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Causal inference and case-control studies with applications related to childhood diabetes
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
2014 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Kausal inferens och fall-kontroll studier med applikationer inom barndiabetes (Swedish)
Abstract [en]

This thesis contributes to the research area of causal inference, where estimation of the effect of a treatment on an outcome of interest is the main objective. Some aspects of the estimation of average causal effects in observational studies in general, and case-control studies in particular, are explored.

An important part of estimating causal effects in an observational study is to control for covariates. The first paper of this thesis concerns the selection of minimal covariate sets sufficient for unconfoundedness of the treatment assignment. A data-driven implementation of two covariate selection algorithms is proposed and evaluated.

A common sampling scheme in epidemiology, and when investigating rare events, is the case-control design. In the second paper we study estimators of the marginal causal odds ratio in matched and independent case-control designs. Estimators that, under a logistic regression model, utilize information about the known prevalence of being a case is examined and compared through simulations.

The third paper investigates the particular situation where case-control sampled data is reused to estimate the effect of the case-defining event on an outcome of interest. The consequence of ignoring the design when estimating the average causal effect is discussed and a design-weighted matching estimator is proposed. The performance of the estimator is evaluated with simulation experiments, when matching on the covariates directly and when matching on the propensity score.

The last paper studies the effect of type 1 diabetes mellitus (T1DM) on school achievements using data from the Swedish Childhood Diabetes Register, a population-based incidence register. We apply theoretical results from the second and third papers in the estimation of the average causal effect within the T1DM population. A matching estimator that accounts for the matched case-control design is used.

Place, publisher, year, edition, pages
Umeå: Umeå universitet , 2014. , 22 p.
Series
Statistical studies, ISSN 1100-8989 ; 48
Keyword [en]
covariate selection, design-weighted estimation, marginal effect, matching, register study, treatment effect, type 1 diabetes mellitus
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-94993ISBN: 978-91-7601-151-5 (print)OAI: oai:DiVA.org:umu-94993DiVA: diva2:757008
Public defence
2014-11-21, Hörsal F, Humanisthuset, Umeå universitet, Umeå, 10:15 (English)
Opponent
Supervisors
Available from: 2014-10-24 Created: 2014-10-20 Last updated: 2014-10-23Bibliographically approved
List of papers
1. Data-driven algorithms for dimension reduction in causal inference
Open this publication in new window or tab >>Data-driven algorithms for dimension reduction in causal inference
2017 (English)In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 105, 280-292 p.Article in journal (Refereed) Published
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.

Keyword
covariate selection, marginal co-ordinate hypothesis test, matching, kernel smoothing, type 1 diabetes mellitus
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-80696 (URN)10.1016/j.csda.2016.08.012 (DOI)000385604500019 ()
Funder
Swedish National Infrastructure for Computing (SNIC), SNIC 2016/1-2Swedish Research Council, 2013-672Swedish Research Council, 07531Riksbankens Jubileumsfond, P11-0814:1
Available from: 2013-09-24 Created: 2013-09-24 Last updated: 2017-12-06Bibliographically approved
2. Estimating a marginal causal odds ratio in a case-control design: analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus
Open this publication in new window or tab >>Estimating a marginal causal odds ratio in a case-control design: analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus
2013 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 32, no 14, 2500-2512 p.Article in journal (Refereed) Published
Abstract [en]

Estimation of marginal causal effects from case-control data has two complications: (i) confounding due to the fact that the exposure under study is not randomized, and (ii) bias from the case-control sampling scheme. In this paper, we study estimators of the marginal causal odds ratio, addressing these issues for matched and unmatched case-control designs when utilizing the knowledge of the known prevalence of being a case. The estimators are implemented in simulations where their finite sample properties are studied and approximations of their variances are derived with the delta method. Also, we illustrate the methods by analyzing the effect of low birth weight on the risk of type 1 diabetes mellitus using data from the Swedish Childhood Diabetes Register, a nationwide population-based incidence register.

Keyword
causal effect, incidence register, potential outcomes, design weighting
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-68956 (URN)10.1002/sim.5826 (DOI)000319880100013 ()
Funder
Riksbankens Jubileumsfond, P11-0814:1Swedish Research Council, 0735
Available from: 2013-05-02 Created: 2013-05-02 Last updated: 2017-12-06Bibliographically approved
3. Estimating marginal causal effects in a secondary analysis of case-control data
Open this publication in new window or tab >>Estimating marginal causal effects in a secondary analysis of case-control data
2017 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 36, no 15, 2404-2419 p.Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Hoboken: Wiley-Blackwell, 2017
Keyword
design-weighted estimation, matched case-control study, propensity score
National Category
Probability Theory and Statistics Public Health, Global Health, Social Medicine and Epidemiology
Research subject
Statistics
Identifiers
urn:nbn:se:umu:diva-94965 (URN)10.1002/sim.7277 (DOI)000402799900007 ()28276084 (PubMedID)
Funder
Swedish Research Council, 07531Riksbankens Jubileumsfond, P11-0814:1
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2017-11-13Bibliographically approved
4. The effects of type 1 diabetes mellitus on school achievements: a Swedish study using register data
Open this publication in new window or tab >>The effects of type 1 diabetes mellitus on school achievements: a Swedish study using register data
(English)Manuscript (preprint) (Other academic)
Abstract [en]

OBJECTIVE: Several characteristics of type 1 diabetes mellitus (T1DM) may affect school achievements and the level of education of individuals with early onset of the disease. Evidence from earlier research showed that a childhood onset of T1DM has a negative impact on schooling among children born in the 1970s and early 1980s. Over the last decades, the management and treatment of T1DM has improved rapidly in several aspects. Additionally, the Swedish grading system changed in the late 1990s. The aim of this study is to investigate if there is an impact of T1DM on educational achievements among children born during the 1980s and early 1990s.

DATA AND METHOD: The study is based on data from the Swedish Childhood Diabetes Register (SCDR) in which Swedish incident cases of T1DM, younger than 15 years, have been reported since 1977. The SCDR, a case-control database, has been linked to several national population registers providing a rich set of variables for the analysis. We analyze the impact of T1DM on final school grades from compulsory and upper secondary school using two approaches: linear regression for estimating the conditional effect, and matching to estimate the average effect within the diabetes group.

RESULTS: The main results from the study indicate slightly lower grades in both compulsory and upper secondary school among children born and diagnosed with T1DM after 1979. The effect was small but significant using both regression and matching.

CONCLUSION: To our knowledge, this is the first study investigating the impact of T1DM on school achievements among individuals that are born during the 1980s and afterwards and have, throughout their childhood, had access to many of the essential treatment improvements seen over the last decades. The results, in line with findings from earlier research, show a small negative impact of the disease among children completing school during 1998-2010. 

Keyword
average effect, educational outcomes, register study
National Category
Social Sciences Interdisciplinary
Identifiers
urn:nbn:se:umu:diva-94969 (URN)
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2014-10-23

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