Probiotic Yogurt Supplement in Reducing Diarrhea in Patients With Metastatic Kidney Cancer Being Treated With Vascular Endothelial Growth Factor-Tyrosine Kinase Inhibitor: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Probiotic Yogurt Supplement in Reducing Diarrhea in Patients With Metastatic Kidney Cancer Being Treated With Vascular Endothelial Growth Factor-Tyrosine Kinase Inhibitor (NCT02944617), a randomized na study. The primary outcome is Number of Participants With Overall Response.

2. Clinical Question

The clinical context is metastatic renal cell carcinoma treated with VEGF-TKI therapy, with interest in whether a probiotic yogurt supplement is associated with differences in outcomes and biomarkers. The registry describes the study as randomized, with a parallel design and no masking. Its stated objective includes studying diarrhea during VEGF-TKI treatment and examining blood and stool samples. However, the primary result available in the registry concerns overall response rather than diarrhea. That distinction matters when interpreting what the numerical comparison can answer: it describes the recorded response endpoint, and should not be recast as an estimate of the effect on diarrhea.

The record also lists clinical benefit rate and progression-free survival as secondary outcomes. The posted numerical secondary result described in the outcome data is clinical benefit rate. A progression-free survival estimate is not reported in the registry record. The trial’s results can therefore be read as a small randomized comparison of overall response, accompanied by a posted clinical benefit result, with important gaps between the broad study rationale and the outcomes for which numerical results are available.

3. Trial Design

This was a randomized, open-label, parallel-arm clinical trial sponsored by City of Hope Medical Center. Its status is completed. The registry record gives a start date of 2016-11-11 and a primary completion date of 2021-12-19. The phase is listed as NA. Enrollment is recorded as 21 participants. The posted primary outcome lists 10 participants in Arm I and 10 in Arm II; the registry record does not explain how these outcome counts relate to the enrollment total.

Arm I received Activia probiotic yogurt once daily during weeks 2-13 of VEGF-TKI treatment. Arm II had no intervention and was instructed to avoid yogurt or yogurt-containing foods and other probiotic supplements for 3 months. The trial is described as unmasked, so participants and those involved in the trial were not assigned masking in the registry design. The record does not report further details about allocation procedures, adherence, or how outcome assessors were kept unaware of assignment.

The posted primary outcome is the number of participants with overall response, using RECIST v1.1 criteria for target lesions assessed by MRI, over up to 3 years. This endpoint differs from the trial title’s emphasis on reducing diarrhea. The brief summary describes the study as examining whether probiotic yogurt reduces diarrhea and says that blood and stool sampling may help inform treatment planning. Those aims provide context, but the posted primary numerical comparison is for overall response.

Clinical benefit rate and progression-free survival are listed among secondary outcomes. The registry record reports numerical results for clinical benefit rate, while a progression-free survival estimate is not reported in the registry record. The outcome information also includes adverse-event counts and deaths by arm. The registry record does not report the trial’s original sample-size justification, original power assumptions, detailed statistical analysis plan, interim-analysis plan, or analysis-population definitions. Those details should not be inferred from the planning illustrations attached to the posted effect estimates.

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 Number of Participants With Overall Response, measured over Up to 3 years.

The primary endpoint is the number of participants with an overall response. The registry defines overall response as complete response or partial response under RECIST v1.1 for target lesions assessed by MRI. Complete response means disappearance of all target lesions; partial response is defined in the record as at least a 30% decrease in the sum of the longest diameters of target lesions. The assessment timeframe is up to 3 years.

A response endpoint gives a binary summary for each participant: the participant either meets the criteria for an overall response or does not. Counting participants with an event makes the outcome straightforward to compare between randomized arms, although the count alone does not describe how long a response lasts or the course of disease for each participant. The registry result reports 2 participants with an overall response among 10 in Arm I and 1 among 10 in Arm II.

Response is used in oncology trials as a structured way to summarize tumor change against prespecified criteria. Standardized criteria make the meaning of “response” more explicit than an informal clinical impression. In this record, the criteria and imaging method are specified, but other details about assessment timing and review procedures are not reported in the registry record.

The endpoint must be distinguished from the study’s stated interest in diarrhea. Overall response measures tumor response; it is not itself a measure of diarrhea frequency, severity, duration, or patient burden. The record’s posted primary comparison therefore informs the response outcome as defined, not the diarrhea-focused question implied by the trial title.

7. Results Posted in the Registry

• Arm II (no Intervention): 10.0% (1/10) versus 20.0% (2/10) on control; risk difference -10.0 percentage points (95% CI -41.0 to 21.0), risk ratio 0.50 (0.05–4.67); unadjusted two-proportion z-test p = 0.531.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Clinical Benefit Rate

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Arm II (no Intervention): 80.0% (8/10) versus 70.0% (7/10) on control; risk difference 10.0 percentage points (95% CI -27.7 to 47.7), risk ratio 1.14 (0.69–1.90); unadjusted two-proportion z-test p = 0.606.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Arm II (no Intervention) and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists clinical benefit rate and progression-free survival as secondary outcomes. Numerical results are posted for clinical benefit rate, while a progression-free survival estimate is not reported in the registry record. This makes the secondary evidence incomplete: the listed outcomes do not all have a reported comparison that can be interpreted alongside the primary result.

The posted clinical benefit comparison has the same broad interpretive pattern as the primary response comparison: the estimate is uncertain, and its confidence interval includes differences in either direction. It does not provide a precise or decisive complement to the primary result. The overall pattern across the available outcome information is therefore limited evidence, with substantial uncertainty rather than a consistent, clearly distinguished effect.

Secondary outcomes can be useful for exploring other aspects of a trial, but they generally carry less weight than a prespecified primary outcome when drawing confirmatory conclusions. Multiple outcomes create more opportunities for chance findings, particularly when many comparisons are examined. The registry record does not report a multiplicity strategy, a hierarchy among secondary outcomes, or whether the analyses were prespecified in a statistical plan. The posted unadjusted secondary comparison should consequently be interpreted cautiously.

The relation between the primary endpoint and the secondary outcomes also matters substantively. Overall response and clinical benefit rate are different outcome definitions; neither should be treated as a direct substitute for a diarrhea outcome. Progression-free survival is listed, but its estimate is absent from the registry results. The study’s diarrhea-focused rationale is therefore not directly resolved by the available primary and secondary comparisons.

Secondary findings can motivate further study or help characterize a trial, but they cannot repair the limited precision of a small primary comparison. The estimates do not establish that one arm is superior, and the available secondary results do not demonstrate that the arms are equivalent. Interpretations should remain tied to each outcome’s definition, its reported estimate, and the degree of uncertainty.

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

• Clinical Benefit Rate
• Progression-free Survival (PFS)

9. Statistical Interpretation

10. Statistical Methods Explained

The main comparison is between two randomized arms: probiotic yogurt and no intervention. Randomization is intended, in general, to make assigned groups comparable on average, including with respect to factors that might affect outcomes. In a small trial, randomization does not guarantee that every relevant characteristic is balanced, and the registry record does not provide enough information to assess baseline balance. The design is parallel, meaning participants are assigned to one arm rather than crossing between strategies. The record describes no masking.

What does a risk difference express?

A risk difference compares the proportion with an event in one group with the proportion in the other group. For the primary endpoint, the posted comparison is Arm II relative to Arm I and reports a risk difference of -0.1. The arm counts are 1 of 10 in Arm II and 2 of 10 in Arm I. A negative value in this comparison indicates that the observed event proportion was lower in Arm II. It is an absolute contrast, not a relative measure. Its interpretation depends on the outcome and the direction in which the arms are compared.

The risk difference is useful because it is expressed on the same absolute scale as the event proportions. It also makes the uncertainty around the estimated difference clinically interpretable: the confidence interval describes a range of effect sizes compatible with the data under the interval method’s assumptions. Here, the primary confidence interval extends from -0.410 to 0.210, so it includes negative and positive values and does not rule out a difference in either direction.

How do relative measures differ?

The registry also reports a risk ratio and an odds ratio for the primary comparison. The risk ratio is 0.5, with a 95% confidence interval from 0.053 to 4.672. A risk ratio compares event proportions as a relative quantity, rather than subtracting them. The odds ratio is 0.444, with a 95% confidence interval from 0.034 to 5.88. An odds ratio compares the odds of an event between groups; odds are not the same as probabilities, especially when events are not rare.

Both relative intervals are very wide. This is consistent with the small number of participants and events in the posted comparison. Neither relative measure should be interpreted in isolation from its interval. The estimates are sensitive to small changes in event counts, and the intervals allow a wide range of possible relative effects. Relative measures and absolute differences answer different descriptive questions; neither removes the uncertainty created by sparse data.

What does the p-value say?

The primary unadjusted p-value is 0.531. In general, a p-value measures how incompatible the observed data are with a specified null hypothesis under the analysis assumptions. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect. The registry labels this result as unadjusted. The record does not report a multiplicity adjustment, so no adjustment should be presumed.

A p-value that does not meet a conventional significance threshold does not establish that the strategies are equivalent. Equivalence requires a design and analysis that define an acceptable range of differences and assess whether the uncertainty lies within that range. Such an equivalence margin is not reported in the registry record. The confidence interval is therefore central to interpreting how much uncertainty remains.

Why does sparse data matter?

Binary outcomes with few participants can yield unstable estimates. When only a small number of participants experience the outcome, changing the status of one participant can noticeably alter the event proportion and the estimated between-arm contrast. General statistical methods for sparse binary data may produce wide confidence intervals, and some methods can behave differently when events are rare or absent. The registry does not report enough analysis detail to identify all computational choices behind the posted comparisons. No alternative method should be assumed.

Over-dispersion is a general issue when observed variability exceeds what a chosen statistical model expects. It is often discussed for count outcomes with more variation than a simple model allows. The posted primary endpoint is a participant count for a binary response outcome, and the record gives no indication that an over-dispersion model was used. It would not be appropriate to claim that over-dispersion was assessed or to infer a model beyond the information reported.

What is and is not reported about analysis?

The registry reports the arm counts, effect estimates, confidence intervals, and unadjusted p-values for the comparisons described. It does not report the detailed statistical analysis plan, analysis populations, handling of missing outcome data, or whether any covariates were included. The posted comparisons can therefore be described as presented, but their full model specification and sensitivity analyses are not available in the registry record.

The registry also lists a planning illustration based on observed effects. This is separate from the trial’s original design justification and does not show that the trial had a particular probability of detecting an effect. The original sample-size assumptions and justification are not reported in the registry record. The planning exercise is useful only as an illustration of how larger samples would be needed to detect specified fractions of the observed effect under the stated assumptions.

In general, the combination of a point estimate, an interval, and a p-value offers complementary information: the estimate gives the direction and scale of the observed contrast, the interval conveys its precision, and the p-value measures compatibility with a null hypothesis under assumptions. For this trial, the small arm counts and broad intervals are particularly important. Statistical interpretation should emphasize that uncertainty rather than treating a single summary statistic as a definitive answer.

11. Confidence Intervals and Precision

Confidence intervals communicate the precision of an estimated comparison. In general, a 95% confidence interval is produced by a method that, across repeated studies under its assumptions, would cover the underlying value in 95% of such intervals. It is not a statement that there is a 95% probability that the particular fixed effect lies inside the observed interval. It is also not a range of outcomes that must occur in future participants.

For the primary risk difference, the estimate is -0.1 and the 95% confidence interval runs from -0.410 to 0.210. The interval includes zero and extends to values on both sides of zero. The data are therefore compatible with a lower observed response proportion in Arm II, a higher one, or little difference. The interval is broad relative to the point estimate, indicating limited precision. The primary event counts, 2 of 10 in Arm I and 1 of 10 in Arm II, help explain why the estimate remains uncertain.

The relative measures are similarly imprecise. The risk ratio is 0.5, with a 95% confidence interval from 0.053 to 4.672. The odds ratio is 0.444, with a 95% confidence interval from 0.034 to 5.88. These ranges include substantially different relative comparisons. The central estimates alone would give an incomplete impression of the evidence.

The secondary clinical benefit risk difference is 0.1, with a 95% confidence interval from -0.277 to 0.477. This interval also includes zero and spans differences in either direction. The estimates do not establish a clear separation between the groups, but the width of the intervals also means they do not establish that the groups are equivalent.

Precision is constrained by the modest number of participants represented in the posted outcome counts and the limited number of events. The registry record does not provide enough information to assess the effects of missing outcomes or alternative analyses on interval width. These intervals should be read as uncertainty about the reported comparisons, not as proof that all values inside them are equally plausible or that values outside them are impossible.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size planning figures are illustrations based on the observed effect, not the trial’s original enrollment plan. For these binary outcomes, the figures are participants per arm, not total events. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The planning exercise considers effects equal to 100%, 75%, and 50% of the observed effect. The registry record does not report the trial’s original sample-size justification or design assumptions.

For the primary endpoint, planning for 100% of the observed effect gives 199 participants per arm for 80% power and 266 participants per arm for 90% power. Planning for 75% of the observed effect gives 379 participants per arm for 80% power and 507 participants per arm for 90% power. Planning for 50% of the observed effect gives 906 participants per arm for 80% power and 1,212 participants per arm for 90% power.

For clinical benefit rate, the corresponding illustrations are 294 and 392 participants per arm for 100% of the observed effect, 540 and 722 participants per arm for 75% of the observed effect, and 1,251 and 1,674 participants per arm for 50% of the observed effect, for 80% and 90% power respectively.

In general, power is the probability that a study will detect a difference of a specified size under the design assumptions if that difference is truly present. The 80% and 90% columns describe different planning targets: the 90% target seeks a higher chance of detecting the specified effect and therefore requires more participants in these illustrations. Smaller effects are harder to distinguish from random variation and require more participants. These values are not observed power; they are planning illustrations based on the observed effect and stated assumptions, and they do not establish what the original trial was designed to detect.

13. How the Sample Size Is Derived

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

Formula

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

Worked calculation for this trial

1. Control proportion p₀ = 0.200; treatment proportion p₁ = 0.100; average p̄ = 0.150.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5050 = 0.9897.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5000 = 0.4208.
4. n = (0.9897 + 0.4208)² / (-0.1000)² = 199.0, rounded up to 199 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 <- 0.2                          # control proportion
p1 <- 0.1                          # 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(2944617)
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 = 199 (80% power), 266 (90% power)
 75% of observed effect: n per arm = 379 (80% power), 507 (90% power)
 50% of observed effect: n per arm = 906 (80% power), 1,212 (90% power)
power.prop.test: 199 
Simulated power at n = 199 per arm: 0.806 (20000 replicates)

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

14. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study evaluates several outcomes, time points, subgroups, or statistical comparisons. In general, the more analyses considered, the greater the chance that at least one result may appear unusual by chance alone. Statistical plans may address this through a prespecified hierarchy, adjustment procedures, or a clear distinction between confirmatory and exploratory analyses. The registry record does not report a multiplicity strategy for the posted results.

The primary outcome is overall response, and the registry lists clinical benefit rate and progression-free survival as secondary outcomes. It also describes biomarker analyses. The record does not state how these outcomes were prioritized analytically, whether the secondary comparisons were prespecified, or whether all planned outcomes and analyses are posted. The clinical benefit comparison is reported with an unadjusted p-value. Its interpretation should not be treated as though a correction for multiple comparisons had been applied.

Missing-data handling is another important component of an analysis plan. The registry record does not report how participants with unavailable outcome assessments were handled, whether all randomized participants were included in each analysis, or how deviations from the assigned strategy were addressed. The primary outcome counts show 10 participants in each arm, while recorded enrollment is 21. The record does not explain this difference, so no particular reason for it should be inferred. It is also not possible from the posted information to determine whether any outcomes were missing.

Analysis populations can differ, for example, according to whether participants are analyzed as randomized or according to treatment received. Such choices can affect interpretation. The registry record does not report the analysis-population definitions for these outcome comparisons. It also does not report sensitivity analyses that might show whether conclusions change under different missing-data assumptions.

These omissions do not imply that the trial lacked a statistical plan or that data were mishandled. They mean that those details are not reported in the registry record and cannot be independently characterized from the posted results. The unadjusted comparisons should be read with that reporting limitation in mind, particularly because the trial includes multiple outcome areas and the available event counts are small.

15. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent non-serious adverse event terms

The adverse-event information reports 0 serious adverse events among 10 participants at risk in Arm I and 0 among 10 at risk in Arm II. Other adverse events were reported for 9 of 10 participants in Arm I and 8 of 10 in Arm II. Deaths are listed for 3 participants in Arm I and 6 in Arm II.

These are descriptive counts. They do not establish that the intervention caused, prevented, or changed the likelihood of an adverse event or death. The record does not provide event-specific details for the other adverse events, and the serious-adverse-event terms list is empty. It is therefore not possible to characterize the nature, timing, severity grading, or attribution of the reported other events from this information.

The small numbers at risk limit the precision of any comparison. The counts should be interpreted as events recorded during the trial, not as a reliable estimate of comparative safety. The registry record does not provide enough detail to determine follow-up duration for each participant, how deaths were classified, or whether the groups differed in exposure time.

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

The posted primary result describes overall response among participants assigned to probiotic yogurt or no intervention. It records 2 responses among 10 participants in Arm I and 1 among 10 in Arm II. The risk-difference estimate is -0.1 for Arm II relative to Arm I, with a broad confidence interval spanning negative and positive values. This is an uncertain comparison from a small number of participants, not a precise estimate of the size or direction of a difference.

The result does not establish that the yogurt strategy improves overall response, nor does it establish that it has no effect. A non-significant p-value does not prove equivalence or absence of an effect. The posted interval allows a range of possible comparisons, including differences that could matter in either direction. Stronger conclusions would require more precise evidence and clear reporting of the analysis methods and populations.

The trial title and brief summary emphasize diarrhea during VEGF-TKI treatment, but the posted primary outcome is overall response. The numerical primary result therefore does not answer whether yogurt reduces diarrhea. A diarrhea outcome estimate is not reported in the registry record. The record’s description of blood and stool sampling also does not supply a numerical biomarker comparison in the posted results.

Clinical benefit rate is reported as a secondary outcome, with an uncertain comparison. Progression-free survival is listed as a secondary outcome, but its result is not reported in the registry record. Neither the available secondary result nor the primary response comparison resolves the trial’s broader diarrhea-focused question.

The adverse-event counts are also descriptive and should not be interpreted as evidence that the intervention caused or prevented harms. The data do not support a definitive comparative safety conclusion. Overall, the posted results characterize outcomes in the two small randomized groups, while leaving considerable uncertainty about differences between them and about the study’s diarrhea and biomarker aims.

17. Why This Trial Matters Statistically

This trial illustrates why a study’s stated clinical motivation and its posted primary endpoint need to be read separately. The trial title and brief summary focus on diarrhea during VEGF-TKI treatment, while the posted primary outcome is overall response under RECIST v1.1. Keeping those questions distinct prevents an outcome that was measured from being used as a substitute for an outcome that is not reported.

It also demonstrates the importance of precision, not just the direction of a point estimate. A numerical difference can appear to favor one arm while the confidence interval remains broad enough to include effects in either direction. In small randomized trials with few events, point estimates can be unstable and should be interpreted alongside intervals and event counts rather than as stand-alone conclusions.

The record is useful for teaching how absolute and relative measures describe the same comparison in different ways, and why both can remain highly uncertain when data are sparse. It also shows how a non-significant unadjusted p-value differs from evidence of equivalence. Lack of a clear statistical distinction is not proof that the strategies have identical outcomes.

Finally, the registry record highlights the value of transparent reporting about analysis populations, missing data, multiplicity, and original sample-size planning. Those details are not reported in the registry record. Their absence limits what can be concluded about the completeness and prespecification of the analyses, without implying that they were not addressed during the trial. Careful reading identifies both what the posted data show and which questions they leave unresolved.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

A central limitation is the small number of participants in the posted outcome comparison: 10 per arm for the primary endpoint. The response counts are 2 and 1, leaving the estimate vulnerable to substantial random variation. The broad confidence interval reflects this limited precision. The registry enrollment total is 21, but the posted outcome counts total 10 in each arm; the record does not explain the difference or provide a participant flow that would clarify it.

The outcome mismatch between the trial’s stated focus and its posted primary endpoint is another trial-specific limitation for interpretation. The title and brief summary emphasize diarrhea, yet the numerical primary result is overall response. A diarrhea estimate is not reported in the registry record, so the posted primary comparison cannot answer the title’s central diarrhea question. Biomarker analyses are described, but numerical biomarker comparisons are not included in the reported outcome information.

The record describes a randomized, unmasked design. Lack of masking can matter for outcomes involving participant reporting or clinical assessment, although the registry record does not provide enough information to determine whether or how this affected assessment of the posted endpoint. Details about allocation procedures and outcome-assessment review are not reported in the registry record.

Other limitations are reporting gaps: the original sample-size justification, detailed analysis plan, multiplicity approach, analysis populations, and handling of missing data are not reported. A progression-free survival estimate is also not reported despite that outcome being listed. The safety information provides limited event detail, and the small counts do not support precise arm comparisons. These limitations constrain interpretation of the available results; they do not by themselves establish that the trial procedures were inadequate.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Probiotic Yogurt Supplement in Reducing Diarrhea in Patients With Metastatic Kidney Cancer Being Treated With Vascular Endothelial Growth Factor-Tyrosine Kinase Inhibitor — NCT02944617.
• Registry record last updated 2024-02-23; status last verified 2024-01.
• Results first posted 2023-04-19.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT02944617 is a completed randomized, open-label, parallel-arm trial sponsored by City of Hope Medical Center. It enrolled 21 participants with metastatic renal cell carcinoma receiving VEGF-TKI therapy. Arm I received Activia probiotic yogurt once daily during weeks 2-13 of treatment; Arm II avoided yogurt, yogurt-containing foods, and other probiotic supplements for 3 months.

The posted primary outcome is overall response assessed using RECIST v1.1 criteria by MRI over up to 3 years. The reported counts are 2 of 10 in Arm I and 1 of 10 in Arm II. The risk-difference estimate is -0.1, with a 95% confidence interval from -0.410 to 0.210 and an unadjusted p-value of 0.531. The interval is broad and includes differences in either direction.

Clinical benefit rate is reported as a secondary outcome; progression-free survival is listed but its estimate is not reported in the registry record. Although the trial’s stated rationale emphasizes diarrhea, a numerical diarrhea comparison is not reported in the registry record. Original sample-size justification, multiplicity strategy, analysis populations, and missing-data handling are also not reported.