Table 1 Statistical methods and machine learning techniques are used in pregnancy literature
From: Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges
Statistical methods | Studies |
|---|---|
T-test | Preeclampsia [15, 17, 22, 24, 30] | Metabolic syndrome [46] | Gestational diabetes[27, 48] Placental abruption [20] | Fetal growth restriction [23] | Asthma in offspring [56] Alternations of breastmilk components of COVID-19 [51] |
Fisher's Exact Test | Preeclampsia [21, 22, 24, 30] | Fetal growth restriction [25] | Gestational diabetes [26] Preterm delivery [29] | Alternations of breastmilk components of COVID-19 [51] |
Mann Whitney Rank Sum Test | Preeclampsia [17, 21, 28, 30] | Obesity and overweight [45] | Fetal growth restriction [25] |
Chi-squared test (include Wald test) | Preeclampsia [13, 24, 30] | Placental abruption [20] | Preterm delivery [29] |
ANOVA | Preeclampsia [13, 22, 28, 30] | Placental abruption [18] | Metabolic syndrome [46] Asthma in offspring [53] |
ROC analysis | Placental abruption [20] | Preeclampsia [21, 22, 28] | Gestational diabetes [26, 27] Preterm delivery [29] |
Logistic regression model (including Hosmer–Lemeshow Goodness-of-Fit Test) | Placental abruption [18, 20] | Gestational diabetes [26] | Asthma in offspring [53, 56] |
Spearman rank correlations | |
Post hoc analyses | Respiratory syncytial virus [4] | Preeclampsia [22, 30] | Asthma in offspring [53] |
Linear regression model (including mixed effect model) | Maternal cardiovascular adaptation [3] | Preeclampsia [24] | Asthma in offspring [54] |
Kaplan–Meier curve | |
Kolmogorov–Smirnov test | |
Principal component analysis (PCA) | Preeclampsia [13] | Metabolic dynamics of timing of delivery [19] |
Cox regression | |
Pearson correlation coefficient | Obesity and overweight [45] |