The Effect of a Nutritional Supplement on Post Prandial Glucose and Lipids in an Adult Population: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for The Effect of a Nutritional Supplement on Post Prandial Glucose and Lipids in an Adult Population (NCT02119325), a randomized na study. The primary outcome is Cmax for Glucose in IFG Status Participants.

2. Clinical Question

The trial also listed outcomes for glucose and triglyceride peaks, areas under the concentration-time curves, and times to peak, as well as remnant-like particle (RLP) cholesterol. These measures address different features of the post-meal response: peak height, cumulative exposure over time, and timing. The primary question is narrower than the full set of listed outcomes, and the primary population differs from the secondary glucose outcomes in overweight healthy participants. The registry record does not report results for every listed secondary measure.

3. Trial Design

This was a randomized, double-blind, two-treatment, two-period crossover study. Participants received both the fibre-rich test drink and the no-fibre placebo drink in separate treatment periods. In a crossover design, each participant can contribute information under both treatments, so comparisons may use within-person changes. The registry record does not report the sequence details, the period length, the interval between periods, or whether a washout was used.

The trial enrolled 96 participants and is listed as completed. The registry dates the study start to 2013-09 and primary completion to 2013-10. The phase is listed as NA. GlaxoSmithKline is identified as the sponsor. The registry describes the target group as healthy overweight adults with impaired fasting glucose; the primary analysis population was the per-protocol population rather than all enrolled participants.

The primary outcome was Cmax for glucose in participants with IFG, reported as a mean with standard deviation and measured in mg/dL. The registry describes blood collection at -35, 10, 20, 30, 40, 50, 60, 75, 90, 105, 120, 150, 180, 210 and 240 minutes. A set of secondary outcomes covers glucose and triglyceride Cmax, AUC and Tmax, along with RLP cholesterol Cmax and AUC. There are 20 outcome measures and 2 reported analyses in the record.

The primary arm summaries each include 26 participants, although the overall enrollment is 96. These figures refer to the posted primary per-protocol summaries, not the total enrollment. The registry record does not explain the reduction from enrollment to the analyzed primary population, give the original sample-size justification, or report the full statistical analysis plan. It also does not report details of allocation concealment, the masking procedures, or the method used to account for crossover sequence and period effects.

4. Participant Flow and Accounting

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

Before assignment: Of the 210 participants screened, 114 participants were not randomised. The reasons for the same were: not meeting study criteria (n=42), lost to follow-up (n=5), and other reasons (n=67).

Period 1

Washout Period 1

Reasons for not completing this period:

Period 2

Analysis population for the primary outcome, as defined in the registry: The per protocol (PP) population was the primary population used for analysis. The PP population included all subjects that fulfil the study entry criteria, receive at least one of the study treatments, have enough plasma samples to estimate the efficacy parameters and are without a major protocol deviation.

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 Cmax for Glucose in IFG Status Participants, measured over Blood samples will be taken at -35, 10, 20, 30, 40, 50, 60, 75, 90, 105, 120, 150, 180, 210 & 240 minutes.

The primary endpoint is Cmax for glucose in participants with IFG. Cmax is the highest measured glucose concentration during the specified post-meal sampling period, expressed in mg/dL. The registry lists blood draws from -35 through 240 minutes. The endpoint is a continuous measure, and the posted arm summaries report means and standard deviations.

A peak measure is used to characterize the height of the observed post-meal response. It differs from AUC, which summarizes concentration over time, and from Tmax, which records the time at which the peak occurs. These endpoints can describe distinct features of the same response: a change in peak height need not imply a similar change in total exposure or timing. That distinction is relevant because the registry includes all three types of glucose outcome among its measures.

Interpreting Cmax depends on how the samples were collected and how the peak was derived from them. The registry describes the sampling times but does not report the detailed calculation procedure, handling of missing samples, or whether peak values were transformed before analysis. The stated primary population was per protocol, with eligibility and sample-availability requirements; the full population definition is not reported in the registry record. The endpoint therefore describes the posted analyzed population and should not automatically be read as an estimate for every enrolled participant.

7. Results Posted in the Registry

• Test: mean 42.76 versus 47.40 on control; difference -4.64 (95% CI -17.54 to 8.26), standardized effect d = -0.20; z-test p = 0.481.

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.

Cmax for Glucose in Overweight Healthy Participants

• 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:
  • Test: mean 38.98 versus 43.64 on control; difference -4.66 (95% CI -9.90 to 0.58), standardized effect d = -0.25; z-test p = 0.081.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Test and the control. The interval gives the range of effects that remain compatible with the data.

AUC for Glucose in Overweight Healthy Participants

• 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:
  • Test: mean 27,164 versus 27,330 on control; difference -165.66 (95% CI -995.07 to 663.75), standardized effect d = -0.06; z-test p = 0.695.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Test and the control. The interval gives the range of effects that remain compatible with the data.

Tmax for Glucose in Overweight Healthy Participants

• 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:
  • Test: mean 25.74 versus 25.78 on control; difference -0.04 (95% CI -4.00 to 3.92), standardized effect d = -0.00; z-test p = 0.984.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Test and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes span several aspects of post-meal glucose and lipid response: peak concentration, cumulative exposure, and time to peak. The available glucose summaries for overweight healthy participants show a general pattern of lower average peak and AUC with the test drink, while the average timing of the peak is very similar between treatments. The unadjusted uncertainty intervals for these posted secondary comparisons include no difference, so the summaries do not provide a precise separation between treatments.

These secondary glucose measures concern overweight healthy participants, whereas the primary endpoint concerns participants with IFG and uses the per-protocol population. They are related to the primary question because they assess post-meal glucose response, but they are not the same endpoint or necessarily the same analysis population. They should not be treated as a direct replication of the primary result.

The registry also lists triglyceride Cmax, AUC and Tmax outcomes, and RLP cholesterol Cmax and AUC outcomes. Results for those listed measures are not reported in the registry record. The record lists 18 secondary outcomes as posted, but detailed numerical summaries in the available results cover only a subset. Consequently, an overall judgment about the full lipid response cannot be drawn from the reported details.

Secondary findings carry less weight when many outcomes are examined without a reported adjustment for multiplicity. Testing multiple questions increases the chance of an apparently notable result arising by chance. The registry record does not report whether the sponsor controlled that risk or designated a hierarchy among secondary endpoints. The secondary results are best read as descriptive and supportive context, not as a replacement for the reported primary analysis or as independent confirmation of it.

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

• Cmax for Glucose in Overweight Healthy Participants
• AUC for Glucose in Overweight Healthy Participants
• Tmax for Glucose in Overweight Healthy Participants
• Cmax for Triglyceride in Overweight Healthy Participants
• AUC for Triglycerides in Overweight Healthy Participants
• Tmax for Triglycerides in Overweight Healthy Participants
• Cmax for RLP Cholesterol in IFG Status Participants
• AUC for RLP Cholesterol in IFG Status Participants

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous: each participant’s glucose Cmax is a numeric measurement in mg/dL. The registry reports group means and standard deviations, and the sponsor’s analysis is identified as ANOVA with a difference in adjusted mean as its parameter. A mean difference expresses the contrast between treatment averages in the endpoint’s original units. The negative estimate indicates that the reported average under the test condition is lower than under placebo, according to the direction used in the comparison. It does not by itself describe how consistent individual participant responses were.

What does an unadjusted recalculation use?

The recalculated estimate is based on posted summary statistics rather than participant-level records. It uses the reported means and standard deviations to form a simple unadjusted comparison. This makes the calculation transparent, but it does not recover information absent from the summaries. In particular, the trial is a crossover design, so the same participant may have observations under both treatments. A paired analysis can use the within-person relationship between those observations; separate arm summaries do not preserve that relationship. The recalculation is therefore not a reproduction of the sponsor’s ANOVA.

The recalculation reports a pooled standard deviation and a standardized effect estimate as descriptive aids. A standardized effect expresses the estimated difference relative to a measure of variability, which can help compare effects measured on different scales. It does not replace the difference in mg/dL, and it does not resolve the discrepancy with the sponsor’s reported analysis. The record does not report enough model detail to explain that discrepancy.

What does a confidence interval communicate?

A confidence interval describes a range of values compatible with the estimate under the analysis and its assumptions. A narrower interval indicates greater precision; a wider interval indicates that the data are compatible with a broader range of effects. Whether an interval includes no difference is one useful feature, but the entire range matters. The sponsor’s 95% interval runs from -9.26 to -0.03 mg/dL, while the recalculated 95% interval runs from -17.54 to 8.263 mg/dL. These different ranges reflect the discrepancy between the analyses; the sponsor analysis takes precedence for interpretation of the trial’s reported result.

A p-value measures how unusual data at least as incompatible with a null hypothesis would be under the specified model, if the null hypothesis and model assumptions held. It is not the probability that the null hypothesis is true, nor does it measure the size or importance of an effect. The sponsor reports p = 0.049, while the unadjusted recalculation reports p = 0.481. Because the results differ, it would be misleading to select one p-value without identifying which analysis it comes from. The record does not report whether the sponsor’s analysis was adjusted for multiple outcomes.

Why does the crossover structure matter?

In a crossover study, each participant receives both treatments in different periods. This can make the within-person comparison informative because stable characteristics shared by that participant are held more nearly constant across treatments. The analysis may need to consider treatment, period, and sequence, and the precision can depend on how strongly a participant’s measurements under the two treatments are related. That relationship is sometimes represented by a within-participant covariance. The registry record does not report the covariance, the treatment sequences, or the details of the sponsor model, so those components cannot be assessed from the posted summaries.

The simple recalculation treats the group summaries as independent information and cannot incorporate the paired structure. This is why it should be read as an unadjusted re-calculation from posted summary statistics, not as the trial’s primary statistical analysis. No replacement crossover analysis can be reconstructed from the means and standard deviations alone.

What role does variability play?

Standard deviations describe spread among individual measurements around their group mean. A large standard deviation relative to a mean difference makes the treatment contrast harder to estimate precisely, particularly when the analyzed group is limited. In a crossover study, however, the relevant variability for a paired contrast can be different from the separate treatment-period standard deviations. Without participant-level paired data or a reported standard error from the sponsor’s model, the marginal standard deviations cannot fully describe the precision of the crossover contrast.

The registry reports an analysis population defined by protocol adherence and adequate plasma samples. Restricting an analysis to participants who meet such criteria can affect both the size and composition of the analyzed population. The record does not report how many enrolled participants were excluded from the primary analysis, why any were excluded, or the handling of missing values. These details matter when assessing the relationship between the analyzed sample and the enrolled population.

How should the secondary analyses be read?

The registry lists multiple secondary outcomes, and the available results include glucose Cmax, AUC and Tmax for overweight healthy participants. These outcomes are not identical to the primary IFG endpoint and are not all analyzed in the same population. Unadjusted secondary p-values, when reported, do not account for the number of questions being examined unless an adjustment is specified. If many outcomes are tested, the chance of at least one apparently notable result can rise. The record does not report a multiplicity strategy, so secondary findings should be treated as exploratory rather than as independent confirmation of the primary result.

More generally, over-dispersion means observed variation is greater than a model expects. When present, it can make standard errors too small and confidence intervals too narrow if the model does not allow for it. The registry does not report a specific over-dispersion assessment, and the posted continuous summaries do not establish that over-dispersion occurred in this trial. This is a general consideration, not a claim about these data.

11. Confidence Intervals and Precision

Precision is reflected by the range of estimates compatible with the analysis, not just by the point estimate. The primary unadjusted recalculation gives a mean difference of -4.64 mg/dL, with a 95% confidence interval from -17.54 to 8.263. This range includes zero and extends in both directions, so the aggregate-summary recalculation leaves substantial uncertainty about the size and direction of the contrast.

The sponsor’s adjusted analysis reports the same point estimate, with a 95% confidence interval from -9.26 to -0.03 mg/dL. This interval is narrower and does not include zero, but its upper end is close to zero. The different intervals should not be combined or treated as interchangeable: they come from different analyses. The registry identifies the sponsor method as ANOVA but does not provide enough detail to explain the difference in precision. Given the discrepancy, the sponsor’s analysis takes precedence when describing the trial result.

The primary summaries include 26 participants per group, while enrollment was 96. The primary analysis used the per-protocol population. The record does not explain the analyzed-sample reduction or report the number and reasons for exclusions. These features limit assessment of how precisely the primary result applies beyond the analyzed participants.

For a crossover study, precision also depends on the within-participant relationship between treatment-period outcomes. The posted marginal summaries do not show that relationship, so the simple recalculated interval cannot reflect it. The sponsor’s interval may incorporate a different model, but the record does not state the model details needed to verify that. The confidence intervals quantify statistical uncertainty under their respective analyses; they do not account for every uncertainty about missing data, population selection, or analysis choices.

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 estimates are planning illustrations based on the observed effect, not the trial’s original design justification. For the continuous outcomes in this record, the figures are participants per arm, not total events. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect.

The 80% power column gives the estimated participants per arm for a design with an 80% chance of detecting the specified effect under those assumptions if that effect is real. The 90% power column gives the corresponding estimate for a design with a 90% chance. These are design probabilities under an assumed effect and analysis framework; they are not a statement about whether the completed trial had a particular chance of success. The registry record does not report the trial’s own sample-size justification or original design assumptions.

Rows based on 100%, 75% and 50% of the observed effect illustrate how the planning estimate changes when the target effect is varied. Smaller effects are harder to distinguish from random variation, so they require more participants under the same planning assumptions. The estimates can become very large when the observed effect is small relative to variability, as with the posted time-to-peak summary. These outputs are not a recommendation for a future trial: they depend on the observed effect and the stated simplifying assumptions, and crossover-specific design considerations are not described in the planning assumptions.

This is not observed power. It is a prospective calculation using an assumed effect size and specified design inputs. The estimates should be read as illustrations of how sample size depends on the target effect and desired power, rather than as evidence that the completed study was adequately or inadequately powered.

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 = -4.640; pooled standard deviation σ = 23.736.
2. n = 2 × (1.96 + 0.842)² × 23.736² / -4.640² = 410.8, rounded up to 411 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 <- -4.64                      # observed difference in means
sigma <- 23.7362247419424                     # 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(2119325)
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 = 411 (80% power), 550 (90% power)
 75% of observed effect: n per arm = 731 (80% power), 978 (90% power)
 50% of observed effect: n per arm = 1,644 (80% power), 2,200 (90% power)
power.t.test: 411.8 
Simulated power at n = 411 per arm: 0.797 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Analyses Reported by the Sponsor

The registry reports the sponsor’s primary result as an ANOVA difference in adjusted mean of -4.64 mg/dL, with a 95% confidence interval from -9.26 to -0.03 and p = 0.049. A separate unadjusted recalculation from the posted means and standard deviations gives the same point estimate, but a 95% confidence interval from -17.54 to 8.263 and p = 0.481. The estimates are therefore not in full agreement: the uncertainty measures and p-values differ.

The sponsor result is the trial’s reported analysis and takes precedence over the unadjusted recalculation. The recalculation is a simple comparison of summary statistics and does not reproduce the sponsor’s ANOVA. This distinction is especially relevant because the trial used a crossover design, while the marginal summaries do not contain the within-participant information needed to reconstruct a paired analysis.

The registry does not describe the sponsor model’s covariates, handling of period or sequence, or other adjustment details. It is therefore not possible to explain from the record why its confidence interval is narrower, or to independently verify the model specification. The sponsor’s reported result should be quoted as reported, while the recalculation is useful as a check on the posted group summaries rather than a substitute for the sponsor analysis.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists 20 outcome measures and reports 2 analyses. It includes a primary glucose Cmax endpoint and a range of secondary glucose and lipid measures. When several endpoints are analyzed, a nominal p-value for one outcome does not automatically account for the number of other tests performed. A multiplicity plan may specify which outcome is primary, how other hypotheses are ordered, or how error rates are controlled. The registry record does not report whether such a plan was used, how many comparisons were included in any adjustment, or whether secondary p-values were adjusted.

The sponsor reports an ANOVA for the primary endpoint, but the record does not provide the full statistical analysis plan or describe which terms were included. It does not report whether treatment sequence and period were modeled, how within-participant correlation was handled, whether any transformations were used, or whether there were interim analyses. Those details are not reported in the registry record and should not be inferred from the study’s crossover design alone.

The primary analysis population was per protocol. Its description includes meeting entry criteria, receiving at least one treatment, having enough plasma samples to estimate efficacy parameters, and being without a major protocol deviation; the final part of the description is truncated. The full definition is not reported in the registry record. The registry does not state how many enrolled participants contributed to the primary analysis beyond the 26 participants shown for each treatment summary, nor does it report reasons for exclusion or a comparison with an intention-to-treat analysis.

Missing-data handling is also not reported in the registry record. The primary population required sufficient samples, but the record does not say how incomplete sampling was handled, whether any values were imputed, or how missing outcomes affected analysis. This matters because exclusions based on available samples or protocol deviations can affect the analyzed population and the interpretation of results. The record also does not report a sensitivity analysis evaluating these choices.

These reporting gaps do not establish that a particular procedure was or was not used. They limit what can be assessed from the registry record, including multiplicity control, missing-data assumptions, and the exact crossover model behind the sponsor’s result.

16. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 1% reporting threshold.

Most frequent non-serious adverse event terms

The registry reports zero serious adverse events among 96 participants at risk in the test group and zero among 96 at risk in the placebo group. It lists no serious adverse event terms. For other adverse events, the record reports zero affected among 96 at risk for the test group and 1 affected among 96 at risk for placebo. These are the recorded counts, not an estimate of whether a difference in safety exists.

The registry does not report deaths as a numeric count; death entries are null, so deaths are not reported in the registry record. The at-risk counts are presented by treatment group, but the record does not explain how crossover exposure periods map to unique participants for these safety summaries. A zero count in the registry means no event was recorded in that category for the listed group; it does not establish that an event is impossible or characterize risk beyond the observed study.

These figures are limited by the sample and follow-up represented in the record. The registry does not provide event narratives, severity details for the other adverse event, or additional information about the timing and duration of safety monitoring. The counts should therefore be read descriptively and separately from the efficacy analyses.

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

The sponsor’s reported primary analysis estimates a lower adjusted mean glucose Cmax with the fibre-rich drink than with placebo. The estimate is -4.64 mg/dL, and the reported 95% confidence interval ranges from -9.26 to -0.03 mg/dL. The interval is close to zero at its upper end, so the result should be read with attention to its uncertainty. The registry’s separate unadjusted recalculation has a wider interval that includes zero; because the analyses differ, the sponsor’s analysis takes precedence for describing the trial’s reported result.

This result does not, by itself, establish what would happen for every participant or in populations beyond the analyzed group. The primary analysis used a per-protocol population, and the primary summaries include 26 participants per treatment. The trial enrolled 96 participants, but the record does not explain the difference between enrollment and the primary analyzed summaries. The full per-protocol definition is also incomplete in the registry record.

The primary endpoint is a measured peak during a specified post-meal sampling period. It is not equivalent to total glucose exposure, timing of the peak, or a broader clinical outcome. The registry lists other glucose and lipid measures, but results for several of them are not reported in the registry record. The available secondary summaries provide related but distinct information and are not a substitute for the primary endpoint.

The registry record does not report the sponsor model’s full specification, multiplicity strategy, missing-data procedures, or original sample-size justification. Those gaps limit assessment of analysis choices and the strength of conclusions across multiple endpoints. The safety figures are sparse descriptive counts and do not characterize uncommon events or risks beyond the study record. The results should be understood as estimates from this completed trial and its reported analysis, not as medical advice or a guarantee of an individual response.

18. Why This Trial Matters Statistically

This trial illustrates why an analysis should be interpreted in light of the design that generated the data. A crossover study can support within-person comparisons, but the posted marginal means and standard deviations do not show the paired relationship between treatment periods. A simple recalculation from those summaries can therefore differ in precision from the sponsor’s analysis, even when the point estimates match. Reporting both the sponsor result and the recalculation makes that distinction visible.

It also shows why confidence intervals matter alongside p-values. The sponsor’s interval is close to zero at one end, while the unadjusted interval is wider and includes zero. Looking only at the point estimate would obscure the disagreement in precision; looking only at a thresholded p-value would omit the range of compatible effects. The registry’s limited model detail means the source of the discrepancy cannot be fully evaluated.

The primary and secondary measures address different features of post-meal response and use different populations. This matters statistically because multiple outcomes can create multiple opportunities for apparently notable results, and because a secondary endpoint in a different population is not a direct substitute for the primary comparison. The record does not describe a multiplicity strategy, so the distinctions among endpoint status, population, and analysis deserve attention.

Finally, the difference between total enrollment and the primary analyzed summaries highlights the importance of transparent analysis populations and missing-data reporting. Without a complete account of exclusions and sample availability, readers cannot fully assess how the per-protocol result relates to all enrolled participants. These are general lessons in interpreting trial results, not claims about unreported procedures in this study.

19. Limitations

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

Several limitations are specific to the record and design. Although the study is described as a two-period crossover, the registry does not report treatment sequence, period duration, washout arrangements, or the analysis model’s handling of sequence and period. These details are important for understanding whether treatment effects could be entangled with period or carryover effects, but their use or absence cannot be inferred from the available record.

The primary outcome summaries include 26 participants per treatment, compared with total enrollment of 96. The registry identifies a per-protocol population with entry, treatment receipt, sample availability and protocol-deviation requirements, but the description is truncated. It does not report the number excluded, reasons for exclusion, missing-data methods, or an intention-to-treat result. This limits assessment of selection into the primary analysis.

The sponsor’s adjusted result and the unadjusted recalculation differ in uncertainty and p-value. The registry identifies the sponsor method as ANOVA but does not report enough detail to explain or reproduce it. The recalculation does not use participant-level paired data, so it cannot account for the crossover correlation. Sponsor analysis takes precedence, but independent assessment of its model is limited.

The registry also does not report the original sample-size justification, the full statistical analysis plan, multiplicity handling, or interim analyses. Outcomes include numerous secondary measures, while results for several listed lipid outcomes are not reported in the registry record. Safety information is limited to event counts, with deaths not reported as a numeric count. These reporting limitations restrict interpretation of the full outcome set and the scope of conclusions.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: The Effect of a Nutritional Supplement on Post Prandial Glucose and Lipids in an Adult Population — NCT02119325.
• Registry record last updated 2015-05-15; status last verified 2015-04.
• Results first posted 2015-05-15.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

NCT02119325 is a completed randomized, double-blind, two-treatment, two-period crossover study sponsored by GlaxoSmithKline. It enrolled 96 participants and compared a 25% fibre health food drink with a 0% fibre drink in overweight adults with impaired fasting glucose. The primary endpoint was glucose Cmax in the per-protocol IFG population.

The sponsor reported an adjusted mean difference of -4.64 mg/dL, with a 95% confidence interval from -9.26 to -0.03 and p = 0.049. An unadjusted recalculation from the posted summaries has the same estimate but a different confidence interval and p-value; the sponsor analysis takes precedence. The registry does not report full model details, the original sample-size justification, the complete analysis plan, or handling of missing data and multiplicity. Serious adverse event counts were zero in both groups; death counts are not reported in the registry record.