Insulin Detemir Compared to Insulin Glargine: Appetite and Calories Consumed in Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Insulin Detemir Compared to Insulin Glargine: Appetite and Calories Consumed in Type 1 Diabetes (NCT00659165), a randomized na study. The primary outcome is Calories Consumed After Fast..

2. Clinical Question

3. Trial Design

This was a randomized, triple-masked crossover study sponsored by the University of New Mexico. The registry record lists an enrollment of 10 and a phase of NA. Its status is completed. The study started in 2008-04, and primary completion is listed as 2010-12. Those dates describe the registry record and do not provide details about the length of an individual participant's treatment periods.

The interventions were insulin detemir and insulin glargine. A crossover design compares treatments across periods within the study framework, rather than assigning each participant to only one treatment throughout. The registry record does not describe the treatment order, allocation ratio, period length, washout, or how the crossover structure was handled in the analysis. Randomization and triple masking are recorded, but the parties masked and the practical masking procedures are not reported in the registry record.

The posted primary outcome was continuous: mean calories consumed after a 24 hour fast, measured after treatment with study insulin for at least 3 weeks. Results are reported as means and standard deviations for each treatment entry, with a comparison of insulin glargine relative to insulin detemir. The outcome record contains no posted analyses, and no secondary outcomes are posted. The registry record identifies the study as a pilot study in its summary, which describes gathering preliminary information about the feasibility of a larger study. It does not report the original sample-size justification or detailed statistical analysis plan.

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.

Before assignment: Three weeks of treatment with each insulin (glargine, detemir) in random order, double blind assignment. Doses of glargine and detemir were equivalent and were based upon existing long acting insulin doses. Doses were titrated up to achieve target fasting glucose \< 150 mg/dl. Both long acting insulins were given once daily before breakfast.

Overall Study

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 Calories Consumed After Fast., measured over Measured after a 24 hour fast, after treatment with study insulin for at least 3 weeks.

The primary endpoint measures the total energy ingested after a 24 hour fast. The registry record expresses the outcome in kcal and reports it as a mean with a standard deviation. Measurement took place after treatment with the study insulin for at least 3 weeks. The endpoint is therefore a measure of calorie consumption after fasting, rather than a direct rating of appetite, hunger, or satiety.

The study summary frames the research question around whether insulin type affects appetite and calories eaten during a meal. Calories consumed offer an observable measure of eating behavior in that setting: they quantify the energy ingested following the fast. That makes the endpoint relevant to the stated research question, but it does not by itself describe why a participant ate a particular amount or how hungry or satisfied the participant felt. No separate appetite measure is posted in the registry record.

The reported mean describes the group's average consumption, while the standard deviation describes the spread of individual measurements around that mean. Neither summary alone shows the full distribution of outcomes. The registry record does not report additional measurement details, such as the meal composition, how consumption was recorded, or whether multiple meal measures were collected.

7. Results Posted in the Registry

• Insulin Glargine: mean 1,357 versus 1,418 on control; difference -61.00 (95% CI -592.82 to 470.82), standardized effect d = -0.10; z-test p = 0.822.

8. Statistical Interpretation

9. Statistical Methods Explained

A continuous endpoint is measured on a scale that can take a range of values. Calories consumed, recorded in kcal, is such a measure. The registry reports a mean for each treatment and a standard deviation. The mean locates the average of the recorded values; the standard deviation describes how spread out those values are around the mean. A standard deviation is not the uncertainty in the mean difference. Uncertainty about a comparison is instead conveyed by its confidence interval and, in a hypothesis-testing framework, its p-value.

What does the mean difference describe?

The registry comparison sets insulin glargine against insulin detemir. Its mean difference is -61.0 kcal, with glargine as the comparison treatment relative to detemir. A negative difference represents a lower mean in the glargine entry. This is a difference in group means, not a claim that every participant consumed less after glargine. Individual measurements can vary around the group averages, and the standard deviations indicate that such variation is relevant to interpreting the means.

The crossover design matters for how a treatment contrast can be estimated. In general, a crossover comparison may use information about how the same participant's outcome changes across treatment periods. That can account for within-participant association. The registry record does not report whether the posted comparison uses paired observations or otherwise accounts for that association. It also does not report period, sequence, or carryover terms. Consequently, the comparison can be described as posted, but the analysis details needed to assess its handling of the crossover structure are not available in the registry record.

How should the confidence interval be read?

A confidence interval expresses the uncertainty around an estimated contrast under the assumptions of the analysis used to construct it. In general, a wider interval indicates less precision than a narrower interval. The interval for this comparison extends from -592.8 to 470.8 kcal. It includes zero, so it is compatible with no mean difference as well as with lower or higher mean consumption for glargine relative to detemir. It also covers a wide range of possible contrasts, so the point estimate should not be interpreted as a precise measurement of the underlying difference.

What does the p-value add?

The reported unadjusted p-value is 0.822. In general, a p-value describes how compatible the observed data are with a specified null hypothesis under the assumptions of the statistical test. It is not the probability that the null hypothesis is true, and it does not measure the size or clinical importance of a difference. Because the registry labels this value unadjusted and does not post analysis details, it should be reported as an unadjusted result, not as evidence that any multiplicity procedure or design-specific adjustment was applied.

The confidence interval and p-value address related but different aspects of inference. The interval shows the range of contrasts compatible with the analysis at its stated confidence level; the p-value summarizes evidence against the null hypothesis under a test. Neither resolves the limitations of a small pilot study or missing analysis details. The registry record does not report the test specification, the confidence-interval method, or whether inference accounts for the crossover pairing.

What does the standardized difference mean?

The pooled standard deviation is 606.7, and the standardized difference is -0.101. A standardized difference expresses a mean contrast relative to a measure of outcome variability, allowing the contrast to be described without retaining the original unit. It does not make an imprecise estimate precise, and it does not explain what the difference means for appetite or other clinical outcomes. The kcal-scale interval remains important because it communicates uncertainty in the original measurement units.

General statistical concepts can help explain why a small study may have an uncertain mean contrast. Greater variability makes it harder to distinguish a treatment contrast from random fluctuation. In count outcomes, over-dispersion means variability exceeds what a basic count model expects and may require a different model; that issue does not apply directly to this continuous calorie endpoint. The registry record does not report a model, variance adjustment, or other method that would justify further assumptions about the analysis.

10. Confidence Intervals and Precision

The mean difference for insulin glargine relative to insulin detemir is -61.0 kcal. Its 95% confidence interval extends from -592.8 to 470.8 kcal. This interval is wide relative to the point estimate and spans both negative and positive values. In practical statistical terms, the posted estimate is not precise: the data are compatible with lower mean consumption for glargine, with little or no difference, and with higher mean consumption for glargine.

In general, a confidence interval describes uncertainty around an estimate under the assumptions of the analysis. It is not a range containing a fixed proportion of individual participants' results, and it is not a probability statement about the true value after the data have been observed. The interval's width matters as much as its midpoint when judging how much the result narrows uncertainty. Here, the interval leaves a broad range of possible contrasts.

The reported standard deviations are 636.0 kcal for insulin detemir and 576.0 kcal for insulin glargine. These describe variation in outcome values, not the precision of the difference by themselves. The registry comparison also reports a pooled standard deviation of 606.7. The study's enrollment was 10, and the registry describes it as a pilot study; the small scale and observed variability are relevant context for the broad interval.

The registry record does not report the confidence-interval construction method or whether it incorporates paired observations from the crossover design. Those details limit assessment of the interval's assumptions. The interval should therefore be interpreted as the posted uncertainty estimate, not as a fully documented account of the analysis procedure.

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, not the trial's own original design assumptions; those original assumptions are not reported in the registry record. The listed planning 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.

For planning for 100% of the observed effect, the figures are 1,554 participants per arm for 80% power and 2,080 participants per arm for 90% power. For planning for 75% of the observed effect, they are 2,761 participants per arm for 80% power and 3,697 participants per arm for 90% power. For planning for 50% of the observed effect, they are 6,213 participants per arm for 80% power and 8,317 participants per arm for 90% power.

In plain terms, the 80% power column gives the illustrated number of participants per arm for a study designed to have an 80% chance of detecting the specified effect under the stated assumptions. The 90% power column gives the corresponding planning target for a 90% chance. These are hypothetical design calculations, not observed power for this trial. The figures show the general planning relationship: targeting a smaller effect requires more participants under the same assumptions. The calculation is based on the observed effect, which is uncertain in this pilot study, so the figures should not be taken as a definitive recommendation for a future design. The planning assumptions also do not describe the trial's original design, and the registry record does not report how its crossover structure might affect a future sample-size calculation.

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 = -61.000; pooled standard deviation σ = 606.742.
2. n = 2 × (1.96 + 0.842)² × 606.742² / -61.000² = 1,553.1, rounded up to 1,554 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 <- -61                      # observed difference in means
sigma <- 606.742119849941                     # 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(659165)
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 = 1,554 (80% power), 2,080 (90% power)
 75% of observed effect: n per arm = 2,761 (80% power), 3,697 (90% power)
 50% of observed effect: n per arm = 6,213 (80% power), 8,317 (90% power)
power.t.test: 1554 
Simulated power at n = 1,554 per arm: 0.798 (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. Multiplicity, Missing Data and Analysis Populations

The registry outcome record lists one primary endpoint, calories consumed after a 24 hour fast, and no secondary outcomes are posted. The record contains no posted analyses. It does not report a complete statistical analysis plan, a prespecified hierarchy of hypotheses, or how multiple comparisons would have been handled if additional outcomes or analyses were conducted. The posted p-value is identified as unadjusted; the registry record does not report a multiplicity adjustment.

Multiplicity is a general issue that arises when many hypotheses are tested. As the number of tests increases, the chance of observing a result that appears unusual by chance can also increase. Whether that concern applies, and what adjustment would be appropriate, depends on the prespecified analyses and research questions. Since the registry record does not provide the full analysis plan, no conclusion about the intended or actual multiplicity strategy is warranted.

Missing-data procedures and analysis populations are also not reported in the registry record. The endpoint entries list 10 observations for each treatment, but this information alone does not establish how missing outcome values were handled, whether all randomized participants contributed to the comparison, or which analysis population was used. The record does not describe exclusions, imputation, sensitivity analyses, or reasons for any unavailable measurements. The posted endpoint counts should be read as the recorded entries, not as evidence about unreported data-management decisions.

The crossover design raises additional analysis questions. In general, treatment sequence, period effects, within-participant correlation, and possible carryover can be relevant to analysis. The registry record does not say whether these were assessed or modeled. Without the protocol or detailed analysis plan in the registry record, it is not possible to establish whether the posted comparison follows a particular prespecified approach or how robust it is to alternative assumptions.

14. 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 5% reporting threshold.

For insulin detemir, the registry lists 10 at risk for serious adverse events and 0 with serious adverse events. It also lists 10 at risk for other adverse events and 0 with other adverse events. The corresponding entries for insulin glargine are 10 at risk for serious adverse events and 0 with serious adverse events, and 10 at risk for other adverse events and 0 with other adverse events.

These entries describe the adverse-event counts recorded in the registry; they do not establish that no other events occurred outside the reported categories or observation period. The registry record does not list serious adverse-event terms. Death counts are not reported in the registry record. The at-risk entries are reported by treatment group in a crossover study and should not be interpreted as separate participant totals.

General safety interpretation depends on the definitions used, the period of observation, and completeness of reporting. Those details are not reported in the registry record. The posted zero counts are limited to the adverse-event entries shown and do not support broader conclusions about safety.

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

The posted results describe mean calories consumed after a 24 hour fast following treatment with insulin detemir or insulin glargine for at least 3 weeks. The mean was 1418.0 kcal for detemir and 1357.0 kcal for glargine. The reported mean difference for glargine relative to detemir was -61.0 kcal, with a 95% confidence interval from -592.8 to 470.8 and an unadjusted p-value of 0.822. The interval indicates substantial uncertainty and includes contrasts in either direction.

These data do not establish that the treatments have the same mean consumption, because the confidence interval is broad and includes a range of possible differences. Nor do they establish a reliable difference in either direction. The point estimate is only one summary of the data, and its direction should not be mistaken for a precise treatment effect. The registry comparison is an unadjusted result, and the analysis details needed to assess treatment pairing in the crossover design are not reported.

The endpoint is calories consumed following fasting. It is not itself a measure of appetite, satiety, long-term energy intake, body weight, or adherence. The study summary describes interest in appetite and calories eaten, but no separate appetite result is posted. The results therefore address the posted calorie endpoint and cannot be extended to outcomes that are not reported.

The registry identifies this as a pilot study and lists enrollment of 10. Its findings are preliminary and imprecise. They can help describe the observed endpoint and the uncertainty around the posted comparison, but they do not provide a definitive answer about the broader research question. The registry record also does not report the original sample-size justification or detailed analysis plan, limiting assessment of design assumptions and analytic choices.

16. Why This Trial Matters Statistically

This study illustrates why an estimated difference should be read together with its uncertainty. The mean calorie-consumption values point in one direction, but the confidence interval extends across zero and covers lower and higher means for glargine relative to detemir. The estimate alone would not show how much uncertainty remains. Reporting the interval makes that uncertainty visible.

It also highlights the distinction between an outcome's observed variability and the precision of a treatment contrast. The standard deviations describe spread in calorie measurements; the confidence interval describes uncertainty in the estimated mean difference under the analysis assumptions. A result can have a numerical point estimate and a p-value while still leaving a broad range of possible effects compatible with the data.

The crossover design makes transparent reporting of methods especially important. In general, within-participant comparisons can require analysis that accounts for the relationship between repeated measurements. The registry record does not describe whether or how the comparison accounts for pairing, treatment period, or sequence. This makes the posted estimate interpretable as a reported contrast, while leaving important methodological questions unanswered.

Finally, the pilot-study context helps distinguish preliminary estimation from definitive evidence. The posted planning illustrations show how assumed effect size and desired power relate to participant requirements under stated assumptions. They do not replace the original study's design rationale, which is not reported, and they are not observed power. Together, the endpoint summaries, interval, design, and missing analysis details demonstrate the importance of examining both the data and the limits of what the registry record documents.

17. 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 enrollment was 10, and the registry describes the study as a pilot. The confidence interval for the mean difference is broad, so the estimate does not tightly constrain the possible contrast. The posted standard deviations also show substantial spread in calorie consumption. These features limit how confidently the observed means can characterize the comparison.

The study used a crossover design, but the registry record does not describe treatment order, period structure, washout, or whether the analysis accounts for within-participant pairing. It also does not report how period or sequence effects were considered. These missing details matter when interpreting a crossover comparison and assessing the assumptions behind its confidence interval and p-value.

Important participant and measurement details are not reported in the registry record. The record does not give further characteristics of the enrolled population, meal composition, or the method used to record calories. It reports an endpoint after a 24 hour fast and treatment for at least 3 weeks, but does not provide additional context about the measurement procedure.

No secondary outcomes or analyses are posted. The registry record does not describe missing-data procedures, analysis populations, the confidence-interval method, or the full statistical analysis plan. It also does not report the original sample-size justification. These limitations mean the posted results can be described, but several aspects of their design and analysis cannot be independently assessed from the registry record.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Insulin Detemir Compared to Insulin Glargine: Appetite and Calories Consumed in Type 1 Diabetes — NCT00659165.
• Registry record last updated 2018-03-22; status last verified 2018-02.
• Results first posted 2011-10-05.
• 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.

21. Record Summary

The registry record describes a completed randomized, triple-masked crossover study sponsored by the University of New Mexico. It lists enrollment of 10 and compares insulin detemir with insulin glargine in a study focused on appetite and calories consumed in type 1 diabetes. The primary endpoint is mean calories consumed after a 24 hour fast, following treatment with study insulin for at least 3 weeks.

The posted mean was 1418.0 kcal for detemir and 1357.0 kcal for glargine. The mean difference for glargine relative to detemir was -61.0 kcal, with a 95% confidence interval from -592.8 to 470.8 and an unadjusted p-value of 0.822. The interval is broad and includes both directions of difference.

The registry record identifies the study as a pilot and does not post secondary outcomes or analyses. Details of the crossover analysis, missing-data handling, and original sample-size assumptions are not reported.