Statistical analysis:
Baseline comparisons of the three surgical groups for women’s characteristics used ANOVA tests for continuous and Chi-square tests for categorical variables.
We used a propensity score matching approach with inverse probability of treatment weighting to balance the baseline differences between the surgical groups and limit indication bias.11Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology 2000;11:550-60,22Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat Med 2004;23:2937-60. A multinomial logistic regression was constructed to estimate each women’s probability of receiving one of the three types of surgeries given their baseline covariates (i.e., the propensity score). Variables of the propensity score model were prespecified before outcome analyses and included age, body mass index, smoking, diabetes, surgical history (hysterectomy, or surgery for stress urinary incontinence or pelvic organ prolapse), physical status score (ASA), menopausal status, and anatomical defect. Stabilized weights were used to estimate the average treatment effect in the entire population, and the extreme weights were truncated.33Austin PC. The performance of different propensity-score methods for estimating differences in proportions (risk differences or absolute risk reductions) in observational studies. Stat Med 2010;29:2137-48. Balance between treatment populations was evaluated by standardized differences of all baseline covariates, with a threshold of 0.1 used to indicate imbalance.16
Survival curves were obtained with the Kaplan-Meier estimator. In the absence of earlier events, we censored events as of December 10, 2019. Two weighted frailty models — one for complications and one for recurrence/reoperations — were used to compare the three surgical groups. The models included a non-parametric estimation of the baseline hazard and a gamma frailty term for the centre effect.44Duchateau L, Janssen P, Lindsey P, Legrand C, Nguti R, Sylvester R. The shared frailty model and the power for heterogeneity tests in multicenter trials. Computational Statistics & Data Analysis 2002;40:603-20.,55Gutierrez RG. Parametric frailty and shared frailty survival models. The Stata Journal 2002;2:22-44.
All statistical tests were two-sided, a p-value <.05 was considered significant. A multiple imputation (R mice package) strategy was used to deal with the missing data. All statistical analyses were performed with the R statistical package version 3.6.1 or later (The R Foundation for Statistical Computing,https://www.R-project.org/).
Patients were not involved in the development of the VIGI-MESH registry. No core outcome sets were used.