The Effect of Paricalcitol Capsules on Reducing Albuminuria in Patients With Type 2 Diabetic Nephropathy Being Treated With Renin-angiotensin System Inhibitors: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for The Effect of Paricalcitol Capsules on Reducing Albuminuria in Patients With Type 2 Diabetic Nephropathy Being Treated With Renin-angiotensin System Inhibitors (NCT00421733), a randomized phase 2 study. The primary outcome is Change From Baseline to the Last On-treatment Measurement in Urine Albumin to Creatinine Ratio (UACR) Levels Determined From the First Morning Void (FMV) Urine Collections Comparing Placebo to the Combined Paricalcitol Treatment Groups (1 Mcg and 2 Mcg)..

2. Clinical Question

3. Trial Design

The registry identifies NCT00421733 as a completed, randomized, parallel-group phase 2 study sponsored by Abbott. The listed enrollment was 281. The study started in 2006-12 and had primary completion in 2009-06. The official title describes a prospective, double-blind, placebo-controlled, multicenter study; the registry’s masking field specifies quadruple masking. The record does not explain which roles were included in that masking designation.

Participants were assigned to paricalcitol 1 mcg, paricalcitol 2 mcg, or placebo. In the 1 mcg group, each dose consisted of one paricalcitol capsule and one matching placebo capsule; in the 2 mcg group, each dose consisted of two paricalcitol 1 mcg capsules. The placebo group received two placebo capsules per dose. The primary outcome timeframe extended from baseline, measured within 1 week before the first treatment, through 24 weeks of treatment.

The primary comparison pooled the two paricalcitol dose groups and compared them with placebo. The posted primary analysis sample sizes were 88 for placebo, 92 for the 1 mcg group, and 92 for the 2 mcg group; the combined paricalcitol group had 184 participants in that analysis. These are analysis-specific counts, not the registry’s enrollment figure. The record states that participants without both baseline and last on-treatment measurements were excluded from the primary efficacy analysis. The original allocation ratio, the detailed randomization procedure, and the trial’s original sample-size justification are not reported in the registry record.

4. Participant Flow and Accounting

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

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Intent-to-treat population, which was all randomized participants who received at least 1 dose of study drug. Subjects without both a baseline and last on-treatment measurement were excluded from the primary efficacy analysis. As such, sample size was N=88 for placebo, N=92 for 1 mcg and for 2 mcg paricalcitol, and N=184 for combined paricalcitol.

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 Change From Baseline to the Last On-treatment Measurement in Urine Albumin to Creatinine Ratio (UACR) Levels Determined From the First Morning Void (FMV) Urine Collections Comparing Placebo to the Combined Paricalcitol Treatment Groups (1 Mcg and 2 Mcg)., measured over Baseline (within 1 week prior to first treatment) through 24 weeks of treatment.

The primary endpoint was change from baseline to the last on-treatment measurement in UACR, assessed from first-morning-void urine collections. UACR expresses the amount of albumin relative to creatinine in urine, using milligrams of albumin per gram of creatinine. The registry states that the UACR values were log transformed before analysis, so the reported change is on a logarithmic scale rather than the original measurement scale.

Baseline UACR was calculated as the mean of three first-morning-void measurements collected within 1 week before the first dose. The last on-treatment value was the mean of three measurements collected within 1 week of the final treatment week. Averaging repeated collections is intended to represent the measurement more stably than relying on a single urine collection, which can vary. That is a general measurement rationale, not a reported reason for choosing this specific endpoint.

The comparison asks whether mean change differed between placebo and the combined paricalcitol groups. A more negative change on the log scale indicates a greater downward shift in the measured ratio, but the registry’s primary reported contrast should be interpreted on its stated log scale. The posted endpoint is a laboratory measure over the treatment period; it is not itself a direct measure of how participants felt or functioned, nor does it establish longer-term clinical outcomes.

7. Results Posted in the Registry

• Combined Paricalcitol 1 Mcg and 2 Mcg: mean -0.18 versus -0.03 on control; difference -0.15 (95% CI -0.31 to 0.01), standardized effect d = -0.22; z-test p = 0.071.
• Paricalcitol 1 Mcg: mean -0.15 versus -0.03 on control; difference -0.12 (95% CI -0.31 to 0.07), standardized effect d = -0.18; z-test p = 0.227.
• Paricalcitol 2 Mcg: mean -0.22 versus -0.03 on control; difference -0.19 (95% CI -0.38 to 0.00), standardized effect d = -0.29; z-test p = 0.050.

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.

Number of Participants Achieving a 15% or Greater Reduction From Baseline to Last On-treatment Urine Albumin to Creatinine Ratio (UACR) Levels.

• 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:
  • Paricalcitol 1 Mcg: 52.2% (48/92) versus 39.8% (35/88) on control; risk difference 12.4 percentage points (95% CI -2.0 to 26.8), risk ratio 1.31 (0.95–1.81); unadjusted two-proportion z-test p = 0.095.
  • Paricalcitol 2 Mcg: 55.4% (51/92) versus 39.8% (35/88) on control; risk difference 15.7 percentage points (95% CI 1.2 to 30.1), risk ratio 1.39 (1.02–1.91); unadjusted two-proportion z-test p = 0.035.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Paricalcitol 1 Mcg and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Paricalcitol 2 Mcg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change From Baseline to the Last On-treatment Measurement in Albumin Levels Determined From 24-hour Urine Collection.

• 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:
  • Paricalcitol 1 Mcg: mean -0.10 versus -0.08 on control; difference -0.02 (95% CI -0.25 to 0.21), standardized effect d = -0.03; z-test p = 0.866.
  • Paricalcitol 2 Mcg: mean -0.44 versus -0.08 on control; difference -0.36 (95% CI -0.62 to -0.10), standardized effect d = -0.44; z-test p = 0.007.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Paricalcitol 1 Mcg and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Paricalcitol 2 Mcg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change From Baseline to the Last On-treatment Observation in Intact Parathyroid Hormone (iPTH) Levels.

• 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:
  • Paricalcitol 1 Mcg: mean -26.70 versus 18.30 on control; difference -45.00 (95% CI -61.15 to -28.85), standardized effect d = -0.81; z-test p = <0.001.
  • Paricalcitol 2 Mcg: mean -50.70 versus 18.30 on control; difference -69.00 (95% CI -86.42 to -51.58), standardized effect d = -1.16; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Paricalcitol 1 Mcg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Paricalcitol 2 Mcg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The secondary outcomes add several kinds of information rather than repeating the primary measure. They include a threshold-based UACR response, albumin change from 24-hour urine collection, and intact parathyroid hormone change. Across these measures, the posted patterns are not identical: some dose comparisons show clearer separation from placebo than others, and the direction or scale of a result depends on the particular outcome. This variation is a reason to interpret each endpoint according to its definition rather than to combine them into a single broad claim.

The threshold-based response is related to the primary UACR question because it uses the same general urine ratio, but it discards information by classifying participants according to whether they reached a specified reduction. The 24-hour urine albumin measure is related to albumin excretion but uses a different collection method and outcome scale. Intact parathyroid hormone is a distinct laboratory outcome. These endpoints can provide context about treatment-period measurements, but they are not substitutes for the primary comparison.

The secondary results do not resolve the primary estimate’s uncertainty. Nor do they establish that differences in these laboratory measures translate into longer-term clinical outcomes. The registry lists analyses for the posted secondary outcomes, but does not report a multiplicity adjustment. When several outcomes and comparisons are examined, unadjusted p-values can make apparently notable findings more likely to arise by chance across the collection of tests. The secondary findings should therefore be considered exploratory or supportive unless a prespecified hierarchy or adjustment is documented; such details are not reported in the registry record.

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

• Number of Participants Achieving a 15% or Greater Reduction From Baseline to Last On-treatment Urine Albumin to Creatinine Ratio (UACR) Levels.
• Change From Baseline to the Last On-treatment Measurement in Albumin Levels Determined From 24-hour Urine Collection.
• Change From Baseline to the Last On-treatment Observation in Intact Parathyroid Hormone (iPTH) Levels.

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous: each participant’s endpoint is a numerical change in log-transformed UACR. The registry reports group means and standard deviations. A mean summarizes the average observed change within a group, while the standard deviation describes how dispersed individual observations are around that group mean. A group mean is not a description of every participant’s response.

What does the mean difference represent?

The recalculated mean difference compares the placebo mean with the mean in a treatment group, expressed as treatment-group change minus placebo change. A negative estimate means the treatment-group mean change was more negative on the stated log scale. For the primary comparison, the estimate was -0.15. The confidence interval expresses the uncertainty around that contrast under the assumptions used for the recalculation. This is an unadjusted comparison based on posted summary statistics, not a reconstruction of the sponsor’s full participant-level model.

The primary registry analysis was ANCOVA. In general, analysis of covariance models an outcome while accounting for covariates; it can adjust the treatment comparison for baseline measurements or other specified factors. The registry identifies ANCOVA but does not give the full model specification, covariates, or handling details. The recalculated comparison uses the posted group summaries and is not adjusted in the same way. Therefore, a small difference between its p-value and the sponsor’s p-value can arise from the different analysis methods without indicating a contradiction.

Why use a log transformation?

The registry says UACR data were log transformed before analysis. In general, transforming a measurement can make a distribution more suitable for a model or reduce the influence of very large values. It also changes the scale of interpretation: differences on the log scale are not simple differences in the original milligram-per-gram units. No back-transformed treatment ratio is posted for the primary comparison, so the log-scale estimate should not be converted into a percentage change.

How should the uncertainty be read?

A confidence interval is a range of values compatible with the observed data under the model and its assumptions. In general, a wider interval indicates less precision than a narrower interval. For a difference, an interval spanning zero includes no difference as a value compatible with the data. It does not prove that the groups are identical; it means the data do not tightly distinguish the contrast from no difference at the interval’s stated confidence level. The registry reports 95% confidence intervals for several analyses, but does not give a primary sponsor confidence interval.

A p-value measures how unusual the observed data, or more extreme data, would be under a specified null model and analysis. It is not the probability that the null hypothesis is true, and it does not measure clinical importance. The sponsor’s primary p-value was 0.071. This should be considered alongside the estimated contrast, its interval, endpoint definition, and analysis population rather than treated as a stand-alone verdict.

What changes for the binary outcome?

The UACR response endpoint counts participants meeting a threshold of a 15% or greater reduction. For this kind of outcome, the recalculation reports risk differences: the difference between the observed proportions in the compared groups. A risk difference is on the probability scale, unlike the mean difference for a continuous outcome. The sponsor’s listed method for these comparisons was Fisher Exact. In general, an exact test is used to assess evidence of association for categorical counts; the registry does not provide further details of the sponsor’s implementation.

Other secondary outcomes are continuous and were analyzed by ANCOVA according to the registry. Their units and measurement procedures differ from the primary endpoint, so their estimates should be interpreted on their own scales. The registry lists standard deviations for the continuous outcomes, but does not give all model details or all covariates used in those analyses.

Why does over-dispersion matter?

For count or binary data, a statistical model can be too confident if the observed variability is greater than the model expects. This general issue is called over-dispersion, and accounting for it can widen uncertainty. The posted binary summary is analyzed with Fisher Exact in the sponsor results, while the simple recalculation uses the displayed counts and proportions. The registry record does not report a separate over-dispersion assessment. The primary endpoint is continuous rather than a count outcome, so over-dispersion is not the central issue for its mean comparison.

All recalculated estimates are simple, unadjusted re-calculations from posted summary statistics. They are useful as a transparent way to interpret the displayed group summaries, but they do not recover participant-level information, adjustment choices, or the full trial analysis plan.

11. Confidence Intervals and Precision

The primary recalculated mean difference was -0.15 log milligram/gram creatinine, with a 95% confidence interval from -0.313 to 0.013. The interval crosses zero. It therefore includes no difference as well as contrasts in either direction, so the point estimate alone gives a more definite impression than the interval supports. The range is compatible with a more negative mean change in the combined paricalcitol group, but it also extends slightly above zero.

Precision concerns how narrowly the data locate an estimate, not whether the estimate is important. In general, a confidence interval becomes narrower when an analysis contains more information or less variability, though its width also depends on the analysis and assumptions. The primary analysis had 88 placebo participants and 184 in the combined paricalcitol group; the posted group standard deviations were 0.61 for placebo and 0.7 for the combined group. These figures describe the analyzed outcome data, not all randomized participants.

Confidence intervals are estimates, not boundaries that guarantee the true value lies within them. Their interpretation depends on the model and analysis being appropriate. The primary interval in this display is an unadjusted recalculation; it is not a reported ANCOVA interval from the sponsor. The registry record reports the sponsor’s primary ANCOVA p-value but does not report a sponsor estimate or confidence interval for that comparison.

The dose-specific intervals also show uncertainty, and the 2 mcg recalculated interval reaches < 0.001 on its upper end. Small differences in the displayed precision should not be treated as substantive distinctions. Taken together, the intervals support attention to uncertainty and estimation rather than a simple yes-or-no reading based only on whether a p-value falls on one side of a chosen threshold.

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 for these comparisons are participants per arm, not total participants across the two arms. They are planning illustrations based on the observed effect, not the sample-size justification used when the trial was designed. The original design assumptions, including the trial’s own target effect and sample-size calculation, are not reported in the registry record.

The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each row uses the stated fraction of the observed effect: 100%, 75%, or 50%. These fractions describe hypothetical effects for planning; they are not additional observed outcomes. The 80% power column indicates the estimated participants per arm for a design with an 80% chance of detecting the specified effect if that effect were real and the planning assumptions held. The 90% power column gives the corresponding per-arm planning figure for a higher chance of detection under those same assumptions.

For a given outcome, planning for a smaller effect requires more participants than planning for a larger effect, because smaller differences are harder to distinguish from random variation. The displayed figures vary across outcomes because their observed effects and variability differ. They should not be read as a claim that the completed trial had the listed power. This is not observed power: it is a prospective planning illustration calculated from the observed effect, not a measure of what the completed trial achieved.

13. How the Sample Size Is Derived

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

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -0.150; pooled standard deviation σ = 0.672.
2. n = 2 × (1.96 + 0.842)² × 0.672² / -0.150² = 315.4, rounded up to 316 participants per arm for 80% power.

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

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- -0.15                      # observed difference in means
sigma <- 0.672316889569197                     # 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(421733)
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 = 316 (80% power), 423 (90% power)
 75% of observed effect: n per arm = 561 (80% power), 751 (90% power)
 50% of observed effect: n per arm = 1,262 (80% power), 1,689 (90% power)
power.t.test: 316.3 
Simulated power at n = 316 per arm: 0.798 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The sponsor’s reported primary analysis was ANCOVA and gave p = 0.071 for the combined paricalcitol groups versus placebo. The unadjusted re-calculation from posted group summaries also gives p = 0.071. This agreement in the displayed p-value is reassuring as a check on the basic contrast, but it does not make the analyses identical: ANCOVA can account for covariates, while the recalculation is based on group-level summary statistics without adjustment.

For the dose-specific primary-endpoint comparisons, the sponsor reported p = 0.229 for 1 mcg and p = 0.053 for 2 mcg. The corresponding unadjusted recalculations are p = 0.227 and p = 0.05. These are close, but the sponsor’s analyses remain the trial’s reported analyses. The recalculated confidence intervals are not replacements for sponsor-model intervals.

The registry record does not give the model details needed to reproduce ANCOVA, such as its complete covariate specification. In general, adjusted and unadjusted estimates or p-values may differ because they use different information and assumptions. No discrepancy is flagged in the record. The sponsor’s own analysis takes precedence where an apparent difference arises, while the summary-statistic recalculation serves as a separate, transparent descriptive check.

15. Multiplicity, Missing Data and Analysis Populations

The registry posts a primary comparison and several other comparisons across outcomes and dose groups. When many statistical tests are performed, the chance of obtaining at least one small p-value by chance can increase. This general issue is called multiplicity. Adjusting for multiplicity can control that risk, but may also change the interpretation and precision of individual tests. The record does not report a multiplicity adjustment, a testing hierarchy, or which comparisons were formally designated as confirmatory beyond identifying the primary outcome.

The primary efficacy population is described as all randomized participants who received at least one dose, with those lacking either baseline or last on-treatment measurement excluded from the primary analysis. This definition is not identical to an analysis of every randomized participant regardless of treatment receipt or measurement availability. The record does not report how missing endpoint measurements were imputed, whether sensitivity analyses were conducted, or the reasons participants might lack a measurement. These details matter because missingness can affect the group summaries and the assumptions behind an analysis.

Different outcomes have different analysis counts. The primary analysis reports 88 participants for placebo and 92 in each paricalcitol dose group; some secondary analyses have other group counts. Those differences reflect the posted outcome-specific analysis samples, but the registry record does not explain all reasons for variation. The enrollment figure should not be assumed to be the denominator for every endpoint.

The registry identifies ANCOVA for the primary outcome and some continuous secondary outcomes, and Fisher Exact for the binary secondary outcome. It does not report the complete statistical analysis plan, model covariates, interim analyses, or detailed missing-data procedures. Those items are not reported in the registry record, so conclusions about prespecification, data handling, or the protection of error rates cannot be independently established from the posted information.

16. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: gastritis (2.2%), hypoglycaemia (2.2%), acute myocardial infarction (2.1%), fluid overload (2.1%), cerebrovascular accident (2.1%).

The registry lists serious adverse events affecting 13 of 93 participants in the 1 mcg group, 19 of 95 in the 2 mcg group, and 12 of 93 in the placebo group. For other adverse events, the listed figures are 38 of 93, 46 of 95, and 46 of 93, respectively. These are counts of participants affected and their corresponding at-risk denominators as posted; they are not a measure of event severity beyond the serious-event classification.

The serious-event terms with listed percentages include gastritis and hypoglycaemia at 2.2% each, and acute myocardial infarction, fluid overload, cerebrovascular accident, and renal failure acute at 2.1% each. The registry record does not establish whether a particular event was caused by study treatment. It also does not report death counts in the posted safety summary. These descriptive figures alone do not establish comparative safety, especially without fuller information about event definitions, timing, follow-up, and statistical comparisons. The safety findings should be interpreted within the trial’s listed groups and record, not as individual medical guidance.

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

The primary result describes the average change in log-transformed UACR over the treatment period among participants included in the primary efficacy analysis. The observed group means moved downward in both placebo and combined paricalcitol groups, with a more negative mean change in the combined group. The recalculated estimate and its confidence interval communicate both the direction of the point estimate and the uncertainty around it. The interval includes zero, so the posted summary does not tightly distinguish the primary contrast from no difference.

This interpretation is limited to the stated endpoint, analysis population, and period through 24 weeks of treatment. It does not establish what happened to kidney function over a longer period, whether the measured change led to a patient-important benefit, or whether effects differ in populations not described by the trial. The study enrolled people with type 2 diabetic nephropathy who were receiving renin-angiotensin system inhibitor therapy; results should not be extended automatically beyond that setting.

A laboratory outcome can be useful for evaluating a biological or treatment response, but a difference in a laboratory measure is not equivalent to proof of a lasting clinical outcome. Likewise, the secondary outcomes may provide context, but multiple unadjusted comparisons and distinct measurement scales require caution. The registry record does not report a multiplicity strategy or longer-term outcome results.

The results also do not show that every participant experienced the group-average change. Standard deviations indicate variation within groups, and group means conceal individual differences. The estimates are statistical summaries, not predictions for an individual. The record does not provide sufficient detail to infer how any individual should be treated or monitored, and these results are not medical advice.

18. Why This Trial Matters Statistically

This trial illustrates why a clinical result should be read through its endpoint definition, analysis population, and uncertainty together. UACR was measured repeatedly at baseline and near the end of treatment, then log transformed for analysis. Those choices shape what the mean difference represents and prevent direct interpretation as a simple change in the original measurement units.

It also shows the distinction between a sponsor’s adjusted analysis and a transparent recalculation from group summaries. The primary p-values agree at the posted precision, while the recalculated confidence interval provides a direct view of uncertainty around the unadjusted contrast. Neither the point estimate nor a p-value on its own captures the full statistical picture.

The trial includes a combined-dose primary comparison alongside dose-specific comparisons and secondary outcomes. This structure makes the primary-versus-secondary distinction and multiplicity important: a collection of nominal results can appear more conclusive than the evidence warrants if the testing plan is unknown. The registry record does not supply that plan or the original sample-size justification, limiting what can be inferred about design decisions.

Finally, the sample-size illustrations demonstrate how planning depends on the effect size assumed. They are not observed power and cannot retrospectively establish whether the completed study was adequately powered for its intended question. Taken together, these features make the study a useful example of interpreting clinical trial summaries without conflating statistical significance, precision, laboratory response, and clinical importance.

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 primary efficacy analysis excludes participants without both baseline and last on-treatment measurements, according to the registry description. The record does not explain the extent or reasons for missing measurements, how missing data were handled in other analyses, or whether sensitivity analyses were performed. This limits assessment of whether the analyzed participants represent the full randomized population.

The registry identifies ANCOVA but does not report the full model specification. The primary sponsor result has a p-value but no reported estimate or confidence interval in the registry record. The unadjusted recalculation therefore cannot substitute for the sponsor’s adjusted effect estimate, and it cannot account for participant-level data or model covariates.

The study’s primary timeframe ended at 24 weeks of treatment. The registry outcome summaries do not establish persistence of measured changes or longer-term clinical consequences. The primary endpoint is a urine laboratory measure, and the posted record does not connect its changes to patient-important outcomes. The trial’s phase 2 designation and enrollment are reported, but its original sample-size justification is not reported in the registry record.

Multiple dose comparisons and secondary outcomes are posted, while multiplicity handling and the complete statistical analysis plan are not reported in the registry record. Safety counts are descriptive; the registry summary does not provide enough detail to attribute events to treatment or fully assess comparative risk. These limitations constrain the conclusions that can be drawn from the posted results.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: The Effect of Paricalcitol Capsules on Reducing Albuminuria in Patients With Type 2 Diabetic Nephropathy Being Treated With Renin-angiotensin System Inhibitors — NCT00421733.
• Registry record last updated 2012-01-20; status last verified 2012-01.
• Results first posted 2010-09-14.
• 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

NCT00421733 is a completed, randomized, parallel-group phase 2 study sponsored by Abbott. It compared paricalcitol 1 mcg and 2 mcg capsules with placebo in participants with type 2 diabetic nephropathy receiving renin-angiotensin system inhibitor therapy. Listed enrollment was 281, and the primary treatment period extended through 24 weeks.

The primary outcome was change in log-transformed UACR from baseline to the last on-treatment measurement, with a combined-dose comparison against placebo. The sponsor reported an ANCOVA p-value of 0.071 for that primary comparison. The unadjusted recalculation gives a mean difference of -0.15, with a 95% confidence interval from -0.313 to 0.013. The interval includes no difference and reflects uncertainty around the estimate.

Secondary laboratory and response outcomes and descriptive adverse-event counts provide additional context, but multiplicity handling, complete model details, missing-data procedures, and the original sample-size justification are not reported in the registry record.