Statistical analysis
The primary outcome was live birth rate (LBR).
Secondary outcomes of the study
included clinical pregnancy
rate(PR), miscarriage rate (MR) and multiple pregnancy rate (MPR).
The baseline characteristics were compared between the two groups. The
normality of continuous variables was examined by normality plots and
Shapiro-Wilk test. Since none of the Continuous variables studied
demonstrated normal distribution by both tests, they are presented as
medians (first quartile, third quartile), while categorical variables
are presented as n (%). Continuous variables were analyzed by
Mann-Whitney U test, and categorical variables were analyzed using
Chi-square test or Fisher’s exact test. All values were two tailed and
P<0.05 was considered to be significant. All analysises were
performed by using SPSS (version 22, IBM).
As two groups were not randomly assigned in clinical practice, potential
confounders and selection biases were accounted for by propensity score
matching 15. Propensity scores were calculated using
logistic regression based on potential variables related to the outcome16. The variables included maternal age, paternal age,
maternal BMI, parity, gravidity, duration of infertility, cause of
infertility, baseline FSH, antral follicle count (AFC), ovarian
stimulation protocol, insemination methods,
endometrial preparation protocol,
endometrial thickness, number of blastocyst vitrified, cycles of ET, day
of blastocyst transferred and the proportion of using top quality
blastocysts. A one-to-one nearest neighbor matching method without
replacement was performed to match data between group G and group GP
with a caliper width equal to 0.03 17. In order to
investigate the effect of group GP in women aged 35 and over and in
women received at least 3 cycles of ET, two groups were stratified by
age and ET order, and PS matching of each subgroup was performed
separately. PS matching was performed by using MatchIt package in R
software.
A generalized estimating equations (GEE) model was conducted to evaluate
the association between the effect of an additional PQE and outcomes due
to including patients contributing multiple cycles 8.
To further verify the results, multivariate GEE models were performed
using pre-matching data to adjust for aforementioned confounders.