Statistical Analysis
preterm preeclampsia, prematurity
EVENT, SECONDARY, REGRESSION, PRETERM DELIVERY, HYPERTENSION, PREECLAMPSIA
We planned the study around seven clusters, each involving 1 center, and therefore 8 steps each of 4 months; and a total trial length of 8×4=32 months. Based on hospital records, we assumed an average of 330 deliveries per month in each center; and 990 per step (allowing for some delay in the full implementation of the new treatment regime). We considered a clinically important improvement would be a fall from 2.0% to 1.5% in the overall rate of preterm delivery with preeclampsia. We assumed a cluster coefficient of variation of 0.4, giving an intracluster correlation of 0.0024. Power calculations were performed using the Stata command steppedwedge.Hospital baseline characteristics of pregnancy outcomes to be reported throughout the data collection period included numbers of patients with preterm preeclampsia, gestational age at delivery of all hospital deliveries, and neonatal inpatient nights in patients with preeclampsia. These characteristics were compared between periods before and after the intervention and were summarized by their means and SDs, medians and interquartile ranges, or numbers and percentages as appropriate. Centers were classified as being exposed to the intervention only after randomization.The primary goal of the study was to test whether a care pathway targeted at preventing unnecessary delivery of patients with preterm preeclampsia would safely reduce the proportion of women delivered in the centers who delivered prematurely. Important independent variables to consider were the clustering effect (ie, the effect of center), calendar time effect (since the intervention was sequentially rolled out), and intervention exposure for each center at each time point, in addition to adjustment for other characteristics. Both individual and cluster-level covariates to be included in the adjustment were prespecified and included maternal age, parity, preexisting chronic hypertension, or other risk factors for prematurity. Null hypotheses and analyses for secondary outcomes took a similar form to that for the primary outcome, with appropriate link functions in the generalized linear mixed model. Summary treatment effect estimates are adjusted and reported (adjusted risk ratios or adjusted odds ratios [aORs]) along with 95% CIs.Data analysis was performed in Stata version16 (StataCorp, College Station, TX). Standard methods were used for the descriptive tables. For the main analysis, event rates were separately estimated for each center and each month of the trial as a proportion of eligible participants with suspected or diagnosed preeclampsia. Because the month of delivery was not typically the same as the month of trial entry, deliveries were not linked to trial entry.Analysis of this data set proved more challenging than expected due to several reasons: (1) an unanticipated bias related to most of the centers with the highest event rates occurring late in the randomization sequence; (2) 2 centers starting the intervention later than planned for operational reasons due to external circumstances beyond our control. As this could be regarded as a random event, the Steering Committee decided to focus on the intervention as actually performed, rather than the intervention as originally planned. This we defined as the intention-to-treat dataset. Further, we established a per-protocol subset of the intention-to-treat dataset, restricted to (1) patients in the Intervention arm with an sFlt-1/PlGF ratio results and (2) patients in the Usual Care arm with no test results.We initially undertook an analysis as indicated in our published protocol.Accordingly, we decided to analyze the data from each center separately. The separate analyses all used binomial regression with the total number of deliveries as the denominator, with a linear effect of time as a covariate and a step change at the point the intervention was introduced. We combined the estimates in a random-effects meta-analysis.Additional risk differences are shown using the nonparametric within-period cluster-level analysis with the Monte Carlo permutation tests method described in Thompson (2018), demonstrated as reliable.As it emerged that sFlt-1/PlGF assays were not universally used outside of the trial population, the data safety and monitoring committee suggested an additional post hoc analysis to investigate the proportion of patients with preterm preeclampsia delivered <37 weeks’ gestation among total patients enrolled. Information about the data safety and monitoring committee is provided in the Results are reported as per CONSORT guidelines.The trial was registered at 