The National Diabetes Prevention Program in Rural Communities: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for The National Diabetes Prevention Program in Rural Communities (NCT05387434), a randomized na study. The primary outcome is Change in Body Weight From 0-6 Months.

2. Clinical Question

3. Trial Design

This was a randomized, single-masked, parallel-group pilot trial of remote National Diabetes Prevention Program delivery in rural communities. The registry lists enrollment as 31 and status as completed. The recorded study dates are 2021-03-03 for the start and 2022-03-05 for primary completion. The phase is listed as NA. The sponsor is the University of Kansas Medical Center.

The two behavioral interventions were DPP-R and DPP-FB. DPP-R used group meetings by Zoom, led by trained Kansas State Research and Extension staff. The description specifies weekly 60-minute meetings over 6 months, followed by monthly 60-minute meetings for the later 6 months. DPP-FB used a private, research-team-moderated Facebook group, with weekly educator posts and discussion prompts. The record describes the delivery approaches, but does not report the precise content or fidelity measures used to assess whether the programs were delivered consistently.

Random allocation and a parallel-group structure mean that participants were assigned to one of the two delivery approaches and followed in that group. The registry identifies masking as single, but the identity or role of the masked party is not reported in the registry record. Details of sequence generation, allocation concealment, and the timing or process of assignment are also not reported in the registry record.

The primary outcome was change in body weight from baseline to 6 months. The posted results include primary-outcome groups of 14 participants for DPP-R and 13 for DPP-FB. Secondary-outcome reporting uses groups of 16 and 15 participants, respectively. These different outcome-specific counts should not be treated as interchangeable; the record does not explain the analysis populations or the reasons for the different counts.

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 Change in Body Weight From 0-6 Months, measured over Baseline - 6 months.

The primary endpoint is change in body weight from baseline to 6 months, measured in kilograms. It is a continuous outcome reported as a mean with a standard deviation. The registry description says weight was measured to the nearest 0.1 kg with a calibrated scale. Participants were weighed between 6 and 11 am after a 12-hour fast and in a standard hospital gown.

Weight change is used as a direct measure of change in body mass over the first 6 months of the program. A negative mean change indicates that the group’s average weight at follow-up was lower than at baseline; a positive mean change would indicate an average increase. The group mean describes the average change, while the standard deviation describes how much individual changes varied around that average. Neither value alone shows the full distribution of participant outcomes.

The recorded means were -6.0 kilograms for DPP-R and -1.7 kilograms for DPP-FB. The estimated mean difference, expressed as DPP-FB relative to DPP-R, was 4.3 kilograms. This contrast describes the observed group averages; it does not by itself establish why the averages differed or whether the difference would be reproduced in a larger study.

7. Results Posted in the Registry

• DPP-FB: mean -1.70 versus -6.00 on control; difference 4.30 (95% CI -0.26 to 8.86), standardized effect d = 0.69; z-test p = 0.064.

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.

Participant Retention

• 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:
  • DPP-FB: 86.7% (13/15) versus 87.5% (14/16) on control; risk difference -0.8 percentage points (95% CI -24.5 to 22.8), risk ratio 0.99 (0.75–1.30); unadjusted two-proportion z-test p = 0.945.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between DPP-FB and the control. The interval gives the range of effects that remain compatible with the data.

Average Program Attendance

• 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:
  • DPP-FB: mean 83.00 versus 69.00 on control; difference 14.00 (95% CI -3.71 to 31.71), standardized effect d = 0.56; z-test p = 0.121.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between DPP-FB and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes address engagement with the program rather than the primary question about body-weight change. Retention describes whether participants remained in the study through the reported follow-up, while average attendance describes participation in program sessions. Across these measures, retention appears similar between delivery groups, while the attendance estimate is higher for DPP-FB. The primary weight-change estimates, in contrast, are more negative for DPP-R. The pattern is not a single consistent ranking across outcome types.

These outcomes can help characterize feasibility and participation, which are relevant to interpreting a remote program, but they cannot be treated as substitutes for the primary endpoint. Attendance and retention capture engagement; weight change captures a measured health-related outcome. A difference in one does not explain or establish a difference in another. Nor does this pilot establish that either delivery approach caused the secondary outcome patterns.

The registry reports no analyses for the secondary outcomes. Their posted comparisons are unadjusted, and the record does not report whether they were prespecified as confirmatory, how multiplicity was addressed, or whether other outcomes were examined. When several outcomes are compared, isolated patterns can arise by chance, and a lack of adjustment can make nominal p-values look more compelling than the overall evidence warrants.

For those reasons, the secondary results are most useful as descriptive signals about participation and as context for a future evaluation. They provide a broader view than body-weight change alone, but they do not remove uncertainty in the primary estimate, and they should carry less inferential weight than a clearly prespecified and appropriately analyzed primary outcome.

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

• Participant Retention
• Average Program Attendance

9. Statistical Interpretation

10. Statistical Methods Explained

The posted comparisons concern two parallel groups: DPP-R and DPP-FB. For a continuous outcome, a mean difference compares the average outcome in one group with the average in the other. The primary comparison is expressed as DPP-FB relative to DPP-R. Because a more negative change represents a greater average reduction in weight, the sign of the difference should be interpreted in relation to that ordering. The reported group means and contrast are descriptive estimates, while the confidence interval conveys uncertainty around the contrast.

What does a standard deviation describe?

A standard deviation describes the spread of individual observations around a group mean. It is not the uncertainty interval for the mean difference and does not say how many participants had a particular outcome. For the primary endpoint, the registry reports standard deviations of 8.0 kilograms for DPP-R and 3.3 kilograms for DPP-FB. When the spread is large relative to the group size, the mean can be estimated less precisely. The spread also matters when planning a future comparison, because greater variability generally requires more participants to distinguish a given difference from background variation.

How should the mean difference and p-value be read?

The mean difference is an estimate in the original measurement units, which makes it interpretable as a contrast in kilograms. The unadjusted p-value is a test statistic translated into a probability under a specified null model and assumptions. It does not measure the size or importance of the contrast. It also does not provide the probability that the null model is true. Since the registry record does not report the full statistical analysis plan, details such as model specification, covariate adjustment, and handling of departures from model assumptions are not reported in the registry record.

The posted primary comparison has an unadjusted p-value of 0.064. The confidence interval extends from a small value below zero to a larger positive value. Together, these results indicate uncertainty about the magnitude and direction of the group contrast. A single p-value should not replace examination of the effect estimate, interval, outcome measurement, and study limitations.

Why can small pilot groups produce uncertain estimates?

With small groups, a few participant outcomes can have a substantial influence on the mean and standard deviation. Estimates may consequently vary from one sample to another, even when the underlying populations and delivery approaches are similar. Randomization helps guard against systematic differences between assigned groups, but it cannot ensure that all relevant participant characteristics are balanced in a small sample. The registry does not report adjusted estimates or participant-level data, so the posted summary statistics cannot show whether the observed contrast is sensitive to particular participants or baseline characteristics.

The primary outcome has 14 participants in DPP-R and 13 in DPP-FB. The secondary outcomes use different denominators. Without a reported account of analysis populations and missing observations, it is not possible to determine from the registry record why the denominators differ or whether outcome availability varied by participant characteristics.

What does over-dispersion mean in general?

For a count or binary outcome, over-dispersion refers generally to greater variability than a chosen statistical model expects. It can make standard errors too small if a model does not account for the extra variability. That concept is not a reported finding about this trial: the primary endpoint and average attendance are continuous measures, and the registry does not report a model-based analysis of the binary retention result. The record does not provide enough detail to assess over-dispersion or model diagnostics.

How should the secondary tests be understood?

The registry lists participant retention and average program attendance as secondary outcomes, but reports no analyses for them. The displayed comparisons are unadjusted. An unadjusted comparison considers an outcome on its own and does not account for the fact that multiple outcomes or comparisons may have been examined. When many outcomes are considered, the chance of seeing an apparently notable result among them can increase. The registry does not report a multiplicity adjustment or a hierarchy of confirmatory and exploratory outcomes.

The registry record gives the summary values and comparison estimates, but the analysis methods used by the trial team are not reported in the registry record. The posted statistical comparisons should therefore be distinguished from a full prespecified analysis, which would require details about the analysis population, missing data, model, and multiplicity handling.

11. Confidence Intervals and Precision

The primary mean difference is 4.3 kilograms for DPP-FB relative to DPP-R. Its confidence interval runs from -0.258 to 8.858 kilograms. A confidence interval is a range of values compatible with the observed data under the method’s assumptions; it expresses uncertainty in the estimated contrast. It is not a range containing a known proportion of individual participant changes, nor does it identify the exact value of the true difference.

The interval includes zero, so the posted results are compatible with little or no difference as well as with differences in either direction. Its width signals meaningful uncertainty about the size of the contrast. The lower end allows a small contrast in the direction opposite to the point estimate, while the upper end allows a substantially larger contrast in the direction of the point estimate. The data therefore do not pin down a single, precise between-group effect.

The group standard deviations provide context for this uncertainty: 8.0 kilograms for DPP-R and 3.3 kilograms for DPP-FB. These describe variation among the observed changes, not the precision of the estimated difference itself. Precision is affected by both variability and the number of observations. The primary results include 14 participants in DPP-R and 13 in DPP-FB, so the estimates are based on small groups.

The posted interval and unadjusted p-value should be read together, while recognizing that the registry record does not report the full analysis procedure used to generate them. Neither statistic resolves questions about missing observations, the analysis population, or the degree to which the findings would generalize beyond this pilot.

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 planning values describe participants per arm, not a total across both arms and not total events. They are illustrations based on the observed effect, rather than a report of the trial’s original design assumptions. The stated planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each planning scenario uses a stated fraction of the observed effect. The trial’s own original sample-size assumptions and justification are not reported in the registry record.

The 80% power column gives the planned number of participants per arm for a hypothetical study designed to have an 80% chance of detecting the specified effect under the stated assumptions. The 90% power column uses a higher target chance of detection, which generally requires more participants under the same assumptions. These figures are planning quantities, not descriptions of the number enrolled in this pilot.

For the primary weight-change comparison, planning for 100% of the observed effect gives 33 participants per arm for 80% power and 44 participants per arm for 90% power. Planning for 75% of the observed effect gives 59 participants per arm for 80% power and 78 participants per arm for 90% power. Planning for 50% of the observed effect gives 131 participants per arm for 80% power and 176 participants per arm for 90% power. The pattern illustrates that smaller effects need more participants to detect, assuming the other planning conditions remain the same.

These figures do not mean the pilot had a particular probability of finding a result, and they are not observed power. They are prospective illustrations based on an effect estimated from a small pilot, so the assumed effect itself may be uncertain. A future study’s sample-size decision would also depend on its objectives and design choices, which are not reported in the registry record.

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 = 4.300; pooled standard deviation σ = 6.205.
2. n = 2 × (1.96 + 0.842)² × 6.205² / 4.300² = 32.7, rounded up to 33 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 <- 4.3                      # observed difference in means
sigma <- 6.20541698840618                     # 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(5387434)
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 = 33 (80% power), 44 (90% power)
 75% of observed effect: n per arm = 59 (80% power), 78 (90% power)
 50% of observed effect: n per arm = 131 (80% power), 176 (90% power)
power.t.test: 33.7 
Simulated power at n = 33 per arm: 0.787 (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 lists one primary outcome and two secondary outcomes: change in body weight, participant retention, and average program attendance. It does not report whether additional outcomes or subgroup comparisons were planned or analyzed. It also does not report a multiplicity strategy, such as adjusting for several statistical tests or specifying a hierarchy in which some outcomes are confirmatory and others exploratory. Without that information, the posted unadjusted secondary comparisons should be interpreted cautiously.

The registry lists no reported analyses for the secondary outcomes. The available comparison estimates do not explain the analysis procedures, and a complete statistical analysis plan is not reported in the registry record. Details such as covariate adjustment, model selection, treatment of outliers, and assumptions checking are therefore not reported in the registry record. The identity of the masked party and the methods of allocation concealment are likewise not reported.

Missing-data handling is also not described. The primary outcome groups contain 14 and 13 participants, while the secondary-outcome groups contain 16 and 15. The reason for the difference in outcome-specific counts is not reported in the registry record. The record does not state how missing weight or participation measurements were handled, whether any imputation was used, or whether analyses included all randomized participants. It is therefore not possible to determine whether the posted comparisons represent an intention-to-treat population, a complete-case population, or another analysis set.

These omissions matter because incomplete follow-up can affect both estimates and their uncertainty, particularly if the likelihood of missing data differs by group or by participant outcome. Without details about participant flow and missingness, readers cannot assess the direction or size of any potential bias. The registry record also does not report interim analyses or stopping rules; their presence or absence should not be inferred.

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.

The registry records zero serious adverse events in each group and zero other adverse events in each group. It also records zero deaths in each group. The at-risk counts are 16 for DPP-R and 15 for DPP-FB in the adverse-event record. These figures describe the events recorded in the registry for the reported groups.

Zero recorded events should not be interpreted as proof that no adverse event occurred or that the approaches have no safety concerns. A small pilot has limited ability to characterize uncommon events, and the registry record does not provide event descriptions, severity criteria, relatedness assessments, or detailed adverse-event collection procedures. The record also does not explain the difference between the adverse-event group counts and the primary-outcome group counts.

The event figures are descriptive. They do not establish a safety comparison between delivery formats, and they should not be used to infer that one approach is safer than the other.

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

The results describe a small randomized pilot comparing two ways of delivering the National Diabetes Prevention Program remotely in rural communities. The registry shows different average weight changes in the two primary-outcome groups, with a mean difference estimate of 4.3 kilograms for DPP-FB relative to DPP-R. The confidence interval is broad and includes zero. This combination means the observed group contrast is uncertain: it does not establish a definitive difference, and it leaves room for a range of possible effects.

The results do not show that one delivery format caused the observed mean difference, nor do they establish that the same contrast would occur in a larger or different population. Randomization is intended to support fair group comparisons, but small groups and incomplete reporting limit how confidently the observed averages can be generalized. Participant characteristics, eligibility details, and the reasons for differing outcome counts are not reported in the registry record.

The secondary outcomes offer additional descriptive information about retention and attendance. They relate to participation and feasibility, but they cannot be taken as evidence that a delivery method produces a particular weight outcome. Their unadjusted status and the lack of reported analysis details further limit inferential claims. Similarly, zero recorded adverse events does not establish that there were no risks.

The trial’s pilot purpose was to compare feasibility and effectiveness of remote delivery, but the registry record does not provide a complete statistical analysis plan or a detailed account of how feasibility was defined. The posted findings are therefore most appropriately treated as preliminary estimates that can inform further evaluation. They neither prove equivalence between the approaches nor demonstrate a definitive advantage for either one.

The appropriate interpretation is bounded by the outcomes and follow-up reported: body-weight change from baseline to 6 months, plus retention and attendance reported over 6 months. Outcomes beyond those measures, longer-term effects, and the experience of participants outside the studied rural-community setting are not established by these results.

17. Why This Trial Matters Statistically

This trial is statistically informative because it places a direct comparison of two remote delivery formats beside outcomes that describe both measured change and program engagement. The distinction matters: a program may be attended or retained differently without that necessarily translating into a similar pattern in body-weight change. Reading the outcomes together helps prevent one measure from standing in for all aspects of program performance.

The pilot also illustrates why an effect estimate should be read with its uncertainty. The primary comparison has a point estimate that favors a more negative mean change in DPP-R, but its confidence interval spans values on either side of zero. The estimate is therefore not a precise summary of a settled difference. A p-value alone would not convey the range of plausible contrasts or the scale of the uncertainty.

The small outcome groups are relevant to planning. Sample-size illustrations based on fractions of the observed effect show that the required number of participants changes substantially when the assumed target effect is smaller. Such illustrations can support planning discussions, but a pilot-derived effect estimate may be unstable, and the registry’s planning assumptions do not substitute for the trial’s unreported original design rationale.

Finally, the record underscores the value of reporting analysis populations, missing-data procedures, multiplicity decisions, and the identity of masked parties. Those details help readers judge how an estimate was obtained and how much confidence to place in it. The registry provides useful descriptive outcomes, while leaving important methodological questions unanswered. Keeping both points in view is central to sound statistical 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.

The trial is a pilot with small outcome-specific groups, so the estimates may be imprecise and sensitive to individual observations. The primary outcome is reported for 14 participants in DPP-R and 13 in DPP-FB, while the secondary outcomes use larger group counts. The registry does not explain these differences, making it difficult to assess the completeness of outcome follow-up or the populations underlying each comparison.

Eligibility criteria and participant characteristics are not reported in the registry record. The description identifies adults in rural communities as the intended population, but does not provide enough information to judge how representative the enrolled participants were of rural communities more broadly. Further, details about the social-media arm’s full intervention content are limited in the record, so replication and comparison of delivery fidelity may be difficult.

Although the registry identifies the trial as randomized and single-masked, it does not report the randomization procedure, allocation concealment, or who was masked. The statistical analysis plan, analysis population, missing-data procedures, and multiplicity handling are also not reported. These gaps restrict assessment of bias and the reproducibility of the posted comparisons.

The primary outcome covers change through 6 months, and the secondary outcomes are reported over 6 months. Longer-term outcomes are not included in the posted results. The registry lists no reported analyses for the secondary outcomes, and the adverse-event record provides counts without detailed event descriptions or collection procedures. Together, these limitations mean that the results are useful as preliminary descriptive evidence, but do not support a complete assessment of comparative effectiveness, longer-term outcomes, or safety.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: The National Diabetes Prevention Program in Rural Communities — NCT05387434.
• Registry record last updated 2023-12-08; status last verified 2023-12.
• Results first posted 2023-11-22.
• 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

NCT05387434 is a completed randomized, single-masked, parallel-group pilot trial sponsored by the University of Kansas Medical Center. It compared the National Diabetes Prevention Program delivered by video conferencing (DPP-R) with delivery through a moderated Facebook group (DPP-FB) for adults in rural communities. The registry lists enrollment of 31 and records a study start of 2021-03-03 and primary completion of 2022-03-05.

The primary outcome was mean body-weight change from baseline to 6 months. The posted group means were -6.0 kilograms for DPP-R and -1.7 kilograms for DPP-FB, with a mean difference of 4.3 kilograms for DPP-FB relative to DPP-R. The confidence interval includes zero, and the comparison remains uncertain. Participant retention and average program attendance were listed as secondary outcomes. The registry does not report secondary analyses or a complete statistical analysis plan.