Anwar T Merchant1, Jihong Liu1, Mark A Reynolds2, James D Beck3, Jiajia Zhang1. 1. Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, South Carolina, USA. 2. School of Dentistry, University of Maryland, Baltimore, Maryland, USA. 3. Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Abstract
BACKGROUND: Survival average causal effect (SACE) can give valid estimates of the periodontal treatment effect on birth outcomes in randomized controlled trials when fetal losses are unequal across the treatment arms. A regression-based method to estimate SACE using ordinary least squares (OLS) regression can be biased if the treatment effect varies across the outcome distribution. In this case quantile regression may be a suitable alternative. METHODS: We compared OLS and quantile regression models estimating SACE to calculate the effect of periodontal treatment on birthweight and gestational age in secondary analyses of publicly available Obstetrics and Periodontal Therapy (OPT) trial data. RESULTS: Periodontal treatment tended to increase birthweight and gestational age at the lowest quantiles, remained flat in the middle quantiles, and trended to decrease both birthweight and gestational age in the highest quantiles. In quantile regression models estimating SACE the β-coefficients: 95% confidence intervals (CI) for the 5th, 50th, and 95th percentiles were 277.5: -141.0 to 696.0 g, 1.4: -107 to 110.3 g, and -84: -344 to 175.3 g for birthweight, and 0.6: -1.0 to 2.2 weeks, -0.1: -0.5 to 0.2 weeks, and -0.6: -1.0 to -0.1 weeks for gestational age. Estimates from OLS models estimating SACE were close to the null, β: 95% CI -4.7: 132.3 to 123.0 g for birthweight, and 0.03: -0.72 to 0.78 weeks for gestational age. CONCLUSIONS: OLS models to evaluate SACE for periodontal treatment effects on birthweight and gestational age may be biased towards the null. Quantile regression may be a preferable alternative.
BACKGROUND: Survival average causal effect (SACE) can give valid estimates of the periodontal treatment effect on birth outcomes in randomized controlled trials when fetal losses are unequal across the treatment arms. A regression-based method to estimate SACE using ordinary least squares (OLS) regression can be biased if the treatment effect varies across the outcome distribution. In this case quantile regression may be a suitable alternative. METHODS: We compared OLS and quantile regression models estimating SACE to calculate the effect of periodontal treatment on birthweight and gestational age in secondary analyses of publicly available Obstetrics and Periodontal Therapy (OPT) trial data. RESULTS: Periodontal treatment tended to increase birthweight and gestational age at the lowest quantiles, remained flat in the middle quantiles, and trended to decrease both birthweight and gestational age in the highest quantiles. In quantile regression models estimating SACE the β-coefficients: 95% confidence intervals (CI) for the 5th, 50th, and 95th percentiles were 277.5: -141.0 to 696.0 g, 1.4: -107 to 110.3 g, and -84: -344 to 175.3 g for birthweight, and 0.6: -1.0 to 2.2 weeks, -0.1: -0.5 to 0.2 weeks, and -0.6: -1.0 to -0.1 weeks for gestational age. Estimates from OLS models estimating SACE were close to the null, β: 95% CI -4.7: 132.3 to 123.0 g for birthweight, and 0.03: -0.72 to 0.78 weeks for gestational age. CONCLUSIONS: OLS models to evaluate SACE for periodontal treatment effects on birthweight and gestational age may be biased towards the null. Quantile regression may be a preferable alternative.