhood onset type-1 diabetics who died after the age of 18 years.
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| 3. |
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| 4. |
- Ciocanea-Teodorescu, Iuliana, et al.
(författare)
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Causal inference in survival analysis under deterministic missingness of confounders in register data
- 2023
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 42:12, s. 1946-1964
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Tidskriftsartikel (refereegranskat)abstract
- Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.
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| 5. |
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| 6. |
- Eikenboom, Anna M, et al.
(författare)
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Quality of Conduct and Reporting of Propensity Score Methods in Studies Investigating the Effectiveness of Antimicrobial Therapy
- 2022
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Ingår i: Open Forum Infectious Diseases. - : Oxford University Press. - 2328-8957. ; 9:4
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Tidskriftsartikel (refereegranskat)abstract
- Background: Propensity score methods are becoming increasingly popular in infectious disease medicine to correct for confounding in observational studies. However, applying and reporting propensity score techniques correctly requires substantial knowledge of these methods. The quality of conduct and reporting of propensity score methods in studies investigating the effectiveness of antimicrobial therapy is yet undetermined.Methods: A systematic review was performed to provide an overview of studies (2005-2020) on the effectiveness of antimicrobial therapy that used propensity score methods. A quality assessment tool and a standardized quality score were developed to evaluate a subset of studies in which antibacterial therapy was investigated in detail. The scale of this standardized score ranges between 0 (lowest quality) and 100 (excellent).Results: A total of 437 studies were included. The absolute number of studies that investigated the effectiveness of antimicrobial therapy and that used propensity score methods increased 15-fold between the periods 2005-2009 and 2015-2019. Propensity score matching was the most frequently applied technique (65%), followed by propensity score-adjusted multivariable regression (25%). A subset of 108 studies was evaluated in detail. The median standardized quality score per year ranged between 53 and 61 (overall range: 33-88) and remained constant over the years.Conclusions: The quality of conduct and reporting of propensity score methods in research on the effectiveness of antimicrobial therapy needs substantial improvement. The quality assessment instrument that was developed in this study may serve to help investigators improve the conduct and reporting of propensity score methods.
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| 7. |
- Gabriel, Erin E., et al.
(författare)
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Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation
- 2024
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Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:3, s. 534-547
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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.
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| 8. |
- Goetghebeur, Els, et al.
(författare)
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Formulating causal questions and principled statistical answers
- 2020
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Ingår i: Statistics in Medicine. - : WILEY. - 0277-6715 .- 1097-0258. ; 39:30, s. 4922-4948
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Tidskriftsartikel (refereegranskat)abstract
- Although review papers on causal inference methods are now available, there is a lack of introductory overviews onwhatthey can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on , where SAS and Stata code for analysis is also provided.
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| 9. |
- Gorbach, Tetiana, 1991-, et al.
(författare)
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Contrasting Identifying Assumptions of Average Causal Effects : Robustness and Semiparametric Efficiency
- 2023
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Ingår i: Journal of machine learning research. - : Microtome Publishing. - 1532-4435 .- 1533-7928. ; 24:197, s. 1-65
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Tidskriftsartikel (refereegranskat)abstract
- Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying assumptions may be plausible; an analyst has to make a delicate choice between these models. In this paper, we study three identifying assumptions based on the potential outcome framework: the back-door assumption, which uses pre-treatment covariates, the front-door assumption, which uses mediators, and the two-door assumption using pre-treatment covariates and mediators simultaneously. We provide the efficient influence functions and the corresponding semiparametric efficiency bounds that hold under these assumptions, and their combinations. We demonstrate that neither of the identification models provides uniformly the most efficient estimation and give conditions under which some bounds are lower than others. We show when semiparametric estimating equation estimators based on influence functions attain the bounds, and study the robustness of the estimators to misspecification of the nuisance models. The theory is complemented with simulation experiments on the finite sample behavior of the estimators. The results obtained are relevant for an analyst facing a choice between several plausible identifying assumptions and corresponding estimators. Our results show that this choice implies a trade-off between efficiency and robustness to misspecification of the nuisance models.
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| 10. |
- Hedström, Erik, et al.
(författare)
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Incidence of fractures among children and adolescents in rural and urban communities : analysis based on 9,965 fracture events
- 2014
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Ingår i: Injury Epidemiology. - : Springer Science and Business Media LLC. - 2197-1714. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Previous work has explored the significance of residence on injuries. A number of articles reported higher rates of injury in rural as compared to urban settings. This study aimed to evaluate the importance of residency on the occurrence of fractures among children and adolescents within a region in northern Sweden.Methods: In a population based study with data from an injury surveillance registry at a regional hospital, we have investigated the importance of sex, age and place of residency for the incidence of fractures among children and adolescents 0-19 years of age using a Poisson logistic regression analysis. Data was collected between 1998 and 2011.Results: The dataset included 9,965 cases. Children and adolescents growing up in the most rural communities appeared to sustain fewer fractures than their peers in an urban municipality, risk ratio 0.81 (0.76-0.86). Further comparisons of fracture rates in the urban and rural municipalities revealed that differences were most pronounced for sports related fractures and activities in school in the second decade of life.Conclusion: Results indicate that fracture incidence among children and adolescents is affected by place of residency. Differences were associated with activity at injury and therefore we have discussed the possibility that this effect was due to the influence of place on activity patterns.The results suggest it is of interest to explore how geographic and demographic variables affect the injury pattern further.
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