A Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Type 1 Diabetes (NCT02258373), a randomized na study. The primary outcome is Percentage of Time in Range of 70 to 180 mg/dl, Measured With CGM.

2. Clinical Question

This is a comparison of monitoring strategies, not a comparison of CGM with no monitoring. Both groups used CGM, and the CGM Only instructions retained BGM use for calibration and specified circumstances. The distinction was whether a BGM confirmation measurement was part of routine management decisions.

The primary endpoint describes time spent within a prespecified sensor glucose range. It is a continuous measure: each participant’s measured value reflects a percentage of time, and the reported group summaries are means with standard deviations. The registry record does not report a threshold or margin that would define how close the strategies must be to establish equivalence or non-inferiority. The results therefore describe the observed group estimates and their uncertainty, rather than establish a formal equivalence or non-inferiority conclusion.

3. Trial Design

This was a randomized, parallel-group trial with no masking. The registry lists the study as completed and records enrollment of 226 participants. The listed study start was 2015-03, and primary completion was 2016-10. The sponsor was Jaeb Center for Health Research. The registry does not report a phase beyond NA.

Participants were assigned to CGM Only or CGM+BGM. In the CGM Only group, blood glucose monitoring remained part of calibration and specified circumstances, while management decisions were to be based on CGM values without routine BGM confirmation. In the CGM+BGM group, participants were instructed to use BGM for calibration and when making management decisions. The registry’s arm descriptions are incomplete in the posted text, so details beyond those stated are not reported in the registry record.

The primary endpoint was the mean percentage of time in the CGM range of 70 to 180 mg/dl between baseline, defined as randomization, and 6 months. The registry reports 148 participants in the CGM Only primary endpoint summary and 76 in the CGM+BGM summary. It notes one participant in each group had no follow-up visit and therefore no CGM data. The reason the primary endpoint group counts differ from the enrollment total is not reported in the registry record.

The registry lists 18 outcome measures and posts 17 secondary outcomes. These include mean glucose, measures of glycemic variability, time in several sensor glucose ranges, change in HbA1c, and a combined measure involving no worsening of HbA1c by greater than 0.3% and no severe hypoglycemia event. The registry record does not report the original sample-size justification, the detailed statistical analysis plan, or the study’s analysis populations.

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: One participant in the CGM Only group and one in the CGM+BGM group never came in for a follow-up visit and therefore had no CGM data.

5. Baseline Characteristics

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

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Time in Range of 70 to 180 mg/dl, Measured With CGM, measured over Between baseline (randomization) and 6 months.

The primary endpoint is the percentage of time with CGM sensor values from 70 to 180 mg/dl, measured between baseline (randomization) and 6 months. The registry reports a mean and standard deviation for each group. This endpoint expresses how much of the monitored time falls within the specified glucose interval, rather than reporting a single glucose measurement or a count of participants.

Time in range is used as a summary of glucose exposure across the measurement period. As a continuous outcome, it can capture differences in the distribution of sensor readings over time that a single measurement might not reflect. The registry’s stated primary objective is to compare the safety and effectiveness of CGM without routine BGM confirmation against CGM used as an adjunct to BGM; the posted primary endpoint specifically measures time in the stated CGM range.

The group means were 63.0 for CGM Only and 65.0 for CGM+BGM. The reported mean difference for CGM+BGM compared with CGM Only was 2.0 percentage points, with a 95% confidence interval from -1.241 to 5.241. The registry does not report a clinical threshold for interpreting a difference in this endpoint, nor a prespecified margin for equivalence or non-inferiority. The numerical estimate should therefore be read as a comparison of the observed group summaries, with its interval expressing uncertainty, rather than as a conclusion that either strategy meets a defined clinical target.

7. Results Posted in the Registry

• CGM+BGM: mean 65.00 versus 63.00 on control; difference 2.00 (95% CI -1.24 to 5.24), standardized effect d = 0.16; z-test p = 0.226.

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.

Mean Glucose

• 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:
  • CGM+BGM: mean 158.00 versus 162.00 on control; difference -4.00 (95% CI -9.83 to 1.83), standardized effect d = -0.18; z-test p = 0.178.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between CGM+BGM and the control. The interval gives the range of effects that remain compatible with the data.

Change in HbA1c

• 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:
  • CGM+BGM: mean 0.00 versus 0.00 on control; difference 0.00 (95% CI -1.54 to 1.54), standardized effect d = 0.00; z-test p = 1.000.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between CGM+BGM and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants With no Worsening of HbA1c by Greater Than 0.3% AND no Severe Hypoglycemia Event

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • CGM+BGM: 72.0% (54/75) versus 81.0% (115/142) on control; risk difference -9.0 percentage points (95% CI -21.0 to 3.1), risk ratio 0.89 (0.76–1.05); unadjusted two-proportion z-test p = 0.129.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between CGM+BGM and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes extend the assessment beyond time in range. They cover average sensor glucose, glycemic variability, time spent at several lower or higher sensor glucose levels, HbA1c change, and a combined outcome involving HbA1c worsening and severe hypoglycemia. Together, these measures describe different aspects of glucose exposure and clinical outcomes; they are related to the primary result but are not interchangeable with it.

The direction of the posted summaries is not uniform across all measures: some group estimates favor one monitoring strategy numerically, while other summaries are similar. This is a reason to consider the pattern as a whole rather than select one result in isolation. The secondary outcomes do not provide a single alternative answer to the primary question, because each measures a different feature and may have a different scale and analysis population.

The registry lists 18 outcome measures and posts 17 secondary outcomes. It reports no formal analyses in its analysis listing. Unadjusted secondary comparisons therefore carry less weight than a prespecified primary analysis: when many outcomes are examined, some estimates may look notable by chance, and the record does not state whether or how the study accounted for multiple testing. The secondary estimates are best treated as descriptive context and as a basis for further questions, not as a collection of independent confirmatory findings.

Differences in endpoint counts also matter when comparing outcomes. The number of participants included in a secondary measure may differ from the primary endpoint count, as the posted HbA1c summaries illustrate. The registry does not explain all such differences or report a detailed missing-data plan. Interpret the secondary pattern with those reporting limitations in mind, and avoid assuming that the same participants contributed to every measure.

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

• Mean Glucose
• Measures of Glycemic Variability: Coefficient of Variation
• Percentage of Time With Sensor Value <70 mg/dl, Measured With CGM
• Percentage of Time With Sensor Values <60 mg/dl, Measured With CGM
• Percentage of Time With Sensor Values <50 mg/dl, Measured With CGM
• Percentage of Days With at Least 20 Minutes of Sensor Glucose Values <60 mg/dl
• Percentage of Time With Sensor Values >180 mg/dl, Measured With CGM
• Percentage of Time With Sensor Values > 250 mg/dl, Measured With CGM

9. Statistical Interpretation

10. Statistical Methods Explained

The primary endpoint is continuous: it is a percentage of time, summarized by a group mean and standard deviation. The posted summaries give the mean and standard deviation for each group, along with an unadjusted comparison of group means. These are descriptive and inferential summaries of the reported endpoint; they do not indicate that a covariate-adjusted or otherwise model-based analysis was performed.

What does a mean difference describe?

A mean difference subtracts one group’s average from the other’s. In the primary comparison, the direction is CGM+BGM compared with CGM Only. The reported estimate of 2.0 means the CGM+BGM group’s average percentage of time in range was higher by 2.0 percentage points in the posted summary. This is a difference between group means, not a change measured in every participant and not a percentage increase.

Group means can be useful for summarizing continuous outcomes, but they do not show the full distribution of participant values. Standard deviations describe how spread out values are around their group mean. The primary standard deviations were 13.0 for CGM Only and 11.0 for CGM+BGM. These measures describe variation among participants; they are not standard errors and do not directly quantify uncertainty in the difference between group means.

How should the confidence interval be read?

A confidence interval gives a range of estimates compatible with the observed data under the statistical method’s assumptions. For the primary comparison, the reported 95% confidence interval is -1.241 to 5.241. It communicates the uncertainty around the estimated mean difference and shows that values below and above zero are compatible with the data. It is not a range containing 95% of individual participant outcomes, nor does it mean there is a 95% probability that the true difference lies within this particular interval.

What does an unadjusted p-value tell us?

The unadjusted p-value measures how unusual data at least as incompatible with a no-difference reference would be under the test’s assumptions. It does not measure the probability that the null hypothesis is true, the size or importance of an effect, or the probability that a result will be replicated. The primary p-value was 0.226. The registry does not report whether a multiplicity adjustment was planned or used, so the p-value should not be treated as an adjusted result.

Comparisons labeled as recalculated are simple, unadjusted calculations based on the posted summary statistics. They do not replace a sponsor’s prespecified analysis. No discrepancy between the recalculated and registry results is flagged in this record. The registry does not report the original statistical analysis plan, so details such as covariate adjustment, model selection, handling of repeated measurements, or prespecified testing procedures are not reported in the registry record.

Why can unequal group sizes matter?

The reported primary endpoint counts are 148 in CGM Only and 76 in CGM+BGM. Unequal group sizes can affect the precision of a comparison: with other features held constant, the smaller group can contribute more uncertainty to the difference estimate. Group sizes also matter when interpreting standard errors and confidence intervals. They do not, by themselves, indicate bias or explain why participants were not included in a particular endpoint summary.

Randomization is intended to support a fair comparison by assigning participants to groups through a chance process. The registry identifies the allocation as randomized, but does not report the sequence-generation method, allocation concealment details, or any stratification factors. Those design details are therefore not reported in the registry record. The trial was unmasked, so participants and study personnel were not blinded according to the registry’s masking entry. The potential consequences of lack of masking depend on the outcome and study procedures; the registry does not provide enough detail to quantify them.

Why does over-dispersion matter in some analyses?

General statistical explanation: over-dispersion occurs when observed variation is greater than a model’s assumptions allow. It can make standard errors too small if the model does not account for the extra variability, leading to confidence intervals that are too narrow and p-values that are too small. This issue can arise in outcomes such as counts or proportions, depending on their model. The posted primary outcome is summarized as a continuous percentage with a mean and standard deviation, and the registry does not report a model-based analysis or an assessment of over-dispersion. It is therefore not possible to say whether this issue affected the trial’s analysis.

The registry gives summary statistics and comparisons for the primary and several secondary outcomes, but reports zero analyses in its analysis listing. This means the record does not provide a formal list of statistical analyses. The posted estimates can still be described, but assumptions and procedures beyond those explicitly stated should not be inferred. The original sample-size justification and the trial’s own design assumptions are also not reported in the registry record.

11. Confidence Intervals and Precision

The primary mean difference is 2.0 percentage points, with a 95% confidence interval from -1.241 to 5.241. The interval extends from a value below zero to one above zero, so the data do not rule out either direction of the mean difference under the calculation’s assumptions. The interval is the main guide to the estimate’s precision: it shows a range of differences compatible with the observed data, rather than identifying the exact difference.

A confidence interval is not a prediction interval for individual participants. It does not describe where most individual time-in-range values fall, and it does not mean there is a 95% probability that the true effect is inside this particular interval. The group standard deviations, 13.0 and 11.0, describe participant-level spread; they answer a different question from the confidence interval around the group difference.

The unadjusted p-value for the primary comparison was 0.226. It should be read alongside the estimate and interval, not as a measure of effect size or clinical importance. The registry does not report a threshold for a clinically meaningful difference or an equivalence margin. Consequently, the confidence interval cannot be judged against such a threshold from the registry record.

Precision is also affected by the number of participants contributing to the endpoint. The reported primary group counts are 148 and 76. The outcome description reports that one participant in each group had no follow-up CGM data, but does not describe other reasons for the analysis counts or the approach to missing data. These details limit how fully the precision and representativeness of the primary estimate can be assessed.

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 the observed effect, not the trial’s original design calculations. For these continuous outcomes, the figures are participants per arm, not a total across both arms and not total events. The stated planning 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 at the stated fraction of the observed effect.

The 80% power column describes the number of participants per arm that the planning calculation estimates would be needed for an 80% chance of detecting the specified effect under those assumptions, if that effect were present. The 90% power column uses a higher target chance of detection and therefore generally calls for more participants per arm. Power is a design property under assumed conditions; it is not the probability that the study conclusion is correct.

Rows planning for 100%, 75%, and 50% of the observed effect use progressively smaller target effects. Smaller effects are harder to distinguish from random variation, so the planning illustrations require more participants per arm to target the same power. The curve presents the sample-size relationship graphically; it should be read as a planning aid under the stated assumptions, not as evidence about the actual result.

These figures are not observed power. They do not show the probability that the completed trial detected an effect, and they do not replace a prospective sample-size calculation based on a prespecified clinically important difference. The original trial’s sample-size assumptions and justification are not reported in the registry record.

13. How the Sample Size Is Derived

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

Formula

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

Worked calculation for this trial

1. Observed difference Δ = 2.000; pooled standard deviation σ = 12.361.
2. n = 2 × (1.96 + 0.842)² × 12.361² / 2.000² = 599.6, rounded up to 600 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                      # observed difference in means
sigma <- 12.3605737643438                     # 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(2258373)
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 = 600 (80% power), 803 (90% power)
 75% of observed effect: n per arm = 1,066 (80% power), 1,427 (90% power)
 50% of observed effect: n per arm = 2,399 (80% power), 3,211 (90% power)
power.t.test: 600.6 
Simulated power at n = 600 per arm: 0.802 (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

Multiplicity arises when several outcomes or comparisons are examined. Even when no true difference exists, looking across many tests can increase the chance that one appears unusual by chance. The registry lists 18 outcome measures and posts 17 secondary outcomes, in addition to the primary outcome. It does not report a multiplicity adjustment, a hierarchy of secondary tests, or whether specific comparisons were designated as confirmatory. Those procedures are not reported in the registry record.

The primary endpoint has a reported unadjusted p-value. Secondary comparisons are also presented as unadjusted, and the registry reports no formal analyses in its analysis listing. Without a stated testing strategy, the secondary p-values should not be interpreted as if each were an independently prespecified confirmatory test. A collection of unadjusted results can be useful for description, but it gives weaker grounds for firm claims than a single prespecified analysis with a clearly reported plan.

Missing data are noted for the primary endpoint: one participant in each group never came in for a follow-up visit and therefore had no CGM data. The primary summaries report 148 participants for CGM Only and 76 for CGM+BGM. Other endpoint summaries use different participant counts. The reasons for all differences in endpoint counts, the extent of missingness for each measure, and any imputation or sensitivity analyses are not reported in the registry record.

The registry identifies the allocation as randomized, but does not report a detailed analysis population such as intention-to-treat or per-protocol. It also does not state how adherence, protocol deviations, or participants without usable measurements were handled. Those omissions restrict assessment of whether the reported comparisons preserve the intended randomized comparison and how sensitive the results might be to missing observations.

The registry record does not report an interim-analysis plan, stopping rules, or the original statistical analysis plan. It is therefore not possible to determine from the record whether multiplicity, missing data, and analysis populations were addressed in prespecified procedures. These are reporting gaps, not evidence that a particular procedure was or was not used outside what the record states.

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.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: nephrolithiasis (1.3%), knee arthroplasty (1.3%), injury (1.3%), hypoglycaemic seizure (1.3%), tachycardia (0.7%).

The registry reports serious adverse events for 4 participants at risk of 149 in CGM Only and 4 participants at risk of 77 in CGM+BGM. Other adverse events affected 6 of 149 participants in CGM Only and 3 of 77 in CGM+BGM. The record reports zero deaths in both groups. These are participant counts, with the at-risk counts shown separately for each group.

The registry also lists serious-event terms with percentages, including nephrolithiasis, knee arthroplasty, injury, hypoglycaemic seizure, tachycardia, and myocardial infarction. The listed percentages are 1.3% for the first four terms and 0.7% for the latter two. The record does not provide enough detail in this summary to attribute each event to a particular monitoring strategy or to establish causation.

Adverse-event counts are descriptive safety information. They should be interpreted in the context of the number of participants at risk, the study period, event definitions, and how events were collected. The registry does not report detailed exposure time, event timing, or causal assessments, so the counts do not by themselves support a causal comparison of safety between groups.

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

The results describe a randomized comparison of two CGM-based monitoring approaches in adults with type 1 diabetes. The primary estimate is the difference in average time spent in the sensor range of 70 to 180 mg/dl between baseline and 6 months. The posted means were 63.0 for CGM Only and 65.0 for CGM+BGM; the estimated mean difference was 2.0 percentage points, with a 95% confidence interval from -1.241 to 5.241.

The interval includes zero, so the data are compatible with no mean difference as well as with a difference in either direction. The estimate does not establish that the groups are equivalent. Equivalence requires a prespecified acceptable range for differences and an analysis designed to test that question; the registry record does not report such a margin. The same limitation applies to a non-inferiority claim: no non-inferiority margin or corresponding plan is reported.

The result also does not establish that either monitoring approach works or fails. It reports an outcome comparison over the stated period, with uncertainty. The registry does not provide a clinical threshold for a meaningful change in time in range, so the practical importance of the estimated difference cannot be determined from the posted result alone.

Secondary outcomes and adverse-event counts add context, but they do not erase the uncertainty in the primary estimate. The secondary analyses are not formally reported in the registry analysis listing, and the record does not state how multiple comparisons were handled. Safety counts are descriptive and do not establish that an event was caused by one strategy.

The findings apply most directly to the participants and procedures represented by this trial record. Information about participant characteristics, adherence, missing-data methods, and the analysis population is limited or not reported. Those gaps, together with the absence of a reported original analysis plan, mean that the registry results should be read as estimates with important uncertainty rather than as a complete account of every analytic decision or a definitive answer to all clinical questions.

17. Why This Trial Matters Statistically

This trial raises a statistical question about how to compare two monitoring strategies when both use continuous sensor measurements but differ in the role of routine blood glucose confirmation. The primary endpoint, percentage of time in a specified sensor range, summarizes measurements across time and is distinct from a single laboratory or point-in-time value. It therefore illustrates how trial conclusions depend on the outcome chosen to represent the study objective.

The record also illustrates why estimates and confidence intervals are more informative than a binary reading of a p-value. The primary comparison has a point estimate favoring CGM+BGM numerically, but its confidence interval includes zero and covers values in either direction. The p-value alone cannot show whether a difference is clinically important, or establish equivalence when no margin is reported.

Other statistical issues are visible in the reporting: the primary group counts are unequal, endpoint counts vary, the study was unmasked, and several secondary outcomes are posted without a formal analysis listing. These features do not determine the result, but they shape how much can be learned from the registry summaries. They also show why transparency about missing-data handling, analysis populations, multiplicity, and prespecified methods matters when interpreting a randomized trial.

Finally, planning calculations based on an observed effect are not substitutes for the trial’s original design justification. A prospective sample-size plan needs a target effect and assumptions chosen before outcomes are observed. The registry record does not report those original assumptions. Keeping planning illustrations distinct from the completed trial’s evidence prevents sample-size calculations from being mistaken for observed power or for proof that a result is conclusive.

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 describes the study as unmasked. Lack of masking may affect participant or study-team behavior and outcome collection, depending on the procedures, but the record does not provide enough detail to assess the extent of any such effect. The primary outcome is based on CGM readings, which are device measurements; the registry does not report additional information about data completeness or sensor wear that would clarify measurement quality.

The primary endpoint counts are 148 for CGM Only and 76 for CGM+BGM, while enrollment is listed as 226. The registry notes one participant in each group without a follow-up visit and CGM data, but does not explain the full relationship between enrollment, endpoint counts, and missing observations. No imputation procedure or sensitivity analysis is reported. Different endpoint counts across outcomes further limit direct comparisons of their summaries.

The registry identifies random allocation but does not report sequence generation, allocation concealment, stratification, or a detailed analysis population. It also does not report the original sample-size justification, an analysis plan, multiplicity procedures, or interim analyses. These omissions restrict evaluation of whether all key decisions were prespecified and how robust the results are to alternative analytic choices.

The posted secondary outcomes cover several measures, but the registry reports no formal analyses in its analysis listing. Without reported multiplicity handling, secondary comparisons should be considered descriptive. The adverse-event summary gives counts and selected terms, but does not provide detailed timing or causal attribution. Finally, no clinical importance threshold for the primary difference is reported, limiting interpretation of the estimate’s practical meaning.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Trial Comparing Continuous Glucose Monitoring With and Without Routine Blood Glucose Monitoring in Adults With Type 1 Diabetes — NCT02258373.
• Registry record last updated 2018-08-09; status last verified 2018-08.
• Results first posted 2018-08-09.
• 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

NCT02258373 is a completed, randomized, parallel-group, unmasked trial sponsored by Jaeb Center for Health Research. It enrolled 226 adults with type 1 diabetes and compared CGM Only with CGM+BGM. The listed study start was 2015-03 and primary completion was 2016-10.

The primary outcome was mean percentage of time with CGM values from 70 to 180 mg/dl between baseline and 6 months. The group means were 63.0 and 65.0, respectively; the reported mean difference for CGM+BGM compared with CGM Only was 2.0, with a 95% confidence interval from -1.241 to 5.241 and an unadjusted p-value of 0.226.

The record posts 17 secondary outcomes among 18 listed outcome measures. It reports no formal analyses in the analysis listing. One participant in each group had no follow-up visit and no CGM data for the primary outcome. Serious adverse events affected 4 participants in each group, and no deaths were reported. The original sample-size rationale, detailed analysis plan, multiplicity procedures, and missing-data methods are not reported in the registry record.