ABSORB III Randomized Controlled Trial (RCT): Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for ABSORB III Randomized Controlled Trial (RCT) (NCT01751906), a randomized na study. The primary outcome is Number of Cardiac Death/TV-MI/ID-TLR (TLF).

2. Clinical Question

3. Trial Design

ABSORB III is described in the registry as a prospective, randomized, single-blind, multi-center trial with a parallel design. The listed enrollment is 2008, and the record gives the trial status as completed. The listed phase is NA. The sponsor is Abbott Medical Devices.

The trial compared Absorb BVS with XIENCE. For the XIENCE group, the registry names XIENCE V, XIENCE PRIME, or XIENCE Xpedition. The primary endpoint timeframe is one year. The analysis population is called the Intent-To-Treat Population set, but the record also says the number analyzed includes subjects who had available follow-up data at the relevant timeframe. This wording describes the posted analysis population; it does not explain how missing follow-up was handled.

The brief summary describes ABSORB III as the pivotal trial supporting US pre-market approval of Absorb BVS. It also explains that ABSORB III PK and ABSORB IV are maintained under one protocol because their designs are related, and says ABSORB IV is a continuation of ABSORB III. The summary states that data from ABSORB III and ABSORB IV will be pooled to support the ABSORB IV primary endpoint. These statements describe the broader protocol context; the results discussed for the primary comparison are the one-year ABSORB III results.

The registry record does not report the original design assumptions, a sample-size justification, the masking procedures, or details of how randomization was implemented. The phase is listed as NA rather than as a clinical-trial phase. Those omissions limit what can be concluded about the design beyond the features stated in the record.

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.

Before assignment: Of total 13,789 eligible population,11,781 patients were excluded due to, 1. Not meeting general eligibility criteria only (n=567); 2. Not meeting angiographic eligibility criteria only (n=10,690); 3. Not meeting both 1 and 2 (n=64); 4. Other reasons (n=460).

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Intent-To-Treat Population set (ITT). The number of participants analyzed includes subjects who had available follow up data at that time frame.

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 Number of Cardiac Death/TV-MI/ID-TLR (TLF), measured over 1 year.

The primary endpoint counts participants who experienced target lesion failure, or TLF, by the one-year timeframe. The registry defines this composite as cardiac death, myocardial infarction attributable to the target vessel, or ischemia-driven target lesion revascularization. Each component represents a different type of event, but the primary measure combines them into a single participant-level outcome.

A composite endpoint can capture several clinically relevant event types in one measure and can make a trial outcome more inclusive than any component considered alone. Its interpretation depends on the component definitions and on which components contribute most to the overall count. The registry gives the TLF definition but does not provide the primary component-specific counts in the results. Therefore, the overall TLF comparison cannot by itself show which component explains the observed group difference.

The primary outcome is reported as a number of participants, and the analysis population is identified as the Intent-To-Treat Population set. The record clarifies that the number analyzed includes those with available follow-up data at the one-year timeframe. It does not report additional detail about missing outcomes or how the composite was adjudicated. The count and comparison should therefore be read as the posted one-year results under the registry’s stated definition, not as evidence about unreported components or follow-up periods.

7. Results Posted in the Registry

• XIENCE: 6.1% (41/677) versus 7.8% (102/1313) on control; risk difference -1.7 percentage points (95% CI -4.0 to 0.6), risk ratio 0.78 (0.55–1.11); unadjusted two-proportion z-test p = 0.161.

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.

Number of Participants With Powered Secondary Endpoint: Angina

• 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:
  • XIENCE: 18.4% (125/678) versus 18.3% (238/1303) on control; risk difference 0.2 percentage points (95% CI -3.4 to 3.8), risk ratio 1.01 (0.83–1.23); unadjusted two-proportion z-test p = 0.926.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between XIENCE and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants With Powered Secondary Endpoint: All Revascularization

• 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:
  • XIENCE: 8.1% (55/677) versus 9.1% (120/1313) on control; risk difference -1.0 percentage points (95% CI -3.6 to 1.6), risk ratio 0.89 (0.65–1.21); unadjusted two-proportion z-test p = 0.449.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between XIENCE and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants With Powered Secondary Endpoint: Ischemia Driven Target Vessel Revascularization (ID-TVR)

• 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:
  • XIENCE: 3.7% (25/677) versus 5.0% (66/1313) on control; risk difference -1.3 percentage points (95% CI -3.2 to 0.5), risk ratio 0.73 (0.47–1.15); unadjusted two-proportion z-test p = 0.177.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between XIENCE and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary outcomes cover several different aspects of participant experience and subsequent procedures, including angina, all revascularization, and ischemia-driven target vessel revascularization. The comparison estimates do not all point in the same direction, and their intervals reflect substantial uncertainty. Taken together, they do not provide a single consistent pattern that would settle the primary question independently of the TLF result.

These endpoints are related to the primary outcome but are not interchangeable with it. TLF combines cardiac death, target-vessel myocardial infarction, and ischemia-driven target lesion revascularization. The secondary measures use their own definitions and may capture events that are not part of that composite, or a broader category of events. A secondary endpoint can therefore provide additional context without serving as a direct replication of the primary comparison.

Unadjusted secondary findings carry less weight when many outcomes are examined. In general, testing multiple outcomes increases the opportunity for results to appear notable by chance unless the analysis plan controls that risk. The registry lists many outcome measures, but does not report a multiplicity adjustment, a hierarchy among the secondary endpoints, or whether each was intended to be confirmatory. Their results should be regarded as supportive or exploratory context unless additional analysis-plan information is available.

The registry includes reported analyses for some of the secondary outcomes and lists the test used for those analyses. The record does not provide the corresponding full analysis specification, including any planned handling of missing outcome data or adjustment for other variables. The posted secondary comparisons can describe the observed data, but they do not by themselves establish a treatment effect or overturn the uncertainty in the primary comparison.

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

• Number of Participants With Powered Secondary Endpoint: Angina
• Number of Participants With Powered Secondary Endpoint: All Revascularization
• Number of Participants With Powered Secondary Endpoint: Ischemia Driven Target Vessel Revascularization (ID-TVR)
• Acute Success- Device Success (Lesion Level Analysis)
• Acute Success: Procedural Success (Subject Level Analysis)
• Number of Death (Cardiac, Vascular, Non-cardiovascular)
• Number of Participants With All Myocardial Infarction (MI)
• Number of Participants With All Target Lesion Revascularization (TLR)

9. Statistical Interpretation

10. Statistical Methods Explained

Several statistical ideas help frame the posted comparisons. The primary endpoint is binary at the participant level: a participant either meets the registry’s TLF definition during the stated timeframe or does not. The posted event counts and analyzed denominators are used to describe the observed frequency in each group. A risk difference expresses the absolute contrast between those group frequencies, with the comparison oriented as XIENCE relative to Absorb BVS.

What does the risk difference represent?

As a general statistical concept, a risk difference subtracts one group’s event proportion from the other group’s event proportion. A value below zero in a comparison oriented as XIENCE relative to Absorb BVS means the observed event proportion is lower in XIENCE. It is an absolute contrast, not a relative measure. The registry also contains relative measures for the primary outcome, but the risk difference provides the direct absolute comparison used in the interpretation above.

The estimated risk difference is subject to sampling uncertainty. The posted interval gives a range of values compatible with the data under the method used to construct it. An interval that includes zero does not settle the question of whether the underlying groups differ. It indicates that the data are not precise enough, under that interval procedure, to exclude a zero difference. The registry record does not state the confidence level or the interval-construction method, so neither should be inferred.

How should a p-value be read?

In general, a p-value describes how surprising data at least as incompatible with a specified null hypothesis would be if that hypothesis and the test assumptions held. It is not the probability that the null hypothesis is true, nor is it the probability that the result occurred by chance. The primary result includes an unadjusted p-value of 0.161. The registry does not report the primary test method or state a decision threshold for that analysis, so the p-value should be read as one component of the posted evidence, alongside the estimate and its interval.

The secondary outcome records list specific tests for some outcomes. For angina, the reported analysis uses a Chi-squared test; for all revascularization and ischemia-driven target vessel revascularization, the reported analyses use Fisher Exact tests. These are tests for comparisons of categorical outcomes. In general, a Chi-squared test relies on an approximation that works best when expected cell counts are adequate, while Fisher’s exact test calculates a probability from the observed contingency table and is often used when counts are limited. The registry does not explain why each test was selected or give further details about the analysis procedure.

Why distinguish posted comparisons from reported analyses?

The comparison statistics include estimates, intervals, and unadjusted p-values calculated from the posted summary information. Separately, the registry lists reported analyses for some secondary endpoints, including the named test and its p-value. These can differ in precision or procedure because an analysis can use a particular test or data-handling approach that is not recoverable from summary counts alone. For the primary endpoint, a reported test method is not listed. The record does not identify covariate adjustment, stratification, or a model for the primary comparison.

Randomization supports a comparison between assigned groups, but it does not remove uncertainty or make every analyzed population identical. The primary record labels the population as ITT while specifying that the number analyzed includes participants with available follow-up. The registry does not report whether missing outcomes were imputed, assumed absent, or handled by another method. The reader should therefore keep the stated availability of follow-up in view rather than assume that every randomized participant contributed an observed primary outcome.

Why can over-dispersion matter?

In general, over-dispersion arises when observed variability is greater than a statistical model expects. It matters especially for outcomes modeled as counts or rates, because underestimated variability can make intervals too narrow and tests too optimistic. The primary outcome is posted as participant counts with a binary interpretation, not as a recurrent-event rate or a continuous measure with a reported dispersion estimate. The record does not report an over-dispersion assessment or a model for repeated events. This concept is useful in general, but there is no basis in the registry record to claim that over-dispersion was present or addressed in this trial.

Multiplicity is another general concern: when many outcomes are tested, the chance of at least one apparently notable result can increase if no adjustment or hierarchy is used. The registry lists a large set of outcome measures and several secondary comparisons. It does not report whether the secondary analyses were adjusted, prioritized, or intended as confirmatory. Their unadjusted results therefore warrant more caution than a prespecified primary comparison would ordinarily receive.

11. Confidence Intervals and Precision

The primary risk-difference estimate is -0.017, and its posted interval runs from -0.040 to 0.006. The interval is an expression of precision: a narrower interval would indicate less uncertainty about the estimated difference, while a wider interval would indicate more. Here, the interval spans zero, so the posted data do not rule out no difference and also allow values on either side of zero. The registry record does not report the interval’s confidence level or construction method, so those details cannot be assigned to it.

The event counts and analyzed denominators give context for that uncertainty. The primary endpoint has 102 events among 1313 analyzed participants in the Absorb BVS group and 41 among 677 in the XIENCE group. The estimate should not be read as an exact description of the underlying difference: it is a sample-based estimate, and its interval shows that the plausible values are not concentrated on one side of zero.

The posted estimate is unadjusted. No adjusted estimate, prespecified precision target, or primary test method is reported in the registry record. The interval therefore should not be presented as accounting for covariates or for multiple endpoint testing. It also does not resolve how the primary endpoint would compare over a different follow-up period, because the result described is for one year.

Precision is distinct from clinical importance. An interval can describe statistical uncertainty without establishing whether any value in it matters clinically. The registry record does not state a threshold for a clinically important TLF difference. The observed estimate and interval can therefore support a description of direction and uncertainty, but not a conclusion about clinical importance on their own.

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 illustrations based on the observed effect, not the trial’s own original design assumptions. For these binary outcomes, the figures refer to participants per arm, not total events. The registry record states the planning assumptions: a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect to detect set as the stated fraction of the observed effect. The original trial’s own sample-size assumptions and justification are not reported in the registry record.

The 80% power column describes the planned number of participants per arm under the stated assumptions for a design with an 80% chance of detecting the chosen effect if that effect were the true one. The 90% power column describes the corresponding planning target for a 90% chance. These are design calculations, not guarantees about a future study’s result. The planning figures are not observed power.

Rows that plan for 100%, 75%, or 50% of the observed effect ask how many participants per arm would be needed to detect effects of those stated sizes under the same assumptions. Smaller effects need more participants to distinguish them from sampling variation. Consequently, the figures can become very large when the effect used for planning is small. They are sensitive to the observed effect, which is itself uncertain, and should be read as planning illustrations rather than as definitive recommendations for a new trial.

The planning assumptions listed in the registry record do not document the design basis used to set enrollment for ABSORB III. They should not be substituted for missing original protocol information or treated as evidence that the completed trial had a particular power.

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 = 0.078; treatment proportion p₁ = 0.061; average p̄ = 0.069.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.3587 = 0.7031.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.3585 = 0.3017.
4. n = (0.7031 + 0.3017)² / (-0.0171)² = 3,443.7, rounded up to 3,444 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 <- 0.0776846915460777                          # control proportion
p1 <- 0.0605612998522895                          # 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(1751906)
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 = 3,444 (80% power), 4,610 (90% power)
 75% of observed effect: n per arm = 6,299 (80% power), 8,431 (90% power)
 50% of observed effect: n per arm = 14,565 (80% power), 19,498 (90% power)
power.prop.test: 3443.7 
Simulated power at n = 3,444 per arm: 0.799 (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

The registry record lists 52 outcome measures and several secondary results, but does not report a multiplicity-control procedure, a testing hierarchy, or which secondary endpoints were prespecified as confirmatory. When multiple outcomes are examined, unadjusted p-values do not account for the greater opportunity to observe an apparently unusual result across the set. For that reason, the secondary findings should be read cautiously and not treated as independent confirmations of the primary endpoint.

The primary outcome is described as analyzed in the Intent-To-Treat Population set. The record further states that the number analyzed includes subjects with available follow-up data at the one-year timeframe. This makes clear that the posted count is tied to available follow-up, but it does not explain the status or outcomes of participants without available follow-up. Imputation, censoring, sensitivity analyses, and other missing-data procedures are not reported in the registry record.

Denominators vary by outcome in the posted results. This means the endpoint comparisons do not all use an identical analyzed population as reported. The record does not explain the reasons for these variations or whether they reflect outcome-specific availability, exclusions, or another cause. This limits direct comparison of the raw counts across outcomes.

The primary analysis method, covariate adjustment, confidence-interval construction, and handling of missing primary outcomes are not reported in the registry record. Nor does the record state whether interim analyses took place or whether any stopping rule applied. These absences do not show that such procedures were or were not used; they mean the registry record does not document them. Interpretation should remain anchored to the population description and statistics that are posted.

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.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: angina pectoris (12.7%), non-cardiac chest pain (7.4%), angina unstable (5.1%), atrial fibrillation (3.9%), myocardial infarction (3.9%).

The adverse-event record lists serious adverse events for 801 participants among 1322 at risk in the Absorb BVS group and 398 among 686 at risk in the XIENCE group. It also lists other adverse events for 1174 among 1322 at risk with Absorb BVS and 601 among 686 at risk with XIENCE. The listed deaths are 85 and 44, respectively. These are counts as recorded; they should not be converted into rates or treated as comparative risks without accounting for the registry’s definitions and analysis details.

The record lists angina pectoris as a leading serious adverse-event term, followed by non-cardiac chest pain, unstable angina, atrial fibrillation, myocardial infarction, and coronary artery restenosis. The registry does not report the timeframe for these adverse-event summaries or explain the denominator used for the listed term percentages. It also does not state in these figures whether events were judged related to a device or procedure. The counts do not establish causation, and deaths in the adverse-event record should not automatically be attributed to either device.

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

The posted primary results describe one-year TLF counts in the two randomized groups and an unadjusted estimate of their difference. The point estimate is lower for XIENCE in the orientation used for the comparison, while the interval includes zero and the unadjusted p-value is 0.161. Together, those statistics describe an observed difference with uncertainty; they do not establish that the groups are identical or that one device is superior.

The primary endpoint is a composite. The overall result does not reveal how many participants experienced each component separately, and it cannot indicate whether any one component drove the comparison. The registry also does not provide the primary component-specific results, the primary test method, or a reported primary analysis p-value. Those omissions limit interpretation of the composite beyond the posted count and comparison.

The results apply to the stated one-year timeframe and the participants included in the posted analyses. They do not by themselves describe later outcomes, nor do they establish how missing follow-up would affect the comparison. The record’s description of available follow-up should be kept in mind when interpreting the analyzed denominators.

Statistical uncertainty and clinical importance are different questions. The registry does not state what size of TLF difference would be clinically important. A p-value and interval cannot supply that judgment on their own. The secondary outcomes provide additional observations but are subject to uncertainty and multiplicity concerns, with no adjustment or hierarchy reported in the registry record.

These results should be understood as a statistical description of the posted trial data, not as medical advice. They do not establish that a treatment works or does not work, and they should not be extended beyond the outcomes, population, and follow-up period documented in the registry record.

17. Why This Trial Matters Statistically

ABSORB III is statistically notable in the registry record because it is described as the pivotal trial supporting US pre-market approval of Absorb BVS, while its primary comparison is accompanied by an interval that includes zero. That combination makes transparent reporting of the estimate, uncertainty, endpoint definition, and analysis population important. A single p-value would not convey the direction or range of the estimated difference.

The primary outcome is a composite, so its definition matters as much as its label. Cardiac death, target-vessel myocardial infarction, and ischemia-driven target lesion revascularization form the stated TLF outcome. Without component-specific results, readers cannot determine how much each component contributed to the composite count. This is a general interpretive issue for composite endpoints, not evidence that any particular component drove this result.

The registry also connects ABSORB III to ABSORB IV under a related protocol and describes planned pooling of data to support the ABSORB IV primary endpoint. That context reinforces the importance of keeping trial-specific results distinct from later or pooled analyses. The one-year comparison reported for ABSORB III should not be treated as a result from the continuation or from a pooled dataset.

Finally, the record illustrates why statistical interpretation requires more than counts. Available-follow-up wording, changing analyzed denominators across outcomes, the absence of documented multiplicity procedures, and limited detail on the primary analysis all affect how confidently the posted results can be generalized. These are not reasons to disregard the observed data; they define the boundaries of what the registry record supports.

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 specific to the posted ABSORB III record affect interpretation. The primary endpoint is a composite, but component-specific primary results are not reported in the results. The combined TLF count therefore cannot show whether cardiac death, target-vessel myocardial infarction, or ischemia-driven target lesion revascularization contributed most to the observed comparison.

The primary analysis population is labeled ITT, while the registry says the number analyzed includes subjects with available follow-up at one year. The record does not explain missing follow-up, outcome ascertainment for participants without available data, or any sensitivity analysis. The analyzed denominators also vary across posted outcomes, and the registry does not give the reasons for those differences.

The registry does not report the primary statistical test, interval method, confidence level, adjusted analysis, or original sample-size justification. It also does not document whether interim analyses or stopping rules were used. For secondary outcomes, multiplicity adjustment and endpoint hierarchy are not reported. These gaps prevent a complete reconstruction of the analysis plan from the registry record.

Safety counts have a separate at-risk population from the primary endpoint analysis, and the adverse-event summary timeframe and event-relatedness criteria are not stated in the information. These figures should not be directly equated with the primary outcome counts. Finally, the primary result is limited to one-year TLF; later follow-up is not characterized by this comparison.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: ABSORB III Randomized Controlled Trial (RCT) — NCT01751906.
• Registry record last updated 2023-10-11; status last verified 2021-01.
• Results first posted 2018-02-13.
• 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

ABSORB III is listed as a completed, prospective, randomized, single-blind, multi-center, parallel trial sponsored by Abbott Medical Devices. It compared Absorb BVS with XIENCE devices in subjects with de novo native coronary artery lesions. The primary outcome was one-year target lesion failure, a composite of cardiac death, target-vessel myocardial infarction, or ischemia-driven target lesion revascularization.

The posted primary comparison gives 102 events among 1313 analyzed participants with Absorb BVS and 41 among 677 with XIENCE. The risk-difference estimate is -0.017, with an interval from -0.040 to 0.006 and an unadjusted p-value of 0.161. The interval includes zero, and the registry record does not report the primary analysis method or a multiplicity procedure. Results are limited by the stated available-follow-up analysis population and by missing details on missing-data handling, endpoint components, and the original design assumptions.