Revisiting the Table 2 fallacy: A motivating example examining preeclampsia and preterm birth

• Main Authors: Bandoli, Gretchen, Palmsten, Kristin, Chambers, Christina D, Jelliffe‐Pawlowski, Laura L, Baer, Rebecca J, Thompson, Caroline A
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: eScholarship, University of California 2018
• Subjects: Epidemiology; Biomedical and Clinical Sciences; Health Sciences; Pediatric; Alcoholism; Alcohol Use and Health; Hypertension; Perinatal Period - Conditions Originating in Perinatal Period; Contraception/Reproduction; Preterm; Low Birth Weight and Health of the Newborn; Substance Misuse; Infant Mortality; Reproductive health and childbirth; Good Health and Well Being; Adult; California; Female; Humans; Infant; Newborn; Maternal Exposure; Pre-Eclampsia; Pregnancy; Pregnancy Complications; Pregnancy Outcome; Premature Birth; Retrospective Studies; Risk Factors; Substance-Related Disorders; measures of effect; perinatal epidemiology; preterm birth; Table 2 Fallacy; Paediatrics and Reproductive Medicine; Public Health and Health Services; Paediatrics; Reproductive medicine; Greenland
• Online Access: https://escholarship.org/uc/item/5450f301

BackgroundA "Table 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.MethodsWe analysed a retrospective population-based cohort of 2963888 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.ResultsThe 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.ConclusionsReporting 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.