Dealing with categorical risk data when extracting data for meta-analysis
نویسندگان
چکیده
A common problem in meta-analysis of observational studies arises when the exposure variable is categorical rather than continuous. These data may be referred to as quantile or quintile (depending on number categories) dose–response data, and this article, term ‘categorical risk data’ will used. reported reflect increase cardiovascular associated with increasing weight gain, alcohol consumption frequency smoking. Further problems arise divide into different numbers categories, same but using thresholds, are missing, include reference categories. make it difficult combine meta-analysis, there methods that can deal these problems. Consider a clinical question an example: How body mass index (BMI) incident atrial fibrillation? Consider three examples for which was focus. The first study1 provided HRs categories BMI: <25 kg/m2, 25≤BMI<30 ≥30 kg/m2. second study2 quintiles. third study3 one unit BMI 4.3% increased fibrillation (HR 1.04, 95% confidence interval (CI) 1.02 1.07). This example HR continuous scale. To carry out from each study need form. form would apply if all scale BMI. For example, desirable derive pooled five units It possible convert …
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ژورنال
عنوان ژورنال: BMJ evidence-based medicine
سال: 2021
ISSN: ['2515-446X', '2515-4478']
DOI: https://doi.org/10.1136/bmjebm-2020-111649