A Study in CKD Patients With Type 2 Diabetes Mellitus and Albuminuria: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study in CKD Patients With Type 2 Diabetes Mellitus and Albuminuria (NCT01847092), a randomized phase 2 study. The primary outcome is Changes in Urine Albumin to Creatinine Ratio (UACR).

2. Clinical Question

The clinical question concerns a laboratory measure of albumin in urine relative to creatinine, rather than a clinical event or a long-term kidney outcome. The primary comparison is the difference between the arms in change from baseline at Week 12. The registry’s primary-outcome description names tenapanor and placebo, while the listed intervention and arm descriptions identify AZD1722 and placebo. The posted comparison is between AZD1722 and placebo; the registry record does not explain the wording difference.

The study also posted a secondary outcome for change in estimated glomerular filtration rate through Week 12. The record does not establish whether either laboratory measure was intended to predict later clinical outcomes. The question answered by these data is therefore bounded: it concerns the observed short-term differences in the recorded measures among participants in this trial, not long-term outcomes or treatment decisions.

3. Trial Design

The registry describes a completed, randomized, parallel-group Phase 2 study sponsored by Ardelyx. Participants were assigned to AZD1722 or placebo. The planned treatment period in the arm descriptions was 12 weeks, and the primary outcome was assessed at Week 12. The record lists 154 enrolled participants and reports 77 participants in each arm for the primary outcome.

The study is described as placebo-controlled and quadruple-masked in the design fields. Its official title describes it as double-blind. Those are the masking descriptions recorded in the registry; the record does not explain the distinction or identify which people were masked. Randomized allocation can reduce systematic differences between groups at the start of a trial, while masking can reduce the influence of expectations on care, assessment, or reporting. These are general design purposes, not proof that bias was absent in this study.

The official title calls the study exploratory and identifies safety, tolerability, and pharmacodynamics as its aims. The brief summary also describes an aim of assessing whether the drug is active in reducing albuminuria. The registry records a primary outcome of change in urine albumin-to-creatinine ratio and a secondary outcome of change in estimated glomerular filtration rate.

The registry record does not report the original sample-size justification, the planned statistical analysis in sufficient detail to reproduce it, interim analyses, or the details of the allocation process. It also does not provide a fuller account of recruitment, follow-up, or participant disposition. These omissions limit how completely the design and analysis can be reconstructed from the record alone.

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:

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 Changes in Urine Albumin to Creatinine Ratio (UACR), measured over Week 12.

The primary endpoint is change in urine albumin-to-creatinine ratio (UACR) from baseline to Week 12, expressed in mg/g. UACR relates the amount of albumin measured in urine to creatinine in the same sample. It is used in kidney research as a measure of albuminuria, the presence of albumin in urine. The study’s stated purpose includes assessing activity in reducing albuminuria, which makes this measure relevant to the question being explored.

The posted arm results are means of a continuous measure: the mean change was -64.6 mg/g in the AZD1722 arm and 104.8 mg/g in the placebo arm. The estimated mean difference was -169.4 mg/g, calculated as AZD1722 relative to placebo. A negative difference indicates that the average change recorded in the AZD1722 arm was lower than the average change in the placebo arm; it does not by itself establish that the difference is reliable or clinically important.

Using a change from baseline focuses the comparison on how the measure moved during the treatment period rather than only on its endpoint value. However, the registry record does not give the baseline UACR values, details of the measurement schedule, or a threshold for interpreting a difference as clinically important. The endpoint therefore describes a short-term laboratory change, not a demonstrated effect on longer-term kidney outcomes.

7. Results Posted in the Registry

• AZD1722: mean -64.60 versus 104.80 on control; difference -169.40 (95% CI -419.46 to 80.66), standardized effect d = -0.21; z-test p = 0.184.

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.

Change in Estimated Glomerular Filtration Rate (mL/Min/1.73 m2) From Baseline to Week 12 Endpoint

• 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:
  • AZD1722: mean -2.80 versus -2.70 on control; difference -0.10 (95% CI -0.48 to 0.28), standardized effect d = -0.08; z-test p = 0.605.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between AZD1722 and the control. The interval gives the range of effects that remain compatible with the data.

The registry posts one secondary endpoint: change in estimated glomerular filtration rate from baseline to the Week 12 endpoint. The recalculated comparison and the sponsor’s ANCOVA analysis do not provide clear evidence of a difference between arms. Overall, the secondary result does not offer a contrasting signal that resolves the uncertainty in the primary UACR comparison.

The secondary measure is relevant to kidney function, while the primary measure captures albuminuria. They address related but distinct aspects of kidney health, and they use different units and statistical summaries. A result on one measure cannot be treated as a direct confirmation or refutation of a result on the other. The registry record does not describe a planned hierarchy linking the endpoints or specify how the secondary outcome was intended to support the primary finding.

Unadjusted secondary results generally carry less weight than a clearly specified primary analysis. When multiple outcomes or comparisons are examined, the chance of seeing an apparently notable result by chance can increase. Statistical approaches that account for multiplicity can control that issue, but the registry record does not report whether any such adjustment was planned or used. The secondary p-value should therefore be read as an unadjusted result, not as a definitive test across all outcomes.

The record identifies 2 outcome measures and posts one secondary outcome. It does not give a complete account of other analyses that may have been planned but not posted. That limits any assessment of the full range of outcomes examined. Taken together, the available secondary information is consistent with cautious interpretation of the primary result rather than a basis for broad conclusions about treatment effects.

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

• Change in Estimated Glomerular Filtration Rate (mL/Min/1.73 m2) From Baseline to Week 12 Endpoint

9. Statistical Interpretation

10. Statistical Methods Explained

The primary measure is continuous, so the posted arm summaries use means and standard deviations. A mean describes the arithmetic average of the recorded changes in a group. A standard deviation describes how dispersed individual measurements are around that group mean. It is not a confidence interval and does not express uncertainty in the estimated mean difference by itself.

What does the mean difference represent?

The recalculated comparison subtracts the placebo mean change from the AZD1722 mean change. The resulting estimate is -169.4 mg/g. In general, a mean difference expresses the average separation between two groups on the original measurement scale. Keeping the units makes the estimate directly interpretable against the UACR measure. The sign depends on the order of subtraction; it does not indicate whether an effect is beneficial or harmful without a defined clinical interpretation.

What does the confidence interval add?

A confidence interval describes the uncertainty around an estimated effect under the assumptions of the method used. In repeated use of the same method, intervals at a stated confidence level are designed to cover the underlying value at that rate. It is not a range containing a specified proportion of individual participants’ measurements, and it does not assign a probability to the true value being inside this particular interval. For the primary comparison, the interval extends from -419.5 to 80.66 mg/g and includes zero, the value corresponding to no mean difference.

Why can variation affect precision?

In general, greater variation among participants makes a group mean less precise when sample size is held fixed. The primary outcome standard deviations, 898.4 mg/g and 668.0 mg/g, are large compared with the absolute size of the estimated mean difference. This helps explain why the interval is broad. A broad interval leaves considerable uncertainty about the size of a possible effect. It does not identify the cause of the variation, which could reflect variation among participants, measurement characteristics, or other factors not described in the registry.

What is an unadjusted recalculation?

The recalculated primary comparison is an unadjusted comparison based on the posted group summary statistics. It is distinct from the sponsor’s mixed-model analysis. An unadjusted comparison does not account for covariates or model structures unless those are built into the calculation. The registry record does not give enough information to reproduce the sponsor model or determine all of its specifications. The recalculation is useful as a transparent check on the reported summaries, but it is not a replacement for the sponsor’s prespecified analysis.

There is no reported discrepancy flag for the primary comparison. The sponsor analysis remains the trial’s reported analysis, while the recalculation offers a simple comparison derived from the posted group summaries. Their p-values differ, but both are consistent with substantial uncertainty rather than a clear separation between groups.

Why can p-values differ across analyses?

A p-value describes how unusual the observed data, or data more extreme, would be under a specified null model and its assumptions. It is not the probability that the null hypothesis is true, nor a measure of the size or importance of an effect. Different analysis methods can produce different p-values because they may use different variance estimates, adjustment variables, handling of repeated measurements, or analysis populations. Which of these explains the difference in this study is not reported in the registry record.

The primary recalculation gives an unadjusted p-value of 0.184, while the sponsor’s mixed-model analysis gives 0.279. These values should be interpreted alongside the estimated difference and its interval, not as stand-alone evidence. The registry record does not report a complete statistical analysis plan or identify the decision threshold used for the sponsor’s primary test.

What does over-dispersion mean in general?

For continuous outcomes, a large standard deviation relative to the group means can signal wide participant-to-participant variability. In other kinds of data, over-dispersion means the observed variability exceeds what a chosen statistical model expects. This matters because standard errors and confidence intervals can be too small if the model understates variability. The term should not be used as a diagnosis of this trial’s data without a specified model and supporting analysis. The registry reports large standard deviations for UACR, but it does not report a formal over-dispersion assessment.

The secondary outcome is also continuous. Its reported results are least-squares means, and its recalculated comparison uses posted summaries with an approximate pooled standard deviation. The corresponding confidence interval is narrower in measurement units than the primary interval, but the measures have different units and scales; their interval widths should not be compared as if they represented the same quantity. The registry record does not provide enough methodological detail to independently assess the assumptions behind the sponsor’s secondary ANCOVA.

11. Confidence Intervals and Precision

The primary estimated mean difference is -169.4 mg/g, with a 95% confidence interval from -419.5 to 80.66. The interval includes zero, the value representing no difference between group means. It also spans values on either side of zero, so the posted summary statistics do not rule out either a lower or a higher mean change with AZD1722 relative to placebo. The estimate should therefore be read with substantial uncertainty, not as a precise measurement of the effect.

In general, a confidence interval combines information about the estimated difference and its precision. A narrower interval indicates more precision under the analysis assumptions; a wider interval indicates less. The primary interval is broad, in keeping with the large spread in the UACR measurements. It does not show the range of individual participants’ responses, and it cannot establish whether any particular participant experienced a change.

The secondary eGFR comparison has an estimated difference of -0.1 mL/min/1.73 m2 and a confidence interval from -0.479 to 0.279. That interval also includes zero. Because eGFR and UACR use different scales and units, the numerical widths of their intervals are not directly comparable. The sponsor analyses report p-values but no estimates or confidence intervals in the registry record, which limits direct comparison of their precision with the recalculated estimates.

Confidence intervals depend on the calculation method and its assumptions. The registry record does not report enough detail to assess all assumptions or reproduce the sponsor models. These intervals describe uncertainty from the posted summary statistics and should be interpreted alongside that limitation.

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 total events. The assumptions listed for the illustration are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each row plans for a stated fraction of the observed effect: 100%, 75%, or 50%. The original trial’s own sample-size assumptions and justification are not reported in the registry record.

The 80% power column gives the number of participants per arm that the planning calculation estimates would be needed to have an 80% chance of detecting the specified effect under the stated assumptions, if that effect and model assumptions were correct. The 90% power column gives the corresponding planning number for a 90% chance. These are design calculations, not guarantees that a study of that size will produce a particular result.

For the primary outcome, planning for 100% of the observed effect gives 343 participants per arm for 80% power and 459 participants per arm for 90% power. Planning for 75% of the observed effect gives 610 and 816 participants per arm, respectively. Planning for 50% gives 1,372 and 1,836 participants per arm. The same pattern applies to the secondary outcome: the listed required numbers increase as the assumed effect becomes smaller.

Smaller effects are harder to distinguish from random variation, so more participants are generally needed to detect them under otherwise unchanged assumptions. These calculations use an observed effect from this study, which is uncertain, and should be treated as planning illustrations. They are not observed power and do not explain why the trial enrolled the number it did.

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 = -169.400; pooled standard deviation σ = 791.627.
2. n = 2 × (1.96 + 0.842)² × 791.627² / -169.400² = 342.8, rounded up to 343 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 <- -169.4                      # observed difference in means
sigma <- 791.626982865036                     # 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(1847092)
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 = 343 (80% power), 459 (90% power)
 75% of observed effect: n per arm = 610 (80% power), 816 (90% power)
 50% of observed effect: n per arm = 1,372 (80% power), 1,836 (90% power)
power.t.test: 343.8 
Simulated power at n = 343 per arm: 0.799 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The registry reports a mixed-model analysis for the primary outcome, with a p-value of 0.279. The recalculated unadjusted comparison from the posted group summaries estimates a mean difference of -169.4 mg/g, with a 95% confidence interval from -419.5 to 80.66 and an unadjusted p-value of 0.184. The p-values are not identical because the analyses use different approaches; the registry record does not provide enough detail to reproduce the mixed model or account for the precise difference.

The recalculation is a simple, unadjusted analysis of the posted summary statistics. It can help readers understand the scale and uncertainty of the comparison, but it does not incorporate any model adjustments that may have been used by the sponsor. The sponsor’s analysis is the reported trial analysis. The registry record does not report an underlying discrepancy between the posted group summaries and the recalculation, but that does not make the two methods interchangeable.

Both analyses leave uncertainty about the primary comparison: the recalculated interval includes zero, and the sponsor analysis reports a p-value of 0.279. The sponsor result should be interpreted using its own method and the information available in the record; the recalculated estimate is supplementary context rather than a substitute for that analysis.

15. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study tests more than one outcome, comparison, time point, or subgroup. In general, the more tests considered, the greater the possibility that at least one result will appear notable by chance if each test is interpreted separately. A prespecified testing hierarchy or an adjustment for multiple comparisons can address this concern, depending on the trial’s aims and analysis plan.

The registry lists a primary UACR outcome and one posted secondary eGFR outcome. The record does not report whether the study adjusted for multiplicity, specified a hierarchy among outcomes, or planned additional comparisons. It also does not describe whether the sponsor’s analyses were prespecified in a statistical analysis plan. Without those details, the posted p-values should be understood as results for their respective analyses, not as a complete multiplicity-controlled assessment.

The record does not report how missing outcome measurements were handled, how many participants had missing data, or whether the analyses used an intention-to-treat, per-protocol, or other defined population. The primary arm summaries each list 77 participants, matching the arm counts shown for that outcome, but these counts alone do not establish that all randomized participants contributed complete data or that no measurements were missing. The registry record also does not describe imputation, sensitivity analyses, or the assumptions used for missing data.

Missing-data methods can affect results if the availability of measurements is related to participants’ outcomes or differs between arms. This is a general concern, not evidence that missingness biased these results. The record does not provide the information needed to evaluate it. Likewise, the absence of a stated analysis population prevents a full assessment of how participants were included in the sponsor analyses. These reporting gaps constrain interpretation of both the primary and secondary comparisons.

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

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hyperkalemia (2.6%), deep vein thrombosis (1.3%), cardiac failure congestive (1.3%).

The registry reports serious adverse events in 3 of 77 participants in the AZD1722 arm and 2 of 77 participants in the placebo arm. It lists hyperkalemia at 2.6%, deep vein thrombosis at 1.3%, and cardiac failure congestive at 1.3% among the leading serious-event terms. No deaths are reported in either arm.

The record also reports other adverse events affecting 45 participants in the AZD1722 arm and 4 in the placebo arm. These are presented separately from serious adverse events and should not be combined with them. The registry summary does not give enough detail about event timing, duration, severity beyond the serious classification, recurrence, or attribution to study treatment. It also does not explain the marked difference in the number of participants with other adverse events.

These counts describe events recorded during the study; they do not establish that AZD1722 caused any particular event or that the two treatments have different safety profiles. Safety comparisons in a study of this size and duration can be imprecise, especially for uncommon events. The registry record does not provide a fuller statistical comparison of adverse events.

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

The primary result estimates the difference between AZD1722 and placebo in mean UACR change at Week 12. The recalculated estimate is -169.4 mg/g, but its confidence interval includes zero and extends across a wide range of possible differences. The sponsor’s mixed-model p-value is 0.279. Together, the posted results do not establish a clear between-arm difference in this endpoint.

This does not mean that the true difference is known to be zero. A confidence interval that includes zero is compatible with no average difference, but it can also be compatible with effects in either direction. The substantial variability in UACR and the short follow-up period limit how precisely the study can characterize the comparison. The record does not report a threshold for a clinically important UACR change, so statistical estimates cannot be translated into a judgment about clinical importance from these data alone.

The secondary eGFR result also does not provide clear evidence of a between-arm difference. It concerns a distinct measure and does not establish what happens to kidney function over a longer period. The record does not report clinical outcomes beyond the laboratory measures described.

The trial’s exploratory Phase 2 design and 12-week treatment period define the scope of the evidence. The results describe measurements from the participants and analyses recorded in the registry. They do not show that AZD1722 works or does not work, establish long-term benefit or harm, or support treatment advice. Further interpretation would require information about the full protocol, analysis plan, missing data, and any later evidence, none of which is established by this record.

18. Why This Trial Matters Statistically

This study illustrates why an estimated difference should be read together with its uncertainty. The primary estimate is negative, but the confidence interval includes zero and is broad. Looking only at the direction of the estimate could overstate what the data establish; looking only at a p-value would omit the range of effects still compatible with the results.

The primary outcome also shows how large participant-to-participant variation can limit precision. The reported UACR standard deviations are large relative to the estimated group difference. A numerical difference between group means is not automatically a precise or clinically meaningful difference. The uncertainty interval, measurement scale, and study context all matter.

The record also distinguishes a sponsor analysis from an unadjusted recalculation. The two approaches give different p-values, while both leave uncertainty about the comparison. This is a useful reminder that statistical results depend on the analysis method and that summary data may not be sufficient to reproduce a model-based analysis.

Finally, the sample-size illustrations show how planning depends on the effect chosen for detection: smaller assumed effects require more participants under the listed assumptions. Those illustrations are not the original justification for this exploratory trial and are not observed power. Together, these features make the record a case in reading estimates, intervals, analysis methods, and planning assumptions as related but distinct parts of statistical interpretation.

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 leaves important design and analysis details unreported. It does not give the original sample-size justification, the full statistical analysis plan, details of interim analyses, or a complete description of the allocation process. It also does not explain the difference between the masking descriptions in the design fields and official title.

The primary outcome description refers to tenapanor and placebo, whereas the intervention and arm descriptions name AZD1722 and placebo. The posted comparison is identified as AZD1722 versus placebo, but the record does not explain the wording difference. This creates uncertainty about the endpoint description as recorded, even though the posted arms and comparison are identifiable.

The primary outcome has large standard deviations and a wide recalculated confidence interval. The reported analyses do not include sponsor estimates or intervals, so the model-based effect size and its precision cannot be compared directly with the unadjusted recalculation. The statistical details needed to reproduce the mixed model and secondary ANCOVA are not reported.

Information about missing outcomes, analysis populations, participant disposition, and sensitivity analyses is also not reported in the registry record. The safety summary gives counts but limited detail about event timing, attribution, and the meaning of the other-event totals. The short 12-week period and exploratory Phase 2 design further limit conclusions about longer-term outcomes. These reporting and design limitations do not prove that the analyses are biased; they identify areas that cannot be assessed from the registry record.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: A Study in CKD Patients With Type 2 Diabetes Mellitus and Albuminuria — NCT01847092.
• Registry record last updated 2020-05-27; status last verified 2020-05.
• Results first posted 2020-05-27.
• 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

NCT01847092 is a completed, randomized, placebo-controlled, parallel-group Phase 2 study sponsored by Ardelyx. It enrolled 154 participants with chronic kidney disease, type 2 diabetes mellitus, and albuminuria. The treatment period described in the arm information was 12 weeks.

The primary outcome was change in UACR at Week 12. The recalculated mean difference was -169.4 mg/g, with a 95% confidence interval from -419.5 to 80.66 and an unadjusted p-value of 0.184. The sponsor’s mixed-model analysis reported a p-value of 0.279. The secondary eGFR comparison likewise did not provide clear evidence of a between-arm difference.

The results are short-term and uncertain, and the registry record does not report several details needed to fully evaluate analysis populations, missing data, multiplicity, or the original sample-size rationale.