Assess the Efficacy of the Low Glucose Suspend (LGS) Feature in the MiniMed Paradigm® X54 System: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Assess the Efficacy of the Low Glucose Suspend (LGS) Feature in the MiniMed Paradigm® X54 System (NCT01148862), a randomized na study. The primary outcome is The Primary Treatment Comparison is the Evaluation of the Duration (Minutes) of Induced Hypoglycemia..

2. Clinical Question

3. Trial Design

The registry describes a randomized, unmasked crossover study. It lists enrollment of 50 and identifies Type 1 Diabetes Mellitus as the condition. The brief summary specifies participants age 16 through 60 years. The study status is completed; the registry lists its start as 2010-06 and primary completion as 2011-11. The phase is listed as NA.

Both conditions used the MiniMed Paradigm® X54 System. In one condition, the Low Glucose Suspend feature was activated; in the other, it was deactivated. The primary purpose was to assess the feature in relation to hypoglycemia induced from exercise. Because the design is identified as crossover, the study compares conditions within a crossover framework. The registry record does not report the order or sequence assigned to participants, the interval between conditions, or whether a washout was used. Those details should not be inferred from the design label alone.

The primary endpoint was the duration in minutes of induced hypoglycemia, described as YSI < 70 mg/dL, over approximately 8 hours per induction experiment. The posted outcome summaries list 50 observations for each condition. These condition-level counts do not, by themselves, explain how participants contributed to the analysis or whether all enrolled participants completed both conditions.

The sponsor is Medtronic MiniMed, Inc. The registry lists two outcome measures and two analyses, while no secondary outcome results are posted in the record. Further information about the original statistical plan, allocation sequence, analysis populations, participant flow, and handling of missing outcome data is not reported in the registry record.

4. Participant Flow and Accounting

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

First Intervention

Washout Period of 2 Weeks

Second Intervention

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.

6. Primary Endpoint

The registry lists the primary outcome as The Primary Treatment Comparison is the Evaluation of the Duration (Minutes) of Induced Hypoglycemia., measured over approximately 8 hours per induction experiment.

The primary endpoint is the duration, in minutes, of induced hypoglycemia. The outcome description defines hypoglycemia as YSI < 70 mg/dL and gives a timeframe of approximately 8 hours per induction experiment. The registry identifies the endpoint as a mean with standard deviation, so the posted summaries describe an average duration and the variation around that average for each condition.

Duration is relevant to the stated research question because the study aimed to assess whether activating Low Glucose Suspend was associated with a different length of induced hypoglycemia than leaving the feature deactivated. It focuses on time spent below the stated threshold during the experimental induction, rather than simply whether the threshold was reached. The mean summarizes the group-level duration, while the standard deviation indicates how dispersed individual measurements were around that mean.

This is a controlled, induced endpoint, not a measure of routine daily experience. The record does not report how the result relates to other clinical outcomes or longer-term events. It also does not provide a separate description of the population included in the primary analysis. Interpretation should therefore stay with the posted endpoint and its experimental setting.

7. Results Posted in the Registry

• LGS Off: Without Low Glucose Suspend (LGS) Feature: mean 170.70 versus 138.50 on control; difference 32.20 (95% CI 2.29 to 62.11), standardized effect d = 0.42; z-test p = 0.035.

8. Statistical Interpretation

9. Statistical Methods Explained

What does a mean difference describe?

A mean difference subtracts one condition's average outcome from the other's. In this comparison, the reported direction is feature-off minus feature-on. A positive value means the average duration was larger with the feature deactivated. The estimate of 32.2 minutes is the magnitude of that difference in the posted summaries. It is not a percentage change, and no percentage interpretation is needed to understand the original unit of minutes.

A mean is useful for describing the central level of a continuous outcome, but it does not describe every participant's result. The standard deviations of 76.68 and 75.91 minutes describe spread around the condition means. Standard deviation is not the same as the uncertainty in a mean or the confidence interval for the difference. A relatively dispersed outcome can coexist with a precisely estimated mean if the data and design support it; precision must be judged from the uncertainty estimate, not from the mean alone.

What does a confidence interval add?

A confidence interval expresses uncertainty in an estimate. In repeated use of a statistical procedure under its assumptions, intervals constructed in this way would cover the target parameter at the stated confidence level at the stated rate. It is not a range containing a fixed proportion of individual participant outcomes, nor does it assign a probability to each value within the particular interval.

The recalculated interval, 2.293 to 62.11 minutes, is for the unadjusted difference in the stated direction. Its width conveys uncertainty about the size of that difference. The interval is compatible with a modest positive difference as well as a larger one. The registry does not post a confidence interval for the sponsor's ANCOVA estimate, so the recalculated interval should not be attributed to that model.

Why are there two p-values?

The unadjusted recalculation gives p = 0.035, while the reported ANCOVA analysis gives p = 0.006. A p-value depends on the analysis method, its assumptions, and the data used. ANCOVA is a model-based analysis that can account for specified covariates; which covariates were used in this analysis is not reported in the registry record. The unadjusted calculation and sponsor analysis therefore answer related but not necessarily identical statistical questions. The difference between their p-values does not imply that either number is a direct measure of effect size.

For a crossover study, participant-level pairing can be important because outcomes from the same person may be related across conditions. The registry does not report the within-participant correlation, sequence details, or full analysis specification. The summary-statistic recalculation cannot recover all information from individual paired measurements. The sponsor's ANCOVA result is the posted result for the sponsor's stated method; the unadjusted result offers a separate, more limited comparison based on the available summaries.

Why can over-dispersion matter?

In general, over-dispersion means that observed variation is greater than a model's assumptions allow. When a model understates variability, its uncertainty estimates can be too small and inferential results can appear more precise than warranted. This is a general statistical consideration, not a finding that over-dispersion occurred in this study. The outcome is continuous and the posted record gives means and standard deviations, but it does not provide enough participant-level information to assess distributional fit, outliers, or model diagnostics.

Similarly, general statistical reasoning distinguishes statistical evidence from practical importance. A p-value assesses compatibility with a specified null model; it does not determine whether a difference matters clinically. The registry does not state a threshold for a clinically important difference in duration, so clinical importance cannot be judged from a reported threshold.

What is and is not documented?

The registry identifies randomization, no masking, and a crossover model. It also reports the endpoint summary, the sponsor's ANCOVA result, and the unadjusted recalculation. The detailed randomization procedure, sequence balance, adjustment variables, missing-data method, interim analyses, and original statistical plan are not reported in the registry record. It is not appropriate to fill those gaps by assuming customary methods.

The number of listed outcome measures and analyses does not establish how many hypotheses were tested or whether multiplicity was addressed. The record does not report a multiplicity adjustment. Consequently, the listed p-values should be read as posted results rather than as evidence that a family-wise error procedure was applied. The calculation methods and any model assumptions remain important to the interpretation of precision.

10. Confidence Intervals and Precision

The unadjusted mean difference is 32.2 minutes, with a 95% confidence interval from 2.293 to 62.11 minutes. This interval quantifies uncertainty in the recalculated mean difference, not variation among individual participants. The endpoints are separated by a sizable range, so the data summaries are consistent with a range of possible positive differences rather than one precisely determined magnitude.

The interval does not include zero, and the corresponding unadjusted p-value is 0.035. These features describe the unadjusted statistical result; they do not establish clinical importance. The registry does not report a threshold for a meaningful difference in duration. Nor does it provide a confidence interval for the sponsor's ANCOVA result, which reports the same point estimate and p = 0.006.

Precision also depends on whether the uncertainty calculation reflects the design. In crossover studies, measurements within a participant can be associated. The registry record does not provide the pairing information or covariance needed to assess how that association was handled in the recalculation. The posted confidence interval should therefore be understood as the uncertainty estimate for the unadjusted summary-statistic calculation, not as a substitute for the sponsor's full analysis or a reconstruction of all crossover features.

The standard deviations are 76.68 minutes for the feature-on condition and 75.91 minutes for the feature-off condition. These measure outcome variability, whereas the confidence interval measures uncertainty in the difference estimate. Keeping these concepts separate helps avoid interpreting the spread of individual durations as the precision of the group comparison.

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 figures are participants per arm, not total events. They are illustrations based on the observed effect rather than the trial's original design. The original design assumptions are not reported in the registry record. The illustrations use a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each scenario asks how many participants per arm would be needed to detect a stated fraction of the observed effect.

For planning at 100% of the observed effect, the figures are 89 participants per arm for 80% power and 118 participants per arm for 90% power. At 75% of the observed effect, they are 157 participants per arm for 80% power and 210 participants per arm for 90% power. At 50% of the observed effect, they are 353 participants per arm for 80% power and 472 participants per arm for 90% power.

In plain language, the 80% power column represents the planning size intended to give an 80% chance of detecting the specified effect under the stated assumptions if that effect is present. The 90% column uses a higher chance of detection, also under those assumptions. The figures increase as the assumed effect becomes smaller: a smaller difference is harder to distinguish from random variation, so more participants are needed in the illustration. These values are not observed power and do not describe the number enrolled or analyzed in this trial.

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 = 32.200; pooled standard deviation σ = 76.296.
2. n = 2 × (1.96 + 0.842)² × 76.296² / 32.200² = 88.1, rounded up to 89 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 <- 32.2                      # observed difference in means
sigma <- 76.2959713877476                     # 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(1148862)
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 = 89 (80% power), 118 (90% power)
 75% of observed effect: n per arm = 157 (80% power), 210 (90% power)
 50% of observed effect: n per arm = 353 (80% power), 472 (90% power)
power.t.test: 89.1 
Simulated power at n = 89 per arm: 0.805 (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 an ANCOVA analysis of mean difference in final values. It gives a mean difference of 32.2 and p = 0.006, with no confidence interval posted. The unadjusted recalculation from the posted condition summaries also gives a difference of 32.2, but its confidence interval is 2.293 to 62.11 minutes and its p-value is 0.035.

The matching point estimates do not make the analyses interchangeable. The sponsor's result is from the stated ANCOVA method, while the recalculation is an unadjusted comparison based on summary statistics. Model specification, covariates, and other details that could explain the difference in inferential results are not reported in the registry record. The two p-values should therefore be identified with their respective analyses rather than combined or treated as a contradiction.

The recalculation is not a correction to the sponsor's result. It provides a transparent comparison using posted summaries, but it cannot recover all details of the crossover data or model. The sponsor's own analysis is the reported analysis for its method; interpretation should preserve that distinction and acknowledge the information the record does not include.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists two outcome measures and two analyses, and it reports a primary outcome analysis. No secondary outcome results are posted in the record. These counts alone do not reveal how many statistical hypotheses were considered, whether any outcomes were exploratory, or whether analyses were planned before results were examined.

A multiplicity adjustment addresses the increased chance of false-positive findings when multiple hypotheses are tested. Whether an adjustment is needed and which procedure is appropriate depend on the prespecified testing strategy. The registry record does not report a multiplicity adjustment or a hierarchy of tests. The posted p-values should therefore be read as the values reported for their stated analyses, without assuming that a family of tests was controlled.

The registry does not report the original statistical analysis plan, interim analyses, or a detailed account of analysis populations. It also does not state how missing outcome data were handled, whether participants who did not complete both conditions were included, or whether the analysis followed an intention-to-treat or other specified principle. The number of observations listed for each condition does not resolve these questions or establish that all enrolled participants contributed complete paired data.

These omissions limit assessment of potential selection and analysis effects. For example, without participant flow and missing-data information, the extent to which the posted summaries represent everyone enrolled cannot be determined from the registry record. Without a full analysis plan, it is not possible to confirm which model choices were prespecified. These are reporting limitations, not evidence that a particular problem occurred.

15. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent non-serious adverse event terms

The serious adverse event figures list 0 affected participants among 50 at risk in the feature-off condition and 0 among 50 at risk in the feature-on condition. The record therefore reports no serious adverse events in either condition during the recorded study period. The registry does not list serious adverse event terms.

For other adverse events, the record lists 21 affected participants among 50 at risk in the feature-off condition and 1 among 50 at risk in the feature-on condition. These are counts reported by condition. They should not be read as proof that the device setting caused or prevented an event, and the registry information does not explain event definitions, timing, severity, or attribution.

Death counts are not reported in the registry record. The safety figures are limited to the categories and information posted; they do not describe all possible risks or establish a comparative safety conclusion. In particular, the absence of reported serious events in these records does not show that serious events are impossible.

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

The main statistical finding is a difference in the posted mean durations of induced hypoglycemia: 138.5 minutes with Low Glucose Suspend on and 170.7 minutes with it off. The unadjusted difference, defined as off minus on, is 32.2 minutes. Its recalculated 95% confidence interval is 2.293 to 62.11 minutes, and its unadjusted p-value is 0.035. The sponsor's ANCOVA reports the same mean difference and p = 0.006, without a posted confidence interval.

Those results describe the endpoint measured during an exercise-related hypoglycemia induction experiment. They do not establish how often hypoglycemia occurs in ordinary use, how long episodes last outside this setting, or whether the observed difference changes other outcomes. The registry does not report a threshold for clinical importance, so the magnitude cannot be classified as clinically meaningful from the record alone.

The statistical evidence also does not settle questions about generalizability. The registry describes participants age 16 through 60 years with Type 1 Diabetes Mellitus, but additional population details are not reported. Study procedures such as the crossover sequence and any washout are also not reported in the registry record. The finding should be interpreted within the documented study design and endpoint rather than extended to settings or populations not described.

Finally, a p-value is not a measure of certainty that the feature works, and a confidence interval is not a guarantee of the true difference's location. The posted results support a comparison of the recorded durations under the analyses reported. They do not by themselves provide a treatment recommendation or establish an overall balance of benefits and risks.

17. Why This Trial Matters Statistically

This study illustrates why the design and analysis need to be read together. The registry calls the trial randomized and crossover, a design in which comparisons may rely on measurements from the same participant under each condition. Yet the posted summaries do not include the paired covariance or the sequence details needed to reconstruct all design-specific aspects. A summary-statistic recalculation can be informative, but its uncertainty should not automatically be assumed to represent every feature of the original analysis.

It also illustrates the difference between a point estimate and its precision. The mean difference is 32.2 minutes, while the unadjusted confidence interval spans 2.293 to 62.11 minutes. The point estimate gives one summary of the observed contrast; the interval makes clear that the exact size remains uncertain. Neither alone establishes clinical importance, which requires context and a meaningful-difference threshold that the registry does not report.

The sponsor's ANCOVA result and the unadjusted calculation offer a further lesson: the same reported point estimate can be accompanied by different p-values when methods differ. Clear reporting of model details, participant flow, missing-data handling, and confidence intervals would help readers evaluate why analyses differ and how robust conclusions are to assumptions.

Finally, the planning illustrations show how assumed effect size influences sample-size requirements. Smaller target effects require more participants under the stated planning assumptions. Those illustrations are distinct from the trial's actual design justification, which is not reported in the registry record. Separating retrospective planning from observed trial results prevents sample-size illustrations from being mistaken for evidence about the study's power.

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 important design and analysis details unspecified. Although it identifies a randomized crossover design, it does not report the assigned condition sequence, period structure, interval between conditions, or washout procedures. These details matter for understanding how order and time effects might enter a crossover comparison.

The record does not describe the primary analysis population, participant flow, or whether all enrolled participants completed both conditions. It lists 50 observations in each condition, but that does not explain whether the observations are paired or how incomplete data were treated. Missing-data methods and the original statistical plan are not reported in the registry record.

The sponsor reports an ANCOVA result, but model covariates and diagnostic information are not reported. The sponsor result has no posted confidence interval. The unadjusted recalculation uses summary statistics and cannot recover participant-level pairing or assess distributional features such as skew and outliers. The record does not state a clinically important difference for the duration endpoint.

The endpoint was measured during induced hypoglycemia associated with exercise, over approximately 8 hours per induction experiment. The registry does not report enough detail to determine how closely this experimental setting represents routine use. Additional population characteristics and results for secondary outcomes are not reported in the registry record. These limits constrain interpretation and generalization; they do not establish that bias or model failure occurred.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Assess the Efficacy of the Low Glucose Suspend (LGS) Feature in the MiniMed Paradigm® X54 System — NCT01148862.
• Registry record last updated 2017-03-30; status last verified 2017-03.
• Results first posted 2017-03-30.
• 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 a completed, randomized, unmasked crossover study of the MiniMed Paradigm® X54 System in participants with Type 1 Diabetes Mellitus. The comparison was between the Low Glucose Suspend feature activated and deactivated. Enrollment is listed as 50, and the brief summary specifies an age range of 16 through 60 years.

The primary endpoint was the mean duration of induced hypoglycemia, defined as YSI < 70 mg/dL, with a timeframe of approximately 8 hours per induction experiment. Posted means were 138.5 minutes with the feature on and 170.7 minutes with it off. The unadjusted difference was 32.2 minutes, with a 95% confidence interval of 2.293 to 62.11 and p = 0.035. The sponsor's ANCOVA result reports the same difference and p = 0.006.

Key details about crossover sequence, missing data, analysis populations, model specification, and the original design assumptions are not reported in the registry record. The statistical findings describe the posted experimental outcome and should be interpreted within those reporting limits.