Diabetes in the Elderly: Prospective Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Diabetes in the Elderly: Prospective Study (NCT01131052), a randomized phase 4 study. The primary outcome is Percent of Participants With a Mean Blood Glucose Concentration of Less Than 70 mg/dL.

2. Clinical Question

The study is registered as NCT01131052, with the title “Diabetes in the Elderly: Prospective Study” and the official title “Diabetes Care in Nursing Home Residents: A Randomized Controlled Study.” Its stated clinical context includes uncertainty about insulin regimens in long-term care. The primary endpoint focuses on a threshold-defined outcome, while the posted secondary measures include glucose and glycosylated hemoglobin summaries. These measures describe different aspects of glycemic status and should not be treated as interchangeable.

3. Trial Design

The registry describes a randomized, parallel-group, Phase 4 study with no masking. The study status is completed. Enrollment is listed as 150, and the arm results report 75 participants in each group. The registry dates the study start to 2011-03 and primary completion to 2013-12. Further details about recruitment, follow-up procedures, and the conduct of randomization are not reported in the registry record.

The two assigned regimens were basal-plus insulin and sliding-scale regular insulin. The basal-plus group received insulin glargine once daily, plus corrective insulin glulisine before meals and at bedtime as needed. The comparison group received sliding-scale regular insulin before meals and at bedtime as needed. The registry lists insulin glargine, insulin, and insulin glulisine among the interventions. Treatment adherence, dose adjustments, and other elements of treatment delivery are not described in the posted record.

The primary outcome was assessed at 3 months and is expressed as a percentage of participants meeting a blood-glucose threshold. The registry also lists secondary measures of mean blood glucose concentration, mean daily blood glucose concentration, and mean glycosylated hemoglobin. The posted secondary summaries are labeled as baseline measures. The registry lists eight outcome measures, but it does not provide reported statistical analyses for the secondary endpoints.

The registry identifies Guillermo Umpierrez as the sponsor. The design description does not establish details such as allocation concealment, the analysis population, missing-data handling, or a prespecified analysis plan. Those details are not reported in the registry record, so they cannot be inferred from the randomized, parallel-group label alone.

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 Percent of Participants With a Mean Blood Glucose Concentration of Less Than 70 mg/dL, measured over 3 months.

The primary endpoint is the percentage of participants with a mean weekly blood glucose concentration below 70 mg/dL at 3 months. It is a threshold-based, binary outcome: each participant is classified according to whether the stated mean glucose measure falls below the threshold, and the group result is expressed as a percentage. The registry describes the measure as “Mean weekly blood glucose concentration less than 70 mg/dL at 3 months.” Details about how weekly measurements were collected or summarized are not reported in the registry record.

A threshold endpoint can make a clinical measure easier to interpret as the proportion of participants meeting a defined criterion. It also simplifies a continuous measurement into a yes-or-no classification. General statistical consideration: this simplification can conceal variation among participants within each category, including how far measurements lie from the threshold. The endpoint therefore answers a focused question about the frequency of values below the stated cutoff, not the full distribution of glucose measurements or every dimension of glycemic control.

The registry reports the endpoint as a percentage of participants. It is distinct from the secondary continuous outcomes, which are reported as means with standard deviations. Those outcomes provide different summaries and do not replace the primary question. The rationale for selecting this particular threshold as the primary endpoint is not reported in the registry record.

7. Results Posted in the Registry

• Sliding Scale Regular Insulin (SSRI): 30.7% (23/75) versus 28.0% (21/75) on control; risk difference 2.7 percentage points (95% CI -11.9 to 17.2), risk ratio 1.10 (0.67–1.80); unadjusted two-proportion z-test p = 0.720.

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.

Mean Blood Glucose Concentration

• 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:
  • Sliding Scale Regular Insulin (SSRI): mean 191.80 versus 198.20 on control; difference -6.40 (95% CI -18.41 to 5.61), standardized effect d = -0.17; z-test p = 0.296.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Sliding Scale Regular Insulin (SSRI) and the control. The interval gives the range of effects that remain compatible with the data.

Mean of Glycosylated Hemoglobin (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:
  • Sliding Scale Regular Insulin (SSRI): mean 6.50 versus 6.80 on control; difference -0.30 (95% CI -0.54 to -0.06), standardized effect d = -0.40; z-test p = 0.015.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Sliding Scale Regular Insulin (SSRI) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Mean of Daily Blood Glucose Concentration

• 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:
  • Sliding Scale Regular Insulin (SSRI): mean 137.70 versus 163.00 on control; difference -25.30 (95% CI -34.40 to -16.20), standardized effect d = -0.89; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Sliding Scale Regular Insulin (SSRI) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The secondary outcomes cover mean blood glucose concentration, mean daily blood glucose concentration, and mean glycosylated hemoglobin. The posted arm summaries and comparisons are labeled baseline, so they describe starting measurements rather than changes during treatment or treatment effects at follow-up. This distinction is central to their interpretation: the primary result addresses a threshold-based outcome at 3 months, while the secondary entries describe continuous measurements at baseline.

Across the baseline measures, the comparisons do not all have the same statistical pattern. Some unadjusted comparisons have p-values below conventional thresholds, while another does not. That variation should be read as exploratory description rather than as a coherent set of demonstrated treatment effects. The registry’s list includes repeated glycosylated hemoglobin entries among its secondary outcomes, but the posted baseline summaries do not supply a reported analysis plan explaining the intended role of each measure.

The secondary outcomes do not directly confirm or contradict the primary result because they differ in measurement type and timing. A continuous mean can describe the overall level of a measurement, while the primary endpoint counts participants meeting a specified threshold. Baseline summaries also cannot establish what happened after assignment. A direct relationship between baseline values and the 3-month endpoint is not reported in the registry record.

Unadjusted secondary results carry less weight when multiple outcomes are examined. General statistical explanation: testing many outcomes creates more opportunities for apparently notable results to arise by chance, even if there is no true effect. A prespecified hierarchy or multiplicity adjustment can help manage that issue, but neither is reported in the registry record. The registry also lists zero reported analyses for the secondary outcomes. Consequently, the posted secondary values are best treated as descriptive baseline information, not as independent proof of benefit or as a substitute for a prespecified primary analysis.

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

• Mean Blood Glucose Concentration
• Mean of Glycosylated Hemoglobin (hbA1c)
• Mean of Daily Blood Glucose Concentration
• Mean of Glycosylated Hemoglobin (hbA1c)
• Mean of Glycosylated Hemoglobin (hbA1c)

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary because it classifies participants by whether their mean weekly blood glucose concentration is below the stated threshold at 3 months. The group summaries are percentages, and the comparison is expressed as a risk difference. A risk difference is the contrast between the proportions in two groups, using the stated order of comparison. It is measured on the proportion scale: zero represents no difference between the group proportions, and values on either side represent differences in opposite directions. The posted comparison reports an estimate of 0.027 and an interval from -0.119 to 0.172.

The arm summaries report 28.0% for basal-plus and 31.0% for sliding-scale regular insulin. The comparison record also lists counts and denominators, as well as proportions used in the comparison. The registry record does not describe the specific statistical procedure used to construct the interval or calculate the p-value. The displayed p-value is unadjusted. No adjusted analysis is reported for the primary endpoint.

What does an interval estimate add?

General statistical explanation: an interval estimate represents uncertainty around an estimated effect under a statistical model. Its width is informative: a wider interval indicates less precision, while a narrower interval indicates greater precision. If an interval includes the null value for a difference, the data are compatible with no difference under the interval procedure, as well as with values on either side of it. An interval is not a guarantee that the true effect lies within its limits, and it does not itself identify which effects are clinically important.

For the primary comparison, the reported interval includes zero and extends in both directions. This supports a cautious reading: the point estimate alone is not enough to establish a difference, and the range does not isolate a single direction. The registry does not state the interval’s confidence level or the method used to construct it, so those details should not be assumed.

How should the p-value be read?

General statistical explanation: a p-value is calculated under a null hypothesis and statistical assumptions. It describes how unusual data at least as incompatible with that null hypothesis would be under that model. It is not the probability that the null hypothesis is true, and it does not quantify the probability that a treatment will help or harm. It also does not indicate whether an observed difference is clinically meaningful.

The primary comparison’s unadjusted p-value is 0.72. This value does not provide persuasive evidence against the null model in the unadjusted comparison. It does not establish equality or rule out meaningful differences. Interpretation also depends on the interval, the endpoint definition, the design, and the analysis choices. Those choices are not fully described in the registry record.

What do the continuous summaries describe?

The posted secondary measures include means and standard deviations for blood glucose concentration and glycosylated hemoglobin. A mean describes the group’s central value, while a standard deviation describes dispersion among observations around that mean. General statistical explanation: the standard deviation describes variability in the measurements, whereas the standard error describes uncertainty in an estimated mean. They are not interchangeable. The posted arm summaries give standard deviations; the comparison records also include standard errors.

For continuous outcomes, the posted comparisons use mean differences and give interval estimates and unadjusted p-values. A mean difference is on the same measurement scale as the underlying outcome. Its direction depends on the order of groups used in the comparison. Statistical interpretation should therefore keep the arm labels and comparison order in view rather than treating a negative or positive sign as inherently favorable.

The secondary summaries are labeled baseline, and the registry lists no reported analyses for them. General statistical explanation: a baseline mean difference is descriptive unless an analysis explicitly connects it to a later outcome or change over time. It should not be read as evidence that one assigned regimen produced a later difference. In particular, the baseline summaries cannot substitute for a reported post-treatment comparison.

What can the registry support?

The registry allows a description of the study’s randomized parallel-group design, assigned regimens, endpoint definition, group summaries, and posted comparison estimates. It does not report the detailed statistical plan, the method used for intervals, covariate adjustment, handling of missing observations, or a defined analysis population. These omissions do not invalidate the posted numbers, but they limit assessment of how the analysis was conducted and whether it followed a prespecified plan.

General statistical explanation: randomization can support a causal comparison when allocation and follow-up are handled appropriately and the analysis respects the design. A registry label of randomized does not establish every such condition. Without information about concealment, adherence, losses to follow-up, and analysis populations, the degree to which the reported comparison preserves the benefits of randomization cannot be fully assessed from this record.

11. Confidence Intervals and Precision

The primary comparison has a point estimate of 0.027 and an interval from -0.119 to 0.172. Because the interval includes zero and spans both negative and positive values, the posted data are compatible with no difference as well as with differences in either direction. The interval’s breadth also means the estimate is not tightly pinned down. The registry does not report the confidence level or construction method for this interval, so the limits should be described as the posted interval rather than assigned an unstated confidence level.

Precision is not the same as whether a result is statistically significant or clinically important. General statistical explanation: an estimate can have a point estimate near zero but still be imprecise if the interval is broad. Conversely, a narrow interval can precisely estimate a small difference that may or may not matter clinically. Determining clinical importance requires a relevant threshold or context, neither of which is reported for this endpoint in the registry record.

The posted primary result gives one summary for each group and a comparison estimate. It does not report additional information that could clarify the precision, such as a detailed account of missing endpoint observations, subgroup estimates, or sensitivity analyses. Those details are not reported in the registry record. The reported p-value of 0.72 is consistent with caution about claiming a clear difference, but the interval is more useful for seeing the range of estimates represented by the comparison.

The precision statement applies only to this endpoint, its stated measurement, and the posted analysis. It should not be transferred to the secondary measures or interpreted as a general judgment about all aspects of care in nursing-home residents.

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 illustrations use the observed effect as a starting point and show planning scenarios for detecting a stated fraction of that effect. For these outcomes, the figures are participants per arm, not totals across arms. The registry’s assumptions for the illustration are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is the stated fraction of the observed effect. The original trial’s own sample-size assumptions are not reported in the registry record.

The 80% power column represents a planning scenario in which a study designed under the stated assumptions would have an 80% chance of detecting the specified effect if that effect and the model assumptions were correct. The 90% column represents the corresponding planning target with a higher chance of detection. These are design concepts, not guarantees about an individual study. This is not observed power.

Rows describing 100% of the observed effect use the observed estimate as the target; rows describing 75% or 50% use smaller target effects. Smaller effects are harder to distinguish from random variation and therefore require more participants under the same assumptions. This pattern appears across the planning illustrations. For example, in the primary endpoint illustration, planning for 100% of the observed effect gives 4,575 participants per arm for 80% power and 6,124 participants per arm for 90% power. Planning for 50% of the observed effect gives 18,056 participants per arm for 80% power and 24,171 participants per arm for 90% power.

These figures are hypothetical planning aids derived from observed estimates, not evidence about the original design or a claim about the study’s power after completion. General statistical consideration: planning values can change substantially with assumptions about the target effect, variability, allocation, and analysis method. The registry does not report the original sample-size justification or its assumptions.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.280; treatment proportion p₁ = 0.307; average p̄ = 0.293.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6439 = 1.2620.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6436 = 0.5417.
4. n = (1.2620 + 0.5417)² / (0.0267)² = 4,574.7, rounded up to 4,575 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
p0 <- 0.28                          # control proportion
p1 <- 0.306666666666667                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^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 (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(1131052)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 4,575 (80% power), 6,124 (90% power)
 75% of observed effect: n per arm = 8,080 (80% power), 10,816 (90% power)
 50% of observed effect: n per arm = 18,056 (80% power), 24,171 (90% power)
power.prop.test: 4574.7 
Simulated power at n = 4,575 per arm: 0.800 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-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 lists multiple outcome measures, including repeated entries for glycosylated hemoglobin, and reports no analyses for the secondary outcomes. It does not report how the investigators handled multiple endpoints, whether they identified a hierarchy among them, or whether any multiplicity adjustment was planned or used. These details are not reported in the registry record.

General statistical explanation: when many outcomes are tested, the chance of obtaining at least one apparently unusual result can increase. This is why interpretation of secondary p-values depends on whether the outcomes and analysis order were prespecified and whether the analysis accounts for multiple testing. The posted secondary p-values are unadjusted, and the registry does not describe any correction. They should therefore be treated cautiously and should not be given the same evidential weight as a clearly prespecified primary analysis.

The registry record does not describe missing-data rates, reasons for missingness, imputation, or sensitivity analyses. It also does not specify whether the primary analysis included all randomized participants, participants with available outcomes, or another defined population. The analysis population is not reported in the registry record. Without those details, it is not possible to assess whether missing outcomes could have affected the group comparison or how robust the result is to different assumptions.

Other elements of the analysis plan are also not reported in the registry record, including covariate adjustment, interim analyses, and procedures for handling protocol deviations or treatment discontinuation. Their absence from the record does not establish that the procedures were absent from the study; it means their use cannot be evaluated from the registry information. A complete interpretation would require those methods and their relationship to the posted results.

15. Safety Results

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

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hospitalization (14.7%), er visit (2.7%), death (1.3%).

The registry reports serious adverse events affecting 9 participants at risk in the basal-plus group and 12 participants at risk in the sliding-scale regular insulin group. Other adverse events are reported for 17 participants at risk in the basal-plus group and 23 participants at risk in the sliding-scale group. These are counts of affected participants in the registry’s adverse-event entries; the record does not provide further detail about event definitions, severity grading, timing, or causal attribution.

The registry’s listed serious-event terms include hospitalization at 14.7%, ER visit at 2.7%, and death at 1.3%. The record does not clarify in the listed terms how these percentages relate to event counts or whether categories overlap. Group-specific death counts are not reported in the registry record. The presence of “Death” in the serious-event terms should therefore not be interpreted as a group-specific count.

These safety summaries are descriptive. They do not establish that an event was caused by an assigned regimen or that one regimen has a higher risk. The registry does not report a formal comparative safety analysis, event-level descriptions, or an analysis accounting for follow-up time. Differences in the displayed counts should not be treated as a causal risk comparison without those details.

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

The posted primary result describes the proportion of participants meeting a blood-glucose threshold at 3 months in each randomized group. The group percentages are 28.0% for basal-plus and 31.0% for sliding-scale regular insulin. The comparison estimate and its interval do not establish a clear difference, and the interval permits values in either direction. This is the appropriate scope of the main statistical conclusion from the posted endpoint.

The result does not demonstrate that the regimens are equivalent. Equivalence requires a prespecified margin and an analysis designed to assess whether differences fall within that margin; neither is reported in the registry record. It also does not prove that there is no clinically important difference, because the interval is not confined to a narrow range around zero and the registry does not state a clinically important threshold.

The result is limited to the endpoint definition, the groups studied, and the 3-month timeframe. It does not establish effects on all dimensions of diabetes care, longer-term glucose control, quality of life, or acute complications. Although the registry background discusses those broader issues, the posted primary result should not be stretched beyond its stated measure. The posted secondary outcomes are baseline summaries and do not supply evidence about later changes.

The registry describes a randomized study, but information about allocation concealment, missing outcomes, analysis populations, treatment adherence, and the detailed analysis plan is not reported in the registry record. Those gaps limit assessment of the comparison’s robustness. The study record can inform statistical understanding of the posted result, but it is not a basis for medical advice or a general recommendation about insulin regimens.

General statistical explanation: failure to show a clear difference is not the same as proving no difference. A result can be inconclusive when estimates are imprecise or when the design does not target equivalence. Conversely, a difference that appears statistically notable would not by itself establish clinical importance. The registry record does not report the threshold needed to make that clinical judgment for this endpoint.

17. Why This Trial Matters Statistically

This trial is statistically useful because it puts two distinct insulin strategies into a randomized parallel-group comparison in a nursing-home population. The registry identifies a focused, threshold-based primary endpoint rather than relying only on a group mean. That makes the estimand—the proportion of participants meeting a defined glucose criterion—different from the continuous summaries also listed in the record.

The result illustrates why an estimate, an interval, and a p-value should be considered together. The primary percentages are close, but the interval reaches in both directions and the unadjusted p-value does not provide a clear signal against the null comparison. The interval helps prevent a point estimate from being mistaken for a precise or definitive answer. It also shows why a non-significant result should not automatically be described as evidence of equivalence.

The registry also illustrates the limits of available outcome reporting. Secondary measures are labeled baseline, no secondary analyses are reported, and key analysis details—including missing-data handling and the original sample-size justification—are not reported in the registry record. Such gaps matter because statistical interpretation depends not only on the numerical summaries, but also on the timing of measurement, the analysis population, and the prespecified plan.

Finally, the planning illustrations underscore that sample-size requirements depend on the effect one aims to detect. Planning for smaller effects requires more participants under the stated assumptions. Those illustrations are not a retrospective measure of what the completed trial could detect; they are planning scenarios based on observed estimates. Together, these features make the study a useful example of how endpoint definitions, precision, multiplicity, and registry completeness shape interpretation.

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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The registry record leaves several trial-specific limitations difficult to assess. It does not report the eligibility criteria in detail, the recruitment process, the number of participants with a measured primary endpoint, or the flow of participants through follow-up. Although enrollment and arm-level denominators are listed, the record does not explain whether all randomized participants contributed to each outcome or how missing observations were handled.

The primary endpoint is reported at 3 months, but measurement frequency, glucose collection procedures, and the calculation of the mean weekly value are not described. The rationale for the below-70 mg/dL threshold is also not reported in the registry record. Without those details, the reproducibility and clinical interpretation of the endpoint cannot be fully assessed.

The registry identifies the study as randomized and unmasked, but it does not report allocation concealment or details of how assignments were generated. It does not describe treatment adherence, insulin dose patterns, protocol deviations, or whether other care differed between groups. These details could be relevant to interpreting outcomes in nursing-home residents, but they cannot be inferred from the intervention descriptions.

The secondary entries are labeled baseline, and the registry reports no analyses for them. No detailed analysis plan, multiplicity strategy, interim-analysis information, or original sample-size assumptions are reported. The adverse-event entries likewise lack event-level detail, group-specific death counts, and a described comparative safety analysis. These limitations concern what can be evaluated from the registry record; they do not establish that unreported procedures were not conducted.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Diabetes in the Elderly: Prospective Study — NCT01131052.
• Registry record last updated 2014-12-31; status last verified 2014-12.
• Results first posted 2014-12-31.
• 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

NCT01131052 is a completed Phase 4 randomized, unmasked, parallel-group study titled “Diabetes in the Elderly: Prospective Study,” with the official title “Diabetes Care in Nursing Home Residents: A Randomized Controlled Study.” Enrollment is listed as 150. The registry dates study start to 2011-03 and primary completion to 2013-12, and identifies Guillermo Umpierrez as sponsor.

The comparison was between basal-plus insulin and sliding-scale regular insulin in nursing-home residents with diabetes. The primary outcome was the percentage of participants with a mean weekly blood glucose concentration below 70 mg/dL at 3 months. The posted group percentages were 28.0% and 31.0%, with a risk-difference estimate of 0.027, an interval from -0.119 to 0.172, and an unadjusted p-value of 0.72. The registry’s secondary summaries are labeled baseline, and no secondary analyses are reported.

Important details about the analysis plan, missing data, analysis populations, and original sample-size assumptions are not reported in the registry record. The primary comparison does not establish a clear between-group difference or demonstrate equivalence.