ADA Linagliptin in Long Term Care: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for ADA Linagliptin in Long Term Care (NCT02061969), a randomized phase 4 study. The primary outcome is Mean Fasting Blood Glucose Level.

2. Clinical Question

The registry summary describes a randomized comparison of the two medication strategies in long-term care residents with type 2 diabetes. It states that the investigators sought to examine glycemic control and hypoglycemia, and also describes planned comparisons of clinical outcomes, resource use, and hospitalization costs. The posted primary result is a comparison of group means for fasting blood glucose at 6 months, not a direct estimate of the frequency of hypoglycemia. The posted secondary outcomes include HbA1c, hypoglycemic events at specified glucose thresholds, insulin dose, cognitive function, acute complications, emergency room visits, and hospital visits. Results for only some of those outcomes are present in the posted results. The registry record does not report a detailed protocol or analysis population description.

3. Trial Design

This was a Phase 4, randomized, parallel-group study with no masking. Emory University is listed as the sponsor. The registry record marks the study as completed. It lists an enrollment of 140. The posted primary outcome summaries report 73 participants in the insulin glargine group and 67 in the linagliptin group; these arm-level figures are reported as posted, without inferring a separate analysis population.

The trial compared two medication approaches over a 6-month period. Linagliptin was given orally once daily at 5mg, with ongoing metformin continued for residents already taking it. Insulin glargine was started at 0.1 unit/kg/day, with the same description of continuing metformin for residents already taking it. The registry summary describes the intended population as long-term care residents with type 2 diabetes and poorly controlled diabetes, specified as HbA1c >7.5% in the summary.

The primary endpoint was mean fasting blood glucose level at 6 months. The listed secondary outcomes cover HbA1c, hypoglycemic events, total daily insulin dose, cognitive function, acute complications, emergency room visits, and hospital visits. Although the summary discusses resource utilization and hospitalization costs, results for those outcomes are not reported in the registry record. Results for changes in cognitive function and hypoglycemic events are also not reported in the registry record.

Randomization can reduce systematic differences between groups at allocation, but the registry record does not report the randomization method, allocation concealment procedures, stratification, or blocking. Because the study was unmasked, participants and study personnel were not described as blinded to assigned treatment. The registry record does not provide the details needed to assess how outcome measurement or other study procedures may have been affected by the lack of masking.

The original design’s sample-size assumptions, planned interim analyses, and detailed statistical analysis plan are not reported in the registry record. The planning illustrations associated with the posted results are based on observed effects and specified illustrative assumptions; they should not be mistaken for the assumptions used to design this completed trial.

4. Participant Flow and Accounting

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

Overall Study

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 Mean Fasting Blood Glucose Level, measured over 6 months.

The primary endpoint is mean fasting blood glucose level, measured in mg/dl, at 6 months. It is a continuous outcome: each resident has a glucose measurement, and the group result is expressed as a mean with a standard deviation. The primary comparison concerns the difference between the linagliptin and insulin glargine group means at the stated time point.

Fasting blood glucose provides a measure of glycemia at a particular sampling condition. A group mean summarizes the average measured level, while the standard deviation describes how dispersed individual measurements are around that group mean. The endpoint description frames the main question as whether average fasting glucose differed between the assigned treatment groups among long-term care residents with poorly controlled diabetes.

In general, a continuous endpoint can be useful for comparing average outcomes across randomized groups because it retains more information about the measured values than a simple classification into categories. Its interpretation is still limited to the chosen measurement and time point. A mean does not show the full distribution of individual values, and it does not by itself describe hypoglycemia, complications, or other clinical outcomes. The registry summary discusses change in HbA1c and hypoglycemia as broader study interests, but the posted primary endpoint is mean fasting blood glucose at 6 months. The baseline value and a change-from-baseline estimate for this primary endpoint are not reported in the registry record.

7. Results Posted in the Registry

• Linagliptin: mean 131.20 versus 136.40 on control; difference -5.20 (95% CI -15.51 to 5.11), standardized effect d = -0.17; z-test p = 0.323.

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.

HbA1c

• 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:
  • Linagliptin: mean 6.82 versus 6.58 on control; difference 0.24 (95% CI 0.02 to 0.46), standardized effect d = 0.37; z-test p = 0.029.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Linagliptin than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Total Daily Dose of Insulin

• 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:
  • Linagliptin: mean 4.03 versus 12.00 on control; difference -7.97 (95% CI -10.28 to -5.66), standardized effect d = -1.10; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Linagliptin than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Number of Participants With Acute Complications

• 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:
  • Linagliptin: 7.5% (5/67) versus 11.0% (8/73) on control; risk difference -3.5 percentage points (95% CI -13.0 to 6.0), risk ratio 0.68 (0.23–1.98); unadjusted two-proportion z-test p = 0.476.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Linagliptin and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover several different domains: another glycemic measure, hypoglycemic events, insulin use, cognitive function, acute complications, and health-service use. Together they provide context around the primary fasting glucose comparison, but they are not interchangeable outcomes. HbA1c reflects glycemia differently from a fasting measurement, while treatment dose, acute complications, and service use address other aspects of the intervention and its consequences.

The posted secondary results do not form a uniformly precise or consistently significant pattern. Some comparisons have intervals that exclude zero or unadjusted p-values below 0.05, while other posted results have intervals spanning both directions. This mix does not establish a single overall secondary conclusion. In particular, an apparent difference on one secondary measure does not resolve uncertainty in the primary comparison or show that the same pattern applies to outcomes that were not reported.

The registry record lists hypoglycemic events, cognitive function, emergency room visits, and hospital visits among the secondary outcomes, but results for these outcomes are not reported in the registry record. As a result, the intended questions about hypoglycemia and health-service use cannot be answered from the posted outcome results. The study summary’s broader discussion of resource use and hospitalization costs should not be mistaken for posted results on those measures.

Unadjusted secondary results carry less weight because multiple outcomes create multiple opportunities to observe an apparently noteworthy result by chance. In general, a multiplicity strategy can control or otherwise address that issue, but the registry record does not report such a strategy. The number and structure of planned tests, any hierarchy among endpoints, and whether particular outcomes were confirmatory are also not reported. Secondary findings are therefore best treated as descriptive or exploratory in the absence of those details, rather than as independent proof.

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

• HbA1c
• Number of Hypoglycemic Events < 70mg/dl
• Number of Hypoglycemic Events < 40mg/dl
• Total Daily Dose of Insulin
• Changes in Cognitive Function
• Number of Participants With Acute Complications
• Total Number of Emergency Room Visits
• Total Number of Hospital Visits

9. Statistical Interpretation

10. Statistical Methods Explained

What does the mean difference represent?

The primary comparison is the difference between the mean fasting blood glucose in the linagliptin group and the mean in the insulin glargine group. The posted estimate is -5.2 mg/dl. The negative sign follows the stated comparison direction: linagliptin relative to insulin glargine. It is an unadjusted comparison of the posted group summaries; a covariate-adjusted estimate is not reported in the registry record.

In general, a mean difference keeps the outcome in its original units. This makes the estimate directly interpretable in mg/dl, but it does not by itself say whether the size of the difference is important. That judgment depends on clinical context, measurement properties, and a meaningful-difference threshold. Such a threshold is not reported in the registry record.

What do the standard deviations describe?

The posted standard deviations describe variation in individual fasting blood glucose measurements within each group. They are not confidence intervals and do not quantify uncertainty in the group difference. In general, greater variability makes a group mean less precisely estimated when other features, including sample size, are held constant. Individual measurements may vary substantially even when group means are close; group averages do not imply that every resident had a value near the mean.

How should the p-value be read?

The unadjusted p-value for the primary comparison is 0.323. In general, a p-value measures how unusual data at least as discrepant as those observed would be under a specified null hypothesis and statistical model. It is not the probability that the null hypothesis is true, nor does it measure the probability that the result will replicate. A p-value also does not quantify clinical importance. Here it is best considered alongside the estimate and confidence interval rather than used as a stand-alone verdict.

The registry record does not report the exact test, model specification, handling of baseline values, or covariates for the primary analysis. Accordingly, the posted estimate and p-value can be described, but the analysis cannot be fully reconstructed from the registry record.

Why does a confidence interval matter?

The primary 95% confidence interval extends from -15.51 to 5.108 mg/dl. In general, a confidence interval is a range of estimates compatible with the observed data under the analysis assumptions. Its width conveys precision: a narrower interval indicates less uncertainty about the estimated quantity, while a wider interval indicates more. The interval includes zero, so the data do not distinguish the observed comparison from no mean difference at the conventional two-sided 0.05 significance level, consistent with the unadjusted p-value.

A confidence interval is not a range containing every individual participant’s result. It also does not automatically account for all sources of uncertainty, such as missing outcomes, selection into analysis, or possible bias related to study conduct. The registry record does not report the missing-data methods or analysis population details needed to evaluate those issues.

What is over-dispersion, in general?

For count outcomes, over-dispersion means that observed variability is greater than a simple count model assumes. When that occurs, a model that ignores the extra variability can understate uncertainty and make intervals too narrow. This is a general consideration for event-count analyses, not a claim about the analysis used in this trial. The posted primary endpoint is continuous, and the registry record does not report a count-model method for secondary event outcomes.

What can randomization contribute?

In general, random assignment helps balance measured and unmeasured characteristics across treatment groups on average, reducing confounding in a comparison of assigned groups. It does not guarantee perfect balance in a particular trial, and it does not remove concerns about outcome availability, adherence, deviations after assignment, or measurement. The registry record identifies the study as randomized but does not report the sequence-generation or concealment methods, or a detailed account of participant flow.

The study was unmasked. In general, lack of masking can affect behavior, co-interventions, and outcome measurement, particularly when outcomes involve decisions or subjective assessment. Fasting blood glucose is a laboratory-type measurement, but the registry record does not describe collection procedures or whether laboratory personnel were masked. No specific effect of unmasking can be inferred from the registry record.

How are standardized effects and planning illustrations different?

A standardized effect expresses a mean difference relative to a measure of outcome variability. It can make continuous effects easier to compare across measures with different units, but it is less directly interpretable than the difference in mg/dl. The posted planning illustrations use fractions of the observed effect to show how hypothetical sample sizes vary with the target effect. Those calculations are not the original trial’s design justification; that justification is not reported in the registry record. They are planning illustrations, not observed power for the completed trial.

More generally, statistical significance, effect size, and precision answer different questions. A p-value reflects compatibility with a null model, a mean difference expresses the estimated change in the outcome’s units, and a confidence interval displays uncertainty around that estimate. None alone establishes clinical importance or eliminates concerns about incomplete reporting.

11. Confidence Intervals and Precision

The primary mean difference is -5.2 mg/dl, and its 95% confidence interval ranges from -15.51 to 5.108 mg/dl. The interval crosses zero and includes negative and positive values. Therefore, the data are consistent with a lower mean fasting blood glucose in the linagliptin group, a small difference, or a higher mean, within the range represented by the interval. The interval is the main guide to the uncertainty in the estimated group contrast.

In general, a confidence interval expresses the precision of an estimate under the analysis assumptions. It does not mean that there is a 95% probability that the true value lies inside this particular calculated interval. Rather, the confidence level describes the long-run behavior of the interval procedure under repeated use and its assumptions. It also does not capture uncertainty arising from unreported analysis decisions or missing data if those are not incorporated into the method.

The primary interval’s breadth means the posted estimate does not pin down a narrow range of possible differences. The record does not state what difference would be considered clinically important, so statistical precision cannot be translated into a judgment about clinical relevance. Nor does the interval describe how fasting glucose values are distributed among individual residents.

Secondary confidence intervals should be considered in their own outcome units and with the same caution about multiple outcomes. An interval for a secondary result is not evidence that the primary endpoint had the same effect. The analysis details and multiplicity adjustments are not reported in the registry record, which limits how confidently the collection of intervals and unadjusted p-values can be treated as a coherent set of confirmatory tests.

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 figures are expressed as participants per arm for 80% power and participants per arm for 90% power. They are not totals across the two arms and, for the continuous outcomes and binary outcome shown, they do not represent a total number of events. The registry record’s planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The target effect is a stated fraction of the observed effect.

In plain language, 80% power describes the chance, under the planning assumptions and if the target effect is real, that a study would detect an effect using the specified significance threshold. The 90% column represents a higher chance of detection under those same assumptions. Higher power generally requires more participants when other assumptions are held constant. These figures are planning illustrations based on the observed effect, not observed power for this completed trial.

The rows consider planning for 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect. The smaller target effects require more participants per arm in the posted illustrations. For the primary outcome, the 100% row gives 574 participants per arm for 80% power and 768 for 90% power; the 75% row gives 1,020 and 1,365; and the 50% row gives 2,294 and 3,071. These figures illustrate how a modest observed contrast can imply large sample sizes when the intended detectable effect is smaller.

The trial’s original design assumptions, including its own planned target effect and sample-size justification, are not reported in the registry record. The planning illustrations therefore should not be used to infer that the trial was designed around the posted effect or to judge its original design without that information.

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 = -5.200; pooled standard deviation σ = 31.429.
2. n = 2 × (1.96 + 0.842)² × 31.429² / -5.200² = 573.4, rounded up to 574 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 <- -5.20000000000002                      # observed difference in means
sigma <- 31.4290090313973                     # 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(2061969)
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 = 574 (80% power), 768 (90% power)
 75% of observed effect: n per arm = 1,020 (80% power), 1,365 (90% power)
 50% of observed effect: n per arm = 2,294 (80% power), 3,071 (90% power)
power.t.test: 574.4 
Simulated power at n = 574 per arm: 0.795 (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. Multiplicity, Missing Data and Analysis Populations

The registry record lists a primary endpoint and numerous secondary outcomes, but does not report a multiplicity adjustment, testing hierarchy, or procedure for controlling false-positive findings across outcomes. The individual secondary p-values are unadjusted. In general, when many hypotheses are tested, the chance of at least one apparently unusual result can increase even if no treatment differences exist. Without a stated multiplicity plan, isolated secondary p-values should be interpreted cautiously.

The registry record does not report an analysis plan specifying which outcomes were intended to be confirmatory, how the primary endpoint was tested, or whether analyses were prespecified. A reported-analyses list is empty for the posted outcomes. This absence limits assessment of whether results reflect the planned analysis or later analytic choices; it does not, by itself, establish that analyses were changed.

Missing-data handling and the analysis population are not reported in the registry record. The record does not describe exclusions, withdrawals, outcome availability, imputation, or whether the analysis followed an intention-to-treat principle. The posted arm-level sample sizes are available for the outcomes, but those figures alone do not establish whether all randomized participants contributed data or how missing measurements were handled.

Randomization is a design feature, while the analysis population determines how assigned participants are represented in the statistical comparison. In general, excluding participants after randomization can undermine the balance created by random assignment if exclusions relate to treatment or outcomes. Missing measurements can also affect precision and may bias estimates depending on why they are missing and how they are handled. Because the registry record does not describe these matters, their impact cannot be evaluated from the posted results.

The original statistical plan, interim analyses, and sample-size justification are not reported in the registry record. No conclusion about whether an interim analysis occurred, whether stopping rules applied, or whether the planned analysis was followed can be drawn from the available record.

15. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hospitalized for altered mental status (1.5%), hospitalized for severe aortic stenosis (1.5%), amputation of the left 2nd toe (1.5%), wound infection (1.5%), hospitalized for repair craninectomy (1.4%).

The posted serious adverse event figures list 6 participants affected in the insulin glargine group and 4 in the linagliptin group. The corresponding group figures at risk are 73 and 67. The record also lists other, non-serious adverse events affecting 16 and 12 participants, respectively. Deaths are listed as 0 in the insulin glargine group and 2 in the linagliptin group.

These are counts as recorded, not adjusted comparisons of risk. The registry record does not provide a statistical comparison for these safety counts, details of event ascertainment, follow-up completeness, or an account of whether events were judged related to treatment. The counts therefore do not establish a treatment-caused difference or demonstrate that risks are equivalent.

Several serious-event terms are listed with percentages of 1.5% or 1.4%, including hospitalizations and other serious events. Those event descriptions should be read as reported event terms, not as a complete account of all possible harms. In general, safety interpretation depends on event definitions, duration of follow-up, exposure, and systematic collection in both groups; those details are not reported in the registry record. Serious adverse events and deaths should be considered separately from the primary glycemic endpoint, since they address different outcomes.

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

The posted primary result estimates a difference in average fasting blood glucose between the randomized groups at 6 months. Its point estimate is -5.2 mg/dl for linagliptin relative to insulin glargine, but the confidence interval includes zero and extends in both directions. The result therefore leaves uncertainty about the size and direction of the underlying mean difference. It is not evidence that the groups were exactly equal, and it does not establish a clinically meaningful difference.

The endpoint is a group average at a specified time, not a measure of every resident’s experience. The primary result cannot establish the frequency of hypoglycemia, because that is a distinct outcome and results for the listed hypoglycemic-event outcomes are not reported in the registry record. Likewise, the primary endpoint does not answer questions about cognitive function, complications, emergency care, or hospitalization costs.

The randomized design supports a comparison of assigned groups in principle, but the record does not provide enough information to assess the randomization procedures, analysis population, missing-data handling, or full statistical plan. The study was unmasked, and the registry record does not describe procedures that would clarify whether masking was used for outcome assessment. These reporting gaps limit the strength of conclusions that can be drawn from the summary statistics alone.

Secondary outcomes can add context but involve multiple tests, and the record does not report a multiplicity strategy. Unadjusted secondary findings should not be taken as definitive evidence of differences across all outcomes. Outcomes without posted results cannot be inferred from those that are available.

These data should be understood as a registry-record account of group summaries and statistical uncertainty, not as individualized predictions or medical advice. A broader assessment would require more complete information on study conduct, outcome completeness, analysis methods, and clinical thresholds for meaningful change.

17. Why This Trial Matters Statistically

This trial illustrates why an estimated treatment contrast should be read together with its uncertainty. The primary mean difference is easy to express in the outcome’s original units, but the confidence interval is broad enough to include values in both directions. The point estimate alone would give an incomplete picture; the interval shows that the data do not precisely determine the comparison.

It also illustrates the difference between a primary endpoint and a collection of related secondary outcomes. Fasting glucose, HbA1c, hypoglycemia, insulin dose, complications, and health-service use answer distinct questions. A result on one measure cannot automatically stand in for all the others. Missing secondary results and unadjusted testing further limit any attempt to make a single overall claim from the outcomes.

The trial’s planning illustrations offer another statistical lesson: sample-size requirements depend on the effect a study is designed to detect. Detecting a smaller effect generally requires more participants than detecting a larger one, under otherwise similar assumptions. Those illustrations are useful for planning, but they do not reveal the completed trial’s original design assumptions or its observed power.

Finally, the registry record distinguishes what can be described from what remains unknown. It reports group means, standard deviations, confidence intervals, and some secondary and safety counts. It does not report key analysis details, missing-data procedures, multiplicity handling, or results for several listed outcomes. Careful interpretation means respecting both the numerical results and the limits of the accompanying record.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several trial-specific reporting gaps constrain interpretation. Detailed eligibility criteria, baseline characteristics, randomization procedures, allocation concealment, and participant flow are not reported in the registry record. The arm-level outcome counts are available, but they do not show whether every randomized participant had an observed endpoint or how any missing measurements were addressed.

The analysis population, treatment adherence, protocol deviations, concomitant care beyond the metformin description, and handling of treatment changes are not reported in the registry record. The study was open label, but the registry record does not describe masking of outcome assessors or laboratory personnel. These omissions make it difficult to assess possible sources of bias beyond the statistical uncertainty reflected in the confidence interval.

The statistical plan is also incompletely described. The exact primary analysis method, any baseline adjustment, interim analyses, and multiplicity procedure are not reported in the registry record. The record lists several secondary outcomes, but results for many—such as hypoglycemic events, cognitive function, and emergency room or hospital visits—are not reported. Thus, important parts of the study’s stated aims cannot be evaluated from the posted results.

The primary endpoint is measured at 6 months, and the record does not provide longer-term primary results. A clinical threshold for a meaningful fasting glucose difference is not reported. The results consequently describe the reported group comparison, but do not by themselves establish clinical importance, treatment effects beyond the reported period, or a comprehensive balance of benefits and harms.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: ADA Linagliptin in Long Term Care — NCT02061969.
• Registry record last updated 2018-08-22; status last verified 2018-07.
• Results first posted 2018-08-22.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

The registry record describes NCT02061969, a completed Phase 4 randomized, parallel-group, unmasked study sponsored by Emory University. It compared linagliptin with insulin glargine in long-term care residents with type 2 diabetes over 6 months. The listed enrollment is 140.

The primary endpoint was mean fasting blood glucose at 6 months. The posted mean difference for linagliptin relative to insulin glargine was -5.2 mg/dl, with a 95% confidence interval from -15.51 to 5.108 mg/dl and an unadjusted p-value of 0.323. The interval includes zero, leaving uncertainty about the direction and size of the difference.

Some secondary and adverse event summaries are posted, but results for several listed outcomes and details of the analysis plan, missing-data methods, and multiplicity handling are not reported in the registry record. The available results therefore support a limited description of the reported comparisons rather than a complete assessment of all study questions.