Limitations of the application of fourfold table analysis to hospital data.

By J. Berkson. 1946

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Citations in the corpus

External Validity: From Do-Calculus to Transportability Across Populations (2014, background): “paradox (Berkson, 1946), whereby observations on a common consequence of two independent causes render those causes dependent.”
Causal inference in statistics: An overview (2009, background): “This special handling of colliders (e.g., Z → X ← UX)) reflects a general phenomenon known as Berkson’s paradox (Berkson, 1946), whereby observations on a common consequence of two independent causes render those causes dependent.”
3. The Foundations of Causal Inference (2010, background): “For example, if X represents a treatment variable, Y a response variable, and Z some covariate that affects the amount of treatment received, then the distribution P(z, y | do(x0)) gives the proportion of individuals that would attain response level Y = y and covariate level Z = z under the…”
Early recognition of multiple sclerosis using natural language processing of the electronic health record (2017, background): “…of the Random sample set enabled us to obtain a more accurate measurement of sensitivity and specificity of classification of patients known to have MS as well as the true prevalence of MS in our adult population which would allow calculation of positive and negative predictive values [37, 38].”
Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain (2012, background): “This process reduced the possibility of distortion to the partial correlation matrix due to Berkson’s paradox (Berkson, 1946), which could occur if we were to partial out negative correlations introduced when regressing out global mean signal (Murphy, Birn, Handwerker, Jones, & Bandettini, 2009).”
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. (2007, unknown): “However, they can present problems if the exposure of interest affects the risk of developing or being hospitalized for the control condition(s) [43,44].”
Application of Support Vector Machine for Prediction of Medication Adherence in Heart Failure Patients (2010, methodology): “Finally, our data were mainly based on patient information, and so, Berkson’s bias [26] may contribute to inaccurate or excessive approval.”
Reinterpreting comorbidity: a model-based approach to understanding and classifying psychopathology. (2006, background): “…is not an artifact of a specific sampling frame, especially in the sense that comorbid cases should be more common in the clinic than in the population at large, even if probabilities of seeking help for specific diagnoses are independent (a phenomenon termed “Berkson’s bias”; Berkson 1946).”