Initiation of Continuous Glucose Monitoring at Diagnosis of Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Initiation of Continuous Glucose Monitoring at Diagnosis of Type 1 Diabetes (NCT02734667), a randomized na study. The primary outcome is Time Spent in Blood Glucose Range (70-180mg/dL).

2. Clinical Question

3. Trial Design

The registry describes a randomized, open-label, parallel-group trial. Random allocation means assignment to the study groups was intended to use a chance-based process; open-label means masking was not used. A parallel design compares groups followed under their respective assigned approaches rather than having each participant receive both approaches in sequence. Further details of the randomization procedure and allocation concealment are not reported in the registry record.

The study compared starting continuous glucose monitoring at diagnosis of type 1 diabetes with usual care for 6 months after diagnosis. The intervention group description specifies non-adjunctive use of continuous glucose monitoring for 6 months. The registry lists the intervention as a device. It does not provide a more detailed description of usual care, so no assumptions about its specific components are warranted.

The study is listed as completed, with enrollment of 120. Its recorded start was 2016-02 and primary completion was 2020-02. The phase is listed as NA. The registry record does not report the original sample-size justification, the assumptions used to design the trial, or why the primary-outcome summaries include 42 participants in the intervention group and 18 in the usual-care group. Those group counts describe the posted outcome summaries and should not be treated as an explanation of recruitment, retention, or analysis eligibility.

The primary outcome was assessed in youths only. Other listed outcomes concern quality of life, diabetes-related problems and distress, mood, anxiety, sleep, fear of hypoglycemia, and confidence related to hypoglycemia. The record lists 16 outcome measures, three analyses, and 13 posted secondary outcomes. The presence of those additional measures makes it important to distinguish the primary glycemic endpoint from other questions the study considered. The registry record does not report enough detail to infer the timing or statistical handling of every secondary outcome.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

Analysis population for the primary outcome, as defined in the registry: This outcome was assessed in youths only.

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.

Population: Only youth were assessed for baseline characteristics.

6. Primary Endpoint

The registry lists the primary outcome as Time Spent in Blood Glucose Range (70-180mg/dL), measured over Collected entire period for 6 months post-baseline; primary endpoint is 14 day period around that 6-month time point.

The primary endpoint is mean percentage of time spent in the blood glucose range of 70-180mg/dL. It is a continuous measure: each participant’s glucose readings contribute to a measure of the share of monitored time within the specified range, and the group summaries report means and standard deviations. The outcome is described as derived from objective data downloads rather than from a questionnaire.

The registry identifies this range-based measure as an important glycemic outcome and says it was selected as the primary outcome. It was calculated from values recorded over the 14-day period around the six-month assessment time point. The broader collection period is described as the entire period for 6 months post-baseline, while the primary endpoint specifically concerns the 14-day window around that six-month time point. Keeping these time descriptions distinct matters: the posted primary endpoint is not a summary of every day across the full follow-up period.

A mean provides a group-level summary of the observed endpoint, while the standard deviation describes variability among observations within a group. The means were 64.99 for CGM at diagnosis and 62.54 for usual care; their reported standard deviations were 18.39 and 19.1, respectively. The endpoint is useful for comparing groups on time in the stated target range, but it does not by itself describe all glucose outcomes, diabetes-related distress, or the experience of every participant.

7. Results Posted in the Registry

• CGM at Diagnosis of T1D: mean 64.99 versus 62.54 on control; difference 2.45 (95% CI -7.98 to 12.88), standardized effect d = 0.13; z-test p = 0.645.

8. Statistical Interpretation

9. Statistical Methods Explained

What does the mean difference represent?

For a continuous outcome, a difference between group means expresses how far apart the group averages are on the outcome’s scale. Here the outcome is percentage of time in the stated blood glucose range. The recalculated mean difference is 2.45, comparing the CGM-at-diagnosis group with usual care. A positive value means the first group’s posted mean is higher; it does not mean every participant had a higher value. The group means are 64.99 and 62.54, and the standard deviations are 18.39 and 19.1.

The recalculation uses the posted group summaries and is unadjusted. An unadjusted comparison does not account for covariates through a regression model. This is a general distinction between ways of estimating a group contrast, not a claim about any unreported adjustment in the trial. The registry’s own analysis is described as linear regression and reports a t statistic and p-value. The registry does not provide further details about the model specification or variables included.

What does the standard deviation tell us?

The standard deviation describes how dispersed observations are around a group mean. A larger standard deviation indicates more spread on the outcome scale, while a smaller one indicates values are more concentrated around the mean. It does not indicate uncertainty in the mean itself; uncertainty in an estimated mean or difference is represented by a standard error or confidence interval. In the posted summaries, the standard deviations are 18.39 for CGM at diagnosis and 19.1 for usual care. These group-level summaries do not reveal the full shape of each distribution or the pattern of individual outcomes.

In general, the standard deviation and the standard error answer different questions. The standard deviation describes variation among observations. The standard error describes the expected variability of an estimate across repeated samples under a statistical model. A confidence interval is built from an estimate and its uncertainty, with the exact method depending on the analysis. For the recalculated comparison, the posted summary statistics support an unadjusted interval; the sponsor’s regression result is reported separately and has no confidence interval recorded.

How should the confidence interval be read?

The recalculated 95% confidence interval for the mean difference runs from -7.98 to 12.88. In general, a confidence interval gives a range of values reasonably compatible with the data and the assumptions used to calculate it. It is not a guarantee that the true difference lies within that particular interval, and it is not a range containing a stated proportion of individual participants’ outcomes. Here it includes negative and positive values, so the posted unadjusted summaries are compatible with differences in either direction as well as with little or no difference.

The interval’s width is relevant to precision. A wide interval permits a broad range of possible effect sizes; a narrow interval would indicate greater precision on the same scale. Its inclusion of zero means the unadjusted interval does not rule out no difference under the calculation’s assumptions. It also includes values that would represent a higher mean in either group. The interval should be interpreted alongside the point estimate rather than as a standalone conclusion.

What does the p-value test?

The unadjusted recalculated p-value is 0.645. The sponsor’s regression analysis reports p = 0.64. In general, a p-value measures how unusual data at least as discrepant as those observed would be if a specified null model and its assumptions held. It does not give the probability that the null is true, and it is not a measure of clinical importance or of the size of an effect. A large p-value is not proof that the groups are equivalent.

The sponsor’s reported test uses a t statistic from a linear regression analysis. A t statistic expresses an estimate relative to its estimated uncertainty under the model. The registry record does not report the regression coefficients, standard errors, model covariates, or a confidence interval for the sponsor’s estimate. Therefore, the reported t statistic and p-value cannot be used to reconstruct details of the model or to infer which adjustments were made.

Why distinguish the sponsor analysis from the recalculation?

The sponsor’s regression analysis is the analysis reported by the trial record. The unadjusted recalculation provides a transparent comparison derived from the posted means, standard deviations, and group sizes. These methods may differ because a regression can model the outcome and potentially account for other variables, whereas the recalculation compares the summary means directly. The record does not report enough model detail to say whether or how the regression adjusted for other factors. The discrepancy indicator is false, so no conflict is flagged between the posted sponsor result and the recalculation; nevertheless, they remain distinct analyses, and the sponsor’s analysis is the trial’s reported result.

In general, adjusting for prespecified prognostic variables can change an estimated contrast or its precision, but it does not automatically make an analysis more reliable. Interpretation depends on the model, the variables, the data, and the analysis plan. Those details are not reported in the registry record, so no claim about the particular advantages or limitations of the sponsor’s regression can be made beyond its stated method and result.

Why can variation and sample size affect precision?

As a general statistical principle, more observations can improve the precision of a group estimate when other features of the data and design are similar. More variation among observations tends to make an estimate less precise, all else equal. Unequal group sizes can also affect the precision of a group comparison. These principles help explain why estimates from small or uneven analysis groups may have broad uncertainty, but they do not identify the cause of the interval in this trial or explain the difference between enrollment and the posted endpoint counts.

Precision is not the same as validity. A narrow interval can still be affected by bias, measurement problems, or an inappropriate analysis. A broad interval may reflect limited information without implying that the estimated effect is wrong. The registry record does not provide enough information about missing outcomes, adherence, protocol deviations, or analysis populations to assess those issues for this endpoint.

Over-dispersion is a general concern when data vary more than a chosen statistical model expects. If a model underestimates variation, standard errors and confidence intervals can be too small and tests can appear more conclusive than warranted. The registry does not report an over-dispersion assessment, and the primary endpoint is analyzed as a continuous measure in the posted summaries. There is no basis in the record to claim that over-dispersion occurred in this analysis.

What can and cannot be inferred from the endpoint?

The endpoint summarizes time in a specified glucose range over a defined assessment window. Its mean describes a group average and its standard deviation describes group spread. Neither conveys all dimensions of glucose patterns, such as timing or duration of excursions, unless those are separately reported. Nor does this endpoint stand in for the listed measures of quality of life, distress, mood, anxiety, sleep, or hypoglycemia-related concerns.

Randomization is intended, in general, to support fair comparison by reducing systematic differences in group assignment. The registry identifies the study as randomized, but does not report the details needed to evaluate the randomization process. The open-label design means masking was not used. For an endpoint derived from objective data downloads, direct measurement may reduce some forms of subjective assessment, but the record does not describe data completeness or the handling of unavailable readings. Statistical results should therefore be read in the context of both the design information and the limits of reporting.

10. Confidence Intervals and Precision

The unadjusted mean difference is 2.45, and its 95% confidence interval extends from -7.98 to 12.88. This interval is broad enough to include negative values, values near no difference, and positive values. The point estimate is positive, but the interval indicates that the data do not pin down the direction or magnitude precisely. The interval is for the difference between group means, not for individual participants’ values.

In general, a confidence interval reflects both the estimate and the uncertainty associated with it under the chosen calculation. If the same study design and outcome were repeated under comparable conditions, estimates would vary; the confidence interval expresses a range of plausible values for the population contrast under the statistical assumptions. It is not a probability statement about the fixed true value after the interval has been calculated.

The posted group means are 64.99 for CGM at diagnosis and 62.54 for usual care. Their standard deviations are 18.39 and 19.1. The spread in each group is substantial relative to the difference in means, while the group counts in the endpoint summaries are 42 and 18. These features are consistent with uncertainty in the group contrast, but the registry record does not report enough detail to attribute the observed interval width to any one factor.

The sponsor’s regression analysis reports p = 0.64 but no confidence interval. The interval comes from the unadjusted recalculation, not from that regression. It should not be presented as though it were the sponsor’s model-based interval. The registry record does not report a sponsor-derived confidence interval for the regression estimate, so its model-based precision cannot be described directly from the record.

Precision is distinct from whether an outcome is important, whether the study was well conducted, or whether the intervention caused a particular result. A broad interval leaves meaningful uncertainty; it does not prove the absence of a group difference. The endpoint-specific result should be read together with the limitations in reporting about missing data and analysis populations.

11. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning display translates the observed unadjusted effect into illustrative participant requirements under stated assumptions. All entries are participants per arm, not total events. The event-count convention for time-to-event outcomes does not apply to this continuous endpoint. The figures are planning illustrations based on the observed effect, not the trial’s original sample-size calculation; the registry record does not report the assumptions used in the original design.

The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set at the stated fraction of the observed effect. The rows planning for 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect show progressively larger participant requirements as the target effect becomes smaller. This is a general feature of power calculations: smaller effects are harder to distinguish from random variation and therefore require more participants, all else equal.

The 80% power column means the participant count per arm estimated to give an 80% chance of detecting the specified effect under the calculation’s assumptions, if that effect were the true one and the model assumptions held. The 90% power column gives the corresponding planning count for a 90% chance. These are prospective design concepts, not probabilities that the trial’s result is correct or that an effect exists.

For example, planning for 100% of the observed effect gives 905 participants per arm for 80% power and 1,212 per arm for 90% power. The smaller-effect planning rows call for more participants per arm. These figures are not observed power: they are hypothetical sample-size illustrations and should not be interpreted as a retrospective measure of the completed trial’s ability to detect an effect.

12. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 2.450; pooled standard deviation σ = 18.601.
2. n = 2 × (1.96 + 0.842)² × 18.601² / 2.450² = 904.8, rounded up to 905 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 <- 2.45                      # observed difference in means
sigma <- 18.6009112216542                     # 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(2734667)
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 = 905 (80% power), 1,212 (90% power)
 75% of observed effect: n per arm = 1,609 (80% power), 2,154 (90% power)
 50% of observed effect: n per arm = 3,620 (80% power), 4,846 (90% power)
power.t.test: 905.8 
Simulated power at n = 905 per arm: 0.799 (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.

13. Analyses Reported by the Sponsor

The registry reports the sponsor’s primary comparison as linear regression, with a t statistic of -0.47 and p = 0.64. A confidence interval for that analysis is not reported in the registry record. The posted arm summaries give means of 64.99 and 62.54, and the unadjusted recalculation from those summaries estimates a mean difference of 2.45, with a 95% confidence interval from -7.98 to 12.88 and p = 0.645.

The recalculation is a simple unadjusted comparison, while the sponsor’s result comes from the reported regression analysis. The registry does not describe the regression model in enough detail to identify its coefficients, covariates, or standard errors. The recalculation should therefore be treated as a separate descriptive and inferential check, not as a replacement for the sponsor’s analysis. The discrepancy indicator is false, so no discrepancy is flagged between the results.

The sponsor’s reported p-value and the recalculated p-value are close, and both indicate limited evidence against a no-difference comparison. This similarity does not make the methods identical and does not supply the missing confidence interval for the regression. The sponsor’s own analysis is the trial’s reported analysis; the unadjusted interval helps describe uncertainty based on the available group summaries.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists 16 outcome measures, three analyses, and 13 posted secondary outcomes. The primary endpoint is one of several outcomes considered, while secondary measures include quality of life, diabetes-related problems and distress, mood, anxiety, sleep, fear of hypoglycemia, and confidence related to hypoglycemia. When many outcomes or comparisons are tested, the chance of obtaining at least one apparently notable result by chance can increase. This is the general statistical issue of multiplicity.

The registry record does not report whether the analysis plan adjusted for multiple outcomes or comparisons, whether any outcomes were prioritized beyond the named primary endpoint, or how the three analyses relate to the listed measures. It is therefore not possible to determine from the record whether multiplicity was controlled or how it should affect interpretation of secondary findings. The primary endpoint remains identifiable from the record, but no unreported multiplicity procedure should be assumed.

The record does not report the approach to missing outcome data, the number of participants with unavailable primary-endpoint measurements, or the reasons endpoint summaries include 42 participants in the CGM group and 18 in usual care. It also does not state whether the analysis used intention-to-treat, per-protocol, or another analysis population. The enrollment figure of 120 cannot, by itself, establish which participants contributed to the reported means or why other observations are not represented in those summaries.

Missing-data methods can affect estimates when outcomes are unavailable, and conclusions may depend on assumptions about those missing values. In general, complete-case summaries describe observed data but may not represent all randomized participants if observation is related to outcomes or group assignment. This is a general caution, not a claim that missingness occurred for a particular reason in this trial. The registry record does not report enough information to assess the missingness mechanism or the sensitivity of findings to alternative assumptions.

Further details that would aid interpretation include the prespecified statistical analysis plan, the regression model specification, the analysis population, endpoint completeness, and any multiplicity strategy. Since these details are not reported in the registry record, interpretation should remain focused on the posted primary-outcome summaries and the stated sponsor analysis.

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 0% reporting threshold.

The serious adverse event summaries report 0 affected participants in the CGM-at-diagnosis group among 42 at risk and 0 in the usual-care group among 18 at risk. The registry also reports 0 participants affected by other adverse events in each group, with the same group-specific at-risk counts. Deaths are reported as 0 in both groups. These are the recorded counts for the listed safety categories.

A zero count means that no affected participants were recorded for that category in the registry summaries. It does not establish that no symptoms, events, burdens, or unrecorded harms occurred. The record does not provide event descriptions for the serious-adverse-event category, and no top serious-event terms are listed. The safety counts should therefore be read as registry-reported observations rather than as proof of absence of risk.

The at-risk counts in the safety summaries are not the same as a complete account of follow-up, data capture, or exposure time. The registry record does not state how safety events were sought, how long each participant contributed to safety monitoring, or how missing safety information was handled. The figures are useful for describing the posted counts but do not, by themselves, establish comparative safety with high precision.

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

The central statistical finding is an unadjusted mean difference of 2.45 in percentage of time in the 70-180mg/dL range, comparing CGM initiated at diagnosis with usual care at the six-month assessment. The group means were 64.99 and 62.54. The unadjusted 95% confidence interval, from -7.98 to 12.88, includes values in either direction. The sponsor’s regression analysis reports p = 0.64. Together, these results indicate that the posted analysis does not provide strong statistical evidence of a group difference on this endpoint.

That interpretation is not the same as concluding that the groups were equivalent. Equivalence requires a defined margin and an analysis designed to test whether differences fall within that margin; the registry record does not report such a margin or an equivalence analysis. Nor does a nonsignificant result prove that the intervention has no effect. The interval permits a range of possible differences, so uncertainty remains about the size and direction of the underlying contrast.

The result applies to the reported outcome and assessment window, in youths, for the comparison of CGM at diagnosis against usual care. It does not automatically answer whether the groups differed on the listed secondary measures, whether participant experiences were similar, or whether other glucose-related outcomes changed. The record lists outcomes concerning quality of life, distress, mood, anxiety, sleep, fear, and confidence, but these should not be conflated with the primary endpoint.

The registry records enrollment of 120, while the primary endpoint summaries include 42 participants in the intervention group and 18 in usual care. The reason for this difference, the analysis population, and the approach to missing data are not reported. These unknowns limit the ability to determine how representative the posted endpoint summaries are of the randomized groups.

Finally, statistical evidence is not a treatment recommendation. The result describes what the registry reports and how uncertain the group comparison is under the stated calculations. It does not establish an individual outcome, a mechanism, or a conclusion beyond the measured endpoint and reported analysis.

17. Why This Trial Matters Statistically

This trial is statistically informative because it illustrates the difference between a point estimate and the precision with which that estimate is known. The unadjusted mean difference is 2.45, but its confidence interval extends from -7.98 to 12.88. A single estimate can appear straightforward, while the interval shows that the data are compatible with a broader range of possible group contrasts. Reading both together prevents the point estimate from being mistaken for a precise or definitive effect.

The record also makes the distinction between a sponsor-reported model and a summary-statistic recalculation visible. The sponsor reports a linear regression result; the recalculation compares the posted group means without adjustment. Their p-values are similar, but the methods are not interchangeable. Reporting the method and its limitations is essential to interpreting a clinical result, particularly when model details and a model-based confidence interval are not available in the registry record.

The gap between the registry enrollment figure and the group counts in the endpoint summaries is another important reporting issue. Without an explanation of endpoint completeness or analysis populations, readers cannot determine why those counts differ or how the result relates to all randomized participants. This does not show that a specific form of bias occurred, but it identifies information needed for a fuller assessment of the evidence.

Finally, the study lists multiple outcomes and analyses. The record does not say how multiplicity was handled, so readers should distinguish the designated primary endpoint from other outcomes and avoid treating every result as if it had the same prespecified status. The case illustrates why uncertainty, analysis methods, outcome selection, and data availability all matter alongside a p-value.

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.

The registry record leaves several trial-specific details unresolved. It identifies the design as randomized, open-label, and parallel, but does not report the randomization procedure or allocation concealment. It describes non-adjunctive CGM use in the intervention group and usual care in the comparison group, but does not give further detail about usual care. These omissions limit the ability to assess how assignment was implemented and what the comparison involved in practice.

Although enrollment is listed as 120, the primary-outcome summaries include 42 participants in the CGM group and 18 in usual care. The record does not explain why those counts differ from enrollment, how many randomized participants contributed outcome data, or whether the summaries reflect a particular analysis population. Missing-data methods, exclusions, protocol deviations, and endpoint completeness are not reported. No specific explanation for the group-count difference can be inferred.

The sponsor’s reported regression analysis is described only as linear regression, with a t statistic of -0.47 and p = 0.64. Model covariates, coefficient estimates, standard errors, and a confidence interval are not reported. The recalculated interval is unadjusted and should not be mistaken for a model-based interval from the sponsor analysis. The registry record also does not report the trial’s original sample-size rationale or design assumptions.

The record lists multiple outcome measures and analyses but does not report a multiplicity strategy. It also does not report the planned hierarchy or the full statistical analysis plan. The primary endpoint concerns youths and a 14-day period around the six-month assessment; it should not be read as a measure of all follow-up days or as a substitute for the secondary psychosocial outcomes.

Finally, the posted safety summaries report zero affected participants in the listed categories but do not describe event ascertainment, monitoring duration, or missing safety information. These limitations constrain interpretation of both the outcome comparison and the broader study record.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Initiation of Continuous Glucose Monitoring at Diagnosis of Type 1 Diabetes — NCT02734667.
• Registry record last updated 2022-03-02; status last verified 2022-02.
• Results first posted 2021-08-17.
• 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 describes NCT02734667 as a completed randomized, open-label, parallel-group study of continuous glucose monitoring initiated at diagnosis of type 1 diabetes. Enrollment is listed as 120. The intervention group started non-adjunctive CGM at diagnosis and continued it for 6 months; the comparison group received usual care for 6 months after diagnosis.

The primary endpoint was mean percentage of time in the 70-180mg/dL blood glucose range, assessed in youths using data from the 14-day period around the six-month time point. Posted means were 64.99 for CGM at diagnosis and 62.54 for usual care. The unadjusted recalculated mean difference was 2.45, with a 95% confidence interval from -7.98 to 12.88 and p = 0.645. The sponsor reported a linear-regression t statistic of -0.47 and p = 0.64.

The record does not report the original sample-size justification, the reason endpoint summary counts differ from enrollment, the missing-data approach, or the analysis populations. Safety summaries list zero serious adverse events, other adverse events, and deaths in both groups.