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  • 1. Barban, Nicola
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundholm, Emma
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Svensson, Ingrid
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Billari, F. C.
    Causal Effects of the Timing of Life-course Events: Age at Retirement and Subsequent Health2017In: Sociological Methods & Research, ISSN 0049-1241, E-ISSN 1552-8294Article in journal (Refereed)
    Abstract [en]

    n this article, we combine the extensive literature on the analysis of life-course trajectories as sequences with the literature on causal inference and propose a new matching approach to investigate the causal effect of the timing of life-course events on subsequent outcomes. Our matching approach takes into account pre-event confounders that are both time-independent and time-dependent as well as life-course trajectories. After matching, treated and control individuals can be compared using standard statistical tests or regression models. We apply our approach to the study of the consequences of the age at retirement on subsequent health outcomes, using a unique data set from Swedish administrative registers. Once selectivity in the timing of retirement is taken into account, effects on hospitalization are small, while early retirement has negative effects on survival. Our approach also allows for heterogeneous treatment effects. We show that the effects of early retirement differ according to preretirement income, with higher income individuals tending to benefit from early retirement, while the opposite is true for individuals with lower income.

  • 2.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Discussion of "Perils and potentials of self-selected entry to epidemiological studies and surveys" by N. Keiding and T.A. Louis2016In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 179, no 2, p. 319-376Article in journal (Other (popular science, discussion, etc.))
  • 3.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Fowler, Philip
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Proxy variables and nonparametric identification of causal effectsManuscript (preprint) (Other academic)
    Abstract [en]

    Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

  • 4.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Fowler, Philip
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Proxy variables and nonparametric identification of causal effects2017In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 150, p. 152-154Article in journal (Refereed)
    Abstract [en]

    Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcomes framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

  • 5.
    Eriksson, Marie
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Glader, Eva-Lotta
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Norrving, Bo
    Asplund, Kjell
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Medicine.
    Poststroke suicide attempts and completed suicides: a socioeconomic and nationwide perspective2015In: Neurology, ISSN 0028-3878, E-ISSN 1526-632X, Vol. 84, no 17, p. 1732-1738Article in journal (Refereed)
    Abstract [en]

    OBJECTIVE: We examined attempted and completed suicides after stroke to determine whether they were associated with socioeconomic status, other patient characteristics, or time after stroke.

    METHODS: This nationwide cohort study included stroke patients from Riksstroke (the Swedish Stroke Register) from 2001 to 2012. We used personal identification numbers to link the Riksstroke data with other national registers. Suicide attempts were identified by a record of hospital admission for intentional self-harm (ICD-10: X60-X84), and completed suicides were identified in the national Cause of Death Register. We used multiple Cox regression to analyze time from stroke onset to first suicide attempt.

    RESULTS: We observed 220,336 stroke patients with a total follow-up time of 860,713 person-years. During follow-up, there were 1,217 suicide attempts, of which 260 were fatal. This was approximately double the rate of the general Swedish population. Patients with lower education or income (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.11-1.68) for primary vs university and patients living alone (HR 1.73, 95% CI 1.52-1.97) had an increased risk of attempted suicide, and patients born outside of Europe had a lower risk compared to patients of European origin. Male sex, young age, severe stroke, and poststroke depression were other factors associated with an increased risk of attempted suicide after stroke. The risk was highest during the first 2 years after stroke.

    CONCLUSIONS: Both clinical and socioeconomic factors increase the risk of poststroke suicide attempts. This suggests a need for psychosocial support and suicide preventive interventions in high-risk groups of stroke patients.

  • 6.
    Fowler, Philip
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Methods for improving covariate balance in observational studies2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment.

    The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias.

    Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects.

    The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.

  • 7.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Department of Statistics, Uppsala University.
    Ornstein, Petra
    The Public Employment Service, Stockholm.
    Bill, Sofia
    The Swedish Social Insurance Agency, Stockholm.
    Bengtsson, Peje
    The Swedish Social Insurance Agency, Stockholm.
    Evaluation of a Vocational Rehabilitation: Using a Proxy for Unobserved ConfoundersManuscript (preprint) (Other academic)
  • 8.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Ornstein, Petra
    Bill, Sofia
    Bengtsson, Peje
    Study protocol for the evaluation of a vocational rehabilitation2017In: Observational Studies, Vol. 3, p. 1-27Article in journal (Refereed)
    Abstract [en]

    This paper presents a study protocol for the evaluation of a vocational rehabilitation, namely a collaboration between the Swedish Social Insurance Agency and the Public Employment Service, where individuals needing support to regain work ability were called to a joint assessment meeting. This protocol describes a matching study design using a lasso algorithm, where we do not have access to outcome data on work ability for the treated. The matching design is based on a collection of health and socio-economic covariates measured at baseline. We also have access to a prognosis made by caseworkers on the expected length of the individual sick leave. This prognosis variable is, we argue, a proxy variable for potential unmeasured confounders. We present results showing balance achieved on observed covariates.

  • 9.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Data-driven Coarsening of Covariates for Causal InferenceManuscript (preprint) (Other academic)
  • 10.
    Genbäck, Minna
    et al.
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation2018In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420Article in journal (Refereed)
    Abstract [en]

    Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due to unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by modeling the treatment assignment mechanism and its correlation with the outcome given the observed confounders, allowing us to derive the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R-package implementing the inference proposed is available.

  • 11.
    Gorbach, Tetiana
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Pudas, Sara
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Lundquist, Anders
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Orädd, Greger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Josefsson, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Salami, Alireza
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Longitudinal association between hippocampus atrophy and episodic-memory decline2017In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 51, p. 167-176Article in journal (Refereed)
    Abstract [en]

    There is marked variability in both onset and rate of episodic-memory decline in aging. Structural magnetic resonance imaging studies have revealed that the extent of age-related brain changes varies markedly across individuals. Past studies of whether regional atrophy accounts for episodic-memory decline in aging have yielded inconclusive findings. Here we related 15-year changes in episodic memory to 4-year changes in cortical and subcortical gray matter volume and in white-matter connectivity and lesions. In addition, changes in word fluency, fluid IQ (Block Design), and processing speed were estimated and related to structural brain changes. Significant negative change over time was observed for all cognitive and brain measures. A robust brain-cognition change-change association was observed for episodic-memory decline and atrophy in the hippocampus. This association was significant for older (65-80 years) but not middle-aged (55-60 years) participants and not sensitive to the assumption of ignorable attrition. Thus, these longitudinal findings highlight medial-temporal lobe system integrity as particularly crucial for maintaining episodic-memory functioning in older age. 

  • 12.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Data-driven Confounder Selection via Markov and Bayesian Networks2018In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 74, no 2, p. 389-398Article in journal (Refereed)
  • 13.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Targeted smoothing parameter selection for estimating average causal effects2014In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 29, no 6, p. 1727-1748Article in journal (Refereed)
    Abstract [en]

    The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.

  • 14.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Persson, Emma
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects2015In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 68, no 1, p. 1-20Article in journal (Refereed)
    Abstract [en]

    We describe the R package CovSel, which reduces the dimension of the covariate vector for the purpose of estimating an average causal effect under the unconfoundedness assumption. Covariate selection algorithms developed in De Luna, Waernbaum, and Richardson (2011) are implemented using model-free backward elimination. We show how to use the package to select minimal sets of covariates. The package can be used with continuous and discrete covariates and the user can choose between marginal co-ordinate hypothesis tests and kernel-based smoothing as model-free dimension reduction techniques.

  • 15.
    Ju, Cheng
    et al.
    Division of Biostatistics, University of California, USA.
    Wyss, Richard
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
    Franklin, Jessica M
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
    Schneeweiss, Sebastian
    Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    van der Laan, Mark J
    Division of Biostatistics, University of California, USA.
    Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data2017In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334Article in journal (Refereed)
  • 16.
    Lindmark, Anita
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals2018In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 37, no 10, p. 1744-1762Article in journal (Refereed)
    Abstract [en]

    To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assumptions about unconfoundedness are required. Since these assumptions cannot be tested using the observed data, a mediation analysis should always be accompanied by a sensitivity analysis of the resulting estimates. In this article, we propose a sensitivity analysis method for parametric estimation of direct and indirect effects when the exposure, mediator, and outcome are all binary. The sensitivity parameters consist of the correlations between the error terms of the exposure, mediator, and outcome models. These correlations are incorporated into the estimation of the model parameters and identification sets are then obtained for the direct and indirect effects for a range of plausible correlation values. We take the sampling variability into account through the construction of uncertainty intervals. The proposed method is able to assess sensitivity to both mediator‐outcome confounding and confounding involving the exposure. To illustrate the method, we apply it to a mediation study based on the data from the Swedish Stroke Register (Riksstroke). An R package that implements the proposed method is available.

  • 17.
    Persson, Emma
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Data-driven algorithms for dimension reduction in causal inference2017In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 105, p. 280-292Article in journal (Refereed)
    Abstract [en]

    In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. In this paper, data-driven algo- rithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness we search for minimal subsets of the covariate vector. Based on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elim- ination procedure testing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented.

    The full text will be freely available from 2019-01-01 00:00
  • 18.
    Persson, Emma
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lind, Torbjörn
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Estimating marginal causal effects in a secondary analysis of case-control data2017In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 15, p. 2404-2419Article in journal (Refereed)
    Abstract [en]

    When an initial case-control study is performed, data can be used in a secondary analysis to evaluate the effect of the case-defining event on later outcomes. In this paper, we study the example in which the role of the event is changed from a response variable to a treatment of interest. If the aim is to estimate marginal effects, such as average effects in the population, the sampling scheme needs to be adjusted for. We study estimators of the average effect of the treatment in a secondary analysis of matched and unmatched case-control data where the probability of being a case is known. For a general class of estimators, we show the components of the bias resulting from ignoring the sampling scheme and demonstrate a design-weighted matching estimator of the average causal effect. In simulations, the finite sample properties of the design-weighted matching estimator are studied. Using a Swedish diabetes incidence register with a matched case-control design, we study the effect of childhood onset diabetes on the use of antidepressant medication as an adult.

  • 19.
    Rompaye, Bart Van
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Goetghebeur, Els
    Evaluating hospital performance based on excess cause-specific incidence2015In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, no 8, p. 1334-1350Article in journal (Refereed)
    Abstract [en]

    Formal evaluation of hospital performance in specific types of care is becoming an indispensable tool for quality assurance in the health care system. When the prime concern lies in reducing the risk of a cause-specific event, we propose to evaluate performance in terms of an average excess cumulative incidence, referring to the center's observed patient mix. Its intuitive interpretation helps give meaning to the evaluation results and facilitates the determination of important benchmarks for hospital performance. We apply it to the evaluation of cerebrovascular deaths after stroke in Swedish stroke centers, using data from Riksstroke, the Swedish stroke registry. 

  • 20.
    Stenberg, Anders
    et al.
    University of Stockholm.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. IFAU, Uppsala, Sweden.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics. Jyväskylä University School of Business and Economics, Jyväskylä, Finland.
    Does formal education for older workers increase earnings?: evidence based on rich data and long-term follow up2014In: Labour, ISSN 1121-7081, E-ISSN 1467-9914, Vol. 28, no 2, p. 163-189Article in journal (Refereed)
    Abstract [en]

    Governments in Europe, Canada and the US have expressed an ambition to stimulate education of older. In this paper, we analyze if there are effects on annual earnings of formal education for participants aged 42-55 at the time of enrolment in 1994-1995. The analysis explores longitudinal population register data stretching from 1982 to 2007. The method used is difference-in-differences propensity score matching based on a rich set of covariates, including indicators of health and labor market marginalization. Our findings underline the importance of long follow up periods and imply positive effects for females, especially so for women with children, and no significant average earnings effects for males. These results differ from earlier studies but are stable to several alternative assumptions regarding unobservable characteristics. Data further indicate that the gender gap in our estimates may stem from differences in underlying reasons for enrolment.

  • 21. Varewyck, M.
    et al.
    Vansteelandt, S.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Goetghebeur, E.
    On the practice of ignoring center-patient interactions in evaluating hospital performance2016In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 35, no 2, p. 227-238Article in journal (Refereed)
    Abstract [en]

    We evaluate the performance of medical centers based on a continuous or binary patient outcome (e.g., 30-day mortality). Common practice adjusts for differences in patient mix through outcome regression models, which include patient-specific baseline covariates (e.g., age and disease stage) besides center effects. Because a large number of centers may need to be evaluated, the typical model postulates that the effect of a center on outcome is constant over patient characteristics. This may be violated, for example, when some centers are specialized in children or geriatric patients. Including interactions between certain patient characteristics and the many fixed center effects in the model increases the risk for overfitting, however, and could imply a loss of power for detecting centers with deviating mortality. Therefore, we assess how the common practice of ignoring such interactions impacts the bias and precision of directly and indirectly standardized risks. The reassuring conclusion is that the common practice of working with the main effects of a center has minor impact on hospital evaluation, unless some centers actually perform substantially better on a specific group of patients and there is strong confounding through the corresponding patient characteristic. The bias is then driven by an interplay of the relative center size, the overlap between covariate distributions, and the magnitude of the interaction effect. Interestingly, the bias on indirectly standardized risks is smaller than on directly standardized risks. We illustrate our findings by simulation and in an analysis of 30-day mortality on Riksstroke.

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