Dietary Acid Load, Kidney Function and Disability in Elderly: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Dietary Acid Load, Kidney Function and Disability in Elderly (NCT02691663), a randomized na study. The primary outcome is Blood Bicarbonate.

2. Clinical Question

3. Trial Design

This was a randomized, quadruple-masked, parallel-group trial comparing oral bicarbonate supplementation with placebo. The registry lists enrollment of 196 and a completed status. The start is recorded as 2016-02, and primary completion as 2018-06-15. The phase is listed as NA. Wake Forest University Health Sciences is named as the sponsor.

The intervention group was assigned 0.3 meq/kg/day NaHCO3 capsules; the placebo group received methylcellulose capsules. The study purpose concerned kidney and physical function, while the primary posted measure is blood bicarbonate. The registry lists chronic kidney disease and disability as conditions. More detailed eligibility criteria, recruitment setting, and participant characteristics are not reported in the registry record.

The primary blood bicarbonate summaries are for baseline, with 41 participants in the supplementation group and 42 in the placebo group. Those counts are smaller than total enrollment, but the reason for the difference is not reported in the registry record. The primary outcome’s timing matters: a baseline comparison describes the groups’ starting measurements rather than change after assignment. The registry also lists a six-month post-baseline adherence outcome and secondary measurements including carbon dioxide blood tests, 400 Meter Walk Time and estimated glomerular filtration rate. The posted summary does not establish that each listed outcome was collected at the same time or in the same participants.

The original design’s sample-size justification, intended analysis populations, allocation procedures, detailed masking procedures and protocol-specific analysis plan are not reported in the registry record. The planning illustrations based on observed effects are separate from the trial’s original design assumptions.

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

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: PI is reporting blood bicarbonate from blood gas measurement as a surrogate of NEA because a percentage of NEA samples could not be accurately measured due to technical issues.

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 Blood Bicarbonate, measured over baseline.

The primary measure is blood bicarbonate, reported as a mean in mmol/L with standard deviation. The registry describes it as a measurement from a blood sample that measures how much carbon dioxide is in the blood. It gives a normal adult result as between 23 and 29 mmol/L. That reference range is descriptive context from the registry, not a basis for inferring the trial’s clinical conclusions.

The record says the principal investigator reported blood bicarbonate from a blood gas measurement as a surrogate of NEA, because a percentage of NEA samples could not be accurately measured due to technical issues. A surrogate is a measurable feature used in place of another quantity that is difficult to measure directly; it is not automatically equivalent to that underlying quantity. The reason for choosing blood bicarbonate is therefore documented, but the degree to which it represents NEA in this trial is not reported in the registry record.

The listed timeframe is baseline. That makes the posted primary comparison a comparison of starting measurements between randomized groups, not a direct estimate of how supplementation changed blood bicarbonate during follow-up. The registry does not report a post-baseline primary blood bicarbonate result or an analysis of change for this primary measure.

7. Results Posted in the Registry

• Oral Bicarbonate Supplementation Group: mean 22.80 versus 23.20 on control; difference -0.40 (95% CI -1.41 to 0.61), standardized effect d = -0.17; z-test p = 0.438.

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.

Percent Adherence: Percentage of Pills Taken

• 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:
  • Oral Bicarbonate Supplementation Group: mean 90.50 versus 85.20 on control; difference 5.30 (95% CI -2.85 to 13.45), standardized effect d = 0.30; z-test p = 0.202.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Oral Bicarbonate Supplementation Group and the control. The interval gives the range of effects that remain compatible with the data.

Carbon Dioxide Blood Test

• 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:
  • Oral Bicarbonate Supplementation Group: mean 35.40 versus 36.50 on control; difference -1.10 (95% CI -2.76 to 0.56), standardized effect d = -0.28; z-test p = 0.194.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Oral Bicarbonate Supplementation Group and the control. The interval gives the range of effects that remain compatible with the data.

400 Meter Walk Time

• 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:
  • Oral Bicarbonate Supplementation Group: mean 6.30 versus 6.30 on control; difference 0.00 (95% CI -0.56 to 0.56), standardized effect d = 0.00; z-test p = 1.000.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Oral Bicarbonate Supplementation Group and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover adherence, blood carbon dioxide, walking performance and kidney function, but the posted summaries include only a subset of the listed measures. Their timeframes also differ: adherence is reported at 6 months post baseline, while the carbon dioxide blood test and 400 Meter Walk Time summaries are baseline measurements. The registry lists estimated glomerular filtration rate among secondary outcomes, but no corresponding result is included in the reported secondary summaries.

Across the posted continuous summaries, the comparisons are descriptive or unadjusted recalculations, not a coordinated analysis of changes over follow-up. The baseline carbon dioxide and walking summaries describe starting measurements. The adherence summary is later, but it is not itself a kidney-function or physical-function endpoint. These results therefore do not supply a follow-up primary blood bicarbonate comparison or establish a common pattern of treatment effects across outcomes.

Unadjusted secondary results carry less weight for several reasons. A trial may examine multiple secondary outcomes, and the more comparisons made, the greater the possibility that some apparent differences arise through chance alone. The registry record does not report a multiplicity strategy or identify which secondary comparisons were formally prespecified for inferential testing. The posted secondary p-values should not be interpreted as if they had been adjusted or confirmed by a full analysis plan.

Secondary outcomes can provide context and help describe participant experience or related measures, but they do not replace the primary endpoint. In this record, the primary outcome is a baseline measurement, while several secondary entries are also baseline summaries. The difference in timing and the absence of reported follow-up analyses limit how much the secondary results can contribute to a conclusion about change over time. The number of outcome measures and the absence of reported analyses further counsel against treating isolated unadjusted comparisons as definitive evidence.

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

• Percentage of Screened Participants Randomized
• Percent Adherence: Percentage of Pills Taken
• Carbon Dioxide Blood Test
• Carbon Dioxide Blood Test
• 400 Meter Walk Time
• 400 Meter Walk Time
• 400 Meter Walk Time
• Estimated Glomerular Filtration Rate (eGFR)

9. Statistical Interpretation

10. Statistical Methods Explained

What does a mean difference describe?

For a continuous outcome, a group mean is the arithmetic average of participants’ measurements. A mean difference compares the two group averages. In the posted primary comparison, the estimate is expressed as oral bicarbonate supplementation minus placebo, so a negative estimate means the measured average was lower in the supplementation group at that measurement time. The result is in mmol/L, the same unit as the outcome.

A mean difference summarizes the location of group averages; it does not describe every participant’s measurement or show how much individuals changed. The standard deviations, 2.3 mmol/L and 2.4 mmol/L, describe how dispersed the measurements were within the respective groups. A standard deviation is not the uncertainty of the estimated difference. Uncertainty in the comparison is described by its confidence interval.

What does the confidence interval convey?

A confidence interval gives a range of estimates compatible with the observed data under the statistical method used. In general, wider intervals indicate less precision, while narrower intervals indicate greater precision. The primary interval runs from -1.411 to 0.611 mmol/L and includes zero. This means the data do not isolate a single direction of baseline difference with high precision. The interval is not a range containing a fixed proportion of individual participants’ values, and it is not proof that every value within it is equally plausible.

How should the p-value be read?

The unadjusted p-value for the baseline comparison is 0.438. In general, a p-value describes how unusual data at least as incompatible with a specified null hypothesis would be if that hypothesis and the model assumptions held. It is not the probability that the null hypothesis is true, the probability that the finding occurred by chance, or a measure of clinical importance. The record does not report an adjustment for multiple comparisons or an inferential analysis plan, so the p-value should be read as an unadjusted recalculation rather than as a complete account of the trial’s planned evidence.

Why distinguish baseline from follow-up?

Randomization is intended to create groups that are comparable on average at the point of assignment, subject to chance variation. A baseline measurement can describe that starting balance. To estimate change associated with assignment, an analysis generally needs post-baseline measurements and a defined comparison, such as change from baseline or a model accounting for baseline and follow-up values. These are general statistical approaches; the particular approach planned for this trial is not reported in the registry record. Since the primary blood bicarbonate timeframe is baseline, its posted mean difference is not a follow-up treatment-effect estimate.

What are the limits of descriptive summaries?

Means and standard deviations are informative for continuous data but do not reveal the distribution’s shape, outliers, or whether missing observations differed between groups. General statistical practice may use diagnostic checks or alternative methods if data are skewed or otherwise poorly represented by a mean and standard deviation. The registry record does not report distributional checks, transformations, or alternative estimators for these outcomes.

Likewise, a simple unadjusted comparison does not account for covariates or repeated measurements. An adjusted model may be appropriate in some trial settings, but the registry does not report whether such a model was planned or performed. The summaries should therefore be interpreted on their own terms rather than treated as if they came from an undocumented model.

What does over-dispersion mean in general?

Over-dispersion is a general concern for some outcomes, especially counts, when their variability exceeds what a statistical model expects. It can make standard errors and confidence intervals too optimistic if the model does not account for the extra variability. The main reported measure is continuous, and the registry does not report an over-dispersion assessment or a count-outcome model. This concept is relevant as general statistical context, not as a reported feature of the trial analysis.

The secondary adherence comparison is also a continuous mean summary, with reported standard deviations. Its unadjusted recalculated mean difference is 5.3 percentage points, with a 95% confidence interval from -2.845 to 13.45 and unadjusted p-value 0.202. Such a result still requires caution: a confidence interval crossing zero does not show equivalence, and an unadjusted secondary comparison does not account for the number of outcomes considered.

Finally, the registry lists 27 outcome measures and zero analyses, while secondary summaries are posted for some measures. Descriptive outcome entries and recalculated comparisons are not the same as a complete prespecified analysis. The outcome hierarchy, formal testing plan, missing-data methods, and multiplicity strategy are not reported in the registry record. Those details would be needed to assess whether the displayed comparisons match the intended primary and secondary inferential procedures.

11. Confidence Intervals and Precision

The primary mean difference is -0.4 mmol/L, with a 95% confidence interval from -1.411 to 0.611 mmol/L. The interval crosses zero, so the observed baseline data are compatible with no group difference as well as with differences in either direction. The interval’s width conveys meaningful uncertainty around the point estimate; the estimate alone should not be read as a precise statement about the underlying difference.

Precision depends on the amount of information available and the variability of the outcome, among other factors. The primary comparison uses 41 participants in the supplementation group and 42 in the placebo group, with standard deviations of 2.3 and 2.4 mmol/L. The registry does not explain why these outcome counts are below total enrollment of 196. It also does not report the handling of missing measurements, which makes it difficult to assess whether the available summaries represent the randomized groups as intended.

A confidence interval is not a guarantee that the true effect lies inside its endpoints. In general, it describes uncertainty under the statistical method and assumptions used. Here the reported comparison is an unadjusted recalculation from group summaries, and no sponsor-reported primary analysis is listed. The interval therefore does not account for any unreported adjustment, repeated-measure structure, or multiplicity plan.

Because the primary measure is recorded at baseline, its precision concerns the difference between baseline means, not the precision of an intervention effect over follow-up. The confidence interval cannot repair that timing limitation. A post-baseline primary estimate and its interval are not reported in the registry record.

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 a total across both arms. The planning 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. These are planning illustrations calculated from the observed effect, not the trial’s original design assumptions; the original design assumptions are not reported in the registry record.

The 80% power column gives an illustrative number of participants per arm for a design with an 80% chance of detecting the assumed effect under the stated assumptions, if that effect and the model assumptions hold. The 90% power column uses a higher target chance of detection and correspondingly gives a larger planning figure for the same assumed effect. For the primary comparison, planning for 100% of the observed effect gives 543 participants per arm for 80% power and 727 for 90% power. Planning for 75% of the observed effect gives 965 and 1,291 participants per arm; planning for 50% gives 2,170 and 2,905.

Smaller effects require more participants to distinguish them from random variation at a given power target. The same logic applies to the other outcomes with planning entries, although their variability and observed estimates differ. These figures are not observed power, do not describe the number actually enrolled or analyzed, and do not show that the study was designed to detect its observed estimate. The registry record does not report the original sample-size justification.

A curve relating assumed effect size to planning sample size represents this same dependence: as the effect one wants to detect becomes smaller, the required sample grows. Such illustrations depend on their assumptions and should not be treated as forecasts of a future study’s exact requirements.

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 = -0.400; pooled standard deviation σ = 2.351.
2. n = 2 × (1.96 + 0.842)² × 2.351² / -0.400² = 542.3, rounded up to 543 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 <- -0.399999999999999                      # observed difference in means
sigma <- 2.3511489179905                     # 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(2691663)
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 = 543 (80% power), 727 (90% power)
 75% of observed effect: n per arm = 965 (80% power), 1,291 (90% power)
 50% of observed effect: n per arm = 2,170 (80% power), 2,905 (90% power)
power.t.test: 543.3 
Simulated power at n = 543 per arm: 0.798 (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 27 outcome measures and zero analyses. It does not report whether the study used a hierarchy of primary and secondary hypotheses, adjusted significance thresholds, or another strategy for controlling multiplicity. Multiplicity matters because testing many outcomes can increase the chance of observing apparently notable results even when there is no underlying effect. This is a general statistical concern; the record does not establish how it was addressed in this trial.

The record also does not report how missing outcome data were handled, whether participants were analyzed according to randomized assignment, or whether any per-protocol or other analysis population was used. The posted primary baseline summaries include 41 participants in the supplementation group and 42 in the placebo group, compared with enrollment of 196. For the adherence summary at 6 months post baseline, the group counts are 34 and 37. The reasons for these different outcome counts and the implications of missing observations are not reported in the registry record.

Missing-data methods can affect estimates if participants without measurements differ from those with measurements. In general, complete-case summaries describe only participants with available data unless additional methods are used. The record does not say whether the posted summaries are complete-case results, whether missing values were imputed, or whether sensitivity analyses were performed. No assumptions about missingness can therefore be verified.

The record does not report a protocol-specific statistical analysis plan, prespecified covariate adjustments, repeated-measure models, or interim analyses. The displayed unadjusted comparisons should not be mistaken for evidence that no other analysis was planned or conducted outside the registry; they simply are not reported in the registry record. These gaps limit evaluation of whether outcomes, analysis populations and statistical procedures were selected before results were known.

15. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: atrial fibrillation (2.4%), transient ischemic attack (2.4%).

The registry reports serious adverse events affecting 1 participant in the oral bicarbonate group and 1 participant in the placebo group. The groups at risk were 41 and 42, respectively. Other adverse events affected 8 participants in each group; the corresponding groups at risk were 41 and 42. No deaths are reported in either group.

Atrial fibrillation and Transient Ischemic Attack are listed as the top serious-event terms, each at 2.4%. The registry summary does not assign these terms to a particular treatment group, so they should not be attributed to either group from these figures alone. The term-level percentages also do not convey event timing, severity beyond the serious classification, or whether events were considered related to the intervention.

These counts are descriptive safety information. They do not by themselves establish whether the groups differ in risk, and the registry record does not report a statistical comparison of adverse events. Interpretation is limited by the number of participants represented, the absence of further event details and the lack of a reported safety analysis plan.

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

The posted primary result says that the baseline mean blood bicarbonate measurements were similar in scale across the randomized groups, with an unadjusted difference estimate of -0.4 mmol/L and a confidence interval that includes zero. It does not show that supplementation changed blood bicarbonate during follow-up: the primary timeframe is baseline, and a post-baseline primary result is not reported in the registry record.

The measure was chosen as a surrogate of NEA because technical issues affected accurate measurement of a percentage of NEA samples. A surrogate may help represent a concept that is difficult to measure, but the registry does not report how strongly blood bicarbonate corresponds to NEA in this study. Nor do the posted primary numbers establish effects on kidney function or physical function, the broader areas named in the study purpose.

The randomized and masked design provides important design context, but it does not remove uncertainty from missing outcome counts, unreported analysis methods, or the baseline timing of the primary result. The record does not report a treatment-effect analysis for change over time, the planned analysis population, or a multiplicity strategy. Secondary summaries are not a substitute for those missing elements and should be read as descriptive or unadjusted results.

Thus, the posted statistics characterize group measurements and their uncertainty; they do not support a conclusion about whether the intervention benefits or harms an individual. They also do not establish equivalence between groups. A conclusion about intervention effects would require appropriately timed outcome comparisons and sufficient reporting of how those estimates were obtained. Such information is not reported in the registry record for the primary measure.

17. Why This Trial Matters Statistically

This trial illustrates why the timing and definition of an endpoint matter as much as its numerical comparison. Blood bicarbonate is named as the primary measure and described as a surrogate for NEA, but the listed timeframe is baseline. A baseline difference can describe starting values; it cannot serve as an estimate of change caused by an assigned intervention. Readers need to check the outcome’s measurement time before interpreting a group contrast as a treatment effect.

The record also provides a useful example of separating descriptive summaries from formal analysis. Means, standard deviations and an unadjusted recalculated difference can convey the observed data and their uncertainty. They do not reveal whether a planned adjusted or longitudinal model was used, how missing outcomes were treated, or how multiple outcomes were handled. These distinctions are central to evaluating clinical trial evidence rather than simply comparing point estimates.

The trial’s surrogate endpoint rationale is also statistically relevant. A proxy measure may be necessary when direct measurement is technically difficult, but its interpretation depends on how well it represents the target concept. The registry explains why blood bicarbonate was reported, while leaving its relationship to NEA in this trial unquantified.

Finally, planning illustrations based on observed effects are different from original design justification and from observed power. Keeping those concepts distinct avoids reading a retrospective planning calculation as evidence that a trial was adequately designed for its stated goals. Together, these issues show why precise reporting of estimands, timing, analysis populations and uncertainty is essential for interpreting randomized trials.

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 major limitation of the available results is that the primary blood bicarbonate measure is reported at baseline. A post-baseline comparison or change analysis for that endpoint is not reported in the registry record, so the primary statistics do not directly describe an intervention effect over time.

The primary summaries include 41 participants in the supplementation group and 42 in the placebo group, although enrollment is 196. The reason for this difference is not reported. The registry also does not report missing-data handling or the analysis population, limiting assessment of selection and attrition. The adherence summaries have different participant counts, and the record does not explain the missing observations there either.

The registry identifies blood bicarbonate as a surrogate of NEA because technical issues prevented accurate measurement of a percentage of NEA samples. It does not report validation of that surrogate within the trial or how closely it reflected NEA. Outcomes such as estimated glomerular filtration rate are listed, but a corresponding result is not included among the secondary summaries described.

Formal analysis details are sparse: the registry lists zero analyses and does not report multiplicity control, covariate adjustment, repeated-measure methods, protocol-specific sample-size justification, or interim analyses. The unadjusted comparisons therefore provide limited information about the intended inferential plan. Safety reporting gives counts and leading serious-event terms but not detailed event narratives, attribution, timing or a formal group comparison.

These limitations concern what is reported in the registry record; they do not establish that unreported procedures or analyses were not conducted elsewhere. They do restrict what can be concluded from the posted summaries alone.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Dietary Acid Load, Kidney Function and Disability in Elderly — NCT02691663.
• Registry record last updated 2023-09-25; status last verified 2023-06.
• Results first posted 2023-09-25.
• 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

NCT02691663, Dietary Acid Load, Kidney Function and Disability in Elderly, is recorded as a completed, randomized, quadruple-masked, parallel-group study sponsored by Wake Forest University Health Sciences. Enrollment was 196. The intervention was 0.3 meq/kg/day NaHCO3 capsules, compared with methylcellulose placebo capsules.

The posted primary measure is baseline blood bicarbonate. The mean difference was -0.4 mmol/L, with a 95% confidence interval from -1.411 to 0.611 and unadjusted p-value 0.438. The registry describes this measure as a surrogate of NEA due to technical issues with accurate measurement of a percentage of NEA samples. The baseline comparison is not a follow-up treatment-effect estimate.

Secondary summaries include adherence at 6 months post baseline and baseline carbon dioxide and walking measures. The record does not report a primary follow-up analysis, missing-data methods, a multiplicity strategy, or the original design’s sample-size assumptions. Safety counts report 1 participant with a serious adverse event in each group and no deaths.