Information Bias

Author(s):  
Lisa TenEyck
Keyword(s):  
2007 ◽  
Author(s):  
Gabriella Pravettoni ◽  
Claudio Lucchiari ◽  
Salvatore Nuccio Leotta ◽  
Gianluca Vago

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Herney Andrés García-Perdomo ◽  
Jessica Fernanda Toro Maldonado

Abstract The aim of this letter was to point out some methodological concerns about an article written by Shi et al. and published in the journal. There is an increasing trend in the isolation of Methicillin-susceptible Staphylococcus aureus bacteremia and a variety of questions regarding the best therapy to treat this condition. These concerns might lead to selection, publication and information bias that prevent the generalization and application of these results in our clinical practice.


2019 ◽  
Vol 4 (6) ◽  
pp. 337-343 ◽  
Author(s):  
Claus Varnum ◽  
Alma Bečić Pedersen ◽  
Per Hviid Gundtoft ◽  
Søren Overgaard

Establishment of orthopaedic registers started in 1975 and many registers have been initiated since. The main purpose of registers is to collect information on patients, implants and procedures in order to monitor and improve the outcome of the specific procedure. Data validity reflects the quality of the registered data and consists of four major aspects: coverage of the register, registration completeness of procedures/patients, registration completeness of variables included in the register and accuracy of registered variables. Survival analysis is often used in register studies to estimate the incidence of an outcome. The most commonly used survival analysis is the Kaplan–Meier survival curves, which present the proportion of patients who have not experienced the defined event (e.g. death or revision of a prosthesis) in relation to the time. Depending on the research question, competing events can be taken into account by using the cumulative incidence function. Cox regression analysis is used to compare survival data for different groups taking differences between groups into account. When interpreting the results from observational register-based studies a number of factors including selection bias, information bias, chance and confounding have to be taken into account. In observational register-based studies selection bias is related to, for example, absence of complete follow-up of the patients, whereas information bias is related to, for example, misclassification of exposure (e.g. risk factor of interest) or/and outcome. The REporting of studies Conducted using Observational Routinely-collected Data guidelines should be used for studies based on routinely-collected health data including orthopaedic registers. Linkage between orthopaedic registers, other clinical quality databases and administrative health registers may be of value when performing orthopaedic register-based research. Cite this article: EFORT Open Rev 2019;4 DOI: 10.1302/2058-5241.4.180097


Author(s):  
James C Doidge ◽  
Katie L Harron

Abstract Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the ‘links’ between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error.


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