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Identifiability and Exchangeability for Direct and Indirect Effects
TLDR
It is shown that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased, and that, even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions. Expand
The impact of confounder selection criteria on effect estimation.
TLDR
The results of a Monte Carlo simulation of several confounder selection criteria, including change-in-estimate and collapsibility test criteria, are presented, compared with respect to their impact on inferences regarding the study factor's effect. Expand
Modern Epidemiology 3rd edition
Methods for trend estimation from summarized dose-response data, with applications to meta-analysis.
TLDR
The authors propose two methods that account for the correlations but require only the summary estimates and marginal data from the studies, which provide more efficient estimates of regression slope, more accurate variance estimates, and more valid heterogeneity tests than those previously available. Expand
Simulation study of confounder-selection strategies.
TLDR
The authors compared the performance of several such strategies for fitting multiplicative Poisson regression models to cohort data, finding that the change-in-estimate and equivalence-test-of-the-difference strategies performed best when the cut-point for deciding whether crude and adjusted estimates differed by an important amount was set to a low value. Expand
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations ofExpand
Modeling and variable selection in epidemiologic analysis.
  • S. Greenland
  • Computer Science, Medicine
  • American journal of public health
  • 1 March 1989
TLDR
An overview of problems in multivariate modeling of epidemiologic data is provided, and some proposed solutions are examined, including model and variable forms should be selected based on regression diagnostic procedures, in addition to goodness-of-fit tests. Expand
Causal diagrams for epidemiologic research.
TLDR
Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates and provide a method for critical evaluation of traditional epidemiologic criteria for confounding. Expand
Generalized Least Squares for Trend Estimation of Summarized Dose–response Data
This paper presents a command, glst, for trend estimation across different exposure levels for either single or multiple summarized case–control, incidence-rate, and cumulative incidence data. ThisExpand
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