Data extraction and outcome indicators
Two authors (Guo ZH and Qiu YJ) independently screened the titles and
abstracts of the studies identified based on inclusion and exclusion
criteria. Full texts were then read to determine which studies would be
finally included in this meta-analysis.
The
Cochrane Risk of Bias and the tool Newcastle–Ottawa Scale were used to
evaluate bias and quality. Statistical heterogeneity in the included
studies was assessed via I2 test and Cochran’s Q test,
and funnel plots and Egger’s test were applied to assess publication
bias10. The I2 > 50%
and Cochran’s Q test p < 0.1 suggest statistically
significant heterogeneity11.
Data were extracted from all
included studies independently by two authors, presented by a standard
form that included author, study type, control group, sample size, mean
gestational age, mean birth weight, and corrected gestational age at
extubation. Any disagreements in
data extraction were settled via discussion. The indicator of the
primary outcome was extubation failure, which was often defined as
requiring reintubation and invasive ventilatory support within 72 h. The
secondary outcomes were rates of related adverse clinical events under
NIV-NAVA compared with conventional NIV in extremely preterm infants
after extubation.
Data synthesis and statistical
analysis
The expression of dichotomous outcome data analyses are risk ratios
(RRs) with 95% confidence intervals (CIs)12, and the
expression of continuous outcome data analyses are mean differences
(MDs) with 95% CIs. Data reported as medians and interquartile ranges
were converted to estimated means and standard deviations via a standard
method13,14. Review Manager (RevMan 5.3; Nordic
Cochrane Centre, Cochrane Collaboration, Copenhagen, Denmark) software
was applied to process meta‑analyses. Variables were analyzed with a
fixed-effects model or a random-effects model depending on statistical
heterogeneity. When p> 0.10 or I2 < 50% a
fixed-effects model was used, otherwise a random-effects model was used.
Trial
sequential analysis
The meta-analysis is often called interim analysis
when applied to new data that are
constantly being updated, and some false-positive conclusions or
false-negative conclusions are unavoidable due to sparse or accumulating
data15. The application of trial sequential analysis
(TSA) may ameliorate these problems in analysis with adjustment of CIs
and restricted thresholds16. In the current study TSA
software (version 0.9.5.10 Beta; Copenhagen Trial Unit, Copenhagen,
Denmark) was used to construct adjusted significance test boundaries by
alpha-spending17. The type 1 error was set as 5% and
boundary type was set as two-sided for conventional boundaries. An
O’Brien Fleming power of 80% was used in alpha-spending boundaries.
Relative risk reduction of 71% was estimated, 20% for the incidence in
the control arm, and the heterogeneity correction was based on model
variance17,18.