Hyperglycemia in Renal Transplantation: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Hyperglycemia in Renal Transplantation (NCT01643382), a randomized na study. The primary outcome is Incidence of Poor Graft Function After Kidney Transplant.

2. Clinical Question

The posted primary outcome counts were 13 participants in the tight glucose control arm and 22 in the standard glucose control arm, with 30 participants in each arm. The registry record reports a risk difference of 0.3 for the standard arm relative to the tight arm. This describes the observed difference in the recorded outcome, not a treatment effect established independently of the study design and analysis.

The registry description motivates the study by noting that kidney transplant recipients with diabetes are at higher risk for poor initial graft function, and identifies short-term graft function as the question of interest. The record does not report additional eligibility criteria, participant characteristics, or a narrower population definition. Any inference should therefore remain tied to the participants enrolled and the outcome as registered, rather than being extended to unreported patient groups or outcomes.

3. Trial Design

The registry record describes a randomized, parallel-group study with no masking. Enrollment was 60. The record lists 30 participants in the Tight Glucose Control arm and 30 in the Standard Glucose Control arm. The study status is completed. The recorded start was 2012-08, and the primary completion was 2014-08.

Random allocation is intended to create groups whose differences are not systematically chosen by participants or study staff, although chance differences between groups can remain. A parallel-group design assigns each participant to one arm for comparison, rather than comparing multiple assigned treatment periods within each participant. No masking means treatment assignment was not concealed from participants and/or study personnel as described in the registry field; the record does not specify which individuals were aware of assignment or how outcome assessment was conducted.

The intervention descriptions distinguish an insulin infusion for tight glucose control from subcutaneous insulin doses every few hours based on blood sugar for standard glucose control. The record does not specify glucose targets, the duration of each approach, dose algorithms, adherence, or other details of treatment delivery. It also does not report a protocol for crossovers or deviations from assigned treatment.

The study lists one outcome measure, the primary incidence of poor graft function after kidney transplant, with a timeframe of 7 days after transplant. No secondary outcomes are posted. Although the study is classified as randomized, details about sequence generation, allocation concealment, stratification, and the original sample-size justification are not reported in the registry record. The record also does not describe an analysis population, such as whether participants were analyzed according to assigned group regardless of treatment received.

4. Participant Flow and Accounting

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

Overall Study

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Incidence of Poor Graft Function After Kidney Transplant, measured over 7 days after transplant.

The primary endpoint counts participants who experienced poor initial graft function after kidney transplant. It is a binary participant-level outcome: each participant either met the definition or did not. The registry record sets the assessment timeframe at 7 days after transplant.

Poor graft function is defined as the occurrence of delayed graft function (DGF) or slow graft function. DGF is defined as a decrease in serum creatinine of <10%/day for 3 consecutive days after transplant. Slow graft function is defined as serum creatinine >3 mg/dL 5 days after transplant without dialysis. The outcome combines these two criteria into a single measure; the posted result does not separate participants meeting one criterion from participants meeting the other.

This kind of endpoint can capture early graft-function problems as defined in the registry, and a participant count makes the two randomized groups directly comparable on whether the event occurred. It does not, by itself, describe the degree or duration of kidney-function change, nor does it summarize function beyond the stated timeframe. Interpretation also depends on consistently applying the stated creatinine and dialysis criteria. The registry record does not report how measurements were collected, how missing measurements were handled, or how endpoint status was adjudicated.

7. Results Posted in the Registry

• Standard Glucose Control: 73.3% (22/30) versus 43.3% (13/30) on control; risk difference 30.0 percentage points (95% CI 6.2 to 53.8), risk ratio 1.69 (1.07–2.69); unadjusted two-proportion z-test p = 0.018.

8. Statistical Interpretation

9. Statistical Methods Explained

The primary outcome is binary at the participant level: the registry counts whether each participant met the poor-graft-function definition during the stated timeframe. A binary outcome can be summarized by the number of participants with the event and the number assessed in each arm. Here, the counts are 13 of 30 in the tight glucose control arm and 22 of 30 in the standard glucose control arm. These counts retain the scale of the observed data without implying that the outcome is a continuous measure of kidney function.

What does the risk difference describe?

A risk difference subtracts the event probability in one group from the event probability in the comparison group. It is expressed on an absolute probability scale, so its interpretation concerns the gap between the groups rather than a multiplicative comparison. The reported risk difference is 0.3 for standard glucose control relative to tight glucose control. A positive value in this stated comparison indicates a larger observed event probability in the standard arm. The registry record gives a confidence interval for this estimate, and that interval is above zero.

How do the risk ratio and odds ratio differ?

A risk ratio compares event probabilities by division. The registry reports a risk ratio of 1.692, with a confidence interval from 1.066 to 2.688. A value greater than 1 in the specified direction indicates a higher observed probability in the standard arm relative to the tight arm. An odds ratio compares event odds rather than probabilities; the reported odds ratio is 3.596, with a confidence interval from 1.216 to 10.64. The two measures use different scales, so the odds ratio is not interchangeable with the risk ratio or the risk difference. For interpretation in a clinical setting, the underlying arm counts help keep the relative measures grounded in the observed participants.

What does a confidence interval add?

A confidence interval represents uncertainty around an estimate under the statistical method used to construct it. It gives a range of values that are compatible with the observed data and the method's assumptions; it is not a range containing a known fixed effect with certainty. The interval can show both the estimated direction and how precisely that estimate is determined. In this study, the reported intervals for the relative measures extend from values closer to no difference to values indicating a larger relative contrast. The registry record does not state the interval-construction method.

What does the p-value describe?

The unadjusted p-value is 0.018. In general, a p-value measures how unusual data at least as discrepant as the observed data would be under a specified null hypothesis and the assumptions of a test. It is not the probability that the null hypothesis is true, nor a measure of the probability that the result will replicate. Because the registry record does not identify the test, its assumptions, or an adjustment strategy, the p-value cannot be independently linked to a particular analysis from the record alone.

Why can the measures tell different-looking stories?

Absolute and relative measures answer distinct questions. The risk difference describes a gap in probabilities, while a ratio describes a multiplicative contrast. The odds ratio additionally uses odds, which are calculated differently from probabilities. It is therefore expected that their numeric values do not match. For a binary outcome that occurs in a substantial number of participants in both arms, an odds ratio can look larger than a risk ratio. This is a general statistical property, not a separate finding about the biological mechanism or about treatment action.

All these comparisons summarize the same posted outcome counts, but each emphasizes a different scale. They do not provide independent replications of the result. Nor do they show the distribution of creatinine values or distinguish delayed from slow graft function, since the posted endpoint combines those criteria. No adjusted estimate, subgroup analysis, or participant-level analysis is reported in the registry record.

The record lists no analysis descriptions. It does not report the statistical test, a regression model, covariate adjustment, handling of protocol deviations, or the method used to address missing observations. The reported comparison should accordingly be understood as the posted unadjusted summary, without attributing an undocumented method to the study team. General statistical explanations of measures do not fill those gaps: the actual methods used for the registry result remain not reported in the registry record.

Over-dispersion is a concern for some count models when the observed variability is greater than the model expects. It is generally relevant when modeling counts or rates, not when the endpoint is a single binary event indicator per participant as registered. That distinction does not resolve other methodological uncertainties; it simply clarifies the kind of outcome being summarized. The record does not report repeated event counts or a count-rate analysis for the primary endpoint.

10. Confidence Intervals and Precision

Precision concerns how much uncertainty surrounds an estimate. The registry reports a risk-difference estimate of 0.3, and its confidence interval lies above zero. The reported risk-ratio estimate is 1.692, with a confidence interval from 1.066 to 2.688. For the odds ratio, the estimate is 3.596, with a confidence interval from 1.216 to 10.64.

Both relative-measure intervals include a range of effect sizes rather than a single settled value. The risk-ratio interval is compatible with a comparatively modest relative contrast as well as a larger one. The odds-ratio interval is particularly broad on its own scale, extending from 1.216 to 10.64. This indicates that the odds-ratio estimate is not very precise: the data are compatible with materially different magnitudes of association, even though the interval is above 1.

The risk-difference interval being above zero indicates that the posted interval does not include no absolute difference, under the method used. The precise endpoints are not repeated because their registry values exceed the allowed display precision. The registry record does not state how the interval was calculated, so its method-specific coverage properties cannot be checked from the record.

All estimates arise from 30 participants per arm and the 7-day composite endpoint. A confidence interval does not account for every uncertainty. It does not correct for unreported missing data, outcome-assessment procedures, allocation details, or selective reporting. The interval also does not indicate whether the estimated difference is important to patients or clinicians. It quantifies statistical uncertainty under assumptions, rather than resolving questions about generalizability or the practical meaning of the endpoint.

11. 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. They are illustrations based on the observed effect, not the trial's own design calculation. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The planning effect is set to the stated fraction of the observed effect. The original trial's sample-size assumptions are not reported in the registry record.

The 80% power column gives the illustrative number of participants per arm for a design with an 80% chance of detecting the specified effect under the planning assumptions, if that effect and the assumptions were correct. The 90% power column gives the corresponding per-arm number for a design with a 90% chance. Power is a design property under a specified model and effect, not a statement about the probability that the observed result is true.

For planning at 100% of the observed effect, the figures are 42 participants per arm for 80% power and 55 per arm for 90% power. For planning at 75% of the observed effect, they are 76 and 101 participants per arm, respectively. For planning at 50% of the observed effect, they are 174 and 232 participants per arm, respectively. Smaller effects need more participants to distinguish them from chance variation at the stated planning settings.

These are planning illustrations, not observed power. The 60 participants enrolled in this study should not be interpreted through a retrospective power calculation using the observed effect; the figures instead show how hypothetical future sample sizes change as the target effect and desired power change. The registry record does not report the trial's original design justification, so these planning assumptions should not be attributed to the investigators.

12. 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 = 0.433; treatment proportion p₁ = 0.733; average p̄ = 0.583.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6972 = 1.3665.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6642 = 0.5590.
4. n = (1.3665 + 0.5590)² / (0.3000)² = 41.2, rounded up to 42 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 <- 0.433333333333333                          # control proportion
p1 <- 0.733333333333333                          # 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(1643382)
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 = 42 (80% power), 55 (90% power)
 75% of observed effect: n per arm = 76 (80% power), 101 (90% power)
 50% of observed effect: n per arm = 174 (80% power), 232 (90% power)
power.prop.test: 41.2 
Simulated power at n = 42 per arm: 0.827 (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.

13. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when several outcomes, comparisons, timepoints, or subgroup analyses are tested. Conducting multiple tests can increase the chance that at least one result appears unusual by chance. The registry record lists one outcome measure, the primary poor-graft-function endpoint, and reports no secondary outcomes. It reports one unadjusted comparison. Whether additional outcomes, analyses, or comparisons were specified or conducted is not reported in the registry record.

The record does not report a multiplicity strategy, such as a hierarchy of outcomes or an adjustment to p-values or confidence intervals. The unadjusted p-value of 0.018 should therefore be read as the posted unadjusted result, not as a result known to have been adjusted for a family of tests. The record also does not identify the test or its assumptions. It is not possible to infer a broader analysis plan from the single posted comparison.

Missing data are another important issue. The posted arm sizes total the stated enrollment, but the registry record does not explicitly report the number with an observed primary outcome, whether any outcome data were missing, or how missingness was handled. It also does not state whether participants were analyzed in their assigned arms, only if they received their assigned approach, or according to another analysis population. The distinction matters because excluding participants or reclassifying them can affect a randomized comparison.

No participant flow, protocol-deviation summary, or sensitivity analysis is reported in the registry record. Such information would help readers assess whether the analyzed groups remained comparable and whether the result depends on assumptions about unavailable outcomes. Its absence does not establish that data were missing or that analyses were inappropriate; it means the record does not document these matters.

The registry record also does not report a prespecified statistical analysis plan, covariate adjustment, subgroup analyses, or interim analyses. No assumptions about those procedures should be inferred. The posted results are consequently best interpreted as a limited record of the primary endpoint and unadjusted comparison, with multiplicity handling, missing-data procedures, and analysis-population definitions not reported in the registry record.

14. Safety Results

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

Deaths

Other (non-serious) adverse events

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

The registry reports serious adverse events for 30 participants at risk in each arm, with 0 affected in the Tight Glucose Control arm and 0 affected in the Standard Glucose Control arm. For other adverse events, it likewise reports 30 participants at risk in each arm and 0 affected in each arm. The registry lists no serious adverse-event terms.

The separate deaths field records 0 deaths in the tight arm and 2 in the standard arm. The record does not say whether these deaths were considered related to an intervention, nor does it give causes or timing. The deaths field should be read separately from the serious and other adverse-event counts; the registry does not explain how those categories relate to one another.

These figures are counts recorded for the study, not estimates of comparative safety with precision. Zero reported affected participants in an adverse-event category does not establish that no events could occur, and the record provides no detailed event descriptions or follow-up duration for safety. The registry record also does not describe event-collection methods or attribution procedures. The figures should therefore be read as what was posted, without inferring cause, absence of risk, or a safety difference.

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

The posted primary results show 13 participants meeting the poor-graft-function definition in the tight glucose control arm and 22 in the standard glucose control arm, with 30 participants in each arm. The reported risk difference is 0.3 in the direction of standard glucose control relative to tight glucose control. The risk ratio is 1.692, and the odds ratio is 3.596. These estimates describe the observed groups on the registered composite endpoint at 7 days after transplant.

The record does not establish that the observed contrast is a causal effect independent of assumptions, nor does it show what would happen in another population or setting. Randomization is an important design feature, but the registry record does not provide enough information to evaluate allocation procedures, missing outcome handling, analysis populations, or outcome assessment. The unadjusted p-value and confidence intervals summarize statistical evidence and uncertainty under methods that are not fully described in the record.

The endpoint combines delayed and slow graft function. The posted result does not show how many participants met each component, provide continuous creatinine measurements, or describe graft function after the 7-day timeframe. It does not establish effects on later kidney function, survival, quality of life, or other outcomes not posted. The registry lists no secondary outcome results.

The findings also do not establish the clinical importance of the observed difference. Statistical comparisons do not by themselves determine whether a result matters to patients, clinicians, or transplant programs. The registry record supplies no threshold for a clinically important difference and no decision framework. It would be inappropriate to turn the posted comparison into individualized medical advice or a recommendation about glucose-control approaches.

In general, an estimate is more informative when interpreted alongside its uncertainty, design, outcome definition, and completeness of reporting. In this record, the arm counts and comparative measures give a clear description of the primary posted result, while unreported methods and limited endpoint scope constrain what can be concluded. The result should be understood as a comparison of the two randomized groups in this completed study, not as proof of a general treatment effect.

16. Why This Trial Matters Statistically

This trial illustrates how a clinical question can be converted into a participant-level binary endpoint and a comparison between randomized groups. The endpoint defines poor initial graft function through two clinical criteria and asks whether either occurred within the registered timeframe. That operational definition makes the outcome countable, but it also means that the composite result cannot reveal which component drove the contrast.

The study also shows why reporting absolute and relative measures together can help interpretation. The risk difference of 0.3 expresses an absolute contrast, while the reported risk ratio of 1.692 and odds ratio of 3.596 express relative comparisons on different scales. Each measure answers a distinct statistical question. Their confidence intervals add information about uncertainty that a point estimate or p-value alone cannot convey.

At the same time, precision and transparency matter. There were 30 participants in each arm, and the odds-ratio confidence interval extends from 1.216 to 10.64. The interval indicates that the magnitude is not estimated narrowly. The registry does not report the statistical test, analysis population, missing-data methods, or a multiplicity approach. These omissions limit reproducibility and make it harder to evaluate how robust the posted comparison is.

The planning illustrations underscore that sample size depends on the effect targeted and the desired power: smaller effects require more participants under the stated assumptions. Those illustrations are not the original design justification and are not observed power. Separating future planning from interpretation of the completed study prevents an observed result from being mistaken for proof that the design had a particular probability of detecting an effect.

More broadly, the record emphasizes that a statistically interpretable result still has boundaries. The outcome is short-term, composite, and specific to the registered population and interventions. Understanding what the estimate does and does not answer is central to sound clinical-trial interpretation.

17. 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 contains limited detail about the enrolled population beyond the listed conditions and total enrollment. Age, baseline characteristics, eligibility criteria, and the distribution of participant characteristics by arm are not reported in the registry record. Without those details, readers cannot assess how closely the participants resemble a particular clinical population or whether the groups differed on measured baseline features.

The open-label design means masking was not used, but the record does not say who knew assignments or whether endpoint assessment was blinded. The primary outcome includes serum creatinine thresholds and dialysis status, and the record does not describe measurement schedules, adjudication, or procedures to ensure consistent endpoint classification across arms. The combined outcome also prevents interpretation of delayed and slow graft function separately.

The registered timeframe is 7 days after transplant, so the posted primary result is limited to early graft function. The record does not report later graft outcomes or any secondary results. Details of insulin dosing, glucose targets, treatment duration, adherence, crossovers, and deviations are not reported in the registry record, limiting description of how the intended approaches were implemented.

Analysis details are also sparse. The record does not report the test used for the unadjusted p-value, an analysis plan, missing-data handling, an analysis population, or adjustment for other variables. It does not report an original sample-size justification. The absence of these details does not prove that methods were not used; it limits what can be verified from the registry record.

Finally, the safety information is limited to event counts, with no serious adverse-event terms and no details about the two deaths recorded in the standard arm. Timing, cause, relationship to intervention, and event-collection procedures are not reported. These limitations should be kept in view when interpreting both the primary comparison and the safety counts.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Hyperglycemia in Renal Transplantation — NCT01643382.
• Registry record last updated 2020-11-05; status last verified 2020-10.
• Results first posted 2020-11-05.
• 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.

21. Record Summary

NCT01643382, titled Hyperglycemia in Renal Transplantation, is a completed randomized, unmasked, parallel-group study sponsored by the University of California, San Francisco. Enrollment was 60, split as 30 participants in each arm. The recorded start was 2012-08, and primary completion was 2014-08.

The primary endpoint was the incidence of poor graft function 7 days after transplant, defined by delayed or slow graft function criteria. The posted counts were 13 participants in the tight glucose control arm and 22 in the standard glucose control arm. The unadjusted p-value was 0.018; the record reports a risk difference of 0.3, a risk ratio of 1.692, and an odds ratio of 3.596.

No secondary outcome results or analysis descriptions are posted. Serious and other adverse events affected 0 participants in each arm; the deaths field records 0 in the tight arm and 2 in the standard arm. Details about the analysis plan, missing data, and the deaths are not reported in the registry record.