When the entire population is the sample: strengths and limitations in register-based epidemiology
Author(s) -
Lau Caspar Thygesen,
Annette Kjær Ersbøll
Publication year - 2014
Publication title -
european journal of epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.825
H-Index - 111
eISSN - 1573-7284
pISSN - 0393-2990
DOI - 10.1007/s10654-013-9873-0
Subject(s) - medicine , epidemiology , population , data collection , data quality , sample (material) , selection bias , missing data , confounding , data science , statistics , environmental health , computer science , pathology , metric (unit) , operations management , chemistry , mathematics , chromatography , economics
Studies based on databases, medical records and registers are used extensively today in epidemiological research. Despite the increasing use, no developed methodological literature on use and evaluation of population-based registers is available, even though data collection in register-based studies differs from researcher-collected data, all persons in a population are available and traditional statistical analyses focusing on sampling error as the main source of uncertainty may not be relevant. We present the main strengths and limitations of register-based studies, biases especially important in register-based studies and methods for evaluating completeness and validity of registers. The main strengths are that data already exist and valuable time has passed, complete study populations minimizing selection bias and independently collected data. Main limitations are that necessary information may be unavailable, data collection is not done by the researcher, confounder information is lacking, missing information on data quality, truncation at start of follow-up making it difficult to differentiate between prevalent and incident cases and the risk of data dredging. We conclude that epidemiological studies with inclusion of all persons in a population followed for decades available relatively fast are important data sources for modern epidemiology, but it is important to acknowledge the data limitations.
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