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- Andersson, Björn, 1984-, et al.
(author)
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Sensitivity analysis of violations of the faithfulness assumption
- 2014
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In: Journal of Statistical Computation and Simulation. - : Taylor & Francis Group. - 0094-9655 .- 1563-5163. ; 84:7, s. 1608-1620
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Journal article (other academic/artistic)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, et al.
(author)
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Impact of Parental Socioeconomic Status on Excess Mortality in a Population-Based Cohort of Subjects With Childhood-Onset Type 1 Diabetes
- 2015
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In: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 38:5, s. 827-832
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Journal article (peer-reviewed)abstract
- OBJECTIVE: 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 patients with childhood-onset type 1 diabetes.RESEARCH DESIGN AND METHODS: Subjects recorded in the Swedish Childhood Diabetes Registry (SCDR) from 1 January 1978 to 31 December 2008 were included (n =14,647). The SCDR was linked to the Swedish Cause of Death Registry (CDR) and the Longitudinal Integration Database for Health Insurance and Labour Market Studies (LISA).RESULTS: At a mean follow-up of 23.9 years (maximum 46.5 years), 238 deaths occurred in a total of 349,762 person-years at risk. In crude analyses, low maternal education predicted mortality for male patients only (P = 0.046), whereas parental income support predicted mortality in both sexes (P < 0.001 for both). In Cox models stratified by age-at-death group 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: Exposure to low SES, mirrored by the need for income support, increases mortality risk in patients with childhood-onset type 1 diabetes who died after the age of 18 years.
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| 3. |
- Berhan, Yonas, 1970-, et al.
(author)
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Impact of parental socioeconomic status on excess mortality in subjects with childhood onset type-1 diabetes
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Other publication (other academic/artistic)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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| 4. |
- Berhan, Yonas, 1970-, et al.
(author)
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Impact of parental socioeconomic status on excess mortality in subjects with childhood onset type-1 diabetes
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Other publication (other academic/artistic)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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| 5. |
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| 6. |
- Ciocanea-Teodorescu, Iuliana, et al.
(author)
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Causal inference in survival analysis under deterministic missingness of confounders in register data
- 2023
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In: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 42:12, s. 1946-1964
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Journal article (peer-reviewed)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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| 9. |
- de Luna, Xavier, et al.
(author)
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Covariate selection for the non-parametric estimation of an average treatment effect
- 2011
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In: Biometrika. - : Oxford University Press. - 0006-3444 .- 1464-3510. ; 98:4, s. 861-875
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Journal article (peer-reviewed)abstract
- Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.
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