Efficacy and Safety of Recombinant Human Growth Hormone on Height Velocity in Subjects With Idiopathic Short Stature: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Efficacy and Safety of Recombinant Human Growth Hormone on Height Velocity in Subjects With Idiopathic Short Stature (NCT01778023), a randomized phase 3 study. The primary outcome is Height Velocity (Ht-V).

2. Clinical Question

The clinical question is about the relative height-velocity results for these schedules under the trial conditions. Because the schedules differ both in when treatment begins and in the duration of treatment before assessment, the comparison does not isolate every possible effect of treatment timing or duration. The record identifies the trial as phase 3, randomized, open-label, parallel-group, multicentre and interventional. It reports 54 participants enrolled.

The primary endpoint description says that height velocity was calculated by Novo Nordisk. The registry population definition includes all randomized subjects in Group A who received at least one dose and all randomized subjects in Group B; it also notes four subjects with missing height at baseline. The available record does not provide detailed eligibility criteria, participant characteristics, or an account of how the four missing baseline measurements affected each analysis. Those details are not reported in the registry record.

3. Trial Design

This was a phase 3, randomized, open-label, parallel-group, multicentre trial of recombinant human growth hormone in subjects with idiopathic short stature in Korea. The official title describes a 12-month trial. Enrollment was 54, and the registry status is completed. The record gives a start date of 2013-01-17 and a primary completion date of 2014-12-17.

The two groups followed different schedules. Group A was assigned 12-month growth hormone treatment. Group B had six months without treatment and then six months of growth hormone treatment. The primary endpoint is described as height velocity after six months of treatment. The schedule difference matters when interpreting a group contrast: the results compare the assigned regimens at the endpoint defined in the registry, rather than comparing growth hormone with no treatment for the full trial period.

Randomization is reported, but the randomization method, allocation concealment procedures and any stratification factors are not reported in the registry record. Masking is listed as none, so the trial was open-label. The record does not explain whether outcome measurement or analysis was masked despite that design. The original protocol’s sample-size justification and design assumptions are not reported in the registry record.

The primary outcome analysis population is described as a full analysis set. It included all randomized subjects in Group A who received at least one dose and all randomized subjects in Group B. The endpoint-specific sample sizes reported with the primary outcome are 32 for Group A and 15 for Group B; the record also notes four subjects with missing height at baseline. Safety counts use different at-risk totals for Group A and Group B. The registry does not give a complete flow of participants from enrollment through each analysis population, so the reasons for differences in the reported counts are not reported in the registry record.

The primary outcome is continuous, expressed in cm/year, and reported as a least-squares mean with standard error. The registry also lists secondary outcomes involving height standard deviation score, IGF-related factors, bone age, adverse events and height velocity. The timing and analysis details for outcomes beyond those explicitly described 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: Full analysis set (FAS) - included all randomised subjects in Group A who received at least one dose of the trial product and all randomised subjects in Group B. Four (4) subjects had missing height at baseline visit.

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 Height Velocity (Ht-V), measured over After 6 months of treatment.

Height velocity (Ht-V) measures change in height per year and is expressed in cm/year. The primary endpoint description defines it as the change in height per year after six months of treatment. The registry says Novo Nordisk calculated this measure. It is used to quantify the rate of height change over a specified interval rather than height at a single visit.

The results are reported as least-squares means, with standard errors as the measure of dispersion. A least-squares mean is a model-based group mean; the record labels the reported parameter this way but does not provide enough detail to reconstruct the sponsor’s model specification. The standard error describes the uncertainty in the estimated mean, rather than the spread of individual participants’ measurements.

The two schedules make the endpoint’s timing important. Group A received growth hormone treatment for 12 months, while Group B first had six months without treatment and then six months of growth hormone treatment. The outcome timeframe is stated as after six months of treatment. The interpretation should therefore stay anchored to the schedules and endpoint timing described in the registry, rather than treating the result as a direct estimate of every possible treatment schedule or longer-term height outcome. The record does not report a final-height endpoint.

7. Results Posted in the Registry

• Group B: 6-month Untreated + 6-month GH Treatment: mean 6.87 versus 12.02 on control; difference -5.15 (95% CI -6.17 to -4.13), standardized effect d = -3.12; z-test p = <0.001.

8. Secondary Efficacy Endpoints

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

Change in Ht-SDS (Height Standard Deviation Score)

• 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 errors.
• Result:
  • Group B: 6-month Untreated + 6-month GH Treatment: mean 0.19 versus 0.76 on control; difference -0.57 (95% CI -0.71 to -0.43), standardized effect d = -2.50; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Group B: 6-month Untreated + 6-month GH Treatment than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change in IGF Related Factors: IGF-I (Insulin-like Growth Factor-I)

• 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 errors.
• Result:
  • Group B: 6-month Untreated + 6-month GH Treatment: mean 28.03 versus 192.58 on control; difference -164.55 (95% CI -215.35 to -113.75), standardized effect d = -1.96; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Group B: 6-month Untreated + 6-month GH Treatment than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change in IGF Related Factors: IGFBP-3 (Insulin-like Growth Factor Binding Protein-3)

• 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 errors.
• Result:
  • Group B: 6-month Untreated + 6-month GH Treatment: mean 0.22 versus 0.89 on control; difference -0.67 (95% CI -1.37 to 0.03), standardized effect d = -0.58; z-test p = 0.059.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Group B: 6-month Untreated + 6-month GH Treatment and the control. The interval gives the range of effects that remain compatible with the data.

The secondary results broadly follow the primary endpoint’s group ordering for the posted continuous measures: Group A has higher estimated values than Group B for height standard deviation score and the two reported IGF-related measures. These measures provide related but distinct information. Height standard deviation score describes height relative to a reference distribution, while IGF-I and IGFBP-3 are biochemical measures. They are not substitutes for height velocity and do not independently establish why the primary groups differed.

The statistical evidence is not equally strong across outcomes. The posted height standard deviation score and IGF-I comparisons have unadjusted p-values below 0.001. The IGFBP-3 comparison has an unadjusted p-value of 0.059 and a confidence interval that crosses zero. That pattern cautions against treating all secondary findings as equally conclusive. These outcomes are secondary, and their results should be read as supporting context rather than as separate confirmation of the primary result.

The registry lists additional secondary outcomes, including change in bone age, occurrence of adverse events and height velocity. It does not report detailed results for all listed outcomes in the same way as the three continuous measures described in the posted secondary summaries. Information not given for those outcomes is not reported in the registry record.

Multiple outcome testing matters because the more comparisons are examined, the greater the chance that some results may look statistically notable even if there is no corresponding underlying difference. The registry does not report a multiplicity adjustment or a formal testing hierarchy for secondary outcomes. As a result, unadjusted secondary p-values carry less weight than the primary analysis, especially when used to make confirmatory claims. The overall pattern is useful for describing the set of outcomes, but it does not remove uncertainty about multiplicity, modeling, or the trial’s limited analysis details.

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

• Change in Ht-SDS (Height Standard Deviation Score)
• Change in IGF Related Factors: IGF-I (Insulin-like Growth Factor-I)
• Change in IGF Related Factors: IGFBP-3 (Insulin-like Growth Factor Binding Protein-3)
• Change in Bone Age
• Occurrence of Adverse Events
• Ht-V (Height Velocity)

9. Statistical Interpretation

10. Statistical Methods Explained

The main reported analysis is an ANOVA comparison, presented by the sponsor as a treatment difference. The primary outcome is continuous and summarized with least-squares means. The registry does not provide the ANOVA specification, covariates, assumptions checks or handling of missing outcome values in enough detail to reproduce the sponsor’s analysis. The reported result should therefore be read as the sponsor’s analysis rather than as a fully reconstructable model.

What does a least-squares mean represent?

A least-squares mean is an estimated group mean derived from a statistical model. It can account for model terms that are included in the analysis, so it need not be identical to a simple arithmetic average. The registry identifies this as the parameter used but does not list the full model terms. The accompanying standard error describes the precision of the estimated mean. It is not the same as the standard deviation, which describes variation among individual observations.

How should the group difference be read?

A difference depends on the order of subtraction. The sponsor reports a positive treatment difference of 5.15, with its confidence interval also positive. The simple unadjusted recalculation compares Group B with Group A and reports -5.15, with a negative confidence interval. These signs reflect opposite comparison directions. The substantive comparison is between the same two group estimates; the sign tells the reader which group is subtracted from which. Neither direction should be interpreted without naming the groups.

The sponsor reports an ANOVA result with p < 0.001. The recalculation reports p < 0.001. A p-value measures how incompatible the data are with a specified null hypothesis under the analysis assumptions. It does not give the probability that the null hypothesis is true, measure the size or clinical importance of a difference, or show how likely the result is to recur. The sponsor’s result is the trial’s reported analysis; the recalculation is based on the posted group summaries and does not replace that analysis.

Why do confidence intervals matter alongside p-values?

A confidence interval gives a range of values compatible with the data and statistical model at the stated confidence level. The primary sponsor interval is 4.09 to 6.21 for its reported direction. The recalculated interval is -6.167 to -4.133 for the reverse direction. Both intervals are away from zero, which agrees with the reported small p-values. The interval also conveys the scale and precision of the estimate, information that a p-value alone does not provide. A confidence interval is not a guarantee that the true value lies in the interval, nor does it include uncertainty from every possible design or analysis limitation.

What does over-dispersion mean in general?

In general, over-dispersion occurs when observed variability is greater than a model assumes. If it is ignored, standard errors can be too small and confidence intervals too narrow, making statistical evidence appear stronger than it is. That concept is relevant when reviewing statistical models, but the registry does not report an assessment of over-dispersion for this continuous endpoint. The posted record does not establish that over-dispersion was present.

The recalculation uses posted means, standard errors and group sizes to form a simple unadjusted comparison. Its pooled standard deviation is marked as approximate in the calculation record, reflecting that the group summaries provide standard errors rather than individual-level variability. The resulting standardized effect estimate is likewise a summary-based calculation, not the sponsor’s ANOVA estimate. Such calculations can help readers understand the magnitude and direction of a contrast, but they cannot reproduce adjustment or other features of the sponsor’s model that are not reported.

Secondary continuous outcomes are also summarized with least-squares means and standard errors. Their accompanying unadjusted comparisons provide useful descriptive context, but testing several outcomes raises the chance that at least one result may appear notable by chance. The registry does not report a multiplicity adjustment or a prespecified hierarchy for secondary testing. That absence makes the secondary p-values less decisive than a primary result defined in advance and reported as the principal comparison.

Finally, the registry’s full analysis set definition is not identical in its wording for the groups: it includes randomized Group A subjects who received at least one dose and all randomized Group B subjects. The outcome-specific analysis counts and missing baseline heights should be considered when evaluating how closely the estimates represent everyone randomized. The record does not report enough participant-level detail to independently assess the effects of missing data or reproduce the fitted models.

11. Confidence Intervals and Precision

For the primary outcome, the sponsor reports a treatment difference of 5.15 with a 95% confidence interval from 4.09 to 6.21. This interval expresses uncertainty around the estimated contrast under the sponsor’s analysis. Because the full ANOVA specification is not reported in the registry record, the interval should be interpreted as the result of that reported analysis rather than as an independently reproducible model output.

The unadjusted recalculation gives a mean difference of -5.15 cm/year for Group B relative to Group A, with a 95% confidence interval from -6.167 to -4.133. Its direction is reversed relative to the sponsor’s positive treatment difference. The interval is bounded away from zero, so the summary-based comparison is consistent with a group difference under its assumptions. The sponsor’s interval and the recalculated interval are not identical in their endpoints; the recalculation is not a substitute for the sponsor’s model-based interval.

Confidence intervals communicate both direction and precision. A narrower interval indicates less statistical uncertainty around an estimate, while a wider interval permits a broader range of values. They do not capture all uncertainty about trial conduct, measurement, missing observations or applicability to other populations. Here, the primary intervals indicate relatively constrained uncertainty for the estimated contrast within the analyses reported. The uncertainty surrounding generalization beyond these participants and schedules remains a separate issue.

For the secondary IGFBP-3 comparison, the unadjusted interval extends from -1.366 to 0.026 and crosses zero. That result is less conclusive statistically than the primary result. The registry’s secondary comparisons are unadjusted, and the record does not describe a correction for testing multiple outcomes. The strength of evidence should therefore be judged by the prespecified primary result and the design context, not by treating each secondary interval as an independent confirmatory finding.

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 figures are planning illustrations based on fractions of the observed effect, not the trial’s original sample-size plan. The assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The original design assumptions and sample-size justification are not reported in the registry record. For these continuous outcomes, the figures are participants per arm, not total participants. The registry’s note that time-to-event planning tables give total events does not apply to this continuous-outcome comparison.

The 80% power column gives an illustrative number of participants per arm for a hypothetical study to have an 80% chance of detecting the specified effect, if the planning assumptions hold and the true effect is as specified. The 90% power column uses a higher target chance of detecting that effect and therefore generally requires more participants per arm. These are planning quantities, not a measure of the probability that the trial result is true. This is not observed power.

Rows that use 100%, 75% or 50% of the observed effect ask what planning numbers would follow if a future study aimed to detect the full observed contrast or a smaller fraction of it. For example, planning for 75% of the observed effect uses a less pronounced target than planning for the entire observed effect. Smaller effects are harder to distinguish from random variation, so they require more participants per arm under the stated assumptions. The table and curve should be read as sensitivity illustrations, not as proof that a future trial with those sample sizes will achieve its target.

The observed effect comes from a small trial and may be imprecise or larger than a future effect. The planning figures also rely on summary-statistic calculations and simplified assumptions. They do not account for every feature that might affect a real trial, such as loss to follow-up, allocation imbalance or details of the original analysis model. The registry does not report the sample-size rationale used when this trial was designed, so the illustrative figures should not be attributed to the trial’s protocol.

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, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -5.150; pooled standard deviation σ = 1.648.
2. n = 2 × (1.96 + 0.842)² × 1.648² / -5.150² = 1.6, rounded up to 2 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
delta <- -5.15                      # observed difference in means
sigma <- 1.64827317045581                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^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 (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(1778023)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 2 (80% power), 3 (90% power)
 75% of observed effect: n per arm = 3 (80% power), 4 (90% power)
 50% of observed effect: n per arm = 7 (80% power), 9 (90% power)
power.t.test: 3 
Simulated power at n = 2 per arm: 0.409 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-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 sponsor’s reported primary analysis is an ANOVA with a treatment difference of 5.15, a 95% confidence interval from 4.09 to 6.21, and p < 0.001. It reports the contrast in the direction that yields a positive difference, consistent with Group A’s higher least-squares mean height velocity of 12.02 cm/year compared with Group B’s 6.87 cm/year.

The simple unadjusted recalculation compares Group B with Group A and reports -5.15 cm/year, with a 95% confidence interval from -6.167 to -4.133 and p < 0.001. The negative sign follows from that reversed comparison order. Both analyses indicate the same direction of group ordering when the contrast is interpreted with its stated direction. The registry record does not mark a discrepancy between the sponsor analysis and recalculation.

The recalculation uses posted summary statistics and does not reproduce any model adjustments that may have been used in the sponsor’s ANOVA. The registry does not give enough detail about the sponsor model to verify its specification independently. The sponsor analysis is therefore the reported trial analysis, while the recalculation is best understood as a transparent, unadjusted check on the summary values. Differences between interval endpoints or p-value formatting should not be taken as evidence of contradictory findings.

15. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when several outcomes or comparisons are tested. Even if each test is considered separately, examining multiple results increases the possibility of chance findings. The registry lists several secondary outcomes and reports unadjusted comparisons for the posted continuous secondary measures. A multiplicity adjustment, a prespecified hierarchy for testing secondary outcomes, or an explanation of how confirmatory claims were controlled is not reported in the registry record. Secondary p-values should therefore be treated as exploratory or supportive rather than as a set of independent definitive tests.

Missing data also affect interpretation. The primary endpoint description notes four subjects with missing height at baseline. The registry defines the full analysis set as all randomized Group A subjects who received at least one dose and all randomized Group B subjects. It does not report a full participant flow for each outcome, the reasons for missing data, or the method used to handle missing endpoint observations. Sensitivity analyses for missing values are not reported in the registry record.

The endpoint-specific primary counts are 32 for Group A and 15 for Group B, while the safety at-risk counts are 36 and 15. These counts apply to different reported contexts and should not be assumed to represent identical analysis populations. The record does not explain the difference between the Group A primary and safety counts. It also does not provide enough detail to determine how inclusion, exclusions or missing measurements affected the sponsor’s model estimate.

The analysis population definition itself is asymmetric in wording: Group A participants had to be randomized and receive at least one dose, whereas all randomized Group B participants are included. The registry does not explain the rationale for that distinction. The population definition, missing baseline heights, and outcome-specific counts are relevant when considering how broadly the result represents randomized participants. Without additional details, the posted results can be described, but the influence of missingness and population choices cannot be fully assessed.

The registry does not report interim analyses, the original statistical analysis plan, or a complete account of all analytic decisions. Those matters are not grounds to infer that a particular procedure was or was not used. They remain unreported in the registry record.

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: pneumonia (6.7%), tonsillar hypertrophy (6.7%), hydrocele (2.8%), pharyngotonsillitis (2.8%), kawasaki's disease (2.8%).

The safety results report serious adverse events among 4 of 36 participants at risk in Group A and 2 of 15 participants at risk in Group B. These are counts of participants affected, not counts of all individual events. The record separately lists other adverse events in 20 of 36 participants in Group A and 10 of 15 in Group B. Serious and other adverse-event categories should not be combined or treated as interchangeable.

The registry lists pneumonia and tonsillar hypertrophy at 6.7% each, and hydrocele, pharyngotonsillitis and Kawasaki's disease at 2.8% each, among the top serious adverse-event terms. The record does not give enough detail in these entries to assign each listed term to a treatment group or to establish causality. A reported event during a trial does not by itself show that the intervention caused it.

Deaths are not reported in the registry record. The available adverse-event summary does not provide event onset, duration, severity detail beyond the serious classification, or a comparison adjusted for exposure time. These figures describe the recorded safety experience; they do not establish that either schedule is free of risk or that the observed counts would be the same in another population.

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

The posted data show a difference in estimated height velocity between the two randomized treatment schedules at the stated endpoint. Group A’s least-squares mean is 12.02 cm/year, compared with 6.87 cm/year for Group B. The sponsor’s ANOVA reports a treatment difference of 5.15, with a 95% confidence interval from 4.09 to 6.21. The unadjusted calculation expresses the contrast in the reverse direction and also reports an interval away from zero. These findings describe the trial’s comparison; they do not predict the result for a particular person.

The result is specific to subjects with idiopathic short stature studied in Korea, the schedules used, the endpoint timing and the analysis population stated in the registry. It does not establish what would happen under other schedules, in other populations, or over a longer period. The record does not report a final-height outcome, so the primary height-velocity result should not be treated as evidence about final height.

Statistical evidence and clinical importance are different questions. A small p-value indicates that the observed contrast is not easily reconciled with a no-difference hypothesis under the analysis assumptions; it does not establish that the size of the difference is clinically important. The registry record does not provide a threshold for clinical importance. Nor do the results establish the mechanism behind the group contrast.

The open-label design, incomplete reporting of missing-data handling and analysis details, and unadjusted secondary comparisons are relevant limitations. The trial also compares schedules that differ in treatment timing and duration before the endpoint, so the contrast should be interpreted as a regimen comparison. The results are not medical advice and do not determine what treatment is appropriate for an individual.

18. Why This Trial Matters Statistically

This trial is statistically informative because it reports a randomized comparison of two treatment schedules and identifies a continuous primary endpoint with its measurement scale, timing and analysis population. The sponsor also reports a model-based treatment difference, confidence interval and p-value. These features let readers consider direction, magnitude and uncertainty rather than relying on a single significance statement.

The contrast also illustrates why the order of subtraction must be clear. The sponsor’s treatment difference is positive, while the unadjusted recalculation for Group B relative to Group A is negative. Both use the same group estimates, but describe the comparison in opposite directions. Naming the groups prevents a sign change from being mistaken for a disagreement in the substantive result.

Other statistical lessons arise from the record’s limitations. Least-squares means and standard errors are not raw participant-level summaries, and the full sponsor model is not reported. A summary-based recalculation can provide a check but cannot recover unreported model terms. The distinction between primary and secondary outcomes also matters: multiple unadjusted secondary tests offer less confirmatory evidence than the primary comparison.

Finally, the record documents different analysis and safety counts, notes missing baseline heights, and gives an unusual difference in the full analysis set wording between groups. Those details show why population definitions and missing-data methods are part of interpreting a result, not merely administrative information. The evidence is most useful when read alongside these design and reporting features, rather than as an isolated p-value.

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.

The open-label design is an important trial-specific limitation. Masking is listed as none, and the registry does not describe whether height measurement or analysis was masked. The record also does not report the randomization method, allocation concealment, or any stratification factors. These omissions limit assessment of how assignment and measurement procedures were implemented.

The primary analysis population description and counts warrant attention. Group A’s full analysis set includes randomized participants who received at least one dose, while all randomized Group B participants are included. The endpoint-specific counts are 32 and 15, compared with safety at-risk counts of 36 and 15. Four subjects had missing height at baseline. The reasons for count differences, handling of missing outcome values and sensitivity analyses are not reported in the registry record.

The endpoint is height velocity after six months of treatment, but the assigned schedules differ: Group A received 12 months of treatment, while Group B had six months untreated and then six months treated. This is a comparison of the schedules as described, not an isolated test of a single treatment-duration component. The registry does not report final height, so the result addresses the stated velocity outcome rather than later height.

The registry does not provide the full ANOVA model, the original sample-size justification, or a complete statistical analysis plan. It does not report an adjustment for multiple secondary tests. The trial’s enrollment was 54, while the reported outcome populations are smaller or differ by analysis type; a complete participant flow is not reported. These limits make it difficult to assess model robustness, missing-data effects and the strength of secondary findings beyond the posted summaries.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Efficacy and Safety of Recombinant Human Growth Hormone on Height Velocity in Subjects With Idiopathic Short Stature — NCT01778023.
• Registry record last updated 2017-06-02; status last verified 2017-05.
• Results first posted 2016-10-05.
• 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

NCT01778023 is a completed phase 3, randomized, open-label, parallel-group, multicentre trial of recombinant human growth hormone in Korean subjects with idiopathic short stature. It enrolled 54 participants and compared 12-month growth hormone treatment with six months untreated followed by six months of treatment.

The primary outcome was height velocity in cm/year after six months of treatment. The registry reports least-squares means of 12.02 for Group A and 6.87 for Group B. The sponsor’s ANOVA reports a treatment difference of 5.15, a 95% confidence interval from 4.09 to 6.21, and p < 0.001. The simple unadjusted recalculation uses the reverse group direction.

Secondary measures and adverse events provide additional context, but their interpretation is limited by unadjusted multiple comparisons and incomplete reporting of analysis and missing-data procedures. The registry does not report the original sample-size rationale, full model specification or final-height results.