Efficacy of Alogliptin and With Pioglitazone in Patients With Type 2 Diabetes.: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Efficacy of Alogliptin and With Pioglitazone in Patients With Type 2 Diabetes. (NCT00655863), a randomized phase 3 study. The primary outcome is Change From Baseline in Postprandial Incremental Area Under the Curve for Total Triglycerides at Week 16..

2. Clinical Question

The trial asked whether alogliptin, taken once daily by itself or alongside pioglitazone, changed postprandial lipid measures in people with type 2 diabetes compared with placebo. Its primary question concerned the change from baseline to Week 16 in the incremental area under the curve for total triglycerides after a meal. The area was measured over the 0-to-8-hour period after dosing.

The reported primary comparisons concern each active-treatment group versus placebo. They do not establish a direct comparison between alogliptin alone and the combination. The outcome is a laboratory measure of post-meal triglyceride exposure over a specified period, rather than a clinical event or a measure of long-term health. The registry record does not report a clinical outcome question beyond these lipid and other metabolic measures.

3. Trial Design

The registry describes a Phase 3, randomized, double-blind, placebo-controlled, parallel-group study. It also records quadruple masking. Three groups are listed: placebo once daily, alogliptin 25 mg once daily, and alogliptin 25 mg once daily with pioglitazone 30 mg once daily. The registry enrollment is 71, and the study status is completed.

The stated purpose was to compare alogliptin alone and in combination with pioglitazone against placebo on postprandial lipid measures in people with type 2 diabetes. Random allocation and a placebo comparison provide a framework for comparing changes between groups. Masking is intended, in general, to reduce the influence of treatment knowledge on participant experience, study conduct, and outcome assessment. The record does not specify which four roles are included in the quadruple masking designation.

The primary outcome is measured from baseline to Week 16. A Week 4 change in the same triglyceride area-under-the-curve measure is listed as a secondary outcome. Other secondary outcomes cover lipid and lipoprotein measures, post-meal changes over time in GLP-1, glucose, insulin and glucagon, and glycosylated hemoglobin. The registry record lists 17 posted secondary outcomes.

The registry records the study start as 2007-07 and primary completion as 2009-12. The original sample-size justification, the planned number of participants in each group, the allocation ratio, and the trial's original statistical assumptions are not reported in the registry record. The record also does not report whether interim analyses were planned or conducted.

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: Participants with a historical diagnosis of type 2 diabetes mellitus were enrolled in one of three, once-daily (QD) treatment groups.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The full analysis set included all randomized participants who took at least 1 dose of double-blind study medication. Participants who were in the full analysis set and had a baseline and at least 1 post-baseline assessment were included for this analysis. Missing values were imputed using last observation carried forward.

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 From Baseline in Postprandial Incremental Area Under the Curve for Total Triglycerides at Week 16., measured over Baseline and Week 16..

The primary endpoint is the change from baseline to Week 16 in postprandial incremental area under the plasma concentration-time curve for total triglycerides. The measure covers the period from 0 to 8 hours after dosing following a meal, and is reported in mg.h/dL. An area under a concentration-time curve combines the level of a measurement with its duration over a specified observation period. The incremental form focuses on change relative to the starting level rather than simply describing the whole concentration-time profile.

In general, a post-meal area-under-the-curve measure can summarize exposure across several time points, rather than relying on a single measurement after eating. That can be useful when the research question concerns the course of a response over time. The endpoint is continuous, and the registry reports least-squares means with standard errors for the treatment groups.

The treatment-group estimates are negative, indicating decreases from baseline on the reported change scale. The active-treatment groups have more negative estimated changes than the placebo group. The endpoint is a biochemical measure, however, and does not by itself establish a change in symptoms, complications, or other clinical outcomes. The registry record does not report a threshold for a clinically important difference in this measure, so the clinical importance of a particular change cannot be determined from the endpoint definition alone.

7. Results Posted in the Registry

• Alogliptin 25 mg QD: mean -346.96 versus -39.73 on control; difference -307.23 (95% CI -439.55 to -174.90), standardized effect d = -1.30; z-test p = <0.001.
• Alogliptin 25 mg QD + Pioglitazone 30 mg QD: mean -293.44 versus -39.73 on control; difference -253.71 (95% CI -391.49 to -115.93), standardized effect d = -1.08; z-test p = <0.001.

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.

Change From Baseline in Postprandial Incremental Area Under the Curve for Total Triglycerides at Week 4.

• 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 errors.
• Result:
  • Alogliptin 25 mg QD: mean -288.49 versus -16.29 on control; difference -272.20 (95% CI -447.73 to -96.67), standardized effect d = -0.91; z-test p = 0.002.
  • Alogliptin 25 mg QD + Pioglitazone 30 mg QD: mean -279.12 versus -16.29 on control; difference -262.82 (95% CI -442.76 to -82.89), standardized effect d = -0.88; z-test p = 0.004.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Alogliptin 25 mg QD than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Alogliptin 25 mg QD + Pioglitazone 30 mg QD than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary-outcome inventory covers postprandial lipids and lipoproteins, post-meal time-course measures of GLP-1, glucose, insulin and glucagon, and glycosylated hemoglobin. The record lists 17 posted secondary outcomes. The Week 4 triglyceride area-under-the-curve outcome is numerically reported, while numerical findings for many of the other listed outcomes are not reported in the registry record.

For the Week 4 triglyceride measure, the posted estimates show more negative changes in both active-treatment groups than in placebo, with unadjusted comparisons favoring a larger decrease in the active groups on the reported change scale. This is directionally consistent with the primary Week 16 pattern for the same measure. It provides context across two measurement times, but the Week 4 analysis is secondary and does not replace the primary endpoint or constitute a separate clinical outcome.

In general, multiple secondary analyses create more opportunities for apparently notable results to arise by chance, especially when each is interpreted without an adjustment for the number of comparisons. The registry record does not report a multiplicity adjustment or a hierarchy for interpreting secondary endpoints. Accordingly, unadjusted secondary p-values carry less weight than a prespecified primary analysis when the multiplicity strategy is unknown. They are useful for describing patterns and generating questions, but should not be treated as independent confirmation of the primary result.

Because many secondary numerical results are not reported in the registry record, the overall pattern across all listed measures cannot be assessed from the posted information. The outcome list describes what was assessed; it does not establish that every measure changed in the same direction or had a statistically notable result.

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

• Change From Baseline in Postprandial Incremental Area Under the Curve for Total Triglycerides at Week 4.
• Change From Baseline in Postprandial Incremental Area Under the Curve Changes for Lipid Parameters.
• Change From Baseline in Postprandial Incremental Area Under the Curve for Lipoprotein Parameters.
• Postprandial Changes Over Time From Baseline for Glucagon-like Peptide-1 (GLP-1)
• Postprandial Changes Over Time From Baseline for Glucose
• Postprandial Changes Over Time From Baseline for Insulin
• Postprandial Changes Over Time From Baseline for Glucagon
• Change From Baseline in Glycosylated Hemoglobin

9. Statistical Interpretation

10. Statistical Methods Explained

The primary endpoint is continuous: it is a numerical measure of change in postprandial triglyceride area under the curve. The registry reports least-squares means and standard errors for the treatment groups. A mean describes the central value of a continuous measure; a standard error describes uncertainty in an estimated mean. In general, a smaller standard error indicates a more precise estimate, while a larger one indicates greater sampling uncertainty.

What does the sponsor's ANCOVA estimate?

The posted sponsor analyses use ANCOVA and label the parameter as a mean difference in final values. In general, analysis of covariance compares groups while accounting for a specified covariate or covariates. It can therefore yield an adjusted comparison rather than a simple subtraction of two displayed group means. The registry record identifies ANCOVA but does not report the complete model specification, including the covariates or other details needed to reproduce the analysis fully.

The sponsor's reported estimates compare placebo with alogliptin and placebo with the alogliptin-plus-pioglitazone group. The registry reports confidence intervals and p-values for those comparisons. These are the sponsor's own analyses. Since the discrepancy flag is false, the posted simple recalculations are not identified as discrepant, but the recalculations remain unadjusted summaries and should not be treated as a replacement for the sponsor's ANCOVA.

What are the recalculated comparisons?

The recalculated mean differences use the posted group-level estimates and standard errors to make unadjusted comparisons. Their confidence intervals describe a range of values compatible with those calculations under their assumptions. Their p-values are also unadjusted. They do not reproduce the trial's analysis model, and they do not account for any model features not contained in the posted summaries.

The pooled standard deviations and standardized effect estimates used in the planning illustrations are approximate. The standard deviations are inferred from the reported standard errors, as indicated in the registry-derived calculations. Such estimates rely on assumptions about the summaries and are not direct observations of individual-level variability. In general, a standardized effect expresses a mean difference relative to a measure of variability, which can help illustrate the scale of an effect across measures. It does not change the underlying units or confer clinical importance.

How should the p-values be read?

In general, a p-value describes how incompatible the observed data are with a specified null model, assuming the model and its assumptions hold. It is not the probability that the null hypothesis is true, nor does it measure the probability that a result will repeat. A small p-value is not a measure of the clinical importance of the result. The sponsor reports p < 0.001 for both primary comparisons; the recalculated unadjusted values are also reported as < 0.001.

These comparisons involve two active-treatment groups against one placebo group, and the registry lists many outcome measures. The multiplicity approach, if any, is not reported in the registry record. Without that information, the reported p-values should be understood as the p-values attached to the posted analyses, not assumed to reflect a particular family-wise error adjustment.

Why can variability matter?

In general, variability describes how spread out measurements are within a group. Greater variability makes group means less precise when other features are held constant. If outcome variability is larger than a statistical model allows, standard errors and p-values from that model may be unreliable. This is one reason model fit and distributional assumptions matter. The registry record does not report diagnostic checks or analyses addressing distributional assumptions for the endpoint.

The outcome is an area-under-the-curve measure, and the record describes an analysis population that required baseline and post-baseline assessments. The population definition says missing values were imputed using “last obse” in the registry text, which ends mid-phrase. The full imputation method and its implementation are therefore not reported in the registry record. The record does not provide enough detail to assess how missingness or imputation affected the primary estimates.

In general, statistical interpretation depends on more than the point estimate. The estimate, interval, analysis population, outcome definition, model, missing-data handling, and multiplicity strategy all shape what can be concluded. Some of these details are available in the record, while the full model and analysis-plan information are not reported. The reported results should consequently be read as estimates from the posted trial analyses, with those reporting limits in mind.

11. Confidence Intervals and Precision

The sponsor's confidence intervals for the primary comparisons are 95% intervals. For alogliptin versus placebo, the reported estimate is -307.229 mg.h/dL, with an interval from -443.168 to -171.290. For alogliptin plus pioglitazone versus placebo, the estimate is -253.711 mg.h/dL, with an interval from -394.161 to -113.262. Each interval is entirely negative, so the sponsor's estimates are consistent with a more negative change in the active group than in placebo under the reported analysis.

In general, a confidence interval communicates uncertainty around an estimated effect. It gives a range of values compatible with the data and the statistical method at the stated confidence level; it is not a range that guarantees where the true value lies. The width reflects precision: narrower intervals indicate more precision, while wider intervals indicate less precision, given the model and data.

The recalculated unadjusted intervals are -439.6 to -174.9 for alogliptin versus placebo and -391.5 to -115.9 for the combination versus placebo. They are close to, but not identical with, the sponsor's ANCOVA intervals. This is expected to be interpreted as a comparison of different analytical approaches, not as evidence that one set of intervals can replace the other. The sponsor's reported analysis is the trial analysis; the recalculations are simple unadjusted estimates based on the posted summaries.

Intervals do not indicate whether a difference is clinically meaningful. The registry record does not report a clinically important threshold for the endpoint. Precision should therefore be considered alongside the measurement's clinical meaning, which cannot be resolved from statistical uncertainty alone.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size figures are planning illustrations based on the observed effect, not the original trial's design calculation. Because the primary outcome is continuous, the figures are participants per arm, not total event counts. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two compared arms, and a normal approximation. The illustrations use the stated fraction of the observed effect as the effect to detect.

The 80% power column gives the illustrative number of participants per arm for a design with an 80% chance of detecting the specified effect if that effect is real and the planning assumptions hold. The 90% power column gives the corresponding number for a 90% chance. These are design probabilities under the assumed effect and model, not probabilities that the trial's finding is correct. These values are not observed power.

The rows consider planning for 100%, 75%, and 50% of the observed effect. For example, planning for 75% of the observed effect asks how many participants per arm would be needed to target a smaller effect than the full observed estimate. Smaller effects generally require more participants to detect at the same power and significance level, which is why the illustrations increase as the assumed effect becomes smaller.

The original sample-size justification and its assumptions are not reported in the registry record. The planning figures should therefore not be read as evidence that the trial was designed around these assumptions, nor as a reconstruction of its intended enrollment. They are sensitivity illustrations derived from the observed comparisons and depend on the planning model. The plotted curve, if consulted alongside the figures, represents this same relationship between assumed effect size and required participants per arm.

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 = -307.229; pooled standard deviation σ = 236.243.
2. n = 2 × (1.96 + 0.842)² × 236.243² / -307.229² = 9.3, rounded up to 10 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 <- -307.229                      # observed difference in means
sigma <- 236.243443443803                     # 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(655863)
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 = 10 (80% power), 13 (90% power)
 75% of observed effect: n per arm = 17 (80% power), 23 (90% power)
 50% of observed effect: n per arm = 38 (80% power), 50 (90% power)
power.t.test: 10.3 
Simulated power at n = 10 per arm: 0.786 (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 registry reports sponsor ANCOVA estimates of -307.229 mg.h/dL for alogliptin versus placebo and -253.711 mg.h/dL for alogliptin plus pioglitazone versus placebo. The corresponding sponsor confidence intervals are -443.168 to -171.290 and -394.161 to -113.262, with p < 0.001 for both comparisons.

The simple unadjusted recalculations from the posted group summaries give estimates of -307.2 mg.h/dL and -253.7 mg.h/dL, respectively. Their intervals and p-values are also posted alongside the recalculations. The estimates are close to the sponsor's, but the confidence limits differ because the recalculation is not the sponsor's ANCOVA model. The sponsor's reported comparison is the principal trial analysis; the recalculation is a transparent, simplified check based on available summary statistics.

The discrepancy flag is false, so the record does not identify a conflict requiring the recalculation to take precedence. Neither analysis can be fully evaluated from point estimates alone. The full ANCOVA specification and analysis-plan details are not reported in the registry record, which limits independent assessment of the sponsor's modeling choices.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary endpoint, multiple secondary outcomes, and two primary active-versus-placebo comparisons. These create a set of statistical questions rather than a single isolated test. In general, conducting many tests without an adjustment raises the chance that at least one result may appear statistically notable by chance. A prespecified hierarchy, family-wise adjustment, or other multiplicity strategy can define how such results should be interpreted.

The multiplicity strategy is not reported in the registry record. The posted p-values should therefore be read as the values associated with their respective reported analyses; the record does not establish whether they are adjusted for the two primary comparisons, the multiple secondary endpoints, or both. The record also does not report interim analyses or any interim-testing adjustment.

The primary population is described as the full analysis set: randomized participants who took at least one dose of double-blind study medication. For the endpoint analysis, the description further requires a baseline and at least one post-baseline assessment. This makes the analyzed set narrower than a definition that includes every randomized participant regardless of treatment exposure or outcome availability. The registry reports an enrollment of 71, but the primary group counts are the counts contributing to the endpoint summaries.

The missing-data text says values were imputed using “last obse” before the registry description cuts off. The full method is not reported in the registry record, so the exact procedure, its scope, and any sensitivity analyses cannot be assessed. The amount and pattern of missing outcome data are also not reported. These omissions matter because imputation assumptions can affect estimated changes and their uncertainty.

The complete statistical analysis plan, full ANCOVA covariate specification, and details of any sensitivity analyses are not reported in the registry record. The posted results can be described as reported, but the available information does not support a full audit of every modeling and missing-data decision.

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 3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: concussion (4.5%), fall (4.5%), head injury (4.5%), gastric ulcer (4.0%), nephrolithiasis (4.0%).

The registry reports serious adverse events for the safety groups: none in the placebo group, 2 in the alogliptin group, and 3 in the alogliptin-plus-pioglitazone group. The corresponding numbers at risk are 24, 25, and 22. These are group-level counts reported for the safety data and should be read separately from the primary efficacy analysis counts.

The registry also reports other adverse events affecting 15 participants in the placebo group, 19 in the alogliptin group, and 13 in the combination group. The record lists terms including concussion, fall, head injury, gastric ulcer, and nephrolithiasis among the serious-event terms. Death counts are not reported in the registry record.

These counts describe events recorded during the study; they do not establish that a treatment caused an event. Small group sizes and the limited information posted restrict comparisons of safety patterns. The registry record does not provide enough detail to evaluate event timing, severity beyond the serious-event classification, exposure duration, or causal attribution. The figures should therefore be read as descriptive safety reporting, not as a definitive comparative safety assessment.

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

The primary findings show that the estimated Week 16 change in postprandial total-triglyceride area under the curve was more negative in each active-treatment group than in the placebo group. The sponsor's ANCOVA comparisons report negative mean differences, confidence intervals that remain below zero, and p < 0.001 for both active-versus-placebo comparisons. In statistical terms, the posted analyses are consistent with a difference between each active group and placebo on this measured endpoint.

That result has a defined scope. It concerns a continuous post-meal lipid measure over the stated 0-to-8-hour interval, assessed as change from baseline at Week 16. It does not directly answer whether participants experienced fewer symptoms, fewer diabetes complications, or better long-term health. The registry record does not report a clinically important threshold for the measure, so statistical separation cannot by itself establish the clinical importance of the estimated change.

The comparisons are each against placebo. They do not establish whether alogliptin alone differs from alogliptin with pioglitazone. The estimates also come from a particular trial population and analysis definition; the registry record does not support assuming that the same estimates apply to other populations, settings, or durations.

The results do not show that all secondary outcomes changed in the same way. Numerical findings for many listed secondary measures are not reported in the registry record, and the multiplicity approach is not reported. Interpretation should distinguish the sponsor's primary analyses from the unadjusted secondary results and simplified recalculations.

Finally, the record's missing-data description is incomplete, and the full ANCOVA specification is not reported. These limitations do not erase the posted estimates, but they constrain how completely their robustness can be evaluated. The appropriate conclusion is about the reported statistical contrast on the measured endpoint, not a broader claim about overall treatment benefit or harm.

18. Why This Trial Matters Statistically

This trial illustrates the distinction between a treatment-group change and a treatment contrast. The arm estimates describe change from baseline within each group; the primary comparison asks how those changes differ between each active group and placebo. Keeping those quantities distinct helps prevent a within-group decrease from being mistaken for a treatment effect by itself.

It also shows why adjusted and unadjusted results may be close without being identical. The registry reports ANCOVA as the sponsor's method, while the simple recalculations use posted summary statistics. They answer related but not identical analytical questions. Reporting the sponsor analysis alongside a transparent unadjusted check can help readers see the scale and direction of the comparison while retaining the model-based analysis as the trial result.

The endpoint raises another important interpretive issue: a statistically precise difference in a laboratory measure is not automatically a clinically important difference. Confidence intervals and p-values address statistical uncertainty under assumptions; clinical meaning requires context, including a meaningful threshold or a link to outcomes that matter to participants. The registry record does not provide such a threshold.

Finally, the record demonstrates the importance of reporting multiplicity, missing-data methods, and model details. These features determine how multiple results are interpreted and how robust estimates are to incomplete observations and modeling choices. Where those details are absent, readers can describe the posted results but should keep the scope of inference limited. Those distinctions are useful when evaluating trials that report several metabolic outcomes and comparisons.

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.

The registry record leaves important analysis details unspecified. It identifies ANCOVA for the sponsor's primary comparisons but does not report the full model specification, including covariates. The multiplicity strategy for the two primary comparisons and numerous secondary outcomes is not reported. Interim analyses and any related adjustment are also not reported in the registry record.

The primary analysis population requires treatment exposure and baseline plus at least one post-baseline assessment. The missing-data description ends after “last obse,” so the full imputation rule is not reported in the registry record. The extent and pattern of missingness, and sensitivity analyses for missing data, are not available in the posted information. These limits prevent a full assessment of how incomplete observations could influence the estimates.

The comparison is placebo-based; the record does not report a direct statistical comparison between alogliptin alone and the combination. The trial enrollment is 71, and the group summaries are based on the endpoint analysis counts. The resulting estimates and safety counts should be interpreted in the context of the study's reported size and scope rather than treated as comprehensive evidence across populations or durations.

The endpoint is a postprandial biochemical measure, and the record does not report a clinically important threshold. The registry lists many secondary outcomes, but numerical results for many are not reported in the registry record. The posted evidence therefore cannot establish a uniform pattern across all secondary measures or connect the primary measure to clinical outcomes.

The original sample-size rationale and statistical design assumptions are not reported in the registry record. The planning illustrations use observed effects and stated modeling assumptions, and do not reconstruct the original trial design.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Efficacy of Alogliptin and With Pioglitazone in Patients With Type 2 Diabetes. — NCT00655863.
• Registry record last updated 2013-05-27; status last verified 2013-05.
• Results first posted 2013-05-27.
• 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 describes a completed Phase 3, randomized, double-blind, placebo-controlled, parallel-group trial of alogliptin alone and alogliptin with pioglitazone versus placebo in people with type 2 diabetes. The primary endpoint was change from baseline to Week 16 in postprandial incremental area under the curve for total triglycerides.

The sponsor reports negative mean differences versus placebo for both active-treatment comparisons, with 95% confidence intervals below zero and p < 0.001. Simple unadjusted recalculations are broadly consistent with those estimates. The Week 4 result for the same measure is directionally similar, while numerical findings for many other listed secondary outcomes are not reported in the registry record.

The record does not report the complete ANCOVA specification, multiplicity approach, full missing-data method, original sample-size justification, or a clinically important threshold for the primary endpoint. The results support interpretation of the reported statistical contrasts on this biochemical measure, with those reporting limitations in view.