Statistiska metoder för registerbaserade studier med tillämpningar på stroke

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

Sensitivity analysis for unobserved confounding in causal mediation analysis allowing for effect modification, censoring and truncation

Causal mediation analysis is used to decompose the total effect of an exposure on an outcome into an indirect effect, taking the path through an intermediate variable, and a direct effect. To estimate these effects, strong assumptions are made about unconfoundedness of the relationships between the exposure, mediator and outcome. 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 possible impact of violations. In this paper we present a method for sensitivity analysis to not only unobserved mediator-outcome confounding, which has largely been the focus of previous literature, but also unobserved confounding involving the exposure. The setting is estimation of natural direct and indirect effects based on parametric regression models. We present results for combinations of binary and continuous mediators and outcomes and extend the sensitivity analysis for mediator-outcome confounding to cases where the continuous outcome variable is censored or truncated. The proposed methods perform well also in the presence of interactions between the exposure, mediator and observed confounders, allowing for modeling flexibility as well as exploration of effect modification. The performance of the method is illustrated through simulations and an empirical example.

truncSP : an R package for estimation of semi-parametric truncated linear regression models

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

Mediation analyses of the mechanisms by which socioeconomic status, comorbidity, stroke severity, and acute care influence stroke outcome

BACKGROUND AND OBJECTIVES: Low socioeconomic status (SES) is associated with increased risk of death and disability after stroke, but interventional targets to minimize disparities remain unclear. We aim to assess the extent to which SES-based disparities in the association between low SES and death and dependency at three months after stroke could be eliminated by offsetting differences in comorbidity, stroke severity, and acute care. METHODS: This nationwide register-based cohort study included all 72 hospitals caring for patients with acute stroke in Sweden. All patients registered with an acute ischemic stroke in the Swedish Stroke Register in 2015-2016 who were independent in activities of daily living (ADL) at the time of stroke were included. Data on survival and SES the year before stroke were retrieved by cross-linkage with other national registers. SES was defined by education and income, and categorized into low, mid, and high. Causal mediation analysis was used to study the absolute risk of death and ADL-dependency at 3 months depending on SES, and to what extent hypothetical interventions on comorbidities, stroke severity, and acute care would equalize outcomes. RESULTS: Of the 25,846 patients in the study, 6,798 (26.3%) were dead or ADL-dependent three months after stroke. Adjusted for sex and age, low SES was associated with an increased absolute risk of 5.4% (95% CI: 3.9%-6.9%; p<0.001) compared to mid SES, and 10.1% (95% CI: 8.1%-12.2%; p<0.001) compared to high SES. Intervening to shift the distribution of all mediators among patients with low SES to those of the more privileged groups would result in absolute reductions of these effects by 2.2% (95% CI: 1.2%-3.2%; p<0.001), and 4.0% (95% CI: 2.6%-5.5%; p<0.001), respectively, with the largest reduction accomplished by equalizing stroke severity. DISCUSSION: Low SES patients have substantially increased risks of death and ADL-dependency three months after stroke compared to more privileged patient groups. This study suggests that if we could intervene to equalize SES-related differences in the distributions of comorbidity, acute care, and stroke severity, up to 40 out of every 1000 patients with low SES could be prevented from dying or becoming ADL-dependent

Socioeconomic status and survival after stroke : using mediation and sensitivity analyses to assess the effect of stroke severity and unmeasured confounding

Background: Although it has been established that low socioeconomic status is linked to increased risk of death after stroke, the mechanisms behind this link are still unclear. In this study we aim to shed light on the relationship between income level and survival after stroke by investigating the extent to which differences in stroke severity account for differences in survival. Methods: The study was based on patients registered in Riksstroke (the Swedish stroke register) with first time ischemic stroke (n = 51,159) or intracerebral hemorrhage (n = 6777) in 2009–2012. We used causal mediation analysis to decompose the effect of low income on 3-month case fatality into a direct effect and an indirect effect due to stroke severity. Since causal mediation analysis relies on strong assumptions regarding residual confounding of the relationships involved, recently developed methods for sensitivity analysis were used to assess the robustness of the results to unobserved confounding. Results: After adjustment for observed confounders, patients in the lowest income tertile had a 3.2% (95% CI: 0.9–5.4%) increased absolute risk of 3-month case fatality after intracerebral hemorrhage compared to patients in the two highest tertiles. The corresponding increase for case fatality after ischemic stroke was 1% (0.4–1.5%). The indirect effect of low income, mediated by stroke severity, was 1.8% (0.7–2.9%) for intracerebral hemorrhage and 0.4% (0.2–0.6%) for ischemic stroke. Unobserved confounders affecting the risk of low income, more severe stroke and case fatality in the same directions could explain the indirect effect, but additional adjustment to observed confounders did not alter the conclusions. Conclusions: This study provides evidence that as much as half of income-related inequalities in stroke case fatality is mediated through differences in stroke severity. Targeting stroke severity could therefore lead to a substantial reduction in inequalities and should be prioritized. Sensitivity analysis suggests that additional adjustment for a confounder of greater impact than age would be required to considerably alter our conclusions

Socioeconomic status and stroke severity : Understanding indirect effects via risk factors and stroke prevention using innovative statistical methods for mediation analysis

Background: Those with low socioeconomic status have an increased risk of stroke, more severe strokes, reduced access to treatment, and more adverse outcomes after stroke. The question is why these differences are present. In this study we investigate to which extent the association between low socioeconomic status and stroke severity can be explained by differences in risk factors and stroke prevention drugs. Methods: The study included 86 316 patients registered with an ischemic stroke in the Swedish Stroke Register (Riksstroke) 2012–2016. Data on socioeconomic status was retrieved from the Longitudinal integrated database for health insurance and labour market studies (LISA) by individual linkage. We used education level as proxy for socioeconomic status, with primary school education classified as low education. Stroke severity was measured using the Reaction Level Scale, with values above 1 classified as severe strokes. To investigate the pathways via risk factors and stroke prevention drugs we performed a mediation analysis estimating indirect and direct effects. Results: Low education was associated with an excess risk of a severe stroke compared to mid/high education (absolute risk difference 1.4%, 95% CI: 1.0%-1.8%), adjusting for confounders. Of this association 28.5% was an indirect effect via risk factors (absolute risk difference 0.4%, 95% CI: 0.3%-0.5%), while the indirect effect via stroke prevention drugs was negligible. Conclusion: Almost one third of the association between low education and severe stroke was explained by risk factors, and clinical effort should be taken to reduce these risk factors to decrease stroke severity among those with low socioeconomic status

The Importance of Integrating Clinical Relevance and Statistical Significance in the Assessment of Quality of Care--Illustrated Using the Swedish Stroke Register.

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.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.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.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