What question did this study set out to answer?

To model the time-dependent effects of past exposure histories on disease risk by estimating a spline-based weight function within a generalized linear model.

December 1, 2000Biometrics

Analysis of Exposure‐Time‐Response Relationships Using a Spline Weight Function

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Authors

MHMichael HauptmannCardio-Oncology JWJürgen WellmannJohannes Gutenberg University Mainz JLJay H. LubinDartmouth College

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Implication

Case-control study demonstrates time-dependent exposure weighting for lung cancer risk from smoking histories, suggesting improved estimation of historical exposure impacts.

Key Points

To model the time-dependent effects of past exposure histories on disease risk by estimating a spline-based weight function within a generalized linear model.
Modeled the exposure-time-response weight function as a cubic B-spline within a generalized linear model framework.
Evaluated the statistical properties of the model using simulation studies and applied the method to empirical smoking and lung cancer data from a case-control study in Germany.
Simulation results demonstrated that the cubic B-spline weight function accurately recovers the shape and timing of exposure effects on disease risk.
Application to German case-control data delineated the specific temporal impact of past smoking increments on overall lung cancer risk.

Cite This Study

Hauptmann et al. (2000) studied this question.

synapsesocial.com/papers/6a763a42666dd073ee008fee https://doi.org/10.1111/j.0006-341x.2000.01105.x

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Implication

Case-control study demonstrates time-dependent exposure weighting for lung cancer risk from smoking histories, suggesting improved estimation of historical exposure impacts.

Analysis of Exposure‐Time‐Response Relationships Using a Spline Weight Function

Discussion

Member takes

Implication

Key Points

Cite This Study

Analysis of Exposure‐Time‐Response Relationships Using a Spline Weight Function

Discussion

Member takes

Implication

Key Points

Cite This Study

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