Use of Artefill® for Moderate to Severe Atrophic Acne Scar Correction: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Use of Artefill® for Moderate to Severe Atrophic Acne Scar Correction (NCT01559922), a randomized phase 3 study. The primary outcome is ASRS Responder Rate at 6 Months.

2. Clinical Question

3. Trial Design

The record describes a Phase 3, randomized, double-blind, parallel-group, placebo-controlled trial. The sponsor was Suneva Medical, Inc. The study is listed as completed. Its recorded start was 2012-02, and its primary completion was 2013-04. The registry lists atrophic acne scar as the condition.

Participants were allocated to Artefill or Placebo, with the latter identified as normal saline. Artefill is described as a dermal filler and classified as a device; normal saline is classified as a drug. The record does not give further detail about the procedures for delivering either intervention, the duration of treatment, allocation concealment, or which people were masked. Although double masking is recorded, the identities of the masked groups are not specified.

The listed enrollment was 175. The primary outcome results give 50 participants for Placebo and 97 for Artefill. The outcome population is called the Full Analysis Population and is described as including only subjects that passed screening. The registry record does not explain the relationship between the enrolled population and these outcome denominators, or give the number screened, excluded, or missing from the outcome analysis.

The primary outcome was measured 6 months post-treatment. The registry record lists one outcome measure and no secondary outcomes posted. Details about the trial’s original sample-size justification, planned allocation ratio, statistical analysis plan, interim analyses, or protocol deviations are not reported in the registry record. Those details should not be inferred from the posted outcome counts or from the planning illustrations based on the observed result.

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

Analysis population for the primary outcome, as defined in the registry: Full Analysis Population included only subjects that passed screening

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 ASRS Responder Rate at 6 Months, measured over 6 months post-treatment.

The primary endpoint is the ASRS responder rate at 6 months post-treatment. It is reported as a percentage of participants. A participant was considered a responder if at least 50% of treated scars showed an ASRS improvement of at least 2 points, as assessed by a blinded evaluator. Thus, the endpoint reduces the assessment across treated scars to a participant-level yes-or-no classification: responder or not responder.

A responder-rate endpoint can make a clinical threshold easier to interpret than a mean change alone. It answers how many participants met a specified improvement criterion, rather than describing the average score change across all participants. The threshold also means that participants with different amounts of improvement may count alike if they both meet the definition. Conversely, a participant just short of the threshold is classified differently from one who meets it. The registry record does not define the ASRS acronym or provide further detail about the scale’s scoring or validation.

The endpoint is reported with a 95% confidence interval for each arm. The recorded time point is 6 months post-treatment; the record does not give other primary-outcome time points. The results therefore address the stated responder definition at that time point, not every possible measure of scar change or every aspect of treatment safety.

7. Results Posted in the Registry

• Artefill: 67.0% (65/97) versus 32.0% (16/50) on control; risk difference 35.0 percentage points (95% CI 19.1 to 51.0), risk ratio 2.09 (1.37–3.21); unadjusted two-proportion z-test p = <0.001.

8. Statistical Interpretation

9. Statistical Methods Explained

Several statistical ideas help interpret a binary responder outcome. The endpoint classifies each participant as either meeting or not meeting the response criterion. The responder rate is the proportion of participants in an arm who meet that criterion. It is not a continuous score and does not show how far each participant’s scar assessment changed. The threshold used requires at least 50% of treated scars to have an ASRS improvement of at least 2 points, according to a blinded evaluator.

What do the arm-specific percentages describe?

The Placebo responder rate is 32.4%, and the Artefill responder rate is 67.4%. Each percentage is an estimate for its trial arm, based on the outcome population listed for that arm: 50 for Placebo and 97 for Artefill. A percentage is often easy to read, but it does not remove uncertainty from finite sample sizes. The 95% confidence intervals for these rates are 17.3% to 52.2% and 51.3% to 80.2%, respectively. They describe a range of values compatible with the data under the interval method’s assumptions; they are not a guarantee that the true rate lies inside the interval.

How does a risk difference work?

The risk difference is an absolute comparison of two proportions. The posted estimate is 0.350, with a 95% confidence interval of 0.191 to 0.510. It is expressed on the proportion scale. An absolute contrast is useful because it describes the separation between the arms in the outcome’s original probability scale. Its interpretation still depends on the responder definition and the population used for analysis.

How do risk ratios and odds ratios differ?

The risk ratio, reported as 2.094 with a 95% confidence interval of 1.366 to 3.211, compares the responder proportions as a ratio. The odds ratio, reported as 4.316 with a 95% confidence interval of 2.081 to 8.953, compares the odds of response. Odds are not the same as probabilities, and an odds ratio should not be read as a risk ratio. These relative measures can look numerically different because they use different scales. The record does not state that either relative measure was the prespecified primary comparison.

What does the p-value indicate?

The unadjusted p-value is p < 0.001. In general, a p-value describes how unusual data at least as incompatible with a specified null hypothesis would be if that hypothesis and the calculation’s assumptions held. It is not the probability that the null hypothesis is true, and it does not quantify the probability that the observed result occurred by chance. The registry record does not report the specific statistical test used for the sponsor’s analysis. The posted comparison is an unadjusted recalculation from summary statistics; its p-value should not be mistaken for a documented adjusted analysis.

Why are confidence intervals useful?

A confidence interval conveys both the estimate and its precision. In general, narrower intervals indicate greater precision, while wider intervals indicate more uncertainty about the size of the underlying value. The endpoints depend on the data and the method used to construct the interval. For the risk difference, the reported interval runs from 0.191 to 0.510. That interval is above zero, while still allowing a range of possible effect sizes. Confidence intervals do not address unmeasured bias, missing-data problems, or whether the responder threshold captures every outcome of interest.

What does unadjusted mean?

In general, an unadjusted comparison contrasts the groups without accounting for additional variables in a statistical model. Adjustment can be used to account for prespecified factors, but the registry record does not report a sponsor analysis plan or covariates for the primary comparison. The posted estimates are recalculated from the arm summary information and are not a substitute for any analysis that may have been conducted outside the registry record. The discrepancy indicator is false; under the stated reporting convention, the sponsor’s own analysis would take precedence if a discrepancy were flagged.

Why can a binary outcome hide detail?

Turning a continuous or graded assessment into a responder classification simplifies the result, but discards some information. People who meet the threshold can have different degrees of improvement, while those who fall short can also differ substantially. The observed responder rate therefore describes only how many crossed the defined threshold. The record does not include secondary outcomes or additional score summaries that would show other aspects of change. General statistical interpretation should keep that limitation separate from the numerical comparison itself.

10. Confidence Intervals and Precision

The arm-specific estimates are accompanied by 95% confidence intervals. Placebo has a responder rate of 32.4%, with an interval from 17.3% to 52.2%. Artefill has a responder rate of 67.4%, with an interval from 51.3% to 80.2%. These intervals show that the estimated rates are not exact descriptions of the underlying response proportions; each is subject to sampling uncertainty.

The interval for the risk difference is 0.191 to 0.510 around an estimate of 0.350. It gives a range of plausible values under the calculation’s assumptions, rather than identifying a single precise effect size. The interval for the risk ratio is 1.366 to 3.211 around 2.094, and the interval for the odds ratio is 2.081 to 8.953 around 4.316. Each interval is on the scale of its corresponding measure, so they cannot be compared by width alone without considering those different scales.

Confidence intervals are useful for assessing precision, but they do not capture every source of uncertainty. They do not show whether missing outcomes, selection, measurement, or analysis choices could affect the estimates. The outcome population is described as including only subjects that passed screening; further details about the analysis population and missing data are not reported in the registry record. The posted intervals therefore need to be interpreted within the limits of the recorded methods and population information.

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 planning figures are participants per arm, not total events. They illustrate the number of participants per arm under different fractions of the observed effect. For planning for 100% of the observed effect, the illustration gives 31 participants per arm for 80% power and 41 participants per arm for 90% power. For planning for 75% of the observed effect, it gives 56 participants per arm for 80% power and 74 participants per arm for 90% power. For planning for 50% of the observed effect, it gives 123 participants per arm for 80% power and 164 participants per arm for 90% power.

In general, power is the chance that a planned study will detect an effect of a specified size under a defined statistical model, if that effect is present. The 80% and 90% columns therefore describe different planning targets: the 90% target requires a design intended to have a higher chance of detecting the assumed effect than the 80% target. Smaller effects require more participants in these illustrations because they are harder to distinguish from random variation.

The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The target effect is the stated fraction of the observed effect. These are planning illustrations based on the observed effect, not the trial’s original design assumptions, which are not reported in the registry record. They are not observed power.

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 = 0.320; treatment proportion p₁ = 0.670; average p̄ = 0.495.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7071 = 1.3858.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6623 = 0.5574.
4. n = (1.3858 + 0.5574)² / (0.3501)² = 30.8, rounded up to 31 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 <- 0.32                          # control proportion
p1 <- 0.670103092783505                          # 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(1559922)
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 = 31 (80% power), 41 (90% power)
 75% of observed effect: n per arm = 56 (80% power), 74 (90% power)
 50% of observed effect: n per arm = 123 (80% power), 164 (90% power)
power.prop.test: 30.8 
Simulated power at n = 31 per arm: 0.821 (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 lists one outcome measure and no secondary outcomes posted. The primary outcome is the ASRS responder rate at 6 months post-treatment. No multiplicity adjustment or strategy for controlling false-positive findings across multiple outcomes or time points is reported in the registry record. Because no secondary outcomes are posted, the available record does not show how additional outcomes, if any, were handled.

The record identifies the outcome population as the Full Analysis Population and says that it included only subjects that passed screening. It does not give a more detailed definition of that population, report participant flow from enrollment to analysis, or explain how participants with missing outcome data were treated. Missing-data methods, reasons for missingness, and sensitivity analyses are not reported in the registry record. The outcome denominators are 50 for Placebo and 97 for Artefill, while listed enrollment is 175; the record does not explain the difference between those population counts.

The primary comparison is presented as an unadjusted recalculation from summary statistics. The sponsor’s analysis methods, including any covariate adjustment, handling of protocol deviations, or prespecified analysis population beyond the screening statement, are not reported in the registry record. The registry record also does not report interim analyses, subgroup analyses, or a detailed statistical analysis plan. Their absence from the record should not be interpreted as evidence that such procedures were or were not used.

In general, multiplicity matters because conducting several statistical tests can increase the chance of at least one apparently notable result. Missing-data choices can also affect estimates if outcomes are unavailable for some participants. These are general methodological concerns; the record does not provide enough detail to determine their impact in this trial.

14. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: cholecystitis acute (2.0%), breast cancer (recurrent) (2.0%), back pain (1.0%), depression (1.0%), meningitis (1.0%).

The registry lists serious adverse events affecting 2 participants at risk in the Placebo group and 3 participants at risk in the Artefill group. The corresponding numbers at risk are 50 and 97. Other adverse events affected 0 participants in each group. Deaths are not reported in the registry record.

The listed serious adverse event terms and percentages are Cholecystitis acute, 2.0%; Breast Cancer (recurrent), 2.0%; Back pain, 1.0%; Depression, 1.0%; and Meningitis, 1.0%. The record does not assign these listed terms to a particular arm, so they should not be attributed to Placebo or Artefill from this listing alone. It also does not provide event-level descriptions, timing, severity beyond the serious-event classification, or judgments about whether events were related to treatment.

These counts describe what the registry record lists; they are not a complete assessment of comparative safety. The groups have different reported numbers at risk, and the record does not explain follow-up completeness or how adverse events were collected. The absence of listed other adverse events or reported deaths should not be read as proof that none occurred, because deaths are not reported and the event-recording details are unavailable.

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

The posted primary result describes the proportion of participants meeting a specific responder threshold at 6 months post-treatment. That rate was 67.4% in the Artefill arm and 32.4% in the Placebo arm. The posted comparison reports a risk difference of 0.350, with a 95% confidence interval of 0.191 to 0.510, and an unadjusted p-value of p < 0.001. Together, these statistics describe the observed contrast and its statistical uncertainty for the registry-defined endpoint.

The result does not describe the average amount of scar improvement, because the endpoint classifies participants according to whether they crossed a threshold. It does not establish how long any response persisted beyond the recorded time point, or how results might differ for other populations or outcome definitions. The record does not define the ASRS acronym or provide detailed scale information.

The posted comparison does not by itself establish the overall balance of benefits and risks. The safety record lists serious events, but does not assign the listed serious-event terms to an arm or report deaths. Further, details about missing outcomes, the sponsor’s statistical analysis, and the relationship between enrollment and the outcome population are not reported in the registry record. Those limitations constrain how fully the numerical estimates can be interpreted.

In general, statistical significance and clinical importance are different questions. A p-value describes compatibility with a statistical null under assumptions; it does not determine whether a result is meaningful to every participant or whether an intervention should be used. The estimates should be understood as evidence about the specified responder outcome in the recorded trial population, not as a complete account of all relevant outcomes.

16. Why This Trial Matters Statistically

This trial illustrates how a binary responder definition turns a graded clinical assessment into a proportion that is straightforward to compare between randomized groups. That simplicity comes with a trade-off: the responder rate communicates whether participants crossed a stated threshold, but it does not show the full range of changes in treated scars or how far participants were from the threshold.

The result also shows why absolute and relative measures should be read together. The risk difference of 0.350 describes the contrast on the proportion scale; the risk ratio of 2.094 and odds ratio of 4.316 describe relative contrasts using different scales. Their confidence intervals express uncertainty around each estimate. Keeping the measure and its scale clear helps prevent an odds ratio from being mistaken for a risk ratio or an absolute difference.

The record’s population and analysis details matter to interpretation. Listed enrollment is 175, while the outcome results give denominators of 50 and 97, and the outcome population is described as including only subjects who passed screening. Without participant-flow and missing-data information, readers cannot determine from the record how the analyzed population relates to everyone enrolled.

Finally, this example separates a registry result from a complete statistical report. The record does not include secondary outcomes or a detailed sponsor analysis plan, and the posted comparison is an unadjusted recalculation from summary statistics. These gaps make transparent reporting of analysis populations, missing-data handling, and prespecified methods important for judging both precision and the scope of a trial’s conclusions.

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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

Several limitations specific to this record affect interpretation. The trial lists 175 enrolled participants, but the primary outcome results are reported for 50 Placebo participants and 97 Artefill participants. The Full Analysis Population is described as including only subjects that passed screening, but participant flow, screening counts, reasons for exclusion, and the handling of missing outcomes are not reported in the registry record. It is therefore not possible to reconstruct how the outcome population relates to total enrollment.

The outcome is a threshold-based responder rate at 6 months post-treatment. The registry record does not define the ASRS acronym, report other score summaries, or post secondary outcomes. This limits assessment of changes that do not fit the responder threshold and of outcomes beyond the primary endpoint. No further follow-up result is posted in the record.

The record identifies the trial as randomized and double-blind, but does not describe the randomization procedure, allocation concealment, or which groups of people were masked. The sponsor’s detailed statistical methods, analysis plan, sample-size justification, and interim-analysis approach are not reported in the registry record. The posted comparative statistics are unadjusted recalculations from summary data rather than a documented account of the sponsor’s full analysis.

The safety listing reports serious-event counts, but the listed event terms are not assigned to arms in the record. Deaths are not reported. The available adverse-event information therefore does not provide a complete account of event timing, attribution, or comparative safety.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Use of Artefill® for Moderate to Severe Atrophic Acne Scar Correction — NCT01559922.
• Registry record last updated 2019-11-01; status last verified 2016-12.
• Results first posted 2017-02-14.
• 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

NCT01559922 is a completed Phase 3 randomized, double-blind, parallel-group, placebo-controlled trial sponsored by Suneva Medical, Inc. It studied Artefill, described as a dermal filler, against normal saline for atrophic acne scars. The recorded start was 2012-02, and primary completion was 2013-04. Listed enrollment was 175.

The primary outcome was the ASRS responder rate at 6 months post-treatment. Response required at least 50% of treated scars to show an ASRS improvement of at least 2 points, based on a blinded evaluator’s assessment. The posted rates were 32.4% for Placebo and 67.4% for Artefill. The comparison reported a risk difference of 0.350, a 95% confidence interval of 0.191 to 0.510, and p < 0.001.

The record lists one outcome measure and no secondary outcomes posted. Details about missing data, the sponsor’s statistical analysis plan, and the relationship between enrollment and the outcome population are not reported in the registry record.