Clinical Trial Evaluating the Efficacy and Safety of Technosphere® Inhalation Insulin (TI) Inhalation Powder Using the Gen2 Inhaler: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Clinical Trial Evaluating the Efficacy and Safety of Technosphere® Inhalation Insulin (TI) Inhalation Powder Using the Gen2 Inhaler (NCT01196104), a randomized phase 3 study. The primary outcome is Change in HbA1c (%) From Baseline to Week 16.

2. Clinical Question

The trial record describes randomized allocation to parallel groups, with no masking. The primary outcome population is identified as the Safety Population with data available at baseline and Week 16. The posted primary results show average HbA1c changes of -1.218 in the Technosphere insulin plus insulin glargine arm and -1.265 in the insulin aspart plus insulin glargine arm. These are changes from baseline, not final HbA1c values.

The clinical question is a comparison of two treatment combinations over a 16-week treatment period. The registry record does not report a prespecified margin for noninferiority or equivalence in the primary outcome description. A formal claim about either would require interpreting the sponsor’s registered analysis in light of its design and margin, and the margin is not reported in the registry record.

3. Trial Design

This was a Phase 3b, multicenter, open-label, randomized, forced-titration trial with a parallel-group design. It compared Technosphere insulin inhalation powder plus insulin glargine with insulin aspart plus insulin glargine in people with type 2 diabetes. The planned treatment period was 16 weeks. The registry record reports enrollment of 39 and lists the trial status as terminated.

Because the trial was open label, participants and study personnel were not masked to treatment assignment. Open-label conduct can affect behavior, treatment adjustments, and the reporting or assessment of outcomes, particularly outcomes that involve participant reports. HbA1c is a laboratory measure, but knowledge of assignment may still affect aspects of care during the study. Randomization is intended to make treatment groups comparable on average, though it cannot guarantee balance in a trial of this size.

The registry lists the primary outcome population as the Safety Population with data available at baseline and Week 16. The posted arm counts for that outcome are 16.0 for Technosphere insulin plus insulin glargine and 14.0 for insulin aspart plus insulin glargine. These are the counts associated with the primary endpoint results, not the enrollment figure. Other posted safety counts are different, reflecting the populations described for those safety summaries.

The registry lists a start date of 2010-09 and a primary completion date of 2011-08. It does not report why the trial was terminated, whether termination affected follow-up or outcome collection, or how many participants completed treatment. The record also does not report the original design assumptions, such as the expected effect used when planning enrollment. Those details should not be inferred from the posted outcome data or from the planning illustrations.

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: After a 1 week to 5 week run-in period, subjects were randomized to receive either TI Inhalation Powder in combination with insulin glargine, or insulin aspart in combination with insulin glargine. 105 Screened/46 Eligible. 39 subjects were randomized; 59 screen failures and 7 were screened but not randomized.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Safety Population, participants with data available at Baseline and Week 16

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 Change in HbA1c (%) From Baseline to Week 16, measured over Baseline to Week 16.

The primary endpoint was change in HbA1c from baseline to Week 16, expressed as a percentage of total hemoglobin. HbA1c reflects glucose exposure over time and is commonly used as a continuous measure of glycemic status in clinical trials. A change score compares a participant’s value at follow-up with their baseline value; a negative change indicates a lower follow-up value than baseline.

The registry describes the primary measure as a least-squares mean and identifies standard deviation as the dispersion measure. The primary population is participants in the Safety Population who had data available at both baseline and Week 16. The posted group values are -1.218 for Technosphere insulin plus insulin glargine and -1.265 for insulin aspart plus insulin glargine.

Using a continuous outcome preserves information about the size and direction of changes rather than reducing results to a threshold such as improved or not improved. Comparing average changes can therefore describe whether the groups’ observed shifts were similar or different. It does not, by itself, establish the clinical importance of a difference. That judgment requires a clinically meaningful threshold and a design that supports the intended claim; such a threshold is not reported in the registry record.

The outcome is limited to the stated baseline-to-Week 16 period. It does not describe longer-term HbA1c patterns, and the registry record does not report the handling of participants lacking measurements at either time point.

7. Results Posted in the Registry

• Insulin Aspart + Insulin Glargine: mean -1.27 versus -1.22 on control; difference -0.05 (95% CI -0.16 to 0.06), standardized effect d = -0.31; z-test p = 0.396.

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.

Number of Subjects Reporting Cough Episodes

• 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:
  • Insulin Aspart + Insulin Glargine: 0.0% (0/18) versus 21.1% (4/19) on control; risk difference -21.1 percentage points (95% CI -39.4 to -2.7); unadjusted two-proportion z-test p = 0.039.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Insulin Aspart + Insulin Glargine than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Number of Subjects Reporting Intermittent Coughing Episodes

• 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:
  • Insulin Aspart + Insulin Glargine: 0.0% (0/18) versus 21.1% (4/19) on control; risk difference -21.1 percentage points (95% CI -39.4 to -2.7); unadjusted two-proportion z-test p = 0.039.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Insulin Aspart + Insulin Glargine than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Baseline Forced Expiratory Volume in 1 Second (FEV1)

• 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:
  • Insulin Aspart + Insulin Glargine: mean 2.83 versus 2.97 on control; difference -0.14 (95% CI -0.59 to 0.31), standardized effect d = -0.20; z-test p = 0.540.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Aspart + Insulin Glargine and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists a broad set of secondary outcomes, including HbA1c, fasting and post-prandial glucose, glucose measures from continuous monitoring, body weight, and treatment satisfaction. These outcomes address different aspects of glycemic measures, variability, physical measures, and participant experience. They are not all interchangeable with the primary HbA1c change and should not be treated as a single overall measure of treatment effect.

The secondary results include cough-related participant counts and a baseline measure of forced expiratory volume in one second. These results concern different questions from the primary endpoint: the cough measures are participant-reported events over the study period, while baseline FEV1 describes lung function at entry rather than a change during treatment. The primary result concerns average HbA1c change. The secondary results can add context, but they do not replace or directly confirm the primary comparison.

Some secondary comparisons are reported without a corresponding sponsor analysis, and the cough comparisons have unadjusted p-values. When many outcomes are examined, the chance of obtaining apparently notable results by chance increases unless the analysis accounts for multiplicity. The registry record does not report a multiplicity adjustment or a hierarchy specifying which secondary outcomes were confirmatory. Unadjusted secondary findings should therefore be regarded as exploratory evidence rather than treated as definitive standalone conclusions.

The secondary outcomes also differ in measurement scale, timing, and meaning. A pattern across them cannot be reduced to a single favorable or unfavorable direction without a prespecified synthesis plan and complete interpretation of each measure. The posted information does not establish that the secondary findings explain the primary result or change its precision. Their role is to provide additional descriptive and hypothesis-generating context, with the primary endpoint retaining its distinct place in the registered analysis.

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

• To Evaluate the Effect of Each Treatment on HbA1c
• Comparison of Fasting Plasma Glucose (FPG) Levels at Randomization and Throughout the Study
• Comparison of Post-prandial Glucose (PPG) Levels at Randomization and Throughout the Study
• Glycomark and Fructosamine Levels Measured Throughout the Study
• Seven-point Glucose at Randomization and Throughout the Study
• Glycemic Excursions and Variability as Assessed Through Continuous Glucose Monitoring (CGM)
• Changes in Body Weight at 16 Weeks
• Treatment Satisfaction as Assessed by Subject Treatment and Health Outcomes Questionnaires

9. Statistical Interpretation

10. Statistical Methods Explained

What does a mean change describe?

A mean change is the average of participants’ follow-up values minus their baseline values, as defined by the analysis. It summarizes the direction and size of change across a group. A negative mean change indicates that the follow-up value was lower than baseline on average. Individual participants can have changes that differ from the group mean, so the mean does not describe every participant’s experience or the spread of individual responses.

The primary registry measure is identified as a least-squares mean. In general, a least-squares mean is an average estimated from a statistical model, potentially accounting for other variables in that model. The registry record does not specify the model details for the posted primary arm estimates. It does report an ANCOVA method for the sponsor’s comparison, but does not identify the covariates used. The exact adjustments behind the reported estimates therefore cannot be reconstructed from the registry record.

What is the unadjusted recalculation?

The recalculation compares the posted arm summaries without covariate adjustment. It uses the group means and their reported spread to estimate an average difference, its uncertainty, and an unadjusted p-value. The calculation is intended as a transparent check based on summary statistics; it is not a replacement for the trial’s prespecified analysis. The recalculated standard deviation is approximate, as indicated in the analysis details, so the resulting uncertainty and p-value should be interpreted as approximate too.

An unadjusted comparison does not account for baseline variables or other prognostic factors through a regression model. Random allocation helps support a fair comparison, but in a limited sample, chance imbalances can remain. An adjusted analysis can account for variables specified in its model, but adjustment is only interpretable when the model and covariates are known. Those details are not reported in the registry record for the sponsor’s ANCOVA.

How should the p-value be read?

A p-value describes how compatible the observed data are with a specified null model, assuming that model and the analysis assumptions are appropriate. It is not the probability that the null hypothesis is true, and it does not measure the size or clinical importance of a difference. The unadjusted p-value for the primary comparison is 0.396. The sponsor reported a p-value of 0.828 for its ANCOVA analysis. These values refer to different analyses and should not be treated as if they came from a single calculation.

A nonsignificant result is not evidence that two treatments are identical. To establish equivalence or noninferiority, a trial ordinarily needs a prespecified margin and an analysis designed to assess that margin. Although the sponsor’s analysis is categorized in the registry as noninferiority or equivalence, the margin and its justification are not reported in the registry record. The category alone is not enough to determine whether the intended criterion was met.

Why does dispersion matter?

Dispersion describes how much participant results vary around a group average. Greater variability makes it harder to estimate a group mean precisely from a given amount of data. Standard deviations are reported for the primary outcome, while the recalculation uses a pooled standard deviation to express variability across arms. The posted summary-based calculation indicates that the standard deviation is approximate. That approximation is another reason to distinguish the recalculation from the sponsor’s model-based analysis.

In general, over-dispersion means that variability is greater than a statistical model expects. When a model understates variability, standard errors can be too small and confidence intervals too narrow. The primary outcome is continuous and is summarized using standard deviations; the registry record does not report an over-dispersion assessment or indicate that over-dispersion was present. The general concept should not be mistaken for a finding about this trial.

What does a confidence interval add?

A confidence interval gives a range of estimates compatible with the data under the method used. It reflects uncertainty in the estimated contrast, not a range containing a specified proportion of individual participant outcomes. A wider interval signals less precision than a narrower interval. Whether the interval includes zero helps describe whether a no-difference value is compatible with the estimate, but interval width and the clinical meaning of values across the interval also matter.

For the primary outcome, both the unadjusted recalculated interval and the sponsor’s reported interval include zero. The sponsor interval is much wider than the recalculated interval. Since the registry record does not give the full ANCOVA specification, the reason for that difference cannot be identified from the reported information alone.

11. Confidence Intervals and Precision

The primary comparison is not estimated with high precision. The unadjusted recalculated confidence interval includes zero and extends to both negative and positive differences. This means the posted summaries are compatible with no average difference, but also with contrasts in either direction. The estimate near zero should not be read as proof that the treatments produce identical changes.

The sponsor’s reported ANCOVA interval also includes zero and is wider than the interval from the unadjusted recalculation. A wider interval represents greater uncertainty under that analysis. The sponsor reported a p-value of 0.828, whereas the unadjusted recalculation gives 0.396. Because the methods differ, the estimates of uncertainty should be understood as analysis-specific. The ANCOVA covariates and other model details are not reported in the registry record, so the reason for the difference cannot be assessed fully.

The primary outcome counts are 16.0 and 14.0, and the posted summaries therefore contain limited information for estimating the contrast. Small amounts of data can lead to substantial uncertainty, particularly when outcome variability is present. The confidence interval, not a single p-value, conveys how much uncertainty remains about the size and direction of the contrast.

Precision is distinct from clinical importance. A narrow interval could still cover effects that are not clinically important, while a wide interval can leave uncertainty about effects that might matter. The registry record does not report a clinically meaningful threshold for HbA1c change or a noninferiority margin. It is therefore possible to describe statistical uncertainty, but not to conclude from these results alone whether the range of plausible effects meets a clinical criterion.

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 planning illustrations give participants per arm for a two-group comparison. They are based on the observed effect and use a two-sided significance level of 0.05, equal allocation between arms, and a normal approximation. The effect used for planning is set at the stated fraction of the observed effect. These assumptions describe the illustrations, not the original trial design; the trial’s own design assumptions are not reported in the registry record.

For the primary HbA1c comparison, planning for 100% of the observed effect gives 162 participants per arm for 80% power and 217 participants per arm for 90% power. Planning for 75% of the observed effect gives 287 participants per arm for 80% power and 385 participants per arm for 90% power. Planning for 50% of the observed effect gives 646 participants per arm for 80% power and 865 participants per arm for 90% power.

In plain terms, the 80% power column describes the number of participants per arm that the calculation estimates would be needed to detect the chosen effect with 80% probability under its assumptions, if that effect were present. The 90% column uses a higher probability of detecting the same chosen effect and therefore calls for more participants per arm. For either target, a smaller effect is harder to distinguish from random variation and requires more participants. The planning illustrations are not observed power; they are hypothetical design calculations based on the observed effect, not a measure of what the completed trial achieved.

These figures should not be used to infer why the registry reports enrollment of 39 or why the trial was terminated. The original sample-size rationale and termination reason are not reported in the registry record. The planning assumptions also do not establish what effect would be clinically meaningful.

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 = -0.047; pooled standard deviation σ = 0.152.
2. n = 2 × (1.96 + 0.842)² × 0.152² / -0.047² = 161.4, rounded up to 162 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 <- -0.0473000000000001                      # observed difference in means
sigma <- 0.151669157096246                     # 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(1196104)
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 = 162 (80% power), 217 (90% power)
 75% of observed effect: n per arm = 287 (80% power), 385 (90% power)
 50% of observed effect: n per arm = 646 (80% power), 865 (90% power)
power.t.test: 162.4 
Simulated power at n = 162 per arm: 0.797 (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. Analyses Reported by the Sponsor

The sponsor’s reported primary comparison used ANCOVA and gave a mean difference of -0.047, a 95% confidence interval spanning zero, and a p-value of 0.828. The unadjusted recalculation from the posted summary statistics also gives a difference close to zero, but its confidence interval and p-value differ. The recalculation uses an approximate pooled standard deviation and does not include model-based covariate adjustment.

The sponsor analysis and recalculation should not be treated as duplicate estimates with interchangeable uncertainty. ANCOVA can incorporate covariates, while the recalculation is unadjusted; the registry record does not report the ANCOVA covariates or full model specification. The difference in interval width therefore cannot be attributed to a specific modeling choice from the record alone. The sponsor analysis is the registered analysis, and its stated method should be considered alongside the simpler recalculation.

The registry categorizes the sponsor analysis as noninferiority or equivalence. However, the noninferiority or equivalence margin and its justification are not reported in the registry record. The reported estimate and interval alone cannot establish that a formal noninferiority or equivalence criterion was met. The record does not flag a discrepancy between the sponsor result and the posted summaries.

15. Multiplicity, Missing Data and Analysis Populations

The registry record lists multiple secondary outcomes, but does not report a multiplicity adjustment, a prespecified testing hierarchy, or which secondary comparisons were intended to support confirmatory claims. When investigators test many outcomes, some results may appear statistically notable through chance alone. Adjustments or a prespecified hierarchy can help control how strongly those results are interpreted. Without that information, unadjusted secondary p-values should be read cautiously and should not be treated as independent confirmation of the primary finding.

The primary endpoint population is described as participants in the Safety Population with data available at baseline and Week 16. The record does not report how missing baseline or follow-up measurements were handled, whether any values were imputed, or whether sensitivity analyses assessed the effect of missing data. It also does not explain whether termination affected outcome availability. These omissions matter because analyses limited to participants with observed measurements can be affected if missingness is related to treatment, outcome, or participant characteristics.

The sponsor’s comparison is described as ANCOVA, but the registry record does not report its covariates, model specification, or assumptions. The record identifies the endpoint summaries as least-squares means and standard deviations, but does not give enough detail to reconstruct the full analysis. Consequently, the unadjusted recalculation is useful as a transparent comparison based on posted summaries, but it cannot verify every aspect of the sponsor’s model.

The record identifies the primary endpoint analysis population, but does not report an intention-to-treat analysis population or explain how participants were analyzed in relation to randomization. It also does not report a plan for interim analyses or the original sample-size justification. Those items should not be inferred from the posted outcome results. The available information supports describing the reported primary comparison and its uncertainty, while leaving important questions about missing-data methods, multiplicity, and analysis implementation unresolved.

16. 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.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: angina unstable (5.6%), rib fracture (5.3%).

The registry reports serious adverse events affecting 1 of 19 participants in the Technosphere insulin group and 1 of 18 in the comparator group. These are descriptive counts within the reported at-risk groups. They do not, on their own, establish whether the event patterns differ between treatments, and they do not provide a precise estimate of comparative safety.

The record also lists serious event terms including Angina unstable at 5.6% and Rib Fracture at 5.3%. These terms and percentages should be read as the listed serious-event information, not as a complete account of all safety outcomes or as evidence of a causal relationship. The registry record does not report deaths; death values are not available in the posted safety summary.

Safety interpretation is limited by the size and duration of the trial, the open-label design, and the information reported in the registry. The counts describe events recorded in the study and do not establish that an event was caused by a treatment. Broader safety conclusions would require fuller event descriptions, follow-up context, and more complete comparative information.

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

The primary result means that the posted group averages for change in HbA1c were similar over the 16-week treatment period, and the estimated unadjusted contrast was close to zero. The confidence interval includes zero, so the results do not clearly distinguish a difference in either direction from no average difference under that calculation. The sponsor’s ANCOVA result also has an interval that includes zero.

This does not prove that the two treatment combinations are equivalent or that their effects are identical. A nonsignificant comparison can occur when the true difference is small, when the data are too limited to estimate the difference precisely, or through random variation. The confidence intervals show uncertainty, and the registry does not report the clinical threshold or noninferiority margin needed to judge whether all plausible differences would be acceptably small.

The result also does not establish that either treatment is effective or ineffective in a broader clinical sense. The posted values are changes observed among participants with the specified primary endpoint data, in the stated trial setting and period. They do not describe longer-term outcomes, outcomes in other populations, or the results that might occur under different treatment procedures.

Finally, the primary endpoint is one measure among several outcomes listed in the record. Secondary findings may provide additional context, but unadjusted analyses across multiple outcomes carry greater risk of chance findings. The trial’s terminated status, unexplained in the registry record, and unreported details about missing data and analysis models further limit what can be concluded. The most defensible reading is a description of the observed primary changes with substantial remaining uncertainty about the between-group contrast.

18. Why This Trial Matters Statistically

This trial illustrates why an estimate, its uncertainty, and the analysis method should be considered together. The posted arm means are close, but a point estimate alone cannot show how precisely the treatment contrast is known. The unadjusted interval includes zero, and the sponsor’s ANCOVA interval is wider. Reading only the point estimate or only a p-value would leave out important information about uncertainty.

It also demonstrates the distinction between a failure to detect a difference and evidence of equivalence. The registry labels the sponsor analysis as noninferiority or equivalence, but does not report the margin required to evaluate such a claim. Without the margin and its justification, the statistical label cannot be translated into a conclusion that the treatments are sufficiently similar.

The sample-size illustrations show how planning depends on the effect one intends to detect: smaller effects require more participants under the stated assumptions. Those illustrations are not the original design rationale, which is not reported in the registry record, and they do not show the probability that this trial detected an effect after completion.

Finally, the record highlights the limits of reading multiple secondary outcomes without a reported multiplicity plan, and of interpreting safety counts from a limited, short-duration trial. Together, these issues make the registry results useful for understanding the statistical evidence while underscoring why the design, analysis, precision, and reporting context all matter.

19. 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 are specific to this trial record. It was open label, so treatment assignment was known to participants and study personnel. The study used forced titration, but the registry record does not provide enough detail about titration procedures to assess how they were applied or whether they differed in practice between arms. The treatment period was 16 weeks, so the primary result addresses only that follow-up period.

The trial status is terminated, but the reason for termination and its consequences for recruitment, follow-up, and outcome availability are not reported in the registry record. Enrollment was 39, while the primary endpoint summaries include 16.0 and 14.0 participants. The record does not explain the difference between these counts and enrollment or report how missing outcome data were handled. It is therefore not possible to assess whether the primary analysis represents all randomized participants.

The registry does not report the original sample-size justification or the model details and covariates for the sponsor’s ANCOVA. It also does not report a noninferiority margin, a multiplicity plan, or sensitivity analyses. The combination of limited endpoint counts and incomplete statistical detail restricts assessment of the precision and robustness of the reported comparison.

Finally, several secondary measures are listed, but the registry record does not report a multiplicity strategy for interpreting them together. The safety summary gives counts and selected event terms, but it does not provide a complete clinical narrative of events or report deaths. These limitations do not invalidate the posted numbers; they define the boundaries of what can be inferred from them.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Clinical Trial Evaluating the Efficacy and Safety of Technosphere® Inhalation Insulin (TI) Inhalation Powder Using the Gen2 Inhaler — NCT01196104.
• Registry record last updated 2014-10-30; status last verified 2014-10.
• Results first posted 2014-10-21.
• 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.

23. Record Summary

The registry record describes NCT01196104, a Phase 3b, multicenter, open-label, randomized, forced-titration parallel trial in people with type 2 diabetes. It compared Technosphere insulin inhalation powder plus insulin glargine with insulin aspart plus insulin glargine over a 16-week treatment period. The record lists enrollment of 39 and status as terminated.

The primary endpoint was change in HbA1c from baseline to Week 16. Posted mean changes were -1.218 and -1.265, respectively. The unadjusted recalculation gives a contrast close to zero, with a confidence interval including zero; the sponsor reported an ANCOVA analysis that also had an interval including zero. The record does not report the noninferiority margin or several details needed to assess model specification, missing data, and multiplicity.

The secondary outcomes cover several distinct measures, and safety summaries report serious-event counts in both groups. These results provide additional context but do not remove uncertainty in the primary comparison. The reason for termination is not reported in the registry record.