A Study of SIMPONI® to Arrest Beta-cell Loss in Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study of SIMPONI® to Arrest Beta-cell Loss in Type 1 Diabetes (NCT02846545), a randomized phase 2 study. The primary outcome is Active Treatment Period: C-peptide Area Under the Concentration-time Curve (AUC) Calculated From a 4 Hour Mixed Meal Tolerance Test (MMTT) at Week 52.

2. Clinical Question

3. Trial Design

This was a Phase 2, randomized, double-blind, parallel-group study sponsored by Janssen Research & Development, LLC. The registry records an enrollment of 84 and a completed status. The study start date was 2016-08-26 and the primary completion date was 2019-05-21. The arms were golimumab and matching placebo. The registry does not report the planned allocation ratio, randomization details, or the methods used to maintain masking.

The active-treatment period ran from Week 0 to Week 52. The golimumab arm description says participants received subcutaneous doses intermittently, based on weight and/or body surface area. It also says participants meeting response criteria at Week 52 may enter an open-label extension to receive golimumab; the description ends with “50 w,” and further details are not reported in the registry record. The placebo arm description specifies a matching placebo. The safety record also includes an off-therapy follow-up period from Week 52 to Week 104 and an open-label extension period.

The primary endpoint was C-peptide AUC from a 4-hour mixed-meal tolerance test at Week 52. Its analysis population is described as the Full Analysis Set: randomized participants who took at least one dose, with the outcome count limited to those evaluable for this measure. The analyzed counts were 25 for placebo and 50 for golimumab. These outcome-specific counts need not equal the enrollment entry because the endpoint population is defined by evaluability and treatment exposure.

The registry lists 12 outcome measures and three posted secondary analyses. It names secondary outcomes covering insulin use, HbA1c, hypoglycemic event rates, C-peptide over time, and safety and tolerability. The original protocol’s sample-size justification, intended analysis populations beyond the primary description, and planned statistical analysis details 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.

Active Treatment Period: Week 0-52

Reasons for not completing this period:

Off-therapy FU:Wk 52-104 & OLE: Wk 0-60

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Full Analysis Set (FAS) included all randomized participants who took at least one dose (complete or partial) of study agent. Here 'N' (number of participants analyzed) included all participants who were evaluable for this outcome measure.

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Active Treatment Period: C-peptide Area Under the Concentration-time Curve (AUC) Calculated From a 4 Hour Mixed Meal Tolerance Test (MMTT) at Week 52, measured over Week 52.

The primary endpoint was the C-peptide area under the concentration-time curve (AUC) at Week 52, calculated from a 4-hour mixed-meal tolerance test. The registry defines the measure as the mean area under the C-peptide level time curve over the 4-hour period divided by the duration. The unit is picomoles per milliliter (pmol/mL). A mixed meal provides a standardized stimulus, while serial C-peptide measurements characterize the response over the test period.

C-peptide is used as a measure related to the body’s own insulin production. In this study, the endpoint was chosen to address the stated aim of determining whether golimumab could preserve beta-cell function in children and young adults with newly diagnosed type 1 diabetes. This is a biomarker-based outcome; it is not itself a direct measure of daily functioning or long-term clinical outcomes. The registry record lists several other outcomes, including insulin use, HbA1c, hypoglycemic event rates, and safety measures.

As a general statistical point, an AUC compresses repeated measurements over time into a summary measure. It can represent both the level and pattern of the measured response during the test, but interpretation depends on the test procedures, data completeness, and analysis methods. The registry’s endpoint description specifies the test duration and calculation, but additional details of handling missing test measurements are not reported in the registry record.

7. Results Posted in the Registry

• Golimumab: mean 0.64 versus 0.43 on control; difference 0.21 (95% CI 0.02 to 0.40), standardized effect d = 0.51; z-test p = 0.032.

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.

Active Treatment Period: Change From Baseline in Insulin Use in Units Per Kilogram Body Weight Per Day

• 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:
  • Golimumab: mean 0.06 versus 0.24 on control; difference -0.18 (95% CI -0.32 to -0.05), standardized effect d = -0.68; z-test p = 0.009.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Golimumab than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Active Treatment Period: Change From Baseline in Glycosylated Haemoglobin (HbA1c) at Week 52

• 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:
  • Golimumab: mean 0.35 versus 0.61 on control; difference -0.26 (95% CI -0.98 to 0.46), standardized effect d = -0.15; z-test p = 0.480.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Golimumab and the control. The interval gives the range of effects that remain compatible with the data.

Hypoglycemic Event Rates

• Endpoint type: Event rate: events per unit of follow-up, compared as a rate ratio.
• Test: Poisson rate ratio, based on the estimated number of events and follow-up time.
• Result:
  • Golimumab: 39.01 versus 43.36 (events per patient-year) on control; rate ratio 0.90 (95% CI 0.84–0.97, approximate, Poisson model); p ≈ 0.003.
• Interpretation:
  • The 95% confidence interval excludes no difference (a ratio of 1), so the event rate is statistically lower for Golimumab than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The secondary outcomes extend the primary biomarker question to insulin use, HbA1c, hypoglycemic event rates, and safety. They address different aspects of the study and use different scales, analysis populations, and statistical methods. Their pattern is not a single unified replication of the primary C-peptide finding: some posted comparisons show group differences with intervals excluding zero or one, while another model-based comparison is compatible with no difference. The endpoints should therefore be read individually rather than treated as interchangeable measures.

These outcomes can help readers understand whether the primary endpoint sits alongside changes in related measures, but they do not directly validate or invalidate the primary result. C-peptide AUC is a stimulated biomarker outcome; insulin use and HbA1c are separate measures, and hypoglycemic events are analyzed as rates. Differences among results can reflect the distinct questions, measurement scales, event variability, analysis populations, or uncertainty in each estimate.

Unadjusted secondary results carry less weight because the registry lists multiple outcomes and does not report a multiplicity-control plan or a prespecified hierarchy for interpreting them. As a general statistical principle, when many hypotheses are examined, some apparently unusual results can occur by chance even if no underlying differences exist. This does not make secondary outcomes uninformative, but it argues against treating an isolated p-value as confirmatory evidence.

For the insulin-use and HbA1c endpoints, the record includes mixed-model analyses in addition to the summary-based comparisons. For hypoglycemic event rates, it includes a Poisson regression analysis. These analyses are not identical in method to the simple unadjusted comparisons, and the full model specifications are not reported in the registry record. Overall, the secondary outcomes provide context, but their interpretation remains exploratory unless the trial’s prespecified analysis plan and multiplicity strategy are available.

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

• Active Treatment Period: Change From Baseline in Insulin Use in Units Per Kilogram Body Weight Per Day
• Active Treatment Period: Change From Baseline in Glycosylated Haemoglobin (HbA1c) at Week 52
• Hypoglycemic Event Rates
• Active Treatment Period: C-peptide Area Under the Concentration-time Curve (AUC) Calculated From a 4 Hour Mixed Meal Tolerance Test (MMTT) Over Time
• Percentage of Participants With Treatment Emergent Adverse Events (TEAEs) as a Measure of Safety and Tolerability
• Percentage of Participants With Serious Adverse Events
• Percentage of Participants With Severe Infections Through Week 52 and Week 104
• Active Treatment Period: Percentage of Participants With Study Agent Injection Site Reactions Up to Week 52

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous, and its posted group summaries are means and standard deviations. A mean describes the average measured outcome in a group, while a standard deviation describes variation among individual measurements around that average. General statistical explanation: these summaries make a mean difference a natural unadjusted way to compare the groups, but they do not describe the full distribution or account for any covariates.

What does the mean difference represent?

The reported contrast is the golimumab group mean minus the placebo group mean. The posted means are 0.64 and 0.43 pmol/mL, and the recalculated unadjusted difference is 0.21 pmol/mL. A positive value means the posted mean is higher in the golimumab group on this endpoint’s scale. That arithmetic description does not by itself establish clinical importance or explain why the groups differ.

The analysis counts were 50 for golimumab and 25 for placebo. The registry describes the primary population as randomized participants who took at least one dose, with only participants evaluable for the endpoint included in the outcome count. The registry does not report a detailed participant flow for this endpoint, reasons for missing measurements, or the exact method used to handle missing data.

How should the confidence interval be read?

A confidence interval expresses uncertainty around an estimate under the assumptions of the method used. For the unadjusted mean difference, the 95% interval extends from 0.018 to 0.402 pmol/mL. In general, intervals that are narrower indicate greater precision, while wider intervals leave more uncertainty about the size of the contrast. A confidence interval is not a range guaranteed to contain the true value in this particular study, and it is not a measure of clinical relevance.

The registry’s primary result includes a p-value of 0.032 for the unadjusted comparison. A p-value is a measure of compatibility between the observed data and a null hypothesis, conditional on the statistical model and its assumptions. It is not the probability that the null hypothesis is true, and it does not measure the probability that a result will replicate. The full method used for an official primary analysis is not reported in the registry record, so the posted unadjusted result should be distinguished from a model-based analysis that might have been specified in the protocol.

Why do outcome-specific analysis counts matter?

Different outcomes can have different numbers of participants analyzed because data availability and evaluability may vary by measure. The primary C-peptide analysis has 25 placebo and 50 golimumab participants. The insulin, HbA1c, and hypoglycemia outcomes have their own analysis counts and methods. Comparing their counts with overall enrollment without accounting for these definitions can be misleading. General statistical explanation: complete-case summaries describe participants with usable outcome data; if those participants differ systematically from those without data, the observed estimate may not represent the full randomized population.

How do secondary models differ?

The registry reports a mixed models analysis for change in insulin use and HbA1c. A mixed model is a general approach that can account for relationships among repeated measurements, depending on its specification. The registry gives least-squares mean estimates, confidence intervals, and p-values for these outcomes, but the complete model specifications, covariance assumptions, covariates, and missing-data assumptions are not reported in the registry record.

For hypoglycemic event rates, the reported analysis is a Poisson regression model. A rate compares event counts with time at risk, and a rate ratio compares the modeled rates between groups. General statistical explanation: Poisson models are designed for count data, but event counts can be more variable than the model assumes. Over-dispersion matters because it can make standard errors and confidence intervals too small if not addressed. The registry does not report whether over-dispersion was assessed or how it was handled.

The registry also posts recalculated unadjusted contrasts for secondary outcomes. These are based on posted group summaries and are not the same as the sponsor’s reported model analyses. Where a reported model analysis is available, it should be read as a distinct estimate with its own stated method. The discrepancy flag is false, so no conflict between the recalculated summaries and the sponsor’s reported analyses is flagged in this record.

What does multiplicity change?

Multiplicity arises when several outcomes or comparisons are tested. As a general statistical principle, considering many tests can increase the chance that at least one result appears notable by chance. The registry lists 12 outcome measures and three posted secondary analyses, but the prespecified testing hierarchy and any adjustment for multiple comparisons are not reported in the registry record. This limits how strongly isolated secondary p-values can be interpreted.

11. Confidence Intervals and Precision

The primary unadjusted mean difference is 0.21 pmol/mL, with a 95% confidence interval from 0.018 to 0.402. The interval lies above zero, but its limits allow for uncertainty in the size of the difference. The estimate is based on 25 placebo participants and 50 golimumab participants who were evaluable for the primary outcome. Unequal analysis counts and the total amount of outcome information are relevant when judging precision.

In general, a confidence interval gives a range of values reasonably compatible with the observed data under the analysis assumptions. It is not a guarantee that the true effect lies within the interval, and it does not show the range of individual participant outcomes. The reported group standard deviations, 0.388 for placebo and 0.423 for golimumab, describe within-group variability rather than uncertainty about the group means. Those are different quantities.

The registry record does not report a separate sponsor-model confidence interval for the primary endpoint. The interval discussed for the primary comparison is therefore the unadjusted recalculation from the posted summary statistics. Its interpretation depends on the assumptions behind that calculation, and it does not incorporate adjustments that may have been made in an analysis not posted in the registry record.

Precision is also distinct from importance. A narrow interval can still include effects of little practical relevance, while a wide interval can leave substantial uncertainty even when its point estimate is notable. The registry record does not specify a minimum clinically important difference for C-peptide AUC. Therefore, the interval describes statistical uncertainty but cannot alone determine how meaningful the difference is in clinical terms.

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 illustrations show hypothetical participants per arm for continuous outcomes and the other non-time-to-event outcomes. They are planning calculations based on the observed effect, not the original trial’s documented design rationale. The stated assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. Time-to-event illustrations, if present, are expressed as total events using the Schoenfeld method; the figures in this study’s listed comparisons are participants per arm.

The 80% power column describes the number of participants per arm that the planning calculation estimates would be needed to have an 80% chance of detecting the specified effect under its assumptions. The 90% power column gives the corresponding planning count for a 90% chance. These are planning probabilities under a hypothetical repeated-study framework, not probabilities that the observed result is correct.

The rows reduce the target effect to 100%, 75%, and 50% of the observed effect. A smaller effect is harder to distinguish from random variation, so the planning illustration requires more participants per arm. For example, the primary comparison’s row for 100% of the observed effect lists 61 participants per arm for 80% power and 81 for 90% power; the row for 50% lists 242 and 324, respectively. These are not the enrollment numbers for the trial.

This is not observed power. The trial’s original sample-size justification, target effect, and planning assumptions are not reported in the registry record. The illustrations use the observed estimates after the fact, which means they are best read as sensitivity-oriented planning aids, not as evidence that the study was adequately or inadequately powered.

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 = 0.210; pooled standard deviation σ = 0.412.
2. n = 2 × (1.96 + 0.842)² × 0.412² / 0.210² = 60.4, rounded up to 61 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 <- 0.21                      # observed difference in means
sigma <- 0.411821496774366                     # 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(2846545)
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 = 61 (80% power), 81 (90% power)
 75% of observed effect: n per arm = 108 (80% power), 144 (90% power)
 50% of observed effect: n per arm = 242 (80% power), 324 (90% power)
power.t.test: 61.3 
Simulated power at n = 61 per arm: 0.796 (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. Multiplicity, Missing Data and Analysis Populations

The registry record identifies a primary endpoint and lists multiple secondary and safety outcomes. It does not report a complete statistical analysis plan, the prespecified hierarchy of hypotheses, or whether and how multiplicity was controlled. This is important because testing several outcomes can increase the chance of observing at least one nominally notable result by chance. Secondary p-values should therefore be interpreted as outcome-specific evidence, not as a set of independent confirmations.

The primary endpoint’s posted contrast is an unadjusted recalculation from group summaries. The registry does not report a separate primary model result, whether covariates were included in a planned primary model, or whether any sensitivity analyses were conducted. For secondary outcomes, some sponsor-reported analyses use mixed models and Poisson regression, but full model specifications are not reported in the registry record.

The primary population is described as randomized participants who took at least one dose, with the outcome count restricted to participants evaluable for the measure. The registry does not describe the handling of missing C-peptide results, the amount or reasons for missing data, or whether participants were analyzed according to randomized assignment in every analysis. It also does not report imputation methods or sensitivity analyses for missing outcomes. General statistical consideration: if outcome availability is related to treatment assignment or the unobserved outcome, analysis of evaluable participants alone can introduce bias.

The registry includes an overall enrollment entry and outcome-specific counts, but a detailed flow of participants through randomization, treatment, follow-up, and each analysis is not reported in the registry record. Further details about interim analyses, stopping rules, subgroup analyses, and multiplicity adjustments are also not reported in the registry record. These omissions limit independent assessment of whether the posted comparisons match all prespecified analyses and how much weight should be placed on individual secondary findings.

15. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: seizure (20.0%), diabetic ketoacidosis (8.2%), cholelithiasis (3.6%), diplopia (1.8%).

The safety record reports serious adverse events and other adverse events by study period and group. During active treatment from Week 0 to Week 52, 1 of 28 participants in the placebo group and 1 of 56 in the golimumab group had a serious adverse event. The corresponding counts for other adverse events were 21 of 28 and 45 of 56. These are counts among the participants listed as at risk for each group and period; they are not direct measures of comparative risk adjusted for follow-up time.

During off-therapy follow-up from Week 52 to Week 104, serious adverse events were reported for 1 of 25 placebo participants and 4 of 49 golimumab participants. Other adverse events were reported for 14 of 25 and 26 of 49, respectively. In the open-label extension group, 1 of 5 participants had a serious adverse event and 3 of 5 had other adverse events. No deaths were reported in the listed periods.

These small group counts should be read descriptively. The record does not provide a statistical comparison of safety outcomes, event duration, severity grading beyond the serious-event classification, or full details about repeated events. The serious-event terms listed include seizure, diabetic ketoacidosis, cholelithiasis, and diplopia; the registry does not make the denominators for those listed percentages clear in this summary.

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

The posted primary result is a difference in mean C-peptide AUC at Week 52 between the participants analyzed in the golimumab and placebo groups. The group means were 0.64 and 0.43 pmol/mL, with an unadjusted mean difference of 0.21 and a 95% confidence interval from 0.018 to 0.402. This describes the observed contrast in this endpoint and its statistical uncertainty under the recalculation assumptions.

It does not establish that every participant experienced a similar difference, because a group mean does not describe individual responses. It also does not establish the clinical importance of the estimate: the registry record does not report a threshold for a meaningful change in C-peptide AUC. The endpoint is a biomarker measure related to beta-cell function, not a direct summary of all clinical outcomes or long-term health.

The record’s stated study purpose was to determine whether golimumab could preserve beta-cell function in children and young adults with newly diagnosed type 1 diabetes. The posted result addresses the Week 52 endpoint in the participants evaluable for that measure. It cannot by itself establish what would happen beyond the observed study periods, in populations not represented by the trial, or under different treatment schedules. The registry record does not report results sufficient to answer every such question.

Interpretation is also limited by missing details about the primary analysis plan, multiplicity, missing-data handling, and the rationale for the trial’s enrollment target. The recalculated unadjusted result should not be mistaken for a complete substitute for those methods. The secondary outcomes and safety counts add context, but their multiple measures, different analysis populations, and differing methods make them less straightforward to combine into a single conclusion.

As a general statistical point, evidence from one randomized study is best understood in relation to its population, endpoint definition, analysis methods, and uncertainty. The registry result is an estimate with a confidence interval, not a guarantee about a future individual or a recommendation about treatment.

17. Why This Trial Matters Statistically

This trial is statistically notable because it places a biomarker outcome at the center of a randomized, double-blind comparison and reports an estimate with an interval rather than only a binary conclusion. The primary C-peptide AUC result gives readers a group-level contrast at Week 52 and makes its uncertainty visible. That supports a more informative reading than a p-value alone, while still leaving questions about clinical meaning and precision.

The record also illustrates why outcome-specific analysis counts matter. The overall enrollment entry is 84, while the primary endpoint analysis included 25 placebo participants and 50 golimumab participants. The endpoint population is defined by exposure and evaluability, but the reasons for the analysis counts are not reported in the registry record. This makes participant flow and missing-data methods relevant to interpretation.

Several secondary measures cover related but distinct concepts, and their posted analyses use more than one statistical approach. Reading these results together requires attention to endpoint definitions, models, and multiplicity, rather than treating every outcome as an equivalent test of the same hypothesis. The safety record likewise separates active treatment, follow-up, and extension periods, and its counts are descriptive rather than adjusted comparisons.

Finally, the planning illustrations show how target sample sizes vary when the effect assumed for planning becomes smaller. They are not the original design justification and are not observed power. This distinction matters because post hoc planning calculations cannot establish whether the trial’s original design was suitable. Together, the endpoint estimate, its precision, the analysis population, and the unreported methods define both the statistical contribution and the limits of the registry record.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several limitations are specific to interpreting this record. The primary outcome counts are 25 for placebo and 50 for golimumab, while the enrollment entry is 84. The registry defines the primary analysis population as randomized participants who took at least one dose and were evaluable for the outcome, but does not explain the difference between those counts or report reasons for missing or unevaluable measurements.

The primary contrast discussed is an unadjusted recalculation from posted means and standard deviations. A separate sponsor-reported primary model estimate is not reported in the registry record. The record also does not report the original sample-size justification, the planned primary analysis details, or the treatment of missing primary outcomes. These gaps prevent a full assessment of the relation between the posted calculation and the intended analysis.

The registry lists multiple outcomes, but does not report a multiplicity plan or hierarchy for secondary hypotheses. The secondary results should therefore be interpreted cautiously, particularly where conclusions could depend on isolated p-values. Model details for the reported mixed-model and Poisson analyses are also not reported in the registry record.

The study includes an open-label extension and an off-therapy follow-up period, but the arm description for the extension is incomplete. Safety counts are presented across periods with different at-risk group sizes, and the record does not provide a statistical comparison or explain repeated-event handling. The serious-event term percentages do not have clear denominators in this summary.

Finally, the available primary outcome is a Week 52 biomarker measurement. The registry record does not report a clinically meaningful threshold for this measure. The result therefore should be read as an estimate on the endpoint’s scale, not as a complete account of long-term outcomes or clinical importance.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study of SIMPONI® to Arrest Beta-cell Loss in Type 1 Diabetes — NCT02846545.
• Registry record last updated 2025-02-04; status last verified 2025-01.
• Results first posted 2022-07-14.
• 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.

22. Record Summary

The registry record describes NCT02846545, a completed, randomized, double-blind, parallel-group Phase 2 study of golimumab versus matching placebo in children and young adults with newly diagnosed type 1 diabetes. The listed enrollment is 84. The active-treatment period was 52 weeks, and the primary endpoint was C-peptide AUC from a 4-hour mixed-meal tolerance test at Week 52.

The primary analysis included 25 placebo participants and 50 golimumab participants. Their posted means were 0.43 and 0.64 pmol/mL, respectively; the recalculated unadjusted mean difference was 0.21, with a 95% confidence interval from 0.018 to 0.402 and p = 0.032. Secondary outcomes include insulin use, HbA1c, hypoglycemic event rates, and safety measures. The registry does not report several details needed for full evaluation, including the original sample-size justification, complete analysis plan, multiplicity approach, and primary-endpoint missing-data methods.