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  • 1.
    Broström, Göran
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Att läsa i Umeå är ett bra val2005In: Västerbottens-Kuriren, no 9 december, p. 4-Article in journal (Other (popular science, discussion, etc.))
  • 2.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Sensitivity analysis of the unconfoundedess assumption in observational studies2009Report (Other academic)
  • 3.
    Degerman, Sofie
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Domellöf, Magdalena
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Linder, Jan
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Haraldsson, Susann
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Elgh, Eva
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Long Leukocyte Telomere Length at Diagnosis Is a Risk Factor for Dementia Progression in Idiopathic Parkinsonism2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 12, article id e113387Article in journal (Refereed)
    Abstract [en]

    Telomere length (TL) is regarded as a marker of cellular aging due to the gradual shortening by each cell division, but is influenced by a number of factors including oxidative stress and inflammation. Parkinson's disease and atypical forms of parkinsonism occur mainly in the elderly, with oxidative stress and inflammation in afflicted cells. In this study the relationship between blood TL and prognosis of 168 patients with idiopathic parkinsonism (136 Parkinson's disease [PD], 17 Progressive Supranuclear Palsy [PSP], and 15 Multiple System Atrophy [MSA]) and 30 controls was investigated. TL and motor and cognitive performance were assessed at baseline (diagnosis) and repeatedly up to three to five years follow up. No difference in TL between controls and patients was shown at baseline, nor any significant difference in TL stability or attrition during follow up. Interestingly, a significant relationship between TL at diagnosis and cognitive phenotype at follow up in PD and PSP patients was found, with longer mean TL at diagnosis in patients that developed dementia within three years.

  • 4.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    On statistical methods for labor market evaluation under interference between units2016Report (Refereed)
    Abstract [en]

    Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be satised. One requirement is that the outcomes of individuals are not affected by the treatment given to other individuals, i.e., that the no-interference assumption is satisfied. This assumption might, in many situations, not be plausible. However, recent progress in the research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field of policy evaluation and if there are some measures to be taken when planning an evaluation study in order to be able to use a particular method. In addition, we give examples on how interference has been dealt within some evaluation applications including, but not limited to, labor market evaluations, in the recent past.

  • 5.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundquist, Anders
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Student active learning online and in the classroom by combining the best of Flipped Classroom and MOOCs when teaching statistics2017In: Royal Statistical Society 2017 International Conference, Glasgow, 4-7 september 2017, 2017Conference paper (Other academic)
  • 6.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundquist, Anders
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Student active learning online and in the classroom by combining the best ofFlipped Classroom and MOOCs2017In: Universitetspedagogiska konferensen 2017.: Undervisning i praktiken – föreläsning, flexibelt eller mitt emellan?, 2017, p. 37-37Conference paper (Other academic)
  • 7.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Effects of college choice on income: estimation and sensitivity analysis2006Licentiate thesis, monograph (Other academic)
  • 8.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Profile likelihood for semiparametric regressionManuscript (preprint) (Other academic)
  • 9.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Sensitivity Analysis of Untestable Assumptions in Causal Inference2011Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis contributes to the research field of causal inference, where the effect of a treatment on an outcome is of interest is concerned. Many such effects cannot be estimated through randomised experiments. For example, the effect of higher education on future income needs to be estimated using observational data. In the estimation, assumptions are made to make individuals that get higher education comparable with those not getting higher education, to make the effect estimable. Another assumption often made in causal inference (both in randomised an nonrandomised studies) is that the treatment received by one individual has no effect on the outcome of others. If this assumption is not met, the meaning of the causal effect of the treatment may be unclear.

    In the first paper the effect of college choice on income is investigated using Swedish register data, by comparing graduates from old and new Swedish universities. A semiparametric method of estimation is used, thereby relaxing functional assumptions for the data.

    One assumption often made in causal inference in observational studies is that individuals in different treatment groups are comparable, given that a set of pretreatment variables have been adjusted for in the analysis. This so called unconfoundedness assumption is in principle not possible to test and, therefore, in the second paper we propose a Bayesian sensitivity analysis of the unconfoundedness assumption. This analysis is then performed on the results from the first paper.

    In the third paper of the thesis, we study profile likelihood as a tool for semiparametric estimation of a causal effect of a treatment. A semiparametric version of the Bayesian sensitivity analysis of the unconfoundedness assumption proposed in Paper II is also performed using profile likelihood.

    The last paper of the thesis is concerned with the estimation of direct and indirect causal effects of a treatment where interference between units is present, i.e., where the treatment of one individual affects the outcome of other individuals. We give unbiased estimators of these direct and indirect effects for situations where treatment probabilities vary between individuals. We also illustrate in a simulation study how direct and indirect causal effects can be estimated when treatment probabilities need to be estimated using background information on individuals.

  • 10.
    Lundin, Mathias
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimation of causal effects in observational studies with interference between units2014In: Statistical Methods & Applications, ISSN 1618-2510, E-ISSN 1613-981X, Vol. 23, no 3, p. 417-433Article in journal (Refereed)
    Abstract [en]

    Causal effects are usually estimated under the assumption of no interference between individuals. This assumption means that the potential outcomes for one individual are unaffected by the treatments received by other individuals. In many situations, this is not reasonable to assume. Moreover, not taking interference into account could result in misleading conclusions about the effect of a treatment. For two-stage observational studies, where treatment assigment is randomized in the first stage but not in the second stage, we propose IPW estimators of direct and indirect causal effects as defined by Hudgens and Halloran (J Am Stat Assoc 103(482):832-842, 2008) for two-stage randomized studies. We illustrate the use of these estimators in an evaluation study of an implementation of Triple P (a parenting support program) within preschools in Uppsala, Sweden.

  • 11.
    Xavier, de Luna
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 8, p. 1767-1784Article in journal (Refereed)
    Abstract [en]

    We evaluate the effects of college choice on earnings using Swedish register databases. This case study is used to motivate the introduction of a novel procedure to analyse the sensitivity of such an observational study to the assumption made that there are no unobserved confounders – variables affecting both college choice and earnings. This assumption is not testable without further information, and should be considered an approximation of reality. To perform a sensitivity analysis, we measure the departure from the unconfoundedness assumption with the correlation between college choice and earnings when conditioning on observed covariates. The use of a correlation as a measure of dependence allows us to propose a standardised procedure by advocating the use of a fixed value for the correlation, typically 1% or 5%, when checking the sensitivity of an evaluation study. A correlation coefficient is, moreover, intuitive to most empirical scientists, which makes the results of our sensitivity analysis easier to communicate than those of previously proposed methods. In our evaluation of the effects of college choice on earnings, the significantly positive effect obtained could not be questioned by a sensitivity analysis allowing for unobserved confounders inducing at most 5% correlation between college choice and earnings.

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