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Study protocol for the evaluation of a vocational rehabilitation
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik. (Stat4Reg)
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik. (Stat4Reg)
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2017 (Engelska)Ingår i: Observational Studies, Vol. 3, s. 1-27Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper presents a study protocol for the evaluation of a vocational rehabilitation, namely a collaboration between the Swedish Social Insurance Agency and the Public Employment Service, where individuals needing support to regain work ability were called to a joint assessment meeting. This protocol describes a matching study design using a lasso algorithm, where we do not have access to outcome data on work ability for the treated. The matching design is based on a collection of health and socio-economic covariates measured at baseline. We also have access to a prognosis made by caseworkers on the expected length of the individual sick leave. This prognosis variable is, we argue, a proxy variable for potential unmeasured confounders. We present results showing balance achieved on observed covariates.

Ort, förlag, år, upplaga, sidor
2017. Vol. 3, s. 1-27
Nyckelord [en]
Love Plots, Matching Design, Observational Study, Proxy Variable
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-133847OAI: oai:DiVA.org:umu-133847DiVA, id: diva2:1089292
Tillgänglig från: 2017-04-19 Skapad: 2017-04-19 Senast uppdaterad: 2018-06-09Bibliografiskt granskad
Ingår i avhandling
1. Methods for improving covariate balance in observational studies
Öppna denna publikation i ny flik eller fönster >>Methods for improving covariate balance in observational studies
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Metoder för att förbättra jämförbarheten mellan två grupper i observationsstudier
Abstract [en]

This thesis contributes to the field of causal inference, where the main interest is to estimate the effect of a treatment on some outcome. At its core, causal inference is an exercise in controlling for imbalance (differences) in covariate distributions between the treated and the controls, as such imbalances otherwise can bias estimates of causal effects. Imbalance on observed covariates can be handled through matching, where treated and controls with similar covariate distributions are extracted from a data set and then used to estimate the effect of a treatment.

The first paper of this thesis describes and investigates a matching design, where a data-driven algorithm is used to discretise a covariate before matching. The paper also gives sufficient conditions for if, and how, a covariate can be discretised without introducing bias.

Balance is needed for unobserved covariates too, but is more difficult to achieve and verify. Unobserved covariates are sometimes replaced with correlated counterparts, usually referred to as proxy variables. However, just replacing an unobserved covariate with a correlated one does not guarantee an elimination of, or even reduction of, bias. In the second paper we formalise proxy variables in a causal inference framework and give sufficient conditions for when they lead to nonparametric identification of causal effects.

The third and fourth papers both concern estimating the effect an enhanced cooperation between the Swedish Social Insurance Agency and the Public Employment Service has on reducing sick leave. The third paper is a study protocol, where the matching design used to estimate this effect is described. The matching was then also carried out in the study protocol, before the outcome for the treated was available, ensuring that the matching design was not influenced by any estimated causal effects. The third paper also presents a potential proxy variable for unobserved covariates, that is used as part of the matching. The fourth paper then carries out the analysis described in the third paper, and uses an instrumental variable approach to test for unobserved confounding not captured by the supposed proxy variable.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet, 2017. s. 29
Serie
Statistical studies, ISSN 1100-8989 ; 52
Nyckelord
causal effect, coarsening, discretisation, proxy variables, register study, swedish social insurance agency, unobserved variables
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
urn:nbn:se:umu:diva-139523 (URN)978-91-7601-751-7 (ISBN)
Disputation
2017-10-10, Hörsal G, Humanisthuset, Umeå, 10:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2017-09-19 Skapad: 2017-09-15 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

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