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Estimating marginal causal effects in a secondary analysis of case-control data
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)ORCID iD: 0000-0002-1174-5126
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
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. Vol. 36, no 15, 2404-2419 p.
Keyword [en]
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: urn:nbn:se:umu:diva-94965DOI: 10.1002/sim.7277ISI: 000402799900007PubMedID: 28276084OAI: oai:DiVA.org:umu-94965DiVA: diva2:756913
Funder
Swedish Research Council, 07531Riksbankens Jubileumsfond, P11-0814:1
Available from: 2014-10-20 Created: 2014-10-20 Last updated: 2017-11-13Bibliographically approved
In thesis
1. Causal inference and case-control studies with applications related to childhood diabetes
Open this publication in new window or tab >>Causal inference and case-control studies with applications related to childhood diabetes
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Kausal inferens och fall-kontroll studier med applikationer inom barndiabetes
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
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:nbn:se:umu:diva-94993 (URN)978-91-7601-151-5 (ISBN)
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

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