Bioprosthetic Mesh to Expand the Lower Pole in Tissue Expander Reconstruction: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Bioprosthetic Mesh to Expand the Lower Pole in Tissue Expander Reconstruction (NCT01310075), a randomized na study. The primary outcome is Number of Participants With Complications After Tissue Expander Replacement With Implant.

2. Clinical Question

3. Trial Design

The registry record describes a prospective, randomized, double-masked, parallel-group controlled trial. Its official title identifies the study as a trial of bioprosthetic mesh to expand the lower pole in tissue expander reconstruction. The listed condition is breast cancer, and the sponsor is M.D. Anderson Cancer Center. The record marks the study completed, with a start date of 2011-02 and primary completion on 2017-11-08.

Enrollment is listed as 90. The primary outcome description states that 66 of those randomized participants (74%) were evaluable for the primary measure. The posted outcome results use the labels Arm 1 and Arm 2, with 31 participants and 35 participants, respectively, in the outcome data. The registry does not identify which of those labels corresponds to Alloderm or Surgimend. It also lists a no-mesh control label, but the description is blank and the posted comparison does not establish its role.

The listed devices are Alloderm and Surgimend. For Alloderm, the description says a piece measuring 6 x 12 cm or 6 x 16 cm was trimmed into a semicircle and sewn into the inframammary fold, with the smooth side against the implant. For Surgimend, a 10 x 15 cm piece of fenestrated material was sewn to the fold, with its curved side along the fold. Both descriptions specify vicryl suture.

Randomization and masking describe important design features, but the registry record does not report the allocation sequence method, who was masked, or how masking was maintained. The original protocol, statistical plan, and trial-specific sample-size assumptions are not reported in the registry 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.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Of the 90 randomized participants, 66 (74%) were evaluable for the primary measure

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 Participants With Complications After Tissue Expander Replacement With Implant, measured over through study completion, an average of 1 year.

The primary endpoint counts participants who experienced complications after tissue expander replacement with an implant. It is expressed as a number of participants with an event, rather than as a continuous measure such as a score or measurement. The outcome timeframe is through study completion, described in the registry record as an average of 1 year.

A count of participants with complications is a binary-outcome summary: each evaluable participant is classified according to whether a complication was recorded for the endpoint. Such an endpoint can give a clinically interpretable summary of how often participants experienced at least one qualifying event. It does not, by itself, describe the number, severity, timing, or duration of complications for each participant. Those details depend on definitions and additional reporting.

The endpoint is relevant to the trial's stated aim of comparing complication rates by mesh type. The brief summary also names post-surgical appearance as an interest, but no secondary outcome results are posted in the registry record. The primary endpoint description does not provide a detailed complication definition, nor does it specify how multiple complications in one participant were handled. Those details are not reported in the registry record.

7. Results Posted in the Registry

• Arm 2: 28.6% (10/35) versus 32.3% (10/31) on control; risk difference -3.7 percentage points (95% CI -25.9 to 18.6), risk ratio 0.89 (0.43–1.84); unadjusted two-proportion z-test p = 0.745.

8. Statistical Interpretation

9. Statistical Methods Explained

For the primary binary endpoint, the posted summaries give the number of participants with a complication and the number of participants represented in each arm. Arm 1 is reported as 10 events among 31 participants; Arm 2 is reported as 10 among 35. A risk is the proportion of participants in a group who experienced the event. The registry's comparison reports group risks of 0.323 and 0.286, respectively. Because these values exceed the permitted display precision, the interpretation above focuses on the event counts and reported effect estimate without restating them.

What does a risk difference represent?

In general, a risk difference subtracts one group's event risk from the other group's event risk. The registry reports the Arm 2 versus Arm 1 risk difference as -0.037. A negative value indicates that the estimated event risk is lower in the first named group of that contrast, Arm 2, than in Arm 1. This is a descriptive direction for the posted comparison, not proof of a treatment effect. The registry record does not provide a device mapping for these arm labels, so the estimate cannot be assigned to a specific mesh from the posted comparison alone.

Unlike a relative measure, a risk difference is expressed on the absolute risk scale. In general, it describes the difference in event probability between groups rather than the proportional change. For clinical interpretation, the absolute scale can be useful because it relates directly to how often an event occurs in each group. Here, however, the uncertainty around the estimate is substantial: the reported confidence interval includes values below and above zero. A value of zero on the risk-difference scale represents no difference between group risks.

What does the confidence interval add?

In general, a confidence interval gives a range of values compatible with the data under the method used to construct it. It conveys uncertainty in the estimated effect and is not a guarantee that the true effect lies within the interval. For this comparison, the interval crosses zero and includes differences in either direction. That pattern is consistent with the unadjusted p-value of 0.745, while neither result establishes equality or rules out potentially meaningful differences.

The registry also lists a risk ratio and an odds ratio for the comparison. In general, a risk ratio compares event risks by division, while an odds ratio compares event odds. Relative measures can express the association differently from an absolute risk difference, but all depend on the same underlying outcome data and remain subject to uncertainty and missing outcome information. The posted summary should not be read as several independent replications of the result: these measures are alternate ways of describing the same arm comparison.

What is the role of the p-value?

The unadjusted p-value is 0.745. In general, a p-value quantifies how unusual data at least as discrepant as those observed would be under a specified null hypothesis and statistical model. It is conditional on those assumptions. It does not express the size of the estimated difference, does not establish whether an effect matters clinically, and is not the probability that chance alone produced the result. Effect estimates and confidence intervals are therefore essential companions to a p-value.

The record characterizes the posted outcome as a participant count and lists no dispersion measure. For a binary endpoint, the event count and the number of participants in each arm are the core summary inputs. No adjusted model, covariate set, or alternative analysis is reported in the registry record. The posted comparison is described as unadjusted; no basis is available to assess whether an adjusted analysis would materially change the estimate.

Why can binary outcomes be difficult to estimate precisely?

In general, precision depends on how much information the data contain, including the number of participants and how often the event occurs. When event counts are limited, estimates can be uncertain even if the point estimate appears straightforward. Confidence intervals reflect this sampling uncertainty under the selected method. The registry's interval spanning both negative and positive risk differences illustrates why the point estimate alone should not be treated as a decisive finding.

General statistical methods for binary outcomes can use different approximations or models. Their behavior can vary with sample size, event frequency, and the analysis choices. The registry record does not state the method used to calculate the posted confidence interval or p-value, so those details should not be inferred. The sample-size illustration separately states a normal approximation for planning; that assumption describes the illustration, not necessarily the trial's original analysis.

What can the randomized design support?

In general, random allocation is intended to make comparison groups similar, on average, with respect to factors measured and unmeasured before assignment. A double-masked design can reduce some forms of bias if masking is maintained. These are design protections, not guarantees: missing outcomes, deviations, outcome assessment, and implementation can still affect an estimate. The registry record identifies the trial as randomized and double-masked but does not report enough detail to assess these processes fully.

The posted results concern the evaluable participants described for the primary measure. The record does not specify whether its analysis followed an intention-to-treat approach, how missing outcomes were addressed, or whether participants were analyzed according to randomized assignment. These are important analysis details because the population included in an analysis affects what the estimate represents. Their absence limits a full assessment of the statistical methods and the strength of conclusions that can be drawn from the registry results.

10. Confidence Intervals and Precision

The risk-difference estimate for Arm 2 versus Arm 1 is -0.037. Its confidence interval extends from a negative value to a positive value, crossing zero. On this scale, zero represents no difference in group risks. The interval therefore does not confine the plausible effects to one direction: it includes a lower estimated risk in Arm 2 and a higher estimated risk in Arm 2, relative to Arm 1.

This breadth matters more than the point estimate alone. A point estimate is the single value calculated from the observed data, whereas its confidence interval communicates uncertainty about the underlying effect. When the interval includes values on both sides of no difference, the data do not precisely distinguish the direction of the effect. That is not the same as evidence that the groups are identical. The interval's width leaves room for effects of different sizes and directions.

The confidence interval should be interpreted alongside the reported unadjusted p-value of 0.745 and the event counts. Neither the p-value nor the interval tells whether a difference would be clinically important; that requires context and a prespecified threshold for importance, neither of which is reported in the registry record. The record also does not state the method used to calculate the interval, which limits assessment of the assumptions behind its coverage.

Precision is further relevant because the primary outcome description says that 66 of the 90 randomized participants were evaluable. The registry record does not explain the non-evaluable cases or how missing outcome information was handled. Uncertainty from incomplete outcome data is distinct from the sampling uncertainty represented by the confidence interval and may not be captured by it. As a result, the interval should be read as uncertainty for the posted comparison, not as a complete account of every source of uncertainty in the trial.

11. 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 sample-size display is a planning illustration based on the observed effect, not the trial's original design assumptions. Its figures are participants per arm, not total events. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. For each row, the effect to detect is the stated fraction of the observed effect.

The 80% power column gives the estimated participants per arm for a future study to have an 80% chance of detecting the specified effect under those planning assumptions, if that effect is real and the assumptions hold. The 90% power column gives the corresponding per-arm planning figure for a 90% chance. These are design targets, not observed trial outcomes. This is not observed power.

The display reports 2,444 participants per arm for planning around 100% of the observed effect, 4,382 for 75%, and 9,940 for 50%, each in the 80% power column. The 90% power column gives 3,271, 5,865, and 13,306 participants per arm for those same effect fractions. Smaller effects require more participants to detect under the stated assumptions, which is why the planning figures rise as the fraction of the observed effect gets smaller.

These calculations are illustrations, not a claim that the original trial should have enrolled those numbers. The trial's own sample-size justification and design assumptions are not reported in the registry record. The illustration uses an effect estimated from the posted data, which is uncertain; it should not be treated as a reliable estimate of the effect that a future trial would necessarily observe. The planning numbers also do not repair limitations in outcome completeness or resolve the uncertainty in the posted comparison.

12. 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 = 0.323; treatment proportion p₁ = 0.286; average p̄ = 0.304.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6506 = 1.2752.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6501 = 0.5471.
4. n = (1.2752 + 0.5471)² / (-0.0369)² = 2,443.3, rounded up to 2,444 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 <- 0.32258064516129                          # control proportion
p1 <- 0.285714285714286                          # 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(1310075)
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 = 2,444 (80% power), 3,271 (90% power)
 75% of observed effect: n per arm = 4,382 (80% power), 5,865 (90% power)
 50% of observed effect: n per arm = 9,940 (80% power), 13,306 (90% power)
power.prop.test: 2443.3 
Simulated power at n = 2,444 per arm: 0.804 (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.

13. Multiplicity, Missing Data and Analysis Populations

The registry record reports one outcome measure and no posted secondary outcome results. It also reports zero analyses. The brief summary says that the research aimed to learn whether complication rates and post-surgical appearance differed by mesh type, but the posted outcome information includes only the primary complication count. Results for appearance and other secondary outcomes are not reported in the registry record.

Multiplicity arises when several outcomes, comparisons, time points, or statistical analyses are examined. In general, conducting many tests can raise the chance of a nominally unusual result occurring even when no true effect exists. A prespecified statistical plan can explain which outcomes and comparisons were primary and how multiplicity was handled. The registry record does not report such a plan, so it is not possible to assess prespecification or any multiplicity adjustment. With only the primary comparison posted, there is no basis to infer how unreported outcomes might affect the overall interpretation.

The primary outcome population description says 66 of the 90 randomized participants were evaluable. The outcome arms list 31 participants in Arm 1 and 35 in Arm 2. The record does not describe why some randomized participants were not evaluable, whether they lacked the primary outcome, or how their data were treated. It does not report missing-data methods, imputation, sensitivity analyses, or whether all randomized participants were included in an intention-to-treat analysis.

These omissions matter because analysis population choices can change the meaning of a treatment comparison. In general, analyzing only participants with observed outcomes may be biased if outcome availability is related to assignment or the outcome itself. Randomization protects comparisons at assignment, but it does not by itself solve missing-outcome problems. Without reasons for non-evaluability and a clear analysis population definition, the effect of missing data cannot be assessed from the registry record.

The registry also does not describe protocol deviations, adherence, exclusions after randomization, or adjustments for baseline factors. These are not necessarily flaws in how the trial was conducted; they are details not reported in the registry record. The interpretation should therefore remain limited to the posted unadjusted results and should not assume that the missing analysis details were handled in a particular way.

14. 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 1% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: pain (3.2%), delayed wound healing (3.2%), infection (3.2%), hypophosphatemia (2.9%), muscle spasms (2.9%).

The registry reports serious adverse events for each outcome-labeled arm. Arm 1 has 3 serious adverse events among 31 participants at risk, and Arm 2 has 2 among 35 participants at risk. The listed deaths are 0 in each arm. These are descriptive counts in the registry record; they do not by themselves show whether an event was caused by a device or whether the groups differed meaningfully in safety.

The record also lists other adverse events: 8 among 31 participants at risk in Arm 1 and 18 among 35 at risk in Arm 2. Serious and other adverse event categories should not be conflated. The registry lists serious-event terms with reported percentages, including Pain at 3.2%, Delayed wound healing at 3.2%, Infection at 3.2%, Hypophosphatemia at 2.9%, and Muscle spasms at 2.9%. The record does not explain in this summary how these terms relate to the two arm labels or whether participants could have more than one event.

In general, adverse-event counts require context about definitions, collection, severity, timing, and attribution. The registry record does not provide that detail here, and the arm-to-device mapping is not reported. These figures should be read as reported safety counts, not as a causal comparison or a complete assessment of safety.

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

The posted results give a comparison of the primary complication count between Arm 1 and Arm 2. Each arm has 10 participants with complications, with 31 participants reported in Arm 1 and 35 in Arm 2. The risk-difference estimate is -0.037 for Arm 2 versus Arm 1, and its confidence interval crosses zero. The unadjusted p-value is 0.745. Taken together, the posted estimates do not provide a clear statistical distinction between the two registry-labeled groups on this outcome.

That finding does not establish that the groups have equal complication risks. The confidence interval includes differences in both directions, and the registry record does not report an equivalence margin or an equivalence analysis. Nor does the result establish that a particular mesh is better or worse: the outcome labels Arm 1 and Arm 2 are not mapped to the named devices in the posted comparison. The trial title and device descriptions give context, but they do not resolve that labeling gap.

The results also do not answer every question raised by the brief summary. Although that summary mentions post-surgical appearance, no secondary outcome results are posted. The complication endpoint is described through study completion, an average of 1 year, but the record does not provide more detail about the timing or nature of individual complications. Serious and other adverse event counts are available, but the registry information does not establish causality or provide the detail needed for a comprehensive safety interpretation.

Finally, the primary outcome description states that 66 of the 90 randomized participants were evaluable. The reasons for non-evaluability and the missing-data approach are not reported in the registry record. The posted estimates therefore describe the reported analysis population, while the implications of incomplete outcome information remain uncertain. The result is best understood as an imprecise comparison of the posted arm summaries, rather than proof of no difference or a definitive device-specific conclusion.

16. Why This Trial Matters Statistically

This trial illustrates why a statistical result should be read through its estimate, uncertainty, and reporting context rather than by focusing only on whether a p-value is small. The unadjusted p-value is 0.745, but the more informative interpretation also considers the risk-difference estimate and the confidence interval that crosses zero. Together they show that the posted data do not clearly distinguish the groups, while still allowing effects in either direction.

The endpoint is a count of participants who experienced complications, an outcome with a direct event-based interpretation. Yet the registry record's outcome labels are not mapped to the named devices, and the record also includes a no-mesh control label without a description. Clear labeling is essential for connecting a statistical contrast to the intervention it represents. Without that link, even a correctly reported estimate can be difficult to translate into a device-specific conclusion.

The difference between enrollment and evaluability also highlights the importance of analysis populations and missing outcome information. A randomized design helps create comparable groups, but interpretation depends on who contributes outcome data and how missingness is handled. The registry does not report those methods, so readers cannot fully assess whether the primary comparison preserves the benefits of randomization.

Finally, the sample-size illustration shows how planning requirements depend on the effect a future study aims to detect: smaller effects require more participants under the stated assumptions. The figures are planning calculations based on an uncertain observed estimate, not a retrospective verdict on the trial's design. The original design rationale is not reported in the registry record. Distinguishing those planning illustrations from trial findings is central to sound statistical interpretation.

17. 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 trial-specific reporting gaps constrain interpretation. The registry record gives outcome results for Arm 1 and Arm 2 but does not identify which arm received Alloderm or Surgimend. It separately lists a Control (no mesh) label with a blank description, without clarifying its relationship to the posted two-arm comparison. The device-level meaning of the reported contrast is therefore uncertain.

The primary measure is described as the number of participants with complications after tissue expander replacement with an implant, through study completion, an average of 1 year. A detailed definition of a qualifying complication is not reported in the registry record. Nor does the record explain how recurrent events or multiple complications in one participant were counted. The posted count consequently cannot characterize event burden, timing, or severity in detail.

Although 90 participants were randomized, the primary outcome description says 66 were evaluable. The record does not explain why the remaining randomized participants were not evaluable, how missing outcomes were handled, or whether the analysis followed randomized assignment. This limits assessment of selection bias and of how representative the posted outcome comparison is of all randomized participants.

The record does not report the original sample-size assumptions, a statistical analysis plan, the confidence-interval method, a prespecified equivalence margin, or details of masking and allocation implementation. It also does not post secondary outcome results, including results for post-surgical appearance mentioned in the brief summary. These are limitations of the information in the registry record; they do not establish that the trial lacked such procedures or analyses.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Bioprosthetic Mesh to Expand the Lower Pole in Tissue Expander Reconstruction — NCT01310075.
• Registry record last updated 2019-04-30; status last verified 2019-04.
• Results first posted 2019-04-30.
• 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.

21. Record Summary

NCT01310075 is a completed randomized, double-masked, parallel-group trial sponsored by M.D. Anderson Cancer Center. The record lists breast cancer as the condition and identifies Alloderm and Surgimend devices. Enrollment is 90; the primary outcome description reports that 66 participants were evaluable.

The primary measure counts participants with complications after tissue expander replacement with an implant, through study completion, an average of 1 year. The posted comparison is Arm 2 versus Arm 1, with 10 events in each arm and an unadjusted p-value of 0.745. The risk-difference confidence interval includes values in both directions. The registry does not map the arm labels to the named devices, explain non-evaluability, or post secondary outcome results.