Limitations of the Application of Fourfold Table Analysis to Hospital Data.¶
Berkson, J. (1946). Limitations of the Application of Fourfold Table Analysis to Hospital Data. Biometrics Bulletin, 2(3), 47-53.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Absence as Information
- Two further modes deepen the analysis. Reasoning about selection effects: the population one can observe is bounded by an absence-process one may not have modelled, and Berkson's bias, selection bias, survivorship bias, and publication bias are all failures to model an absence-process — which unifies a scattered set of named biases under a single structural cause.
This sourceIdentifies the selection (Berkson's) bias that arises when the observable population is conditioned by an unmodeled selection process.
Supported in partVerified against the publisher's abstract
Berkson 1946 supplies the hospital-sampling/conditioned-population frame behind Berkson's bias, but names no bias and does not unify survivorship or publication bias under a single cause.
“In the statistical application, the groupings, B and not B, are made after the subgroupings, A and not A, are already determined; that is, all the effects are already produced before the investigation starts.”
- Two further modes deepen the analysis. Reasoning about selection effects: the population one can observe is bounded by an absence-process one may not have modelled, and Berkson's bias, selection bias, survivorship bias, and publication bias are all failures to model an absence-process — which unifies a scattered set of named biases under a single structural cause.
- Selection Bias
- The tension between prevention rigor and the realities of observational data drives methodological choice
This sourceBerkson hospital data case-control collider bias spurious association.
Supported in partVerified against the publisher's abstract
Berkson's abstract contrasts experimental selection-before-treatment with observational selection-after-effect, showing why observational data require different interpretation, but it does not mention prevention rigor.
“In the experimental situation, the groups, B and not B, are selected before the subgroupings, A and not A, are effected; that is, we start with a total group of unaffected animals. In the statistical application, the groupings, B and not B, are made after the subgroupings, A and not A, are already determined; that is, all the effects are already produced before the investigation starts. In the end, the tables of the …”
- The tension between prevention rigor and the realities of observational data drives methodological choice
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