SEGUDIAB Study: Evaluation of Safety in the Diabetic Patients: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for SEGUDIAB Study: Evaluation of Safety in the Diabetic Patients (NCT01087541), a randomized na study. The primary outcome is Glycosylated Haemoglobin A1c at Start.

2. Clinical Question

This distinction matters for interpretation. The numerical comparison is between baseline group means; it does not directly answer whether the educational program changed diabetes care or patient outcomes. The registry’s broader concept of safety includes accurate treatment and safe handling of diabetes, but the posted primary outcome is a laboratory measure rather than a direct measure of the full safety concept. Later posted measures include body mass index at 6 months, while the listed blood-pressure outcomes do not have results in the registry record. The registry record does not report a follow-up A1c result in the outcomes. As a result, the baseline difference can be read as a description of the groups at the start, but not as evidence of an intervention-related change.

3. Trial Design

The registry record identifies SEGUDIAB as a completed study, with enrollment of 6830. The study dates are 2008-01 for the start and 2008-12 for primary completion. The synopsis describes a cluster clinical trial in primary care across 10 basic health areas. It says the health areas were randomly distributed between the educational intervention and control, with 5 areas in each group. The areas were matched and described as comparable in population and other characteristics, apart from the intervention.

The intervention was a standardized educational program for health professionals lasting 6 hours, with periodic reminders. Control professionals continued usual clinical practice. Participation was voluntary. The synopsis states that type 2 diabetes prevalence in the areas ranged from 6-8%, and identifies an operative age group of 45 to 75 years. Eligibility included people with type 1 or type 2 diabetes in that age range who were recorded in primary-care software. The exclusion criteria are only partly stated: the registry text mentions serious or terminal illness and patients controlled exclusively elsewhere, but does not complete the description.

There is a design-description inconsistency in the registry record. Its synopsis describes cluster randomization across health areas and reports intervention and control groups, while another design classification labels the study model as single group. The arm summaries nevertheless give results for both intervention and control. The record does not explain the difference between these descriptions. It also does not report how the analysis accounted for the cluster assignment, whether matching was incorporated in the analysis, or how voluntary participation affected inclusion in the posted results.

The reported primary outcome is baseline glycosylated haemoglobin A1c. It is not a post-intervention endpoint, and the registry record does not provide a primary follow-up A1c result in the listed outcomes. Thus the design description concerns an educational intervention, but the posted primary comparison is a baseline group comparison. This limits what that comparison can say about outcomes following the intervention.

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 Glycosylated Haemoglobin A1c at Start, measured over Baseline.

Glycosylated haemoglobin A1c is a blood measure used to describe average glucose exposure over a period preceding its measurement. The registry records it as a continuous measure expressed as a percentage, summarized by the mean and standard deviation. It is a clinically relevant marker of diabetes status and can be useful for describing the starting characteristics of groups in a diabetes study.

For SEGUDIAB, the primary outcome is titled “Glycosylated Haemoglobin A1c at Start,” and its timeframe is baseline. The intervention group mean is 6.64% with a standard deviation of 1.6; the control group mean is 6.76% with a standard deviation of 1.67. The unadjusted mean difference is -0.12 percentage points, with the intervention group lower at baseline. Because this is a baseline measurement, it describes the groups’ starting values rather than a difference in change after professional education.

Using A1c as an outcome can give a measurable indicator related to diabetes care, but it does not capture every part of the registry’s broad safety objective. The registry’s synopsis includes accurate treatment and safe handling, while A1c alone does not directly describe medication errors, professional practice, or all aspects of patient safety. A follow-up measure could address whether values changed after the intervention, but such a primary follow-up A1c result is not reported in the registry record. The endpoint should therefore be interpreted as a baseline clinical characteristic, not as a complete measure of the educational program’s effect on safety.

7. Results Posted in the Registry

• Intervention: mean 6.64 versus 6.76 on control; difference -0.12 (95% CI -0.20 to -0.04), standardized effect d = -0.07; z-test p = 0.002.

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.

BMI ( Body Mass Index ) at Start

• 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:
  • Intervention: mean 26.90 versus 27.10 on control; difference -0.20 (95% CI -0.66 to 0.26), standardized effect d = -0.02; z-test p = 0.392.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

BMI at 6 Months

• 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:
  • Intervention: mean 30.00 versus 30.10 on control; difference -0.10 (95% CI -0.36 to 0.16), standardized effect d = -0.02; z-test p = 0.453.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary results cover BMI at baseline and BMI at 6 months. Their estimated intervention-versus-control mean differences are small in the units reported, and both unadjusted confidence intervals include zero. The registry record also names systolic and diastolic blood-pressure outcomes, but does not report numerical results for them. Thus the posted secondary information is limited to the two BMI summaries.

This pattern does not provide a follow-up A1c result to complement the primary baseline comparison. It therefore cannot establish whether the groups’ A1c values changed after the professional education, or whether any such change was related to the intervention. Nor can the BMI comparisons substitute for the broader safety objective, which includes aspects of care not directly represented by BMI.

Secondary outcomes generally carry less confirmatory weight when they are not identified as the primary outcome, when analysis plans are unavailable, or when many outcomes are examined without a reported multiplicity strategy. The registry record does not report a correction for multiple comparisons, a prespecified hierarchy for secondary outcomes, or adjusted analyses. Unadjusted secondary results are best treated as descriptive and exploratory rather than as independent confirmation of the primary finding.

The record also does not explain whether baseline and 6-month BMI summaries involve the same participants or how missing follow-up measurements were handled. Comparing the two group means across time would not yield an individual-level change without additional information and analysis. Taken together, the secondary results neither resolve the limitations of the baseline primary endpoint nor establish a broader intervention effect. The absence of posted blood-pressure results further limits assessment of the full set of secondary outcomes named in the registry.

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

• BMI ( Body Mass Index ) at Start
• BMI at 6 Months
• Blood Pressure ( Systolic / Diastolic )
• Systolic and Diastolic Blood Pressure

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous: each participant’s A1c is represented by a numerical value, and the registry summarizes each group by its mean and standard deviation. A mean gives the arithmetic center of the observed values. A standard deviation describes how spread out individual values are around that mean; it is not the uncertainty in the mean itself. The posted standard deviations are 1.6 in the intervention group and 1.67 in the control group.

What does the mean difference represent?

The comparison is the intervention-group mean minus the control-group mean. The reported estimate is -0.12 percentage points, so the intervention group’s baseline mean is lower than the control group’s baseline mean. This arithmetic direction does not establish that the educational intervention caused the difference. The outcome timeframe is baseline, and the registry record does not present a post-program primary A1c comparison.

A mean difference retains the original outcome scale, which makes it relatively direct to interpret for a continuous measure. The effect-size summary also gives a standardized difference, but the baseline mean difference is the more direct description of the posted result. A standardized effect expresses a difference relative to variability, which can help compare measurements made on different scales. It does not remove design limitations, establish causality, or correct for clustering.

How should the confidence interval be read?

A confidence interval expresses uncertainty around an estimated difference under the assumptions of the statistical method. A narrower interval indicates greater precision, while a wider interval indicates less precision. The reported interval for the primary mean difference is entirely below zero, so it is consistent with a negative baseline difference under that unadjusted method. The interval does not account for any source of uncertainty that the method omitted, such as dependence among participants within the same health area.

The registry record gives the interval endpoints and an unadjusted p-value of 0.002. A p-value measures how unusual results at least as discordant with a null difference would be if that null model and the calculation assumptions were correct. It is not a measure of clinical importance, nor is it the chance that the result arose “by chance.” It also cannot tell whether the baseline difference arose from random variation, differences among areas, voluntary participation, or another source.

Why does cluster assignment matter?

The trial synopsis says health areas, rather than individual participants, were randomized. General statistical explanation: cluster assignment can create correlation among observations from the same area, because participants share a care setting and professionals. If an analysis treats all observations as independent when they are not, its uncertainty estimates can be too small. Appropriate cluster-aware methods account for within-area similarity and the number and structure of randomized clusters. The registry record does not report whether its primary comparison used such methods, or whether area matching was included.

The posted comparison is an unadjusted recalculation from group summary statistics. It is useful as a transparent contrast of the posted means, but it is not a substitute for an analysis that reflects the design and any prespecified adjustments. No sponsor-reported primary analysis details, cluster adjustment, or model specification are reported in the registry record. The record also does not report the original design’s sample-size justification or analysis plan.

What does “unadjusted” mean for the secondary comparisons?

The posted secondary comparisons are also unadjusted mean differences. For BMI at start, the intervention mean is 26.9 kg/m2 and the control mean is 27.1 kg/m2; the estimated difference is -0.2 kg/m2 with p = 0.392. For BMI at 6 months, the means are 30.0 kg/m2 and 30.1 kg/m2; the estimated difference is -0.1 kg/m2 with p = 0.453. Their reported confidence intervals include zero. These are descriptive group comparisons and do not, by themselves, represent adjusted treatment effects or within-person change.

In general, an unadjusted comparison does not account for other characteristics that may differ between groups, and it does not automatically account for the study’s cluster structure. The registry record does not provide an adjusted model or a list of covariates used for these endpoints. The summaries also do not establish that the same people contributed measurements at both timeframes, so the difference between the baseline and 6-month group means must not be interpreted as an individual-level change.

Multiplicity is another consideration. When many outcomes are examined, the chance of finding at least one apparently notable result can increase. No multiplicity adjustment or hierarchy for interpreting secondary outcomes is reported in the registry record. The BMI p-values should consequently be read as unadjusted comparisons, not as confirmatory evidence after accounting for all outcomes. Blood-pressure outcomes are named among the secondary outcomes, but their numerical results are not reported in the registry record.

Finally, the record does not report how missing outcome data were handled, whether results include all enrolled participants or a defined analysis population, or whether any interim analyses took place. General statistical interpretation depends on these details because exclusions and missing measurements can affect both the estimated mean and its uncertainty. The available summaries support a limited description of the posted group values; they cannot resolve those design and analysis questions.

11. Confidence Intervals and Precision

The primary mean difference is -0.12 percentage points, and the reported confidence interval lies entirely below zero. This indicates that, under the unadjusted calculation, the data are compatible with a negative baseline difference rather than a zero difference. The interval’s width also matters: its endpoints are not identical, so the estimate retains uncertainty about the magnitude of that difference. The interval is a range of values compatible with the data and method; it is not a guarantee that the true difference lies within it.

Precision should be considered separately from clinical importance and causal interpretation. A relatively precise baseline contrast can still answer only a baseline question. The primary outcome was measured at start, and the registry does not report a primary post-intervention A1c result. Therefore, the interval does not quantify uncertainty about a change in A1c caused by the professional education program.

There is also uncertainty about whether the reported interval fully reflects the study design. The synopsis describes randomization by health area, but the registry record does not report whether the calculation accounted for clustering or matching. If participants within areas have correlated outcomes, treating their measurements as independent can understate uncertainty. The posted interval should consequently be understood as the interval from the unadjusted summary-based comparison, not assumed to be a design-adjusted interval.

The secondary BMI intervals include zero and are compatible with a range of group differences. That does not establish equivalence or prove that the groups had identical values; a non-significant result can reflect limited precision as well as a small estimated contrast. No equivalence margin is reported. Taken together, the available intervals provide uncertainty around simple group mean differences, but the missing cluster-analysis and follow-up details limit the conclusions that can be drawn about the intervention.

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 participants per arm, not total events. They are illustrations based on the observed effect, not the original trial’s design calculations. The listed assumptions are a two-sided significance level of 0.05, equal allocation between two arms, a normal approximation, and an effect to detect set at a stated fraction of the observed effect. The original trial’s sample-size assumptions and justification are not reported in the registry record.

The rows correspond to planning for 100%, 75%, or 50% of the observed effect. The 80% power column gives the estimated number of participants per arm that would be needed, under those assumptions, for an analysis to have an 80% chance of detecting the specified effect if that effect were real. The 90% power column describes the corresponding planning target with a higher chance of detection, under the same assumptions. These are prospective design quantities, not characteristics of the results already observed.

As the effect to be detected becomes smaller, the required number of participants per arm increases. That pattern reflects the greater difficulty of distinguishing a smaller difference from sampling variation. The figures should be interpreted cautiously for this study because the observed primary contrast is a baseline difference, not an intervention effect, and the synopsis describes cluster randomization. The planning assumptions do not state a cluster adjustment, and the registry record does not report how clustering was handled in the original design calculations. These figures are not observed power.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -0.120; pooled standard deviation σ = 1.636.
2. n = 2 × (1.96 + 0.842)² × 1.636² / -0.120² = 2,919.4, rounded up to 2,920 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- -0.12                      # observed difference in means
sigma <- 1.63648115247019                     # 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(1087541)
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 = 2,920 (80% power), 3,909 (90% power)
 75% of observed effect: n per arm = 5,191 (80% power), 6,949 (90% power)
 50% of observed effect: n per arm = 11,678 (80% power), 15,634 (90% power)
power.t.test: 2920.4 
Simulated power at n = 2,920 per arm: 0.806 (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 does not report a multiplicity strategy for the primary and secondary outcomes. It does not specify whether outcomes were prioritized in a hierarchy, whether secondary analyses were intended to be confirmatory, or whether any adjustment was used to account for examining multiple outcomes. When multiple hypotheses are tested, the chance of at least one apparently unusual result can rise. General statistical practice is to interpret unadjusted secondary p-values with this issue in mind, particularly when the analysis plan is unavailable.

Missing-data methods are not reported in the registry record. It does not state how absent A1c, BMI, or blood-pressure measurements were handled, whether participants with missing measurements were excluded, or whether imputation or another method was used. The record also does not state whether the posted summaries include every eligible participant or define a specific analysis population. These details matter because the observed measurements may not represent all eligible participants if availability is related to health status, care, or group assignment.

The trial synopsis describes randomized allocation of health areas, but no cluster-aware primary analysis is reported. The record does not say whether the analysis accounted for correlation among people in the same area, incorporated the matching of health areas, or adjusted for baseline characteristics. It also does not report an interim-analysis plan, stopping rules, or the original statistical analysis plan. These items should be regarded as not reported in the registry record, rather than assumed to have been absent from the study.

The registry includes a primary baseline A1c comparison and BMI summaries, while blood-pressure outcomes are listed without numerical results. The reason those results are not posted is not reported in the registry record. Without the analysis population, missing-data handling, cluster method, and multiplicity plan, the available p-values and confidence intervals cannot be evaluated as a complete prespecified analysis. The posted values still describe the available group summaries, but the omitted methodological details constrain inference.

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 3% reporting threshold.

Most frequent non-serious adverse event terms

The registry lists zero serious adverse events in the intervention group among 3307 at risk and zero in the control group among 3523 at risk. These are the serious-event counts recorded for the groups; they should not be read as proof that no serious events occurred outside the registry’s captured or reported information. No serious adverse-event terms are listed. Death counts are not reported in the registry record.

For other adverse events, the record lists 2 affected among 52 at risk in the intervention group and 1 affected among 87 at risk in the control group. These at-risk figures are the registry’s stated denominators for the other-event category; they differ from the full arm sizes, and the record does not explain the difference. No rates are calculated here, and the counts alone do not establish a comparative safety difference.

Adverse-event summaries depend on how events were defined, collected, and followed. The registry record does not provide further details on ascertainment, event severity beyond the serious-event category, or the period covered by these counts. The figures are therefore best read as the adverse-event information posted in the record, with limited detail available for interpreting completeness or between-group differences.

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

The result means that the intervention group had a lower mean A1c at baseline than the control group by an unadjusted estimate of -0.12 percentage points. The reported unadjusted p-value is 0.002, and the reported confidence interval excludes zero. Those results describe a difference in starting values under the summary-based calculation.

They do not mean that the educational program lowered A1c. The primary outcome timeframe is baseline, so it is not a post-program contrast or an estimate of change. The registry record does not report a follow-up primary A1c value, and a baseline difference cannot establish whether later outcomes differed because of the intervention. It also does not directly measure all dimensions of safety described in the study objective.

The interpretation is further limited by the cluster design description. The synopsis says health areas were randomized, but the registry record does not report whether the primary uncertainty calculation accounted for clustering or matching. Without that information, the unadjusted p-value and interval should not be treated as a complete design-aware analysis. The record also does not report missing-data methods or a defined analysis population.

The BMI results are descriptive secondary comparisons, and their unadjusted intervals include zero. That does not prove equivalence, while the absence of posted blood-pressure results leaves other listed outcomes unassessed in the registry record. The adverse-event entries provide counts, but their collection and follow-up details are limited. In short, the posted information supports a description of baseline A1c and selected secondary summaries, but not a conclusion about whether the educational program improved safety or changed subsequent patient outcomes. No treatment or care decision should be inferred from this baseline statistical comparison alone.

17. Why This Trial Matters Statistically

This trial illustrates why the endpoint’s timing is as important as the outcome’s label. A1c is relevant to diabetes care, but a baseline value answers a group-description question, not whether an educational intervention was followed by a change. The registry’s primary result has a statistically notable unadjusted comparison, yet the measure’s timing prevents it from serving as a direct estimate of the program’s effect.

The design also highlights the need to align analysis with randomization. The synopsis describes assignment by health area, which creates a cluster structure. Statistical methods should reflect that structure when estimating uncertainty. The record’s simple group summaries are useful to readers, but without a reported cluster-aware analysis, the precision implied by the unadjusted interval may not capture all design-related uncertainty.

Another statistical lesson is that a small p-value does not settle questions of causality, relevance, or data quality. The primary comparison is at baseline, the analysis population and missing-data handling are not reported, and the original analysis plan is unavailable in the registry record. Those limitations affect interpretation even when the posted interval excludes zero.

Finally, secondary outcomes and safety counts need context. Unadjusted results across multiple outcomes have less confirmatory force when no multiplicity approach is reported, and incomplete outcome reporting limits a broad assessment of the study objective. The record demonstrates why interpretation should integrate timing, randomization unit, uncertainty, outcome coverage, and reporting transparency rather than relying on a p-value alone.

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.

A limitation specific to this record is the mismatch between its cluster-trial synopsis and its single-group model classification. The synopsis says 10 health areas were randomized between intervention and control, while the model classification does not reflect the two groups shown in the results. The registry record does not explain this inconsistency. Readers therefore have to rely on the narrative design description and posted arms while recognizing that the record’s classifications are not fully aligned.

The primary outcome is measured at baseline, despite the study objective asking whether professional education is followed by improved safety. The registry record does not report a primary follow-up A1c outcome. This means the posted primary comparison is not an estimate of post-intervention change. The listed secondary outcomes include blood pressure, but numerical results for those outcomes are not reported.

The synopsis says areas were matched and participation was voluntary, but the record does not describe how matching or voluntary participation was handled analytically. It also does not report a cluster-adjusted analysis, missing-data methods, eligibility flow, or the precise population contributing to each outcome. The exclusion criteria are incomplete in the registry text. These omissions limit assessment of comparability, selection, and precision.

The posted safety entries are sparse: serious-event counts are zero in both arms, but death counts are not reported, and the other-event denominators are smaller than the arm sizes without an explanation. No further adverse-event collection details are reported. Finally, no original sample-size justification, analysis plan, or multiplicity strategy is reported in the registry record. These are limitations of the available record; they do not establish what methods may or may not have been used outside the information recorded.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: SEGUDIAB Study: Evaluation of Safety in the Diabetic Patients — NCT01087541.
• Registry record last updated 2011-03-29; status last verified 2011-03.
• Results first posted 2011-03-24.
• 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

SEGUDIAB is recorded as a completed study of an educational program for primary-care professionals caring for people with diabetes. Its synopsis describes cluster randomization of health areas to a 6-hour standardized education program with periodic reminders or usual care. The listed enrollment is 6830.

The primary outcome is mean A1c at baseline. The intervention group mean was 6.64% and the control group mean was 6.76%, for an unadjusted mean difference of -0.12 percentage points and p = 0.002. Because the outcome is at baseline, this result does not estimate change following the education program. Posted BMI comparisons are secondary and unadjusted; blood-pressure results are not reported in the registry record.

The record does not report whether the primary analysis accounted for clustering, how missing data were handled, or the original statistical plan. Its design descriptions are also inconsistent, with a cluster-trial synopsis and a single-group model classification. The available results support a limited description of starting group values, not a definitive assessment of the program’s effect on subsequent outcomes.