Andrea Arfè1, Giovanni Corrao2. 1. Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milan 20126, Italy. 2. Unit of Biostatistics, Epidemiology and Public Health, Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, Milan 20126, Italy. Electronic address: giovanni.corrao@unimib.it.
Abstract
OBJECTIVES: Protopathic bias is a systematic error which occurs when measured exposure status may be affected by the latent onset of the target outcome. In this article, we aimed to discuss the benefits and drawbacks of the lag-time approach to address this type of bias. STUDY DESIGN AND SETTING: The lag-time approach consists in excluding from exposure assessment the period immediately preceding the outcome detection date. With the help of simple causal diagrams, we illustrate the rationale and limitations of such strategy. The lag-time approach was illustrated in a case-crossover study, based on the health care utilization databases of the Italian Lombardy Region, on the real-world effectiveness of some respiratory drugs (exposure) in preventing asthma exacerbations (outcome). RESULTS: A total of 7,300 of patients who were admitted to an emergency department (ED) for asthma during 2010-2012 (cases) were included. Use (vs. nonuse) of short-acting beta-agonists (SABAs, an asthma reliever medication) during the 90 days before the ED admission date was associated with an increased risk of the outcome [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.72, 2.22]. This paradoxical finding may be explained by protopathic bias, as SABA use prior the ED admission may be affected by preceding respiratory distress. Indeed, when a 120-day period preceding the ED admission was ignored from drug exposure assessment (lag time), SABAs were found to be associated with a reduced risk of the outcome (OR: 0.81; 95% CI: 0.84, 0.92), as expected. CONCLUSIONS: The lag-time approach can be a useful strategy to circumvent protopathic bias in observational studies.
OBJECTIVES:Protopathic bias is a systematic error which occurs when measured exposure status may be affected by the latent onset of the target outcome. In this article, we aimed to discuss the benefits and drawbacks of the lag-time approach to address this type of bias. STUDY DESIGN AND SETTING: The lag-time approach consists in excluding from exposure assessment the period immediately preceding the outcome detection date. With the help of simple causal diagrams, we illustrate the rationale and limitations of such strategy. The lag-time approach was illustrated in a case-crossover study, based on the health care utilization databases of the Italian Lombardy Region, on the real-world effectiveness of some respiratory drugs (exposure) in preventing asthma exacerbations (outcome). RESULTS: A total of 7,300 of patients who were admitted to an emergency department (ED) for asthma during 2010-2012 (cases) were included. Use (vs. nonuse) of short-acting beta-agonists (SABAs, an asthma reliever medication) during the 90 days before the ED admission date was associated with an increased risk of the outcome [odds ratio (OR): 1.95; 95% confidence interval (CI): 1.72, 2.22]. This paradoxical finding may be explained by protopathic bias, as SABA use prior the ED admission may be affected by preceding respiratory distress. Indeed, when a 120-day period preceding the ED admission was ignored from drug exposure assessment (lag time), SABAs were found to be associated with a reduced risk of the outcome (OR: 0.81; 95% CI: 0.84, 0.92), as expected. CONCLUSIONS: The lag-time approach can be a useful strategy to circumvent protopathic bias in observational studies.
Authors: Chun-Han Lo; Peiyun Ni; Yan Yan; Wenjie Ma; Amit D Joshi; Long H Nguyen; Raaj S Mehta; Paul Lochhead; Mingyang Song; Gary C Curhan; Yin Cao; Andrew T Chan Journal: Gastroenterology Date: 2022-07-01 Impact factor: 33.883
Authors: Soumya G Chikermane; Manvi Sharma; Susan M Abughosh; Rajender R Aparasu; Meghana V Trivedi; Michael L Johnson Journal: Breast Cancer Res Treat Date: 2022-08-15 Impact factor: 4.624
Authors: Peipei Liu; Úna C McMenamin; Brian T Johnston; Peter Murchie; Lisa Iversen; Amanda J Lee; Pauline A J Vissers; Chris R Cardwell Journal: Br J Cancer Date: 2020-05-05 Impact factor: 7.640