Prevention of Preterm Birth Using Cervical Pessary in Pregnant Women With Short Cervix: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Prevention of Preterm Birth Using Cervical Pessary in Pregnant Women With Short Cervix (NCT00706264), a randomized na study. The primary outcome is Spontaneous Delivery Before 34 Completed Weeks.

2. Clinical Question

3. Trial Design

PECEP is identified in the registry as a randomized controlled trial. The design is parallel, with participants assigned to expectant management or pessary placement. The registry lists single masking, but does not report which people involved in the trial were masked or how masking was maintained. Allocation concealment and details of the randomization procedure are not reported in the registry record.

The record gives enrollment as 385. The primary outcome results list 190 participants in each arm. The trial ran from 2007-06, with primary completion recorded as 2014-07, and its status is completed. The phase is recorded as NA. The sponsor is Maternal-Infantil Vall d´Hebron Hospital.

The intervention is described as a silicon ring (Arabin Pessary). One arm is described as expectant management; the other received an Arabin pessary from 23 weeks until 37 weeks. The record does not specify further components of routine care or whether participants could receive additional interventions. It also does not give a detailed account of eligibility criteria beyond the short-cervix population stated in the trial summary.

The primary endpoint is spontaneous delivery before 34 completed weeks. The registry records a timeframe between 24 and 34 weeks. Secondary outcome topics include birthweight thresholds, fetal demise, neonatal death, and several neonatal complications. The registry record lists 24 outcome measures, while 23 secondary outcomes are posted. It does not give a complete analysis plan or a narrative explaining the status of each outcome measure.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Spontaneous Delivery Before 34 Completed Weeks, measured over Between 24 and 34 weeks.

The primary endpoint counts participants with spontaneous delivery before 34 completed weeks. It is a binary outcome at the participant level: each participant either experienced the specified event or did not. The registry describes the measure as a count of participants and gives the timeframe as between 24 and 34 weeks. The endpoint therefore concerns timing and spontaneous onset of delivery, rather than all births before the threshold or a continuous measure of gestational age.

Preterm birth is the condition named in the record, and the endpoint directly captures an event relevant to that trial question. A count-based binary outcome allows the two randomized groups to be compared by how many participants met the definition. The event definition matters: the result should be interpreted as spontaneous delivery before the stated gestational threshold, not as a general measure of every pregnancy or neonatal outcome.

The registry reports 51 participants with the event in the no-pessary group and 12 in the pessary group, with 190 participants listed in each arm for this outcome. The record does not supply a more detailed operational definition of spontaneous delivery or describe how delivery timing and spontaneous onset were adjudicated. The registry also does not explain whether participants with incomplete follow-up were excluded, counted in another way, or handled through a missing-data procedure.

7. Results Posted in the Registry

• Experimental: Pessary: 6.3% (12/190) versus 26.8% (51/190) on control; risk difference -20.5 percentage points (95% CI -27.7 to -13.3), risk ratio 0.24 (0.13–0.43); unadjusted two-proportion z-test p = <0.001.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Birthweight Less Than 1500 g

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Experimental: Pessary: 4.7% (9/190) versus 13.7% (26/190) on control; risk difference -8.9 percentage points (95% CI -14.7 to -3.2), risk ratio 0.35 (0.17–0.72); unadjusted two-proportion z-test p = 0.003.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Experimental: Pessary than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Birthweight Less Than 2500 g

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Placement of Arabin Pessary Since 23 Weeks Until 37 Weeks: 8.9% (17/190) versus 29.5% (56/190) on control; risk difference -20.5 percentage points (95% CI -28.2 to -12.9), risk ratio 0.30 (0.18–0.50); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Placement of Arabin Pessary Since 23 Weeks Until 37 Weeks than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Neonatal Death

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Experimental: Pessary: 0.0% (0/190) versus 0.5% (1/190) on control; risk difference -0.5 percentage points (95% CI -1.6 to 0.5); unadjusted two-proportion z-test p = 0.317.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Experimental: Pessary and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary results cover outcomes with different meanings and event frequencies. Birthweight below the two stated thresholds and neonatal death are among the outcomes with numerical results. Other registered topics include intrauterine fetal demise, intraventricular haemorrhage, respiratory distress syndrome, retinopathy of prematurity, and necrotising enterocolitis. The registry lists 24 outcome measures and posts 23 secondary outcomes, but the record does not provide a full narrative explanation of which outcomes are represented in the available results.

For the birthweight outcomes with posted comparisons, the event counts are lower in the pessary arm than in the no-pessary arm, matching the direction of the primary comparison. This directional consistency may be relevant when considering the overall pattern, but it does not turn secondary outcomes into replications of the primary endpoint. Birthweight and spontaneous delivery before 34 completed weeks measure different aspects of pregnancy and neonatal outcome, and their comparisons can have different precision.

The neonatal-death comparison is based on sparse counts and does not clearly distinguish the groups. This contrasts with the more statistically clear unadjusted comparisons for the primary and birthweight outcomes. A weak or inconclusive result for a rare secondary event does not negate the primary result, but it also cannot establish safety or equivalence for that event.

Secondary results generally carry less weight when many outcomes are examined and no multiplicity strategy is reported. With more comparisons, the chance of obtaining at least one apparently unusual result by chance can increase. The record does not report a hierarchy of secondary outcomes, adjusted p-values, or a family-wise error or false-discovery procedure. Thus, nominal p-values for secondary outcomes should be treated as exploratory evidence rather than definitive confirmation. The broader pattern can add context to the primary comparison, but it does not replace prespecified analysis or a complete account of all outcome results.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Birthweight Less Than 1500 g
• Birthweight Less Than 2500 g
• Intrauterine Fetal Demise
• Neonatal Death
• Intraventricular Haemorrhage
• Respiratory Distress Syndrome
• Retinopathy of Prematurity
• Necrotising Enterocolitis

9. Statistical Interpretation

10. Statistical Methods Explained

The registry reports a binary primary outcome: participants either had spontaneous delivery before 34 completed weeks or did not. For a binary endpoint, a group’s event count is interpreted alongside the number of participants represented for that outcome. A comparison can then describe the difference in event probability between groups. The posted primary result gives the counts and arm sizes, an unadjusted p-value, and a risk-difference interval.

What does the risk difference represent?

A risk difference expresses the absolute contrast between the event probabilities in the two groups, with the sign depending on which group is subtracted from which. In this record, the pessary comparison is oriented against the no-pessary group, and the estimated risk difference is negative. That direction indicates a lower event probability in the pessary group in the posted comparison. The confidence interval for the primary risk difference is entirely below zero, so its range does not include a zero difference. The interval still represents uncertainty about the size of the contrast; it does not remove uncertainty about generalizability, bias, or the correctness of the endpoint classification.

How should the p-value be read?

A p-value describes how compatible the observed data are with a specified null model of no group difference, under the assumptions of the statistical test. The primary comparison reports p < 0.001. This is evidence against that null model in the unadjusted comparison; it is not the probability that the null hypothesis is true, nor a measure of the clinical importance of the result. A small p-value does not correct for limitations in trial conduct or analysis, and it does not indicate the probability that a result will replicate.

What does a confidence interval add?

A confidence interval gives a range of effect estimates compatible with the data under the method used. Its width communicates precision: a narrower interval indicates less sampling uncertainty than a wider one, while the interval’s location indicates the directions and sizes of effects it accommodates. For the primary risk difference, the reported interval is on the side indicating fewer events with the pessary. The registry record does not report enough detail about the interval method to independently evaluate its assumptions.

Why distinguish absolute and relative comparisons?

An absolute measure such as a risk difference expresses how far apart the groups are on the event-probability scale. A relative measure, such as a risk ratio, compares the probabilities as a ratio. Each answers a different descriptive question and depends on the event frequency in the comparison groups. The registry comparison includes relative-effect measures, but the primary interpretation can be grounded in the event counts and absolute direction without treating a relative measure as a direct estimate of benefit for an individual.

What does unadjusted mean?

An unadjusted comparison uses the posted group summaries without accounting for additional covariates or other outcomes. Randomization is intended to support a fair comparison by assigning participants to groups, but it does not guarantee identical group characteristics in a particular trial. The registry record does not report an adjusted analysis or enough information to determine whether one was planned. The reported p-values should therefore be described as unadjusted, rather than as results from a covariate-adjusted model.

Why can sparse outcomes be hard to interpret?

When an event is rare, estimates are often based on very few observed events. That makes the comparison more sensitive to the outcome of each event and generally limits precision. For neonatal death, the posted counts are 1 and 0, the p-value is 0.317, and the risk-difference interval includes zero. This does not establish equivalence; it means the posted comparison does not clearly distinguish a difference from no difference. The registry does not report a separate rare-event modeling method.

What analysis details are absent?

The registry record does not report the original statistical analysis plan, a covariate-adjustment strategy, interim analyses, handling of missing outcomes, or the analysis population definition. It also does not describe the test or interval method in enough detail for independent replication. These omissions limit how fully the statistical assumptions can be assessed. The posted results remain interpretable as registry-reported group counts and unadjusted comparisons, but they should not be mistaken for a complete account of the trial’s statistical conduct.

11. Confidence Intervals and Precision

Precision concerns how much uncertainty remains around an estimated effect. For the primary outcome, the registry reports a risk-difference confidence interval that lies entirely below zero. This indicates that the interval’s range is consistent with fewer events in the pessary group, rather than including an equal event probability. The interval is not a guarantee that the true effect falls within its limits, and it does not address uncertainties that arise from study conduct, outcome definitions, missing data, or applicability beyond the trial.

The comparison also reports p < 0.001. The p-value and confidence interval are related summaries of the same comparison, but they answer different questions: the p-value describes compatibility with a null model, while the interval conveys a range of effect estimates. Neither tells readers whether an effect is important in a particular clinical context. The registry record does not define a threshold for a clinically important difference.

Precision is more limited for outcomes with few events. For neonatal death, the posted counts are 1 and 0, and the interval for the risk difference includes zero. The reported p-value is 0.317. This combination signals that the comparison does not precisely establish a direction or size of difference. It should not be interpreted as evidence that the groups have identical risks.

For the birthweight outcomes, the unadjusted p-values are 0.003 and p < 0.001. The corresponding posted risk-difference intervals are below zero. These comparisons are more informative than a result based on very few events, but the registry record does not report an adjustment for examining multiple outcomes. Their nominal precision does not eliminate the need to account for the broader set of secondary comparisons when interpreting them.

The registry does not report additional precision measures such as a prespecified clinically meaningful range, nor enough statistical-method detail to reproduce the intervals independently. The displayed estimates should therefore be read as uncertainty summaries from the posted analysis, not as a complete assessment of all uncertainty.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are participants per arm, not total participants across the trial. The planning illustration uses equal allocation, a two-sided significance level of 0.05, and a normal approximation. It takes the observed effect as its starting point and considers detecting the full observed effect, 75% of the observed effect, or 50% of the observed effect. These are planning assumptions for an illustration; the original trial design assumptions are not reported in the registry record.

The 80% power column gives the estimated participants per arm for a hypothetical study designed to have an 80% chance of detecting the stated effect under those assumptions, if that effect is real. The 90% power column gives the corresponding per-arm planning figure for a 90% chance. For the primary comparison, the posted rows give 51 participants per arm for 80% power and 67 for 90% power when planning for 100% of the observed effect; 102 and 136 for 75%; and 253 and 338 for 50%.

The increase in the planning figures as the assumed effect gets smaller illustrates a general statistical principle: smaller effects are harder to distinguish from random variation, so more participants are needed under otherwise unchanged assumptions. A higher target power also requires more participants per arm in these illustrations.

These figures are not observed power. They are planning illustrations based on the effect observed in this trial, not evidence about the probability that the completed trial detected a true effect. The registry record does not report the trial’s original sample-size justification, target power, or original design assumptions, so the illustration should not be used to infer what the investigators planned.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.268; treatment proportion p₁ = 0.063; average p̄ = 0.166.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5259 = 1.0308.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5055 = 0.4254.
4. n = (1.0308 + 0.4254)² / (-0.2053)² = 50.3, rounded up to 51 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.268421052631579                          # control proportion
p1 <- 0.0631578947368421                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(706264)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 51 (80% power), 67 (90% power)
 75% of observed effect: n per arm = 102 (80% power), 136 (90% power)
 50% of observed effect: n per arm = 253 (80% power), 338 (90% power)
power.prop.test: 50.3 
Simulated power at n = 51 per arm: 0.828 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when statistical tests are conducted for multiple outcomes or comparisons. If each test is considered separately without adjustment, the chance of at least one apparently significant result due to random variation can increase across the set. This issue is relevant because the registry lists 24 outcome measures and 23 secondary outcomes are posted, alongside the primary endpoint. The outcomes also cover several distinct domains, so they should not automatically be treated as interchangeable measurements of a single effect.

The registry record does not report a multiplicity adjustment, a hierarchy that specifies which secondary outcomes are confirmatory, or a prespecified approach to interpreting the family of tests. The p-values for secondary outcomes should therefore be viewed as unadjusted. Their values may be useful for describing the posted results, but should not be treated as if they independently confirm a treatment effect after accounting for all outcomes examined.

The record also does not report how missing outcome data were handled, whether any outcomes were imputed, or the number of participants with incomplete follow-up for each measure. Arm sizes are listed for the posted outcomes, but those entries do not establish that all randomized participants contributed to every analysis. The analysis population—such as intention-to-treat or per-protocol—is not reported in the registry record.

There are no reported analyses listed in the registry summary. The record does not give an analysis plan, the timing or conduct of interim analyses, or the statistical methods used for the posted comparisons in sufficient detail to assess them fully. It does not report whether the primary analysis was adjusted for baseline factors, how protocol deviations were handled, or how competing events were considered.

These gaps do not invalidate the reported group counts, but they limit assessment of whether all outcomes and analyses were handled as intended and whether missingness could affect the comparisons. Interpretation should distinguish what is posted—counts and unadjusted comparisons—from details that are not reported in the registry record.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

The registry safety record lists serious adverse events for expectant management and pessary placement. For each group, it reports 0 affected participants among 190 at risk. It also reports 0 participants affected by other adverse events among 190 at risk in each group, and 0 deaths in each group. No top serious-event terms are listed.

These entries describe what is reported in the registry; they are not proof that no adverse events occurred outside the record or that every possible harm was assessed. The registry does not provide event-level detail, definitions of serious events, follow-up completeness, or an explanation of how adverse events were collected and coded. It also does not report a statistical comparison of safety outcomes.

Zero events in the posted counts do not establish that the groups have identical safety profiles. In particular, the safety figures should be read alongside the limitations of the registry reporting and the absence of detailed event descriptions. The record supports a statement about the counts it lists, not a broad conclusion about safety.

16. What the Result Does, and Does Not, Mean

The primary posted result is a comparison of spontaneous delivery before 34 completed weeks in a randomized trial of expectant management versus Arabin pessary placement. The event counts are 51 in the no-pessary group and 12 in the pessary group, with 190 participants listed in each arm. The unadjusted comparison reports p < 0.001, and the risk-difference interval lies below zero. The data therefore show a lower observed frequency of this defined event in the pessary group in the trial’s posted results.

This is not the same as proving that pessary placement will have the same effect in every setting or for every pregnant person with a short cervix. The registry record does not give enough detail about eligibility, clinical care, adherence, missing outcomes, or the analysis population to assess all dimensions of transportability and potential bias. It also does not report the original statistical plan or the rationale behind the sample size.

The secondary results offer context but do not establish a comprehensive effect on all pregnancy or neonatal outcomes. Some posted birthweight comparisons have event counts in the same direction as the primary result, while the neonatal-death comparison is based on very few events and is inconclusive. Many outcome measures are listed, and a multiplicity approach is not reported, so secondary p-values deserve caution.

The results do not identify why group outcomes differed, nor do they quantify a person’s individual chance of an outcome. The record’s safety figures list no affected participants or deaths in either arm, but sparse or incomplete reporting cannot establish that there was no risk. These are statistical descriptions of the posted trial data, not medical advice or a recommendation about care.

The trial summary states that pessary placement reduces the rate of spontaneous preterm birth. Statistical interpretation should nevertheless focus on the defined endpoint, the observed counts, and the uncertainty and reporting limitations. The result is evidence about this randomized comparison as recorded, not a guarantee of outcome or a complete assessment of every benefit and harm.

17. Why This Trial Matters Statistically

This trial is statistically notable because it randomized participants to a parallel comparison and reported a clear difference in the primary binary outcome. The event counts and unadjusted comparison allow readers to connect the headline result to the underlying numbers, rather than relying only on a general summary statement. The direction of the posted risk difference and its interval can be interpreted against the explicit endpoint definition: spontaneous delivery before 34 completed weeks.

The record also illustrates why an effect estimate should be considered with its precision and outcome context. The primary comparison and some birthweight comparisons have small reported p-values, while the neonatal-death comparison is based on sparse events and remains inconclusive. A statistical result for one endpoint does not automatically transfer to another endpoint, especially where event counts and clinical meanings differ.

It also demonstrates the importance of distinguishing planned sample size from observed trial results. The posted planning figures are illustrations calculated from the observed effect under stated assumptions; they are not evidence of the trial’s original design rationale or the completed trial’s observed power. The original sample-size justification is not reported in the registry record.

Finally, the breadth of registered outcomes makes multiplicity and complete reporting relevant. The registry does not report how multiple outcomes were handled, nor does it provide a full analysis plan or detailed information about missing data and analysis populations. Readers can interpret the posted comparisons, while recognizing that these reporting gaps restrict how strongly secondary findings can be treated as confirmatory. The trial is a useful example of why statistical significance, precision, outcome definition, and completeness of reporting need to be considered together.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several limitations follow from details that are not reported in the registry record. The record does not specify the full eligibility criteria, the randomization method or allocation concealment, or which participants or staff were masked. Single masking is reported, but its implementation is not explained. These omissions make it difficult to assess the potential for selection or ascertainment bias from the registry entry alone.

The primary endpoint is described as spontaneous delivery before 34 completed weeks, with a timeframe between 24 and 34 weeks, but operational details are not reported. The record does not explain how spontaneous onset was determined, how gestational age was established, or whether endpoint events were adjudicated. It also does not give the analysis population or how incomplete outcome data were handled.

The registry record does not include an original sample-size rationale, the full statistical analysis plan, interim-analysis details, or information about multiplicity. The sample-size figures are explicitly planning illustrations based on the observed effect and assumptions that differ from an unretrieved original protocol. They cannot clarify what effect the investigators originally considered important or what power they intended.

Secondary outcome reporting is incomplete in the sense that the registry lists 24 outcome measures while 23 secondary outcomes are posted, and no full narrative account of their analysis is available. The record does not report adjusted estimates or a multiplicity procedure. Some results, such as neonatal death, are based on very few events, which limits precision. Safety reporting lists zero events but lacks event-level descriptions and details about collection and follow-up.

These are limitations of what the registry record reports; they should not be taken as proof that the trial lacked procedures or analyses that are simply absent from the record. They do, however, constrain independent assessment of design, analysis, and the completeness of the posted results.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Prevention of Preterm Birth Using Cervical Pessary in Pregnant Women With Short Cervix — NCT00706264.
• Registry record last updated 2021-03-17; status last verified 2021-03.
• Results first posted 2021-03-17.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT00706264, titled Prevention of Preterm Birth Using Cervical Pessary in Pregnant Women With Short Cervix, is the PECEP randomized controlled trial. The registry records a parallel design, single masking, enrollment of 385, and completed status. The sponsor is Maternal-Infantil Vall d´Hebron Hospital. The trial dates are recorded as 2007-06 to primary completion in 2014-07.

The primary comparison is spontaneous delivery before 34 completed weeks. The posted counts are 51 of 190 in the no-pessary arm and 12 of 190 in the pessary arm; the unadjusted comparison reports p < 0.001. The registry also posts selected birthweight and neonatal outcomes. Its safety entries report zero serious adverse events, zero other adverse events, and zero deaths in each group. Details about the original sample-size justification, missing data, analysis populations, and multiplicity are not reported in the registry record.