Method developments for the attributable fraction in causal inference

In public health and policy making, understanding the overall impact of an intervention is of essential importance. A way to quantify the disease burden due to some risk factor is by the attributable fraction (AF). The AF is a measure of the proportion of some disease that could be prevented if all would have been unexposed to the risk factor of interest. From the definition of the AF, it is a causal parameter and in order to achieve a causal interpretation of the AF estimate, we have to tackle the challenges of estimating causal effects in observational data. One of them is the problem of confounding, which may cause the researcher to confuse a spurious correlation with a causal effect. In this work, we stress the importance of using model-based adjustment to estimate the AF and develop novel methods for AF estimation. In project I we implemented methods for AF estimation for cross-sectional, case-control (matched and unmatched) and cohort study designs in the statistical software R by the package AF. The package serves as a platform for the novel methods of AF estimation developed in project II-IV. While project I focuses on estimation methods for the AF that rely on the fact that all confounders, sufficient for confounding control, are measured, researchers often face the problem with unmeasured confounding. In some situations, we may have access to clusters that share these unmeasured confounders. Thus, clustered data can be used to adjust for cluster-shared unmeasured confounding. In project II we develop a method that enables estimation of the AF, as a function of time, and adjusts for cluster-shared unmeasured confounders. In practice, confounders may be unmeasured, but not shared within clusters, or we may lack access to clustered data. One remedy is to use an instrumental variable to mimic a randomized controlled trial and estimate the causal effect. In project IV, we developed a method for AF estimation based on instrumental variable analysis. Genetics play an important role in the disease development and the concept of heritability, i.e. the variation in a trait explained by genetic factors, is often used to quantify the role of genetics. However, heritability does not convey any information on the population impact of some disease due to genetics. In project III we show how the AF can be conceptualized for complex traits, with the overall genetic risk as the exposure, and how heritability and the AF are formally related

Birthweight-specific neonatal health : With application on data from a tertiaryhospital in Tanzania

The following study analyzes birthweight-specific neonatal health using a combination of a mixture model and logistic regression: the extended Parametric Mixture of Logistic Regression. The data are collected from the Obstetric database at Muhimbili National Hospital in Dar es Salaam, Tanzania and the years 2009 -2013 are used in the analysis. Due to rounding in the birthweight data a novel method to adjust for rounding when estimating a mixture model is applied. The influence of rounding on the estimates is then investigated. A three-component model is selected. The variables used in the analysis of neonatal health are early neonatal mortality, if the mother has HIV, anaemia, is a private patient and if the neonate is born after 36 completed weeks of gestation. It can be concluded that the mortality rates are high especially for low birthweights (2000 or less) in the estimated first and second components. However, due to wide confidence bounds it is hard to draw conclusions from the data

Birthweight-specific neonatal health : With application on data from a tertiaryhospital in Tanzania

The following study analyzes birthweight-specific neonatal health using a combination of a mixture model and logistic regression: the extended Parametric Mixture of Logistic Regression. The data are collected from the Obstetric database at Muhimbili National Hospital in Dar es Salaam, Tanzania and the years 2009 -2013 are used in the analysis. Due to rounding in the birthweight data a novel method to adjust for rounding when estimating a mixture model is applied. The influence of rounding on the estimates is then investigated. A three-component model is selected. The variables used in the analysis of neonatal health are early neonatal mortality, if the mother has HIV, anaemia, is a private patient and if the neonate is born after 36 completed weeks of gestation. It can be concluded that the mortality rates are high especially for low birthweights (2000 or less) in the estimated first and second components. However, due to wide confidence bounds it is hard to draw conclusions from the data

Birthweight-specific neonatal health : With application on data from a tertiaryhospital in Tanzania

The following study analyzes birthweight-specific neonatal health using a combination of a mixture model and logistic regression: the extended Parametric Mixture of Logistic Regression. The data are collected from the Obstetric database at Muhimbili National Hospital in Dar es Salaam, Tanzania and the years 2009 -2013 are used in the analysis. Due to rounding in the birthweight data a novel method to adjust for rounding when estimating a mixture model is applied. The influence of rounding on the estimates is then investigated. A three-component model is selected. The variables used in the analysis of neonatal health are early neonatal mortality, if the mother has HIV, anaemia, is a private patient and if the neonate is born after 36 completed weeks of gestation. It can be concluded that the mortality rates are high especially for low birthweights (2000 or less) in the estimated first and second components. However, due to wide confidence bounds it is hard to draw conclusions from the data

mrc-ide/EpiEstim: 2.2-3

New CRAN version of EpiEstim including all new features described in Thompson et al. (currently in review in Epidemics journal)

The comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists: A Scandinavian cohort study

Aim To assess the comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 (SGLT2) inhibitors versus glucagon-like peptide-1 (GLP-1) receptor agonists in routine clinical practice. Materials and Methods A cohort study of nationwide registers from Sweden, Denmark, and Norway, including 87 525 new users of SGLT2 inhibitors and 63 921 new users of GLP-1 receptor agonists, was conducted using data from 2013-2018. Co-primary outcomes, analysed using an intention-to-treat exposure definition, were major adverse cardiovascular events (MACE; myocardial infarction, stroke, and cardiovascular death), heart failure (hospitalization or death because of heart failure), and serious renal events (renal replacement therapy, hospitalization for renal events, and death from renal causes). Results Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a higher risk of MACE (adjusted incidence rate: 15.2 vs. 14.4 events per 1000 person-years; HR 1.07 [95% CI 1.01-1.15]), a similar risk of heart failure (6.0 vs. 6.0 events per 1000 person-years; HR 1.02 [0.92-1.12]), and a lower risk of serious renal events (2.9 vs. 4.0 events per 1000 person-years; HR 0.76 [0.66-0.87]). In as-treated analyses, the HR (95% CI) was 1.11 (1.00-1.24) for MACE, 0.88 (0.74-1.04) for heart failure, and 0.60 (0.47-0.77) for serious renal events. In secondary outcome analyses, use of SGLT2 inhibitors versus GLP-1 receptor agonists was not associated with statistically significant differences for the risk of myocardial infarction (HR 1.09 [95% CI 1.00-1.19]), cardiovascular death (HR 0.97 [95% CI 0.84-1.12]), death from renal causes (HR 0.75 [95% CI 0.41-1.35]), or any cause death (HR 1.01 [95% CI 0.94-1.09]), while the risk of stroke was higher (HR 1.14 [95% CI 1.03-1.26]), and the risk of renal replacement therapy (HR 0.74 [95% CI 0.56-0.97]) and hospitalization for renal events (HR 0.75 [95% CI 0.65-0.88]) were lower among users of SGLT2 inhibitors. Conclusions Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a similar risk of heart failure and a lower risk of serious renal events, while use of GLP-1 receptor agonists versus SGLT2 inhibitors was associated with a slightly lower risk of MACE. In as-treated analyses, the associations with MACE and serious renal events increased in magnitude, and the HR for heart failure tended towards a protective association for SGLT2 inhibitors

The comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 receptor agonists: A Scandinavian cohort study

Aim To assess the comparative cardiovascular and renal effectiveness of sodium-glucose co-transporter-2 (SGLT2) inhibitors versus glucagon-like peptide-1 (GLP-1) receptor agonists in routine clinical practice. Materials and Methods A cohort study of nationwide registers from Sweden, Denmark, and Norway, including 87 525 new users of SGLT2 inhibitors and 63 921 new users of GLP-1 receptor agonists, was conducted using data from 2013-2018. Co-primary outcomes, analysed using an intention-to-treat exposure definition, were major adverse cardiovascular events (MACE; myocardial infarction, stroke, and cardiovascular death), heart failure (hospitalization or death because of heart failure), and serious renal events (renal replacement therapy, hospitalization for renal events, and death from renal causes). Results Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a higher risk of MACE (adjusted incidence rate: 15.2 vs. 14.4 events per 1000 person-years; HR 1.07 [95% CI 1.01-1.15]), a similar risk of heart failure (6.0 vs. 6.0 events per 1000 person-years; HR 1.02 [0.92-1.12]), and a lower risk of serious renal events (2.9 vs. 4.0 events per 1000 person-years; HR 0.76 [0.66-0.87]). In as-treated analyses, the HR (95% CI) was 1.11 (1.00-1.24) for MACE, 0.88 (0.74-1.04) for heart failure, and 0.60 (0.47-0.77) for serious renal events. In secondary outcome analyses, use of SGLT2 inhibitors versus GLP-1 receptor agonists was not associated with statistically significant differences for the risk of myocardial infarction (HR 1.09 [95% CI 1.00-1.19]), cardiovascular death (HR 0.97 [95% CI 0.84-1.12]), death from renal causes (HR 0.75 [95% CI 0.41-1.35]), or any cause death (HR 1.01 [95% CI 0.94-1.09]), while the risk of stroke was higher (HR 1.14 [95% CI 1.03-1.26]), and the risk of renal replacement therapy (HR 0.74 [95% CI 0.56-0.97]) and hospitalization for renal events (HR 0.75 [95% CI 0.65-0.88]) were lower among users of SGLT2 inhibitors. Conclusions Use of SGLT2 inhibitors versus GLP-1 receptor agonists was associated with a similar risk of heart failure and a lower risk of serious renal events, while use of GLP-1 receptor agonists versus SGLT2 inhibitors was associated with a slightly lower risk of MACE. In as-treated analyses, the associations with MACE and serious renal events increased in magnitude, and the HR for heart failure tended towards a protective association for SGLT2 inhibitors