SJ-0021 (Gonalef®) Versus Purified Pituitary Gonadotropin (Fertinorm-P®) for Ovulation Induction in Japanese Infertile Women: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for SJ-0021 (Gonalef®) Versus Purified Pituitary Gonadotropin (Fertinorm-P®) for Ovulation Induction in Japanese Infertile Women (NCT01185782), a randomized phase 3 study. The primary outcome is Percentage of Participants With Ovulation.

2. Clinical Question

3. Trial Design

This was a phase III, randomized, single-blind, parallel-group comparative study of SJ-0021 and purified pituitary gonadotropin in Japanese women with infertility and ovulatory disorders. The study aimed to assess induction of follicle development and ovulation and to gather further information on safety and tolerability. The registry lists an enrollment of 300 and marks the study completed. The recorded start was 2007-02, and primary completion was 2007-12.

The two interventions were Gonalef® (follitropin alfa), referred to as SJ-0021, and Fertinorm-P®, described as purified pituitary gonadotropin and labeled u-hFSH in the posted results. The study used a parallel-group design, so participants were assigned to one of the treatment groups rather than crossing between treatments. The registry says the study was single-blind but does not state which participants or study personnel were masked. Further details of randomization, concealment, and masking are not reported in the registry record.

The primary outcome was the percentage of participants with ovulation. The full analysis set included participants who received at least one dose of the investigational medicinal product and had no major Good Clinical Practice violation. The primary outcome assessment was specified for Day 6±1 or 9±1 during the post-treatment assessment period, with clinical pregnancy assessed during Day 35-42 of the post-treatment period. Secondary outcomes addressed follicle development, time and dose to reach a follicle-size threshold, cancellation, follicle maturation, pregnancy, and ovulation-related measures.

The original sample-size rationale, planned allocation ratio, and original statistical design assumptions are not reported in the registry record. The posted enrollment should not be read as evidence about the rationale for the planned sample size. Likewise, the record does not establish which analysis populations beyond the stated full analysis set were prespecified.

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: 300 participants were enrolled in the study; 39 participants discontinued prior to Investigational Medicinal Product (IMP) administration (35 participants for no longer meeting the eligibility criteria 4 due to Adverse Events \[AEs\] or other reasons). Demographic data was not available for the 4 participants who were randomized but not treated.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Full analysis set (FAS) included all participants who received at least 1 dose of IMP and had no major violation of Good Clinical Practice (GCP) such as non-compliance with the agreement, serious protocol violations, etc.

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 Percentage of Participants With Ovulation, measured over On Day 6±1 or 9±1 days during post-treatment assessment period (Day 35-42 of post-treatment period for clinical pregnancy)].

The primary endpoint was the percentage of participants with ovulation. A participant met the registry definition if serum progesterone (P4) was greater than or equal to 5 ng/mL on Day 6±1 or 9±1 during the post-treatment assessment period, or if the participant became clinically pregnant. The outcome therefore combines a laboratory criterion at specified assessment times with clinical pregnancy as an alternative qualifying event.

This type of endpoint translates treatment response into a participant-level event: each person is classified as meeting or not meeting the stated definition. The percentage expresses how many participants in each analysis group met that definition, using the group size as the denominator. It is important to keep the outcome’s operational definition in view. It is not a direct count of follicles, nor does it report the timing or quality of every follicular response; those aspects are addressed separately by secondary outcomes.

The endpoint was chosen to assess ovulation induction, a central aim of the study. The registry describes the trial as intended to provide confirmatory evidence of non-inferiority for induction of follicle development and ovulation. Whether a difference would meet a non-inferiority criterion depends on the prespecified margin and the analysis plan. The margin is not reported in the registry record, so the primary endpoint results alone do not establish how the formal criterion was set.

The outcome was assessed in the full analysis set, defined as participants who received at least one dose and had no major Good Clinical Practice violation. This definition matters when interpreting the percentages: they apply to that analysis population as described, and should not automatically be treated as results for every enrolled participant.

7. Results Posted in the Registry

• u-hFSH: 82.6% (109/132) versus 79.1% (102/129) on control; risk difference 3.5 percentage points (95% CI -6.0 to 13.1), risk ratio 1.04 (0.93–1.18); unadjusted two-proportion z-test p = 0.472.

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 the Dominant Follicle Achieving 18 mm in Mean Diameter

• 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:
  • u-hFSH: 94.7% (125/132) versus 90.7% (117/129) on control; risk difference 4.0 percentage points (95% CI -2.3 to 10.3), risk ratio 1.04 (0.98–1.12); unadjusted two-proportion z-test p = 0.214.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between u-hFSH and the control. The interval gives the range of effects that remain compatible with the data.

Time for Dominant Follicle to Achieve 18 mm in Mean Diameter

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • u-hFSH: mean 12.10 versus 13.10 on control; difference -1.00 (95% CI -2.18 to 0.18), standardized effect d = -0.21; z-test p = 0.097.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between u-hFSH and the control. The interval gives the range of effects that remain compatible with the data.

Total Dose of the Investigational Medicinal Product (IMP) Administered to Participants With Dominant Follicle Achieving 18 mm in Mean Diameter

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • u-hFSH: mean 845.70 versus 959.29 on control; difference -113.59 (95% CI -236.46 to 9.28), standardized effect d = -0.23; z-test p = 0.070.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between u-hFSH and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover several related aspects of ovulation induction: whether a dominant follicle reached a specified mean diameter, how long that took, and the total dose administered among participants who reached the threshold. Other outcomes address hCG cancellation, single follicle maturation, biochemical and clinical pregnancy, and an ovulation definition based on progesterone or clinical pregnancy. Together, these outcomes broaden the description of follicular response and subsequent events beyond the primary participant-level ovulation outcome.

Overall, the secondary results should be considered alongside, rather than in place of, the primary result. They address different stages or definitions of treatment response, and some are restricted to participants meeting a follicle-development criterion. The registry’s primary outcome used a particular progesterone threshold or clinical pregnancy; secondary measures may use distinct thresholds, populations, or time frames. Agreement across related outcomes may provide context, but it does not remove uncertainty in the primary comparison or supply the missing non-inferiority margin.

The record lists 10 secondary outcomes, and secondary analyses include both binary and continuous outcomes. It reports Chi-squared tests for binary comparisons and two-sided t-tests for continuous comparisons. The reported tests are unadjusted, and the registry record does not describe a multiplicity correction or identify a hierarchy for interpreting secondary outcomes.

General: When many outcomes are tested, the chance of obtaining one apparently notable result by chance can increase. A multiplicity plan may control or otherwise address that issue, but no such plan is reported in the registry record. For that reason, unadjusted secondary results carry less confirmatory weight than a prespecified primary analysis. They are useful for describing patterns and generating questions, but an isolated secondary finding should not be treated as independent proof of a treatment difference.

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

• Number of Participants With the Dominant Follicle Achieving 18 mm in Mean Diameter
• Time for Dominant Follicle to Achieve 18 mm in Mean Diameter
• Total Dose of the Investigational Medicinal Product (IMP) Administered to Participants With Dominant Follicle Achieving 18 mm in Mean Diameter
• Human Chorionic Gonadotropin (hCG) Cancellation Rate
• Single Follicle Maturation Rate
• Biochemical Pregnancy Rate
• Clinical Pregnancy Rate
• Ovulation Rate, Where Ovulation is Defined as a Serum P4 Level Greater Than or Equal to 10 ng/mL or Clinical Pregnancy

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary at the participant level: a participant either met the registry definition of ovulation or did not. A percentage summarizes the share meeting the definition in each group. The posted results include group percentages, group counts, and a risk-difference comparison. A risk difference expresses the difference between the two groups’ event proportions; its sign depends on which group is subtracted from which. The recalculated contrast is stated as u-hFSH relative to SJ-0021. The registry’s Delta uses the reverse direction. Keeping that orientation clear prevents a positive or negative sign from being mistaken for a different clinical result.

For the primary outcome, the posted recalculation gives a risk difference of 0.035 and a 95% confidence interval from -0.060 to 0.131. The unadjusted p-value is 0.472. These figures describe a simple, unadjusted comparison based on the posted group summary statistics. They are not a substitute for the sponsor’s stated analysis or for a prespecified non-inferiority analysis. The registry lists a Chi-squared method for its reported primary analysis and identifies the analysis as non-inferiority or equivalence.

What does a confidence interval convey?

General: A confidence interval describes uncertainty around an estimated effect under the assumptions of the method used. A wider interval indicates less precision than a narrower interval. For a difference measure, an interval that includes zero is compatible with no difference as well as with effects in either direction, subject to the model and data. For non-inferiority, interpretation additionally depends on a prespecified margin: the interval must be evaluated against that margin, not simply against zero. The margin is not reported in the registry record.

What does a p-value answer?

General: An unadjusted p-value describes how incompatible the observed data are with a specified null hypothesis under the test’s assumptions. It does not give the probability that the null hypothesis is true, the probability that a treatment is effective, or the size of an effect. A conventional superiority test and a non-inferiority test address different questions. A p-value from a simple superiority-style recalculation does not determine whether a non-inferiority criterion was met. The registry gives no primary p-value for its reported non-inferiority or equivalence analysis.

Why does the analysis population matter?

The registry defines the full analysis set as participants who received at least one dose of the investigational medicinal product and had no major Good Clinical Practice violation. The posted primary outcome is associated with this population. This definition does not necessarily match every possible analysis population, such as all randomized participants or a per-protocol population. The registry record does not report a per-protocol result or explain whether other populations were analyzed. Excluding participants can affect the comparison if exclusions are related to treatment or outcome, so the set used should be considered alongside the result.

How should the non-inferiority analysis be approached?

The study’s stated purpose included confirmatory evidence of non-inferiority. General: A non-inferiority analysis asks whether the effect of one treatment is not worse than the comparator by more than a prespecified acceptable amount. That amount is the non-inferiority margin. The margin, its justification, and details of the prespecified statistical plan are not reported in the registry record. The record does report a Chi-squared analysis with Delta -3.51 and a 95% confidence interval from -13.05 to 6.04. The interval’s direction is opposite to the recalculated contrast, so direction must be kept consistent when comparing results. In the absence of the margin, the reported interval cannot be evaluated against the criterion that would determine non-inferiority.

The displayed planning illustrations are distinct from the trial’s original design. General: Planning calculations estimate the number of participants needed under chosen assumptions, including a target effect and a statistical power level. When such calculations use an effect observed in a completed trial, the estimate is sensitive to uncertainty in that observed effect and to the assumptions used. It is not evidence that the completed trial had a particular probability of finding a result. The registry does not report the original sample-size justification or full design assumptions.

For the secondary continuous outcomes, posted recalculations use mean differences, with standard deviations reported for the group summaries. A mean difference compares group averages in the stated units. General: Standard deviation describes spread among individual observations, whereas a confidence interval for a mean difference describes uncertainty in the estimated contrast. These are different quantities. The registry reports two-sided t-tests for the secondary continuous comparisons. For binary secondary outcomes, it reports Chi-squared tests. These methods address different outcome scales and should be interpreted with the outcome definitions and analysis populations in mind.

All recalculated comparisons are unadjusted. The registry does not report a multiplicity adjustment, missing-data method, or complete prespecified analysis plan. Those omissions limit how far the set of reported p-values can support confirmatory conclusions.

11. Confidence Intervals and Precision

The primary recalculated risk difference is 0.035, with a 95% confidence interval from -0.060 to 0.131. The interval crosses zero, so the posted unadjusted estimate does not exclude no difference under this calculation. It also permits differences in either direction. The width reflects meaningful uncertainty around the point estimate; the point estimate should not be read as if it were known without error.

The registry’s own reported primary analysis gives Delta -3.51 and a 95% confidence interval from -13.05 to 6.04. That contrast is oriented in the reverse direction from the recalculated risk difference and is reported on a percentage-point scale. The interval likewise covers values on both sides of zero. For a non-inferiority interpretation, however, the key reference is the prespecified non-inferiority margin, not zero alone. The margin is not reported in the registry record, so the precision interval cannot establish whether the interval lies within the acceptable boundary.

General: A confidence interval is an estimate of the range of effects reasonably compatible with the data and the statistical method, at the stated confidence level. It is not a guarantee that the true effect falls inside that particular interval, nor does it represent the range of individual participant outcomes. Precision depends on factors such as the amount of information in the data and variability in outcomes. A confidence interval can be more useful than a p-value for assessing which effect sizes remain plausible.

The trial record also contains estimates for secondary outcomes, but their intervals and tests address separate questions. They should not be used to narrow the uncertainty around the primary comparison. The registry does not report the original sample-size rationale or the non-inferiority margin, both of which would help place the primary interval in its design context.

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 sample-size planning illustrations are calculations based on the observed effect, not a report of the trial’s original sample-size design. The listed values for the binary and continuous outcomes are participants per arm. For time-to-event outcomes, a planning display would instead give total events, but no time-to-event planning outcome is described in this record. The stated assumptions for these planning calculations are a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect to detect set to a stated fraction of the observed effect.

The 80% power column describes the planned number of participants per arm under which a study would have an 80% chance of detecting the chosen effect, assuming the planning model and assumptions are correct. The 90% power column uses a higher chance of detecting that same chosen effect and therefore generally requires a larger planned sample. These columns are prospective planning illustrations, not evidence about what the completed trial was capable of detecting.

The rows use different fractions of the observed effect. Planning for 75% of the observed effect means targeting a smaller effect than planning for 100% of the observed effect; planning for 50% means targeting a still smaller effect. Smaller effects are harder to distinguish from random variation and need more participants per arm under the stated assumptions. These figures are not observed power. They are sensitive to the observed effect used as the planning basis and should not be treated as the study’s own sample-size justification. The original design assumptions and rationale are not reported in the registry record.

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 = 0.791; treatment proportion p₁ = 0.826; average p̄ = 0.808.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5568 = 1.0912.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5562 = 0.4681.
4. n = (1.0912 + 0.4681)² / (0.0351)² = 1,978.2, rounded up to 1,979 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 <- 0.790697674418605                          # control proportion
p1 <- 0.825757575757576                          # 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(1185782)
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 = 1,979 (80% power), 2,648 (90% power)
 75% of observed effect: n per arm = 3,579 (80% power), 4,791 (90% power)
 50% of observed effect: n per arm = 8,189 (80% power), 10,962 (90% power)
power.prop.test: 1978.2 
Simulated power at n = 1,979 per arm: 0.802 (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. Analyses Reported by the Sponsor

The registry reports the sponsor’s primary analysis as a Chi-squared non-inferiority or equivalence analysis. It gives Delta -3.51 and a 95% confidence interval from -13.05 to 6.04, but no p-value for this analysis. The recalculated comparison is a simple unadjusted risk difference based on posted group summaries, with the contrast expressed as u-hFSH relative to SJ-0021. Its estimate is 0.035, and its 95% confidence interval is from -0.060 to 0.131.

The two results use opposite directions for the contrast: the sponsor’s reported Delta is expressed as SJ-0021 relative to u-hFSH, while the recalculation expresses u-hFSH relative to SJ-0021. Their numerical estimates and interval endpoints therefore have opposite signs. The sponsor’s result is expressed in percentage points, while the recalculated risk difference is expressed as a proportion.

The recalculation does not recreate every feature of the sponsor’s analysis. It is unadjusted and depends only on the posted summary statistics. The sponsor analysis is the registry’s stated trial analysis, and should be distinguished from a simple recalculation. The registry record does not report the non-inferiority margin or enough detail to independently reproduce the full inferential plan. Consequently, the two analyses can be described and their contrast directions clarified, but the registry record does not permit a complete audit of the formal non-inferiority decision.

15. Multiplicity, Missing Data and Analysis Populations

The registry identifies a primary outcome and lists multiple secondary outcomes, including follicular development, treatment duration and dose, cancellation, maturation, pregnancy, and a further ovulation measure. It reports 11 outcome measures and 9 analyses. The record does not report a multiplicity adjustment, a testing hierarchy, or a plan for choosing among secondary comparisons. Without such information, the reported unadjusted secondary p-values should be interpreted cautiously, particularly when considered as a group.

Missing-data handling is not reported in the registry record. The primary outcome definition requires either a progesterone result at specified assessment times or clinical pregnancy, but the record does not state how unavailable progesterone measurements, incomplete follow-up, or other missing outcome data were handled. It does not describe imputation, assumptions about missingness, or sensitivity analyses. These details matter because missing observations can affect both the outcome percentages and the uncertainty around the treatment contrast.

The primary analysis population is described as the full analysis set, including participants who received at least one dose and had no major Good Clinical Practice violation. The registry does not report a per-protocol analysis or clarify the inclusion of every randomized participant in the primary analysis. It also does not provide a complete accounting of exclusions from each analysis population in the information recorded for this result. Therefore, the primary estimate should be understood as applying to the stated full analysis set, rather than automatically to all enrolled or randomized participants.

The non-inferiority margin, original sample-size justification, and fuller statistical analysis plan are not reported in the registry record. Their absence limits assessment of whether the study was designed and analyzed in a way that adequately addresses the stated confirmatory non-inferiority objective. These gaps do not change the posted estimates, but they restrict what can be concluded from the registry entry alone.

16. 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

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: ovarian hyperstimulation syndrome (0.8%).

The posted serious adverse event figures show 1 affected participant among 129 in the SJ-0021 group and 1 among 132 in the u-hFSH group. The registry lists ovarian hyperstimulation syndrome as the leading serious adverse event term, at 0.8%. The record does not give a group-specific breakdown for that term in the information reported for the serious-event summary.

Other adverse events are recorded for 36 of 129 participants in the SJ-0021 group and 40 of 132 in the u-hFSH group. These counts are separate from the serious adverse event counts and should not be conflated with them. The registry record does not provide a full account of event severity, timing, relatedness, or the detailed terms for all other adverse events in the posted summary. Death counts are not reported in the registry record.

These figures describe recorded events in the trial groups; they do not by themselves establish comparative safety. Interpretation depends on the definitions, duration of observation, event ascertainment, and completeness of reporting. Further detail about those procedures is not reported in the registry record.

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

The registry results describe a randomized comparison of SJ-0021 with purified pituitary gonadotropin for ovulation induction. For the primary outcome, the posted percentages were 79.1% for SJ-0021 and 82.6% for u-hFSH. The unadjusted recalculated risk difference has a 95% confidence interval that includes zero and spans effects in either direction. The registry also reports a non-inferiority or equivalence analysis, but the non-inferiority margin is not reported in the registry record.

These findings do not, by themselves, establish that the treatments are equivalent or that a formal non-inferiority criterion was met. Equivalence and non-inferiority require a prespecified margin and an analysis interpreted against that margin. The record’s reported confidence interval cannot be judged against the missing margin. The unadjusted p-value from the recalculation addresses a different statistical question and should not be substituted for the sponsor’s stated analysis.

The endpoint also has a specific definition: a progesterone threshold at stated assessment times or clinical pregnancy. Its percentage is not a complete description of every aspect of follicular response, pregnancy, or treatment experience. Secondary measures provide additional outcome perspectives, but multiple unadjusted comparisons and unreported multiplicity handling limit the confirmatory weight of those results.

The results apply to the stated full analysis set, not necessarily every enrolled participant. The registry does not report missing-data handling, the original sample-size assumptions, or detailed analysis-plan information. These limitations mean that the posted record supports a description of the observed outcomes and their statistical uncertainty, but not a complete independent reconstruction of the trial’s formal decision process.

General: Statistical evidence concerns uncertainty in group comparisons; it does not determine an individual participant’s outcome. The findings should be read as trial results in the population and outcome definitions recorded, not as a guarantee about results in other populations or settings.

18. Why This Trial Matters Statistically

This trial is statistically notable because its stated objective was confirmatory non-inferiority, not merely a search for any difference between groups. That distinction changes how the primary result must be interpreted. A nonsignificant superiority-style comparison cannot establish non-inferiority; the conclusion depends on the prespecified margin and the confidence interval relative to that margin. The margin is not reported in the registry record.

The record offers both a sponsor-reported non-inferiority or equivalence analysis and enough summary information for a simple unadjusted recalculation. Comparing them illustrates why contrast direction and analysis purpose matter. The estimates are expressed with opposite orientations, and the recalculation is not a replacement for the sponsor’s planned analysis. The registry’s reported interval is central to interpretation, yet it cannot be assessed against a missing margin.

The trial also illustrates how a binary primary endpoint simplifies a clinical response into a participant-level classification. That is useful for comparing event proportions, but it depends on the operational definition, the assessment schedule, and the analysis population. Here, ovulation incorporated a progesterone criterion or clinical pregnancy, while other outcomes described follicle growth, dose, time, and pregnancy-related measures.

Finally, the registry record leaves several design and analysis details unreported, including original sample-size assumptions, missing-data handling, and multiplicity methods. Those omissions do not erase the reported results, but they affect the strength of an independent statistical interpretation. Separating observed estimates, uncertainty intervals, confirmatory objectives, and reporting gaps is essential when reading trial evidence.

19. 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.

The primary statistical limitation is that the non-inferiority margin is not reported in the registry record. Because the study’s stated objective included confirmatory non-inferiority, the margin is needed to assess whether the reported confidence interval satisfies the intended criterion. The registry gives a Delta and confidence interval, but not the decision boundary or its justification.

The record does not report the original sample-size justification, the planned power, or the full design assumptions. The planning illustrations based on observed effects are not a substitute for those original design details. Randomization is stated, but the allocation procedure and concealment are not described. Single blinding is stated, but the masked parties are not identified.

The primary analysis population is defined, but the registry does not report full details of exclusions, a per-protocol analysis, or whether results were assessed in other populations. Missing-data methods and sensitivity analyses are not reported. These matters can be especially relevant to non-inferiority studies, where conclusions may depend on analysis population and assumptions about incomplete observations.

The registry lists multiple secondary outcomes and analyses but does not report a multiplicity strategy or outcome hierarchy. Their unadjusted tests therefore require cautious interpretation. Safety reporting in the posted summary is also limited: detailed descriptions of all events, timing, relatedness, and follow-up are not reported in the registry record. These specific limitations constrain how fully the clinical and statistical results can be evaluated from the registry information.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: SJ-0021 (Gonalef®) Versus Purified Pituitary Gonadotropin (Fertinorm-P®) for Ovulation Induction in Japanese Infertile Women — NCT01185782.
• Registry record last updated 2013-12-27; status last verified 2013-12.
• Results first posted 2012-02-24.
• 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.

23. Record Summary

NCT01185782 is a completed, randomized, single-blind, parallel-group phase III study comparing SJ-0021 (Gonalef®; follitropin alfa) with purified pituitary gonadotropin (Fertinorm-P®) in Japanese women with infertility and ovulatory disorders. The registry lists enrollment of 300, with a recorded start of 2007-02 and primary completion in 2007-12.

The primary outcome was the percentage of participants with ovulation, defined by a serum progesterone threshold at specified post-treatment assessments or clinical pregnancy. The reported group percentages were 79.1% for SJ-0021 and 82.6% for u-hFSH. The registry reports a Chi-squared non-inferiority or equivalence analysis with Delta -3.51 and a 95% confidence interval from -13.05 to 6.04. The non-inferiority margin, original sample-size rationale, missing-data methods, and multiplicity plan are not reported in the registry record.