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Statistical methods for register based studies with applications to stroke
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Statistiska metoder för registerbaserade studier med tillämpningar på stroke (Swedish)
Abstract [en]

This thesis adds to the area of register based research, with a particular focus on health care quality and (in)equality. Contributions are made to the areas of hospital performance benchmarking, mediation analysis, and regression when the outcome variable is limited, with applications related to Riksstroke (the Swedish stroke register).

An important part of quality assurance is to identify, follow up, and understand the mechanisms of inequalities in outcome and/or care between different population groups. The first paper of the thesis uses Riksstroke data to investigate socioeconomic differences in survival during different time periods after stroke. The second paper focuses on differences in performance between hospitals, illustrating the diagnostic properties of a method for benchmarking hospital performance and highlighting the importance of balancing clinical relevance and the statistical evidence level used.

Understanding the mechanisms behind observed differences is a complicated but important issue. In mediation analysis the goal is to investigate the causal mechanisms behind an effect by decomposing it into direct and indirect components. Estimation of direct and indirect effects relies on untestable assumptions and a mediation analysis should be accompanied by an analysis of how sensitive the results are to violations of these assumptions. The third paper proposes a sensitivity analysis method for mediation analysis based on binary probit regression. This is then applied to a mediation study based on Riksstroke data.

Data registration is not always complete and sometimes data on a variable are unavailable above or below some value. This is referred to as censoring or truncation, depending on the extent to which data are missing. The final two papers of the thesis are concerned with the estimation of linear regression models for limited outcome variables. The fourth paper presents a software implementation of three semi-parametric estimators of truncated linear regression models. The fifth paper extends the sensitivity analysis method proposed in the third paper to continuous outcomes and mediators, and situations where the outcome is truncated or censored.

Place, publisher, year, edition, pages
Umeå: Umeå universitet , 2016. , 30 p.
Series
Statistical studies, ISSN 1100-8989 ; 49
Keyword [en]
Registers, quality of care, socioeconomic status, hospital performance, stroke, mediation, sensitivity analysis, truncation, censoring
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-125953ISBN: 978-91-7601-553-7 (print)OAI: oai:DiVA.org:umu-125953DiVA: diva2:974830
Public defence
2016-10-21, Hörsal E, Humanisthuset, Umeå, 09:30 (English)
Opponent
Supervisors
Available from: 2016-09-30 Created: 2016-09-23 Last updated: 2016-09-29Bibliographically approved
List of papers
1. Socioeconomic disparities in stroke case fatality: observations from Riks-Stroke, the Swedish stroke register
Open this publication in new window or tab >>Socioeconomic disparities in stroke case fatality: observations from Riks-Stroke, the Swedish stroke register
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2014 (English)In: International Journal of Stroke, ISSN 1747-4930, E-ISSN 1747-4949, Vol. 9, no 4, 429-436 p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Low socioeconomic status (low education and income level) has been found to be associated with increased stroke mortality. However, findings from previous studies on the association between socioeconomic status and case fatality (survival) after stroke have been inconsistent.

AIMS: The study aims to explore the association between socio-economic status and survival after stroke using Riks-Stroke, the Swedish Stroke Register, with emphasis on changes in survival (in)equality with time after stroke.

METHODS: All 76 hospitals in Sweden admitting acute stroke patients participate in Riks-Stroke. Riks-Stroke data on 18- to 74-year-old patients with onset of first stroke during the years 2001-2009 were combined with data from other official Swedish registers. Case fatality was analyzed by socioeconomic status (education, income, country of birth, and cohabitation) and other patient characteristics.

RESULTS: Of the 62 497 patients in the study, a total of 6094 (9·8%) died within the first year after stroke. Low income, primary school education, and living alone were independently associated with higher case fatality after the acute phase. Differences related to income and cohabitation were present already early, at 8-28 days after stroke, with the gaps expanding thereafter. The association between education and case fatality was not present until 29 days to one-year after stroke. Dissimilarities in secondary preventative medications prescribed on discharge from hospital had only a minor impact on these differences.

CONCLUSIONS: Socioeconomic status had only a limited effect on acute phase case fatality, indicating minor disparities in acute stroke treatment. The survival inequality, present already in the subacute phase, increased markedly over time since the stroke event. The socioeconomic differences could not be explained by differences in secondary prevention at discharge from hospital. Large socioeconomic differences in long-term survival after stroke may exist also in a country with limited income inequity.

Place, publisher, year, edition, pages
John Wiley & Sons, 2014
Keyword
stroke, outcome, socioeconomic status, case fatality, registers
National Category
Social Sciences Anesthesiology and Intensive Care
Identifiers
urn:nbn:se:umu:diva-79991 (URN)10.1111/ijs.12133 (DOI)000335664900013 ()23981768 (PubMedID)2-s2.0-84899987546 (Scopus ID)
Available from: 2013-09-05 Created: 2013-09-05 Last updated: 2017-12-06Bibliographically approved
2. The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care - Illustrated Using the Swedish Stroke Register
Open this publication in new window or tab >>The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care - Illustrated Using the Swedish Stroke Register
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2016 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 4, e0153082Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: When profiling hospital performance, quality inicators are commonly evaluated through hospital-specific adjusted means with confidence intervals. When identifying deviations from a norm, large hospitals can have statistically significant results even for clinically irrelevant deviations while important deviations in small hospitals can remain undiscovered. We have used data from the Swedish Stroke Register (Riksstroke) to illustrate the properties of a benchmarking method that integrates considerations of both clinical relevance and level of statistical significance.

METHODS: The performance measure used was case-mix adjusted risk of death or dependency in activities of daily living within 3 months after stroke. A hospital was labeled as having outlying performance if its case-mix adjusted risk exceeded a benchmark value with a specified statistical confidence level. The benchmark was expressed relative to the population risk and should reflect the clinically relevant deviation that is to be detected. A simulation study based on Riksstroke patient data from 2008-2009 was performed to investigate the effect of the choice of the statistical confidence level and benchmark value on the diagnostic properties of the method.

RESULTS: Simulations were based on 18,309 patients in 76 hospitals. The widely used setting, comparing 95% confidence intervals to the national average, resulted in low sensitivity (0.252) and high specificity (0.991). There were large variations in sensitivity and specificity for different requirements of statistical confidence. Lowering statistical confidence improved sensitivity with a relatively smaller loss of specificity. Variations due to different benchmark values were smaller, especially for sensitivity. This allows the choice of a clinically relevant benchmark to be driven by clinical factors without major concerns about sufficiently reliable evidence.

CONCLUSIONS: The study emphasizes the importance of combining clinical relevance and level of statistical confidence when profiling hospital performance. To guide the decision process a web-based tool that gives ROC-curves for different scenarios is provided.

National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-119030 (URN)10.1371/journal.pone.0153082 (DOI)000373608000075 ()27054326 (PubMedID)
Available from: 2016-04-08 Created: 2016-04-08 Last updated: 2017-11-30Bibliographically approved
3. Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
Open this publication in new window or tab >>Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals
(English)Manuscript (preprint) (Other academic)
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 correlation between the error terms of the mediator and outcome models, the correlation between the error terms of the mediator model and the model for the exposure assignment mechanism, and the correlation between the error terms of the exposure assignment 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 data from the Swedish Stroke Register (Riksstroke).

Keyword
mediation, direct effects, indirect effects, sensitivity analysis, sequential ignorability, unmeasured confounding, uncertainty intervals
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-125929 (URN)
Available from: 2016-09-23 Created: 2016-09-22 Last updated: 2016-09-28
4. truncSP: an R package for estimation of semi-parametric truncated linear regression models
Open this publication in new window or tab >>truncSP: an R package for estimation of semi-parametric truncated linear regression models
2014 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 57, no 14, 1-19 p.Article in journal (Refereed) Published
Abstract [en]

Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and finite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

Place, publisher, year, edition, pages
American Statistical Association, 2014
Keyword
truncation, limited dependent variable, semi-parametric estimators, R
National Category
Probability Theory and Statistics Computer Science Mathematics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-88483 (URN)10.18637/jss.v057.i14 (DOI)000341021200001 ()2-s2.0-84899828272 (Scopus ID)
Available from: 2014-05-06 Created: 2014-05-06 Last updated: 2017-12-05Bibliographically approved
5. Uncertainty intervals for unobserved confounding of direct and indirect effects with extensions to censoring and truncation
Open this publication in new window or tab >>Uncertainty intervals for unobserved confounding of direct and indirect effects with extensions to censoring and truncation
(English)Manuscript (preprint) (Other academic)
Abstract [en]

When performing a mediation analysis, i.e. estimating direct and indirect effects of a given exposure on an outcome, strong assumptions are made about unconfoundedness. These assumptions are difficult to verify in a given situation and therefore a mediation analysis should be complemented with a sensitivity analysis to assess the impact of violations. Lindmark et al. (2016) proposed a sensitivity analysis method for parametric estimation of conditional and marginal direct and indirect effects when the mediator and outcome are binary and modeled using probit models. In this paper we extend this to include cases with continuous mediators and outcomes and suggest extensions to the cases when the continuous outcome variable is censored or truncated. Three sensitivity parameters are used, consisting of the correlations between the error terms of the mediator, outcome and exposure assignment mechanism models. These correlations are incorporated into the estimation of the model parameters and sampling variability is taken into account through the construction of uncertainty intervals.

Keyword
mediation, sensitivity analysis, unmeasured confounding, sequential ignorability, uncertainty intervals, censoring, truncation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-125930 (URN)
Available from: 2016-09-23 Created: 2016-09-22 Last updated: 2016-09-28

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