Statistical analysis
We considered potential confounders between 3rd and 4th degree tear and VBAC status to be: maternal age (recorded to the nearest year at prenatal booking and centred at 30 years), mode of birth (as a three level nominal variable: normal vaginal birth, forceps vaginal birth and ventouse/vacuum vaginal birth), completed week of term gestational age (37-42 weeks), birthweight (continuous in grams), episiotomy (binary yes/no), BMI (continuous) and epidural analgesia (binary yes/no). Raw data for the outcome and covariates were presented as number (%) or mean (SD) by exposure status. The proportion of missing data was documented for each covariate (Table 1).
The distribution of covariates between exposure groups was examined using univariable logistic regression for categorical data and Wilcoxon rank-sum test for continuous data, based upon non-missing data. The same tests were repeated as a sub-analysis, looking at the distribution of covariates between women who had a VBAC and had a 3rdor 4th degree tear, and women who had a VBAC but no significant perineal injury.
The adjusted analysis consisted of two parts: multiple imputation of missing covariate data, followed by regression adjustment analysis based upon potential outcome means (POM). We performed multiple imputation consisting of fully conditional specification (FCS) using a predictive mean model for continuous, logistic model for binary and multinominal model for unordered categorical covariates. The imputation model included outcome, exposure and all covariates used in the analysis model. There were no additional auxiliary variables available for inclusion. The number of imputed datasets (20) was set above the highest percentage of missingness and considered adequate if the Monte Carlo error was less than 10% of regression coefficient standard errors. Standard imputation diagnostics included: graphical assessment of convergence; comparison of distributional shape between both imputed (complete) and observed (non-imputed) datasets; and graphical and numerical imputed values and observed values.
The analysis was then performed on each of the 20 imputed datasets, using Regression Adjustment (RA) to derive a pooled estimate of potential outcome means (POM), Risk difference (RD) and Relative risk (RR) with associated 95% confidence limits adjusted for imputation using Rubin’s rules. Regression adjustment estimates the POM separately for both exposure groups and these are then used to derive RD and RR estimates. The model will produce unbiased estimates if both covariate relationships are correctly specified, and no important confounders have been omitted.
Sensitivity analysis was performed using the same regression adjustment model for two complete case cohorts (including and excluding BMI and analgesia in the covariate list). Unadjusted RD and RR are also presented. Analysis was performed using Stata v16, including the multiple imputation and teffects suites. The significance level was two-sided and set at 0.05.