Gut and Azithromycin Mechanisms in Infants and Children II: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Gut and Azithromycin Mechanisms in Infants and Children II (NCT04315272), a randomized phase 4 study. The primary outcome is Malaria Status.

2. Clinical Question

3. Trial Design

The registry describes Gut and Azithromycin Mechanisms in Infants and Children II as a Phase 4, randomized, quadruple-masked, parallel-group trial. It lists the condition as malaria, the sponsor as the University of California, San Francisco, and the status as completed. Enrollment is listed as 449. The registry dates the study start to 2020-08-21 and primary completion to 2022-06-01.

Participants were assigned to a single dose of azithromycin or a single dose of placebo. The arm-level results list 220 participants in the azithromycin group and 227 in the placebo group. Those are the posted group counts; the record does not explain the relationship between them and the listed enrollment figure. Randomized assignment supports a comparison of the groups, but the registry record does not give detail on the allocation sequence, concealment procedures, or the roles covered by quadruple masking.

The primary outcome is malaria status at 2 weeks, assessed with a rapid diagnostic test. The registry also lists clinical malaria at 2 weeks as a secondary outcome. The study summary describes an expansion of an earlier study and planned longitudinal evaluation, but details of the earlier trial’s results are not part of this record. The study’s own original sample-size assumptions, recruitment procedures, and any changes to the design are not reported in the registry record.

The posted results include outcome counts and comparison statistics, but the registry lists no reported analyses. Thus, readers can inspect the registered outcome summaries and the comparisons displayed with them, but cannot determine from this record which analysis population, adjustment strategy, or missing-data procedure was used.

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 Malaria Status, measured over 2 weeks.

The primary endpoint is malaria status at 2 weeks, measured as a count of participants with the event. The registry says a rapid diagnostic test was administered to all children to determine malaria status. This is a binary outcome: each participant is represented as having or not having the measured status for the endpoint, and the results summarize the number recorded in each group.

Using a defined assessment and time point makes the groups comparable on a common outcome, provided the assessment and follow-up were applied consistently. The registry’s stated 2-week timeframe aligns with the study summary’s description of assessing malaria after a 14 day period. The outcome is not a measure of every dimension of malaria or of longer-term health; it is the specific test-based status recorded at this assessment.

The endpoint is useful for a randomized group comparison because it expresses the observed outcome as participant counts and allows the groups’ event frequencies to be compared. The posted record does not report further details about test performance, diagnostic procedures, or how missing test results were handled. Those details matter for understanding how completely the endpoint was measured and how the reported counts relate to all randomized participants.

7. Results Posted in the Registry

• Azithromycin: 8.2% (18/220) versus 7.0% (16/227) on control; risk difference 1.1 percentage points (95% CI -3.8 to 6.1), risk ratio 1.16 (0.61–2.22); unadjusted two-proportion z-test p = 0.651.

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.

Clinical Malaria

• 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:
  • Azithromycin: 2.7% (6/220) versus 2.6% (6/227) on control; risk difference 0.1 percentage points (95% CI -2.9 to 3.1), risk ratio 1.03 (0.34–3.15); unadjusted two-proportion z-test p = 0.956.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Azithromycin and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists one secondary outcome, clinical malaria, assessed at 2 weeks. Its posted counts and comparison are shown in the secondary results table. The overall pattern across the primary and secondary outcomes is that neither comparison shows a clear separation between the groups in the posted summaries. This broad pattern is consistent with the uncertainty visible in the tables, but does not establish equivalence or rule out effects that the available data could not estimate precisely.

The secondary outcome is related to the primary malaria-status question, but it is not interchangeable with the primary endpoint. The record labels the primary outcome as malaria status assessed by rapid diagnostic test and the secondary outcome as clinical malaria. The registry does not give further operational definitions that would establish exactly how the outcomes overlap or differ in measurement. Their results should therefore be read as distinct registered measures rather than combined into a single outcome.

The secondary comparison is unadjusted. General statistical explanation: when multiple outcomes or comparisons are examined, the chance of seeing an apparently unusual result somewhere among them can increase. Adjustments or a prespecified hierarchy can address some multiplicity concerns, but the registry does not report whether either was used. A secondary result consequently carries less weight than a primary result when it is not clear how outcomes were prioritized or whether the analysis plan accounted for multiple comparisons.

The registry also lists no reported analyses. It does not report whether the secondary outcome was prespecified in the same way as the primary endpoint, whether all planned secondary outcomes are posted, or how missing secondary measurements were handled. The posted result is informative as a registry summary, but should not be treated as a definitive standalone test or as a replacement for the primary comparison.

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

• Clinical Malaria

9. Statistical Interpretation

10. Statistical Methods Explained

The posted primary and secondary outcomes are binary participant-count outcomes. For each group, the table gives the number of participants with an event and the number in the group. The comparison table reports a risk difference, relative measures, confidence intervals, and an unadjusted p-value. No reported analyses are listed in the registry, so details of the analysis procedures beyond these posted summaries are not reported in the registry record.

What does a risk difference describe?

General statistical explanation: a risk difference compares the event proportion in one group with the event proportion in another. A value of zero corresponds to no difference between the proportions. Positive or negative values indicate direction according to the subtraction order used by the table. Here, the displayed comparison is for azithromycin relative to placebo. The risk difference is directly interpretable in terms of how far apart the groups’ event frequencies are, but its estimate is still subject to sampling variability.

Why are relative measures also shown?

General statistical explanation: a risk ratio compares event proportions as a ratio, while an odds ratio compares event odds. These relative measures can help describe the same group contrast from another perspective. They are not separate outcomes, and they can appear numerically different from a risk difference because they use different scales. Their confidence intervals show how uncertain the estimates are. The primary table presents these measures alongside the participant counts and risk-difference estimate; readers should consider the counts and intervals rather than interpreting a single measure in isolation.

What does the p-value contribute?

General statistical explanation: a p-value summarizes how unusual data at least as incompatible with a specified no-difference model would be under the assumptions of the test. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect. The registry labels the posted values unadjusted. The primary outcome’s p-value is 0.651, and the secondary outcome’s is 0.956. Neither value should be treated as a substitute for the estimates, confidence intervals, design information, or analysis details.

Why does the confidence interval matter?

General statistical explanation: a confidence interval gives a range of estimates compatible with the observed data and the method used to construct the interval. It conveys precision as well as direction. When an interval for a difference spans zero, the data remain compatible with no difference as well as with differences in either direction, within the range shown. The intervals in the posted tables are broad relative to the point estimates, so the results do not pin down a narrow effect size.

What can be said about the analysis plan?

The registry lists no reported analyses. It does not report whether the comparisons were adjusted for baseline characteristics, whether a particular analysis population was used, or which method was used to calculate the intervals and p-values. The record also does not report handling of missing outcomes or whether there were interim analyses. These omissions limit independent evaluation of the statistical approach. They should not be filled in by assuming a conventional method or reconstructing an unreported protocol.

General statistical explanation: randomized assignment supports estimating the effect of assignment by comparing assigned groups, but a complete analysis also needs transparent rules for participants with missing measurements, deviations from assigned treatment, and outcome definitions. Without those rules, readers cannot fully assess whether the posted counts include all randomized participants or only a subset. The registry’s arm-level counts and comparison statistics remain useful descriptions, but they are not a substitute for a full statistical analysis report.

11. Confidence Intervals and Precision

The confidence intervals in the primary results table are central to reading the comparison. They extend across zero for the risk difference, meaning that the posted results are compatible with no difference and with values in either direction. The point estimate alone therefore gives an incomplete picture. The interval is the more informative guide to how precisely the group contrast has been estimated.

The relative-effect intervals in the table also cover a wide range of possible values. This reflects the uncertainty in the estimates, particularly when the number of recorded events is limited. A confidence interval is not a guarantee that the true effect lies inside it, nor does it assign equal probability to every value within its limits. General statistical explanation: it is a method-based range that would have a stated coverage property across repeated applications under its assumptions.

The secondary outcome also has few recorded events, with 6 in each group. Its confidence interval should be read in the context of that sparse event information. The matching counts do not make the estimate precise, and the interval is more informative than the apparent similarity of the counts alone.

Precision is distinct from whether a result is statistically significant. A non-significant result can still be compatible with meaningful effects in either direction if its interval is wide. The posted tables allow readers to see this uncertainty directly. The registry record does not report whether the analyses accounted for missing measurements or other sources of uncertainty beyond sampling variability.

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 table gives participants per arm, not total events. Its rows describe planning for 100% of the observed effect, planning for 75% of the observed effect, and planning for 50% of the observed effect. The columns show participants per arm for 80% power and participants per arm for 90% power. These are planning illustrations calculated from the observed effect, not the original design assumptions for this trial.

The stated 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 the stated fraction of the observed effect. For example, the row for planning for 75% of the observed effect uses a smaller target effect than the row for planning for 100% of the observed effect. The displayed sample sizes rise as the target effect becomes smaller.

General statistical explanation: power is the chance, under a specified true effect and statistical model, of detecting an effect according to a chosen decision rule. The 80% and 90% columns therefore describe different planning targets; the higher target is associated with a larger required sample in the table. They do not tell us how likely the observed results were to occur or establish that the completed trial had a particular chance of detecting an effect.

The listed values are planning illustrations based on the observed effect, not observed power. The trial’s own original sample-size justification and design assumptions are not reported in the registry record. The curve in the sample-size section should be read in the same planning context as the table: it illustrates how required sample size changes with the target effect under the stated assumptions. It should not be interpreted as additional evidence about the trial’s outcomes.

13. How the Sample Size Is Derived

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

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.070; treatment proportion p₁ = 0.082; average p̄ = 0.076.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.3751 = 0.7352.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.3750 = 0.3156.
4. n = (0.7352 + 0.3156)² / (0.0113)² = 8,596.5, rounded up to 8,597 participants per arm for 80% power.

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

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.0704845814977974                          # control proportion
p1 <- 0.0818181818181818                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(4315272)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 8,597 (80% power), 11,508 (90% power)
 75% of observed effect: n per arm = 15,023 (80% power), 20,110 (90% power)
 50% of observed effect: n per arm = 33,212 (80% power), 44,461 (90% power)
power.prop.test: 8596.5 
Simulated power at n = 8,597 per arm: 0.799 (20000 replicates)

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

14. Multiplicity, Missing Data and Analysis Populations

The record lists a primary malaria-status outcome and a secondary clinical-malaria outcome. It does not report a multiplicity strategy, such as an adjustment to account for testing more than one outcome or a hierarchy for interpreting outcomes. The displayed p-values are unadjusted. Because the registry does not report a complete analysis plan, readers cannot determine how the investigators intended to balance evidence across outcomes or whether additional analyses were planned.

The registry lists no reported analyses. In particular, it does not specify whether the primary comparison was adjusted, whether secondary comparisons followed a different analysis method, or whether there were interim analyses. The statistical methods and decision rules beyond the posted summaries are not reported in the registry record. It would be inappropriate to infer these procedures from the fact that the trial was randomized or masked.

Missing-data handling and analysis populations are also not reported in the registry record. The record does not say how many participants had missing rapid diagnostic test results, whether any participants were excluded from the outcome analysis, or how deviations from assigned treatment were addressed. The arm-level counts are presented, but the record does not clarify whether they represent every randomized participant, all participants with an observed result, or another analysis set.

General statistical explanation: missing outcomes can affect a group comparison if their occurrence or handling differs between groups. Clear reporting usually helps readers understand the extent of missingness and the assumptions used to address it. Without those details, the posted event counts can be described, but the completeness and analysis population behind them cannot be fully assessed. These reporting gaps do not show that a particular method was or was not used; they mean that it is not reported in the registry record.

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 non-serious adverse event terms

The registry’s serious adverse event summary lists zero serious adverse events in both groups and zero deaths in both groups. This means no serious events or deaths are recorded in these posted group summaries; it does not establish that no such events occurred outside the period or scope captured in the registry results.

The same summary lists 20 other adverse events among 220 participants in the azithromycin group and 32 among 227 participants in the placebo group. These are the posted counts, not a causal comparison. The registry does not provide detail about event types, severity classifications for these other events, timing, relatedness assessments, or whether participants could contribute more than one event.

General statistical explanation: safety counts need context about how events were defined, collected, and attributed, and about the time period during which participants were monitored. The registry’s brief summary focuses on the malaria outcomes and does not provide those additional safety details. The figures should therefore be read as a summary of the adverse-event information posted, not as a complete evaluation of comparative safety.

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

The posted results describe malaria-related outcomes observed in the two randomized groups at the specified assessment times. For the primary endpoint, the event counts are similar in scale, and the comparison’s confidence interval spans both directions around no difference. The unadjusted p-value is 0.651. The secondary comparison also does not show a clear separation in its posted summary. These data do not identify a precise group effect.

That interpretation is not the same as concluding that the groups are identical. A result that does not show a clear difference may reflect uncertainty, and the confidence intervals show the range of contrasts compatible with the data and method. The tables should be read for both the point estimates and their uncertainty rather than reducing the findings to a yes-or-no label.

The results also do not establish a biological mechanism. Although the study title and summary describe interest in gut mechanisms and stool collection, the posted outcomes concern malaria status and clinical malaria. The registry record does not provide results for gut microbiome measures or explain a pathway connecting the intervention to malaria outcomes.

Nor can these registry summaries answer every question about other populations, longer-term outcomes, or outcomes not reported. The trial enrolled children aged 8 days to 59 months in Nouna Town, and the primary malaria assessment is at 2 weeks. The study summary mentions longitudinal evaluation over 6 months, but the outcome results discussed in these tables are listed at 2 weeks.

Finally, the record does not report a full analysis plan, handling of missing outcomes, or analysis populations. Those omissions limit how completely the posted comparisons can be evaluated. The appropriate conclusion is descriptive: the registry reports the stated counts and uncertain comparisons, while leaving important analysis and measurement details unresolved.

17. Why This Trial Matters Statistically

This trial is statistically useful to examine because it combines randomized assignment with a concrete binary outcome and reports participant counts alongside effect estimates and confidence intervals. That combination lets readers move beyond an isolated p-value and consider the direction, scale, and uncertainty of the group contrast. The results also illustrate why a point estimate should not be mistaken for a precise conclusion.

The registry makes visible an important distinction between an observed result and a design target. Its sample-size illustrations are based on the observed effect and explicitly state assumptions about significance level, allocation, and approximation. Those calculations can help readers understand how sample-size requirements change when the target effect changes, but they do not recover the original design rationale, which is not reported in the registry record.

The record also provides a practical example of the limits of registry summaries. It names the design, interventions, outcomes, and event counts, yet does not report analysis populations, missing-data procedures, multiplicity handling, or a complete analysis plan. Statistical interpretation depends on those details as well as on the numerical results.

For biostatisticians and clinical researchers, the key lesson is to read the evidence as a connected set: design, endpoint definition, event counts, estimates, confidence intervals, and reporting completeness. Here, the posted comparisons do not yield a narrow estimate of the group contrast. The registry tables are a starting point for interpretation, not a substitute for the full study report and analysis documentation.

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 registry record does not explain the relationship between the listed enrollment of 449 and the arm-level counts shown in the results. It also does not state whether all randomized children contributed an outcome measurement. Without a participant flow or analysis-population description, readers cannot assess the completeness of outcome ascertainment from the registry summary alone.

The record identifies a rapid diagnostic test for the primary outcome but does not report additional details about the test, the assessment procedures, or how an unavailable or indeterminate result was handled. It also does not define clinical malaria in further detail. These omissions constrain interpretation of how the outcomes were measured and how comparable they were across groups.

The registry does not report baseline characteristics, adherence, treatment deviations, missing-data methods, covariate adjustment, multiplicity handling, or interim analyses. It lists no reported analyses. These are limitations of what can be learned from this record, not evidence that the investigators did not collect or consider such information.

Although the summary describes longitudinal follow-up and a focus on stool collection, the results concern malaria outcomes at 2 weeks. The registry record does not give results for the other stated areas of interest in the posted outcome summaries. The interpretation should remain limited to the measures and timeframes actually reported.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Gut and Azithromycin Mechanisms in Infants and Children II — NCT04315272.
• Registry record last updated 2023-06-29; status last verified 2023-06.
• Results first posted 2023-06-26.
• 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

Gut and Azithromycin Mechanisms in Infants and Children II is listed as a completed Phase 4 randomized, quadruple-masked, parallel-group trial sponsored by the University of California, San Francisco. The registry lists enrollment as 449 and describes a single dose of azithromycin compared with placebo in children aged 8 days to 59 months in Nouna Town, Burkina Faso.

The primary outcome is malaria status measured by rapid diagnostic test at 2 weeks; clinical malaria is listed as a secondary outcome at the same timeframe. The posted group counts and comparisons do not show a clear separation and remain uncertain. The registry lists no reported analyses and does not report key details about analysis populations, missing data, or multiplicity. The tables and planning illustrations should be read as registry summaries rather than a complete analysis report.