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Träfflista för sökning "WFRF:(Waernbaum Ingeborg) srt2:(2005-2009)"

Sökning: WFRF:(Waernbaum Ingeborg) > (2005-2009)

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1.
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2.
  • Petrauskiene, V, et al. (författare)
  • The risk of venous thromboembolism is markedly elevated in patients with diabetes
  • 2005
  • Ingår i: Diabetologia. ; 48, s. 1017–1021-
  • Tidskriftsartikel (refereegranskat)abstract
    • OBJECTIVE: To survey unnatural deaths among teenagers in northern Sweden and to suggest preventive measures. SETTING: The four northernmost counties (908,000 inhabitants, 1991), forming 55% of the area of Sweden. MATERIAL AND METHODS: All unnatural teenager deaths from 1981 through 2000 were identified in the databases of the Department of Forensic Medicine in Umea, National Board of Forensic Medicine. Police reports and autopsy findings were always studied, social and hospital records if present. RESULTS: Three hundred and fifty-five deaths were found, of which 267 (75%) were males and 88 (25%) females. Ninety out of 327 (28%) tested positive for alcohol. Two hundred and forty-eight (70%) were unintentional and 102 (30%) were intentional deaths, and five (1%) were categorized as undetermined manner of death. Unintentional deaths decreased while the incidence of intentional deaths remained unaffected by time. CONCLUSIONS: Injury-reducing measures have been effective concerning unintentional deaths and the fall in young licensed drivers due to the economical recess have probably also contributed to the decrease. However, there were no signs of decreasing numbers of suicides during the study period, which calls for resources to be allocated to suicide prevention.
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3.
  • Waernbaum, Ingeborg, 1972- (författare)
  • Covariate selection and propensity score specification in causal inference
  • 2008
  • Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
    • This thesis makes contributions to the statistical research field of causal inference in observational studies. The results obtained are directly applicable in many scientific fields where effects of treatments are investigated and yet controlled experiments are difficult or impossible to implement. In the first paper we define a partially specified directed acyclic graph (DAG) describing the independence structure of the variables under study. Using the DAG we show that given that unconfoundedness holds we can use the observed data to select minimal sets of covariates to control for. General covariate selection algorithms are proposed to target the defined minimal subsets. The results of the first paper are generalized in Paper II to include the presence of unobserved covariates. Morevoer, the identification assumptions from the first paper are relaxed. To implement the covariate selection without parametric assumptions we propose in the third paper the use of a model-free variable selection method from the framework of sufficient dimension reduction. By simulation the performance of the proposed selection methods are investigated. Additionally, we study finite sample properties of treatment effect estimators based on the selected covariate sets. In paper IV we investigate misspecifications of parametric models of a scalar summary of the covariates, the propensity score. Motivated by common model specification strategies we describe misspecifications of parametric models for which unbiased estimators of the treatment effect are available. Consequences of the misspecification for the efficiency of treatment effect estimators are also studied.
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