Ferric Citrate for the Prevention of Renal Failure in Adults With Advanced Chronic Kidney Disease: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Ferric Citrate for the Prevention of Renal Failure in Adults With Advanced Chronic Kidney Disease (NCT05085275), a randomized phase 3 study. The primary outcome is Number of Participants Achieving a Composite Endpoint of Initiation of Maintenance Dialysis or All-cause Mortality.

2. Clinical Question

The question combines a kidney-disease progression outcome with death from any cause. A participant counted toward the composite after either component occurred. The registry also lists the components separately as secondary outcomes, allowing the composite result to be read alongside the dialysis and mortality counts.

The trial enrolled adults with advanced chronic kidney disease and compared ferric citrate with placebo. The posted primary outcome is a participant count at 9 months, while the sponsor’s reported comparison uses a Cox regression hazard ratio. These are related but distinct summaries: the count describes how many participants experienced the composite, and the hazard ratio describes a time-to-event contrast under the fitted model.

3. Trial Design

NCT05085275 is a phase 3, randomized, parallel-group trial of ferric citrate versus placebo. The registry describes the study as multicenter, double-blind, and placebo-controlled; its masking classification is quadruple. The planned treatment period described in the study summary is 9 months. The registry lists enrollment of 289, a start date of 2022-03-30, primary completion on 2024-01-24, and status as completed.

The ferric citrate group received oral tablets containing 1 gram ferric citrate, equivalent to 210 milligrams of ferric iron, administered with meals or snacks. The placebo tablets matched the active tablets in color and size. The randomized groups included 138 participants in the ferric citrate arm and 136 in the placebo arm for the primary outcome analysis. The primary population was defined as randomized patients who received one or more doses of study drug.

The registry identifies initiation of maintenance dialysis or all-cause mortality as the primary composite endpoint. It also lists serious-adverse-event hospitalizations, initiation of maintenance dialysis, and all-cause mortality among secondary outcomes. The latter two are the components of the primary composite. The record lists four outcome measures in total and three analyses.

Details of the original sample-size justification, the trial’s intended statistical plan, interim analyses, and any prespecified multiplicity procedure are not reported in the registry record. The sample-size illustrations accompanying the posted outcome are not descriptions of the trial’s original design.

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: A total of 289 patients were randomized 1:1 to receive ferric citrate (n=144) or placebo (n=145).

Overall Study

Analysis population for the primary outcome, as defined in the registry: Full analysis set which consists of randomized patients who received one or more doses of study drug.

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.

Population: This patient population includes those patients that were consented, eligible to be enrolled, randomized, and received a Day 1 dose.

6. Primary Endpoint

The registry lists the primary outcome as Number of Participants Achieving a Composite Endpoint of Initiation of Maintenance Dialysis or All-cause Mortality, measured over 9 months.

The primary endpoint is the number of participants who achieved a composite of starting maintenance dialysis or dying from any cause during the 9-month timeframe. A participant meeting either component contributes to the composite; the endpoint therefore represents occurrence of at least one of these events, rather than a measure of the severity of kidney disease or a count of repeated events.

A composite endpoint is used to capture more than one clinically important event within a single outcome. In this trial, dialysis initiation reflects a major change in kidney-disease management, while all-cause mortality includes death regardless of cause. Combining them can make the primary question broader than either component alone. However, the components may occur at different frequencies and can have different meanings, so the composite should be interpreted together with the separate component results.

The posted outcome reports counts at 9 months, and the sponsor’s primary comparison is a Cox regression hazard ratio. The time-to-event analysis incorporates the timing of events, whereas the participant counts provide a straightforward description of how many participants experienced the composite in each group. The registry describes the analysis population as randomized patients who received one or more doses of study drug. Further details about censoring rules and the handling of participants without a recorded event are not reported in the registry record.

7. Results Posted in the Registry

• Ferric Citrate: 25.4% (35/138) versus 33.8% (46/136) on control; risk difference -8.5 percentage points (95% CI -19.2 to 2.3), risk ratio 0.75 (0.52–1.09); unadjusted two-proportion z-test p = 0.125.

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.

Component of Primary - Initiation of Maintenance Dialysis

• 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:
  • Ferric Citrate: 17.4% (24/138) versus 21.3% (29/136) on control; risk difference -3.9 percentage points (95% CI -13.3 to 5.4), risk ratio 0.82 (0.50–1.33); unadjusted two-proportion z-test p = 0.410.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Ferric Citrate and the control. The interval gives the range of effects that remain compatible with the data.

Component of Primary - All-Cause Mortality

• 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:
  • Ferric Citrate: 0.7% (1/138) versus 2.9% (4/136) on control; risk difference -2.2 percentage points (95% CI -5.4 to 1.0), risk ratio 0.25 (0.03–2.18); unadjusted two-proportion z-test p = 0.171.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Ferric Citrate and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary outcomes cover the separate components of the primary composite and serious-adverse-event hospitalizations. The component results follow the same broad pattern as the primary outcome: the ferric citrate group has numerically fewer observed events in the posted counts, while the uncertainty around the comparisons remains substantial. The separate outcomes do not provide a clearer or more definitive account of the primary result.

These outcomes help describe what contributes to the composite. The dialysis component and the all-cause mortality component are not interchangeable events, and their frequencies differ. Their separate results can therefore add context, but they should not be treated as independent confirmation of the composite finding. The serious-adverse-event hospitalization outcome addresses a different aspect of the trial and does not answer the primary time-to-event question.

The registry’s component analyses use Cox regression and report hazard ratios, but confidence intervals are not reported for the secondary analyses. Without those intervals, readers have less direct information about the precision of the estimated comparisons. The posted p-values alone do not indicate effect size or uncertainty.

Unadjusted secondary results carry less weight when multiple outcomes are examined without a reported multiplicity procedure. General: testing several outcomes increases the chance that at least one result may appear notable by chance, even when there is no corresponding underlying effect. A prespecified approach to multiplicity can address how results are interpreted across a family of tests; such an approach is not reported in the registry record. The secondary findings are best treated as descriptive and supportive context, not as stand-alone proof of an effect.

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

• Hospitalization Events Reported as a Serious Adverse Event (SAE) (Excluding Disease-related Hospitalization [e.g., Dialysis Access Placement, Dialysis Initiation, Kidney Transplant] and Elective Procedures)
• Component of Primary - Initiation of Maintenance Dialysis
• Component of Primary - All-Cause Mortality

9. Statistical Interpretation

10. Statistical Methods Explained

The primary endpoint is reported both as participant counts and as a time-to-event comparison. Those summaries answer connected, but not identical, statistical questions. Counts compare the number of participants with an event during the stated timeframe. A Cox regression analysis compares event timing while accounting for the time participants remain under observation without an event, subject to the model and censoring procedures used.

What does a risk difference describe?

General: a risk difference subtracts the event proportion in one group from the event proportion in the other, according to the contrast’s chosen direction. A value of zero represents equal proportions under that contrast. The posted recalculation for ferric citrate versus placebo is -0.085, with a 95% confidence interval from -0.192 to 0.023. The registry’s group estimates are 0.254 for ferric citrate and 0.338 for placebo. The interval extends across zero, so the recalculated data do not precisely distinguish a difference from no difference.

The risk-difference calculation is an unadjusted comparison based on the posted summary counts. It does not use the event times in the same way as a Cox model, and it does not provide an adjusted estimate. Its p-value is 0.125. This is useful as a transparent count-based comparison, but it should not be substituted for the sponsor’s time-to-event analysis.

What does the Cox hazard ratio describe?

General: a hazard ratio compares the instantaneous event rates between groups among participants who have not yet experienced the event, as represented by a time-to-event model. A hazard ratio of 1 is the null value for equal hazards. Unlike a risk difference, a hazard ratio is not a difference in the proportion of participants with an event. It is also not automatically a ratio of the cumulative risks over the whole follow-up period.

The registry reports a Cox regression hazard ratio of 0.73 for the primary outcome, with a 95% confidence interval from 0.46 to 1.14 and p = .1602. The interval includes 1. A hazard ratio below 1 is in the direction of a lower modeled hazard for the ferric citrate group relative to the placebo group, but the interval’s breadth means the estimate remains uncertain. Whether the Cox model’s assumptions were checked, and how censoring was handled, are not reported in the registry record.

How should p-values be read?

General: a p-value describes how incompatible the observed data are with a specified null hypothesis under the statistical model and its assumptions. It is not the probability that the null hypothesis is true, and it does not measure the size or clinical importance of an effect. The sponsor’s p-value of .1602 belongs to the Cox analysis. The recalculated p-value of 0.125 belongs to the unadjusted risk-difference comparison. They differ because the analyses use different summaries and methods; they should not be treated as duplicate tests of an identical estimate.

The registry classifies the sponsor’s primary analysis as non-inferiority. General: a non-inferiority assessment requires a prespecified margin and decision rules that define how much difference would be acceptable. Those details are not reported in the registry record, so the classification alone is not enough to assess whether the formal criterion was met.

What does the analysis population mean?

The primary analysis population is described as the full analysis set, consisting of randomized patients who received one or more doses of study drug. That definition gives a boundary for who was included in the posted primary outcome. It does not by itself explain how missing outcome information, discontinuation, or loss to follow-up was treated. Those procedures are not reported in the registry record.

General: confidence intervals express uncertainty around an estimate under the chosen method and assumptions. They are more informative than a point estimate alone because they show a range of values compatible with the data and model. For the primary count-based estimate, the interval includes zero; for the Cox estimate, it includes 1. These are the relevant null values for the respective measures.

11. Confidence Intervals and Precision

The sponsor’s primary hazard ratio is 0.73, with a 95% confidence interval from 0.46 to 1.14. The interval includes 1, the null value for a hazard ratio. It allows for a lower hazard in the ferric citrate group, but it also extends to a value above 1. The posted result therefore does not pin down the direction or size of the time-to-event contrast with high precision.

The unadjusted risk-difference recalculation is -0.085, with a 95% confidence interval from -0.192 to 0.023. This interval includes zero, the null value for a difference in risks. The interval is compatible with different magnitudes and directions of the count-based contrast. It should be interpreted as an uncertainty range, not as a list of equally likely effects or as a guarantee that the true effect lies within it.

General: a confidence interval reflects both the observed data and the method’s assumptions. Greater precision generally corresponds to a narrower interval. A confidence interval does not indicate the probability that a particular treatment effect is true, and it does not resolve limitations in the analysis, missing-data handling, or study design.

The participant counts and the modest number of endpoint events contribute to the uncertainty in these comparisons. The point estimates have a numerical direction, but their intervals encompass the null values. The intervals are therefore central to interpretation: the posted estimates do not justify treating a directional point estimate as a settled finding.

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 illustrations use the observed effect as a starting point and show planning scenarios for detecting the full observed effect, 75% of the observed effect, or 50% of the observed effect. For this binary participant-count endpoint, the figures are participants per arm, not total events. At 100% of the observed effect, the illustration gives 456 participants per arm for 80% power and 610 participants per arm for 90% power. At 75% of the observed effect, it gives 828 participants per arm for 80% power and 1,108 participants per arm for 90% power. At 50% of the observed effect, it gives 1,899 participants per arm for 80% power and 2,541 participants per arm for 90% power.

General: power is the probability that a planned statistical test will reject its null hypothesis under a specified effect and set of assumptions. The 80% power column represents a design intended to detect the stated effect with 80% probability under those assumptions; the 90% column represents a higher probability of detection. Smaller effects are harder to distinguish from random variation, so the illustration requires more participants per arm as the assumed effect gets smaller.

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. These figures are planning illustrations based on the observed result, not the trial’s original design assumptions, which are not reported in the registry record. They are not observed power. The plotted planning curve should be read as a visual display of the same planning relationship, rather than as additional trial 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 = 0.338; treatment proportion p₁ = 0.254; average p̄ = 0.296.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6455 = 1.2652.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6428 = 0.5410.
4. n = (1.2652 + 0.5410)² / (-0.0846)² = 455.7, rounded up to 456 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 <- 0.338235294117647                          # control proportion
p1 <- 0.253623188405797                          # 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(5085275)
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 = 456 (80% power), 610 (90% power)
 75% of observed effect: n per arm = 828 (80% power), 1,108 (90% power)
 50% of observed effect: n per arm = 1,899 (80% power), 2,541 (90% power)
power.prop.test: 455.7 
Simulated power at n = 456 per arm: 0.802 (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. Analyses Reported by the Sponsor

The registry’s own primary analysis is a Cox regression with a hazard ratio of 0.73, a 95% confidence interval from 0.46 to 1.14, and p = .1602. It is the sponsor’s reported time-to-event analysis. The registry also marks its analysis type as non-inferiority, but the non-inferiority margin and decision criteria are not reported in the registry record. The label should not be taken on its own as evidence that a formal non-inferiority criterion was met.

The simple recalculation from the posted participant counts gives a risk difference of -0.085, a 95% confidence interval from -0.192 to 0.023, and p = 0.125. It is an unadjusted comparison of the reported counts, not a re-creation of the Cox model. A risk difference and a hazard ratio describe different statistical quantities, so their estimates and p-values need not match. Both intervals include the relevant null value, and both summaries are uncertain.

The registry’s discrepancy indicator is false, so no discrepancy between the posted result and the recalculation is flagged. Where a discrepancy exists, the sponsor’s own analysis takes precedence; in this record, the distinction to keep in mind is that the recalculation is a complementary, unadjusted summary and does not replace the reported Cox analysis.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary composite endpoint and secondary outcome measures, including the two components of the composite and serious-adverse-event hospitalizations. The number of outcome measures and posted analyses indicates that more than one result is available for consideration. However, the registry record does not report a prespecified multiplicity procedure, a hierarchy for testing secondary endpoints, or rules for interpreting results across outcomes.

General: when statistical tests are conducted across multiple outcomes, the chance of at least one apparently notable result can increase. Multiplicity procedures are methods for controlling or otherwise accounting for that issue. Without a stated procedure, secondary p-values should be read as unadjusted evidence and interpreted cautiously. The primary endpoint remains the main outcome for the trial question, while secondary results help describe components or additional outcomes but should not be promoted to confirmatory evidence without a documented testing strategy.

The primary population is defined as randomized patients who received one or more doses of study drug. That definition is reported, but other analysis-population rules are not. Details about missing outcome data, withdrawal, censoring, protocol deviations, and sensitivity analyses are not reported in the registry record. These omissions matter because time-to-event estimates can depend on how follow-up ends and how participants without an observed event are treated.

The original sample-size justification and assumptions are also not reported. Interim analyses and any adjustments associated with them are not reported in the registry record. The planning illustration accompanying the results uses stated assumptions, but it should not be mistaken for the study’s original statistical plan. Taken together, the available record identifies the main outcome and analysis population but does not provide enough detail to reconstruct the full prespecified analysis strategy.

16. 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 5% reporting threshold.

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: cardiac failure congestive (4.4%), acute kidney injury (3.7%), anaemia (2.9%), cardiac arrest (2.9%), pneumonia (2.2%).

The serious-adverse-event figures describe participants recorded with at least one serious adverse event: 33 of 138 in the ferric citrate group and 43 of 136 in the placebo group. The adverse-event listing also records deaths in 2 participants in the ferric citrate group and 6 in the placebo group. These are descriptive group counts. They do not establish that study treatment caused or prevented an event, and the registry record does not report a causal attribution for these figures.

The most frequently listed serious adverse-event terms are cardiac failure congestive (4.4%), acute kidney injury (3.7%), anaemia (2.9%), cardiac arrest (2.9%), pneumonia (2.2%), and syncope (2.2%). Such terms describe reported event categories; they do not by themselves indicate how many participants experienced multiple events. Whether categories overlap is not reported in the registry record.

No participants are listed as affected in the other adverse-event category in either group. These safety counts should be read with the group sizes and the trial’s 9-month timeframe in mind. They provide a record of reported events during the study, not a comprehensive estimate of safety in other populations or over other durations.

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

The primary result describes fewer participants with the composite endpoint in the ferric citrate group than in the placebo group, together with a Cox hazard ratio below 1. The uncertainty intervals, however, include the null values for both the time-to-event estimate and the count-based recalculation. The posted data therefore show a directional numerical difference, but they do not precisely establish the size or direction of the underlying contrast.

This is not evidence that the groups are equivalent. A result that does not clearly distinguish the groups can reflect uncertainty as well as a genuinely small difference. Nor does the point estimate alone demonstrate that ferric citrate changes the chance or timing of the composite event. The registry’s non-inferiority classification cannot be evaluated without the margin and decision criteria, which are not reported in the registry record.

The composite combines initiation of maintenance dialysis with all-cause mortality. The separate component outcomes add context, but their counts are limited and their analyses are also uncertain. The primary result should not be interpreted as if both components had the same frequency, meaning, or contribution.

These findings pertain to the trial population and the stated 9-month timeframe. The registry record does not establish how the results would apply to other populations, treatment durations, or clinical settings. The safety counts are also descriptive and do not show causation. Overall, the result is best understood as an imprecise estimate from a randomized comparison, with important details of the analysis plan and missing-data procedures not reported.

18. Why This Trial Matters Statistically

This trial illustrates why statistical interpretation should not stop at the direction of a point estimate or a single p-value. The primary count comparison and the sponsor’s time-to-event analysis offer complementary views of the same broad endpoint, but they measure different quantities. Each estimate is accompanied by uncertainty that includes its null value, so the numerical direction is not sufficient to settle the study question.

The composite endpoint also highlights a common challenge in clinical-trial interpretation. Combining events can create a single primary outcome, while the components may differ in frequency and meaning. Reading the composite alongside its components helps prevent an overall result from obscuring what kinds of events occurred. Here, the component analyses do not remove the uncertainty in the primary result.

The trial is also a useful example of the distinction between a sponsor’s model-based analysis and a simple recalculation from posted counts. The Cox hazard ratio incorporates event timing through a model; the risk difference summarizes the group counts without that same time-to-event structure. These quantities should not be expected to match numerically, and the recalculation should not displace the sponsor’s reported analysis.

Finally, the registry record makes clear that outcome estimates are only part of the evidence needed for a full statistical assessment. The original sample-size justification, missing-data procedures, interim analyses, multiplicity approach, and non-inferiority margin are not reported. Recognizing these gaps is part of interpreting results responsibly, rather than inferring details from the observed estimates.

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.

The registry record does not report the trial’s original sample-size justification or the assumptions used to plan enrollment. The sample-size illustrations based on the observed effect are not a substitute for that original rationale. The record also does not report the non-inferiority margin or decision criteria, despite classifying the sponsor’s primary analysis as non-inferiority.

Important analysis details are not reported, including the handling of missing outcome data, censoring rules, loss to follow-up, sensitivity analyses, and interim analyses. The full analysis set is defined, but that definition does not resolve how incomplete follow-up was managed. The registry does not report a multiplicity strategy for the primary and secondary outcomes.

The primary outcome combines initiation of maintenance dialysis and all-cause mortality, while the posted event counts show that the components occurred with different frequencies. Component results help interpret the composite, but the component analyses are also limited by uncertainty, and their confidence intervals are not reported. The registry’s posted secondary analysis summaries therefore offer less information about precision than the primary Cox analysis.

The analysis population and outcome timeframe also bound what can be inferred. These results concern randomized participants who received one or more doses of study drug and the stated 9-month period. The record does not establish that the same estimates apply beyond that population or timeframe. Serious adverse-event counts are descriptive, and the record does not establish causality or explain whether event categories overlap.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Ferric Citrate for the Prevention of Renal Failure in Adults With Advanced Chronic Kidney Disease — NCT05085275.
• Registry record last updated 2025-08-29; status last verified 2025-04.
• Results first posted 2025-08-29.
• 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

NCT05085275 is a completed phase 3 randomized, parallel-group trial comparing ferric citrate with placebo in adults with advanced chronic kidney disease. The registry describes a multicenter, double-blind, placebo-controlled study with a 9-month primary outcome timeframe. Its primary endpoint is the composite of initiation of maintenance dialysis or all-cause mortality.

The posted primary counts are 35 of 138 participants for ferric citrate and 46 of 136 for placebo. The sponsor reports a Cox hazard ratio of 0.73, with a 95% confidence interval from 0.46 to 1.14 and p = .1602. The unadjusted count-based recalculation also has an interval that includes its null value. The numerical direction favors fewer observed composite events in the ferric citrate group, but the estimates are uncertain.

The registry does not report several details needed to evaluate the full analysis strategy, including the original sample-size rationale, missing-data procedures, multiplicity approach, interim analyses, and non-inferiority margin. Secondary and safety results add context but do not resolve the uncertainty in the primary comparison.