%0 Journal Article %1 Lefering2014 %A Lefering, Rolf %D 2014 %J Injury %K Adult Bias(Epidemiology) ConfoundingFactors(Epidemiology) DataInterpretation EmergencyMedicine Female Germany Germany:epidemiology Humans Male Models MultipleTrauma MultipleTrauma:mortality MultipleTrauma:radiography OutcomeAssessment(HealthCare) Registries Registries:statistics&numericaldata ResearchDesign RetrospectiveStudies Statistical %P S83-8 %R 10.1016/j.injury.2014.08.023 %T Strategies for comparative analyses of registry data. %U http://www.ncbi.nlm.nih.gov/pubmed/25284241 %V 45 Suppl 3 %X The present paper is a description and summary of methods used in non-randomised cohort data where the comparability of the study groups usually is not granted. Such study groups are formed by a diagnostic or therapeutic intervention, or by other characteristics of the patient or the treatment environment. This is a typical situation in the analysis of registry data. The methods are presented together with an illustrative example of whole-body computed tomography in the early phase of treatment of severe trauma cases. The following approaches are considered: (i) unadjusted direct comparisons; (ii) parallelisation; (iii) subgroup analysis; (iv) matched-pairs analysis; (v) outcome adjustment; and (vi) propensity score analysis. All these approaches have in common that they try to separate, or limit, the influence of confounding variables, which are unevenly distributed among the study groups, but also influence the outcome of interest. They differ in the number of confounders being considered, as well as the number of patients regarded. The more sophisticated the approach, the more effectively such confounding factors could be reduced. However, any method used for the reduction of bias depends on the quality and completeness of recorded confounders. Factors which are difficult or even impossible to be measured could thus not be adjusted for. This is a general limitation of retrospective analyses of cohort data.

@article{Lefering2014, abstract = {The present paper is a description and summary of methods used in non-randomised cohort data where the comparability of the study groups usually is not granted. Such study groups are formed by a diagnostic or therapeutic intervention, or by other characteristics of the patient or the treatment environment. This is a typical situation in the analysis of registry data. The methods are presented together with an illustrative example of whole-body computed tomography in the early phase of treatment of severe trauma cases. The following approaches are considered: (i) unadjusted direct comparisons; (ii) parallelisation; (iii) subgroup analysis; (iv) matched-pairs analysis; (v) outcome adjustment; and (vi) propensity score analysis. All these approaches have in common that they try to separate, or limit, the influence of confounding variables, which are unevenly distributed among the study groups, but also influence the outcome of interest. They differ in the number of confounders being considered, as well as the number of patients regarded. The more sophisticated the approach, the more effectively such confounding factors could be reduced. However, any method used for the reduction of bias depends on the quality and completeness of recorded confounders. Factors which are difficult or even impossible to be measured could thus not be adjusted for. This is a general limitation of retrospective analyses of cohort data.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Lefering, Rolf}, biburl = {https://www.bibsonomy.org/bibtex/25a1dfe607c8db5ea6e1423e3d2374c0a/jepcastel}, doi = {10.1016/j.injury.2014.08.023}, interhash = {61eef7ad7f0ac4f3d51bb15eb03f71b3}, intrahash = {5a1dfe607c8db5ea6e1423e3d2374c0a}, issn = {1879-0267}, journal = {Injury}, keywords = {Adult Bias(Epidemiology) ConfoundingFactors(Epidemiology) DataInterpretation EmergencyMedicine Female Germany Germany:epidemiology Humans Male Models MultipleTrauma MultipleTrauma:mortality MultipleTrauma:radiography OutcomeAssessment(HealthCare) Registries Registries:statistics&numericaldata ResearchDesign RetrospectiveStudies Statistical}, month = {10}, note = {Anàlisi de dades; Observacionals; Propensity score; Introductori}, pages = {S83-8}, pmid = {25284241}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Strategies for comparative analyses of registry data.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/25284241}, volume = {45 Suppl 3}, year = 2014 }