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Revisiting the Table 2 fallacy: A motivating example examining preeclampsia and preterm birth
Author(s) -
Bandoli Gretchen,
Palmsten Kristin,
Chambers Christina D.,
JelliffePawlowski Laura L.,
Baer Rebecca J.,
Thompson Caroline A.
Publication year - 2018
Publication title -
paediatric and perinatal epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 88
eISSN - 1365-3016
pISSN - 0269-5022
DOI - 10.1111/ppe.12474
Subject(s) - medicine , poisson regression , confounding , demography , preeclampsia , population , pregnancy , singleton , obstetrics , odds ratio , alcohol abuse , psychiatry , environmental health , sociology , biology , genetics
Background A “Table [Table 2. Risk ratios for association between preeclampsia and preterm birth in a retrospective cohort of 3 million births between 2007 and 2012 in California] Fallacy,” as coined by Westreich and Greenland, reports multiple adjusted effect estimates from a single model. This practice, which remains common in published literature, can be problematic when different types of effect estimates are presented together in a single table. The purpose of this paper is to quantitatively illustrate this potential for misinterpretation with an example estimating the effects of preeclampsia on preterm birth. Methods We analysed a retrospective population‐based cohort of 2 963 888 singleton births in California between 2007 and 2012. We performed a modified Poisson regression to calculate the total effect of preeclampsia on the risk of PTB, adjusting for previous preterm birth. pregnancy alcohol abuse, maternal education, and maternal socio‐demographic factors (Model 1). In subsequent models, we report the total effects of previous preterm birth, alcohol abuse, and education on the risk of PTB, comparing and contrasting the controlled direct effects, total effects, and confounded effect estimates, resulting from Model 1. Results The effect estimate for previous preterm birth (a controlled direct effect in Model 1) increased 10% when estimated as a total effect. The risk ratio for alcohol abuse, biased due to an uncontrolled confounder in Model 1, was reduced by 23% when adjusted for drug abuse. The risk ratio for maternal education, solely a predictor of the outcome, was essentially unchanged. Conclusions Reporting multiple effect estimates from a single model may lead to misinterpretation and lack of reproducibility. This example highlights the need for careful consideration of the types of effects estimated in statistical models.