Hybrid Closed Loop Pivotal Trial in Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Hybrid Closed Loop Pivotal Trial in Type 1 Diabetes (NCT02463097), a na na study. The primary outcome is Change in A1C.

2. Clinical Question

The central statistical question is therefore descriptive: what A1C change was recorded over the stated follow-up period among participants in this single-group investigation? A descriptive change can characterize what happened in the observed group, but it cannot by itself distinguish an effect of the system from other explanations. Without a concurrent comparator, differences over time might reflect the intervention, other changes during the study, natural variation, or measurement variation. The registry lists severe hypoglycemia events and diabetic ketoacidosis events as secondary outcomes, but their numerical results are not reported in the registry record.

3. Trial Design

This was a completed, single-group, multicenter home and hotel clinical investigation in people with type 1 diabetes on insulin pump therapy. The registry records enrollment of 124 participants, a start date of 2015-06, and primary completion in 2016-03. The intervention was the MMT-670G insulin pump used with its closed loop algorithm. Every participant was assigned to the Study Arm described in the record; no separate control arm is listed.

The registry records allocation as NA, masking as NONE, and the study model as SINGLE GROUP. These details matter to interpretation. Since participants were not assigned between a study treatment and a concurrent control group, the study does not provide a randomized treatment contrast. The baseline and 3-month A1C measurements are repeated observations in the study group, not outcomes from two independent treatment arms. The registry does not report a masking procedure, and masking should not be assumed.

The stated purpose was to demonstrate that the closed loop algorithm built into the 670G insulin pump was safe as part of the overall system. The primary outcome listing is change in A1C, but its description explicitly says there is no statistically powered primary endpoint and that change in A1C would be analyzed descriptively. The registry lists severe hypoglycemia events and diabetic ketoacidosis events as secondary outcomes. Their outcome values, definitions in operational detail, and analysis results are not reported in the registry record.

The phase is recorded as NA. Further protocol information—including detailed eligibility criteria, the intended analysis population, and a complete statistical analysis plan—is not reported in the registry record. The record also does not give a sample-size justification. The enrollment value describes how many participants were enrolled; it should not be treated as evidence that the study was sized to detect a specified difference. The study is marked COMPLETED, but completion status alone does not supply the missing endpoint estimates or analysis details.

4. Participant Flow and Accounting

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

Overall Study

Reasons for not completing this period:

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: All subjects wearing the MMT-670G insulin pump, using it with the closed loop algorithm.

6. Primary Endpoint

The registry lists the primary outcome as Change in A1C, measured over Baseline and 3 months.

The primary endpoint is change in A1C, with the measurement described as a mean and its dispersion described as a standard deviation. The stated timeframe is baseline and 3 months, and the unit is percent. In general, A1C is a measure used to characterize glycemic status over time. Comparing measurements at baseline and follow-up can describe how the group’s average value changed during the study period.

The registry explicitly characterizes this endpoint as descriptive rather than statistically powered. That distinction affects what can be concluded: a descriptive estimate can summarize the observed group, but the record does not establish that the study was designed to test a prespecified primary hypothesis with a target level of precision or power. No numerical baseline mean, follow-up mean, mean change, standard deviation, or confidence interval is reported in the registry record. Consequently, the direction, magnitude, and precision of the observed change cannot be assessed from the record.

In general, a mean describes the center of a set of measurements, while a standard deviation describes their spread around that mean. These are different from the uncertainty around an estimated mean change. A confidence interval, when reported, would help convey uncertainty about the estimate; no such interval is reported in the registry record. The endpoint name alone does not establish the size or clinical importance of any change.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry describes a continuous primary outcome summarized as a mean with standard deviation: change in A1C from baseline to 3 months. It also states that this endpoint was not statistically powered and would be analyzed descriptively. The statistical methods can therefore be understood first as methods for describing a group’s measurements, rather than as evidence of a randomized comparison or a planned confirmatory test. Specific procedures beyond that description are not reported in the registry record.

What does a mean change describe?

In general, a mean change summarizes the average difference between each participant’s follow-up measurement and that participant’s baseline measurement, when paired measurements are available. It is a measure of the group’s central tendency. It does not show how similar or different individual participants’ changes were, and it does not reveal whether the distribution of changes was symmetric or affected by unusual values. The registry identifies the endpoint as a mean but does not report the underlying observations, the mean change, or a distributional summary of individual changes.

Pairing matters because baseline and follow-up measurements from the same participant are related observations. A method that recognizes this pairing generally answers a different question from one that treats all measurements as independent. However, the analysis method used for the endpoint is not reported in the registry record. The word “change” identifies the intended quantity, but does not specify the exact calculation, the handling of participants with only one measurement, or any adjustment for other variables.

What does the standard deviation mean?

In general, a standard deviation describes the spread of individual measurements around a mean. It is not the same as a standard error, which describes uncertainty in an estimated mean, and it is not itself a confidence interval. A relatively wide spread can coexist with a precisely estimated mean when many observations contribute, while a narrow spread does not guarantee that the result is unbiased or that it represents people outside the observed study group. The registry names standard deviation as the dispersion measure but does not provide a numerical value for the endpoint.

What would a confidence interval add?

In general, a confidence interval expresses uncertainty around an estimated quantity under the assumptions of the method used. A narrower interval indicates greater precision under those assumptions; it does not remove bias from study design, measurement, missing data, or confounding. A confidence interval for mean change could help readers understand the range of values compatible with the observed data and analysis. No confidence interval for the A1C change is reported in the registry record, so the precision of that result cannot be evaluated from the record.

A p-value, in general, assesses how compatible observed data are with a specified null model and its assumptions. It is not a measure of effect size, clinical importance, or the probability that a hypothesis is true. The registry does not report a p-value or inferential test for the primary outcome. Since the record describes the endpoint as descriptive and not statistically powered, an unreported inferential test should not be inferred from the presence of a primary-outcome label.

Why does the single-group structure matter?

In general, a controlled comparison can help separate changes associated with an intervention from changes that would also occur without it. Random assignment, when used, helps balance known and unknown characteristics between groups on average, while a concurrent comparator can account for changes over the same calendar period. This study is recorded as SINGLE GROUP, with allocation listed as NA and no separate comparator. As a result, a within-group change would not provide the same causal contrast as a randomized comparison. Changes in care, participant behavior, measurement conditions, or the natural course of outcomes could also contribute to a before-and-after difference; the record does not quantify these possible influences.

The registry records masking as NONE. In general, masking can reduce the influence of expectations on some aspects of care, measurement, or outcome reporting. The absence of masking does not by itself show that any particular bias occurred, but it is relevant context for assessing methods. The registry does not give further details about outcome assessment procedures or safeguards against measurement-related bias.

How should planning calculations be read?

In general, sample-size planning asks how many participants a future study might need under specified assumptions about the effect to detect, variability, significance level, allocation, and statistical model. A planning calculation is conditional on those assumptions. The registry’s planning description refers to a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation, with the target effect expressed as a stated fraction of the observed effect. It also specifies that such calculations are illustrations based on the observed effect, not the trial’s own design. They are not evidence that the completed single-group study had that design, and they are not observed power.

Power is a design concept: in general, it is the probability that a specified analysis would detect an effect of a specified size under stated assumptions. Calculating power after observing a result does not establish how convincing the result is. Planning illustrations based on an observed effect can be useful for considering future study designs, but they depend on the estimated effect and assumptions. They cannot replace an uncertainty interval around the original result. Numerical planning results are not reported in the registry record, and they should not be reconstructed from assumptions alone.

What is not specified about the analysis?

The registry record does not report the statistical analysis plan in sufficient detail to identify the precise estimator, the treatment of missing data, any covariate adjustment, or the analysis population. It also does not report whether the primary outcome was analyzed for all enrolled participants or only for participants with measurements at both time points. These omissions limit reproducibility and make it impossible to determine whether the descriptive estimate, if available elsewhere, used all relevant observations or a subset.

Over-dispersion is a general issue for count outcomes when observed variability exceeds what a simple count model assumes. For example, a count model that assumes variance matches the mean may understate uncertainty if events cluster or vary more than expected. That general concern can matter for event outcomes such as adverse events, but the record does not specify a model for the secondary event outcomes. No model, dispersion assessment, or inferential analysis for those outcomes is reported in the registry record.

10. Confidence Intervals for Each Arm

No two-arm comparison is shown for this trial, but each arm's mean can be given with a t-based 95% confidence interval computed from the posted mean, spread and number of participants.

R code and output

arm <- c("Study Arm")
n   <- c(123)
m   <- c(-0.5)
sp  <- c(0.6)
sd  <- sp      # posted standard deviations
for (i in seq_along(n)) {
  se <- sd[i] / sqrt(n[i]); h <- qt(0.975, n[i] - 1) * se
  cat(sprintf("CARM|%s|%d|%.3f|%.3f|%.3f|%.3f\n", arm[i], n[i], m[i], sd[i], m[i] - h, m[i] + h))
}

CARM  Study Arm  123  -0.500  0.600  -0.607  -0.393

11. Multiplicity, Missing Data and Analysis Populations

The registry lists change in A1C as the primary outcome and names severe hypoglycemia events and diabetic ketoacidosis events as secondary outcomes. The primary-outcome description says that the study did not have a statistically powered primary endpoint and that A1C change would be analyzed descriptively. A multiplicity adjustment or formal hierarchy for testing outcomes is not reported in the registry record.

In general, multiplicity arises when several hypotheses or outcomes are tested: the more tests conducted, the greater the chance of at least one apparently notable result under a collection of null hypotheses. Approaches to address it can include prespecifying a primary test, adjusting significance thresholds, or defining an ordered testing strategy. Those approaches should not be assumed for this study. The available record does not describe a confirmatory testing strategy, and it reports no numerical outcome analyses for the named secondary outcomes.

Missing-data handling is also not reported in the registry record. The record does not state how many participants had measurements at both baseline and 3 months, whether observations were excluded, or whether any imputation method was used. In general, missingness can affect a descriptive estimate if participants with observed follow-up differ from those without it. Without the number and pattern of missing measurements and the analysis approach, the possible direction or size of that influence cannot be assessed.

The analysis population is not reported in the registry record. Enrollment of 124 participants does not establish that all 124 contributed to every outcome analysis. The record does not specify whether the primary outcome used an enrolled, treated, or measurement-complete population, or how participants who discontinued or lacked follow-up were handled. These details are important for interpreting both outcome estimates and safety summaries.

12. 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: appendicitis (0.8%), bacterial arthritis of right wrist (0.8%), c-difficile diarrhea (0.8%), worsening rheumatoid arthritis (0.8%).

The registry reports serious adverse events for the Study Arm, with 124 participants at risk and 2 participants affected. It reports other adverse events with 124 at risk and 66 affected, and records 0 deaths. These are descriptive registry counts for the study group. Because there was no separate comparator arm, they do not provide a controlled estimate of how event occurrence differed from another treatment or from a control condition.

The record lists Appendicitis, Bacterial arthritis of right wrist, C-difficile diarrhea, and Worsening Rheumatoid Arthritis among the top serious adverse event terms, each with a registry value of 0.8%. These entries describe terms and percentages as recorded; the record does not provide additional detail about event timing, relatedness assessments, or clinical narratives. The percentages should not be treated as a complete account of all serious events or as proof about causation.

Severe hypoglycemia and diabetic ketoacidosis are named as secondary outcomes, but their numerical results are not reported in the registry record. The adverse-event counts cannot be used to fill that gap. In general, a safety summary should be interpreted alongside its definitions, observation period, participant denominators, event ascertainment, and comparator context. Details beyond the listed counts are not reported in the registry record.

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

The registry record establishes that this was a completed, single-group investigation of the MMT-670G insulin pump used with its closed loop algorithm in people with type 1 diabetes. It records enrollment of 124 participants and describes a safety-focused investigation in home and hotel settings. It also identifies baseline-to-3-month A1C change as a descriptive primary outcome rather than a statistically powered primary endpoint.

The record does not report numerical A1C results. It therefore does not support a description of the direction or magnitude of A1C change, or of the uncertainty around that change. The named secondary outcomes—severe hypoglycemia events and diabetic ketoacidosis events—also lack numerical results in the registry record. While the record includes descriptive adverse-event counts and terms, those data do not supply results for the unreported secondary outcomes.

In general, a before-and-after observation can describe change within a group, but without a comparator it cannot establish what would have happened to that group under another condition. This limitation is especially relevant when the aim is to attribute a change to an intervention. The study’s single-group model means that the registry record does not provide a randomized treatment effect estimate. It also does not report enough analysis detail to assess the treatment of missing data, the analysis population, or the exact estimator used.

The record can be read as documenting the design, stated purpose, and certain safety summaries of a completed study. It cannot be read as a numerical demonstration of the size, precision, or cause of an A1C change, because those outcome estimates are not reported in the registry record. Nor does a completed status, enrollment count, or safety count alone establish efficacy, comparative safety, or generalizability beyond the participants and circumstances described.

14. Why This Trial Matters Statistically

This study is statistically informative as an example of the difference between a descriptive objective and a powered comparative endpoint. The registry explicitly says that there was no statistically powered primary endpoint and that A1C change would be analyzed descriptively. That wording shapes the appropriate expectations: a descriptive endpoint can characterize measurements in the study group, but it does not automatically support a formal claim about a treatment effect.

The single-group structure is another important statistical feature. It makes the baseline-to-follow-up question distinct from the question of whether the intervention changed outcomes relative to an alternative. A randomized comparator can support a direct contrast between groups; this study has no separate comparator arm. Readers should therefore distinguish the stated safety purpose and descriptive A1C objective from a controlled test of comparative benefit or risk.

The record also illustrates why outcome estimates and analysis details are essential alongside a study design description. Enrollment of 124 and a completed status tell readers about the study’s scale and status, but not the observed A1C change or its precision. The absence of numerical primary-outcome results in the registry record prevents an assessment of the size or uncertainty of the result. Likewise, missing-data methods and the analysis population are not reported in the registry record, limiting evaluation of how fully the observations represent enrolled participants.

Finally, planning calculations should be separated from the study’s actual design and findings. General sample-size illustrations can help frame future research under assumptions, but they are not observed power and do not transform a single-group descriptive investigation into a powered controlled trial. This distinction helps keep planning, estimation, and causal interpretation separate.

15. 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 specific to this study follow directly from the registry description. The design is single-group, with allocation recorded as NA and masking recorded as NONE. There is no separate comparator arm, so the record cannot provide a concurrent randomized contrast. Any within-group change would be descriptive and would not, by itself, establish that the system caused the change.

The registry states that the primary A1C endpoint was not statistically powered and would be analyzed descriptively. Numerical primary-outcome results are not reported in the registry record. Without a mean change, dispersion estimate, or uncertainty interval, the magnitude and precision of the endpoint cannot be assessed. Numerical results for the listed severe hypoglycemia and diabetic ketoacidosis secondary outcomes are also not reported in the registry record.

Important analysis details are not reported in the registry record, including the analysis population, the handling of missing measurements, and the precise statistical method for estimating A1C change. The enrollment figure does not establish the number of participants contributing to each outcome. The record also does not provide a sample-size justification, detailed eligibility criteria, or a full statistical analysis plan.

The safety information is descriptive and has no comparator context. Counts of affected participants do not establish comparative risk, and the record does not give further details about event timing, relatedness, or adjudication. The registry’s completed status does not resolve these limitations or substitute for numerical outcome reporting.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Hybrid Closed Loop Pivotal Trial in Type 1 Diabetes — NCT02463097.
• Registry record last updated 2019-04-16; status last verified 2019-04.
• Results first posted 2017-04-18.
• 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.

19. Record Summary

The registry describes NCT02463097, titled “Hybrid Closed Loop Pivotal Trial in Type 1 Diabetes,” as a completed, single-group, multicenter home and hotel investigation in people with type 1 diabetes on insulin pump therapy. Enrollment was 124; the recorded start was 2015-06 and primary completion was 2016-03. Participants in the Study Arm wore the MMT-670G insulin pump with the closed loop algorithm. Allocation is recorded as NA and masking as NONE.

The primary outcome is change in A1C, measured at baseline and 3 months, with a descriptive analysis specified and no statistically powered primary endpoint. Numerical primary-outcome results are not reported in the registry record. Severe hypoglycemia events and diabetic ketoacidosis events are listed as secondary outcomes, but their numerical results are not reported in the registry record. The adverse-event summary records 2 participants affected by serious adverse events, 66 affected by other adverse events, and 0 deaths among 124 at risk for each category. The record does not report the analysis population or missing-data methods.