Tailoring Online Continence Promotion: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Tailoring Online Continence Promotion (NCT05183217), a randomized na study. The primary outcome is User Engagement With Online Mind Over Matter (MOM).

2. Clinical Question

The stated project aim is to understand whether, and how, tailoring an online continence intervention can increase engagement and uptake of health behaviors known to improve bladder and bowel symptoms. The primary endpoint addresses the engagement part of that aim: it records whether participants crossed the specified threshold for interacting with the program during the stated time window. It does not by itself measure whether symptoms improved or whether health behaviors changed.

The registry lists additional outcomes concerning self-reported behavior changes, self-efficacy, health action process responses, barriers to care-seeking, urinary incontinence, and accidental bowel leakage. Those outcomes broaden the questions the project addresses, but the primary comparison is specifically about engagement. Clinical effects or changes in continence symptoms should not be inferred from the engagement result alone.

3. Trial Design

This is a randomized, parallel-group behavioral study comparing an online continence promotion program with tailoring against the same named program without tailoring. Masking is listed as none. The record labels the phase as NA, which is consistent with the registry’s presentation of this as a behavioral rather than a drug-phase study; the record does not give further phase-related detail.

The registry lists enrollment as 445. The arm-level results report 226 participants for the program without tailoring and 219 for the program with tailoring. These are the figures recorded for the groups; the registry record does not explain how enrollment, allocation, or outcome availability were handled for each analysis. The allocation method is listed as randomized, but details such as sequence generation, concealment, and any stratification are not reported in the registry record.

The study status is completed. The recorded start date is 2023-11-30, and primary completion is listed as 2024-05-08. The primary outcome timeframe is 0-4 weeks. The registry identifies the sponsor as University of Wisconsin, Madison. The listed interventions are behavioral programs, one without tailoring and one with tailoring.

The registry lists 21 outcome measures and 13 posted secondary outcomes. These include measures of pelvic floor muscle exercise frequency; urinary-incontinence and generalized self-efficacy; program-specific health action process responses; barriers to seeking care for urinary or bowel incontinence; and urinary and bowel incontinence scores. The registry record does not report a detailed analysis plan, the method used to define or implement tailoring, the schedule for secondary outcome measurement, or the analysis populations.

4. Participant Flow and Accounting

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

Overall Study

Reasons for not completing this period:

5. Baseline Characteristics

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

6. Primary Endpoint

The registry lists the primary outcome as User Engagement With Online Mind Over Matter (MOM), measured over 0-4 weeks.

The primary endpoint is user engagement with online Mind Over Matter (MOM), measured as the proportion of participants who engage with the program at least 4 times during weeks 0-4. The registry also describes the outcome parameter as a count of participants, so the posted arm summaries include participant counts meeting the engagement definition. The outcome is binary at the participant level: each person either meets the stated engagement threshold or does not.

This endpoint is useful for examining whether tailoring is associated with differences in interaction with the online program. Engagement can be an important implementation outcome because a program cannot deliver its intended content to participants who do not interact with it. The project description also names uptake of health behaviors as an area of interest, but meeting the engagement threshold is not itself evidence that a participant adopted a health behavior.

The timeframe gives the period over which engagement is assessed. The endpoint does not describe engagement after weeks 0-4, the quality or duration of interactions, or whether participants completed particular program components. Those details are not reported in the registry record. Nor does the primary outcome directly measure bladder or bowel symptoms, treatment response, or quality of life. Such outcomes appear among the secondary measures, but they are distinct from the primary engagement measure.

7. Results Posted in the Registry

• Online Continence Promotion Program With Tailoring: 16.9% (37/219) versus 7.5% (17/226) on control; risk difference 9.4 percentage points (95% CI 3.3 to 15.4), risk ratio 2.25 (1.30–3.87); unadjusted two-proportion z-test p = 0.002.

8. Statistical Interpretation

9. Statistical Methods Explained

For a binary outcome, each participant is classified according to whether the event occurred. In this trial, the event is engagement with online MOM at least 4 times during weeks 0-4. The arm-level event counts are 17 of 226 for the program without tailoring and 37 of 219 for the program with tailoring. A proportion summarizes the frequency of that event within each group.

What does a risk difference describe?

In general, a risk difference subtracts the event proportion in one group from the event proportion in the comparison group. It is an absolute measure: it describes the separation between the two group proportions on the probability scale. The reported risk difference is 0.094 for tailoring compared with no tailoring, with an interval from 0.033 to 0.154. This value should be read on the same scale as the underlying proportions; it is not a relative multiplier.

The risk difference can be directly useful when the question is how far apart the observed group proportions are. It does not summarize the contrast in the same way as a ratio. In general, absolute differences depend on the baseline event frequency, so the same relative contrast can correspond to different absolute differences when the baseline frequency differs. No additional absolute measures need to be calculated to interpret the reported estimate.

How do risk ratios and odds ratios differ?

In general, a risk ratio divides one group’s event proportion by the other group’s event proportion. A value above one means the event proportion is higher in the numerator group. The reported risk ratio is 2.246, with an interval from 1.304 to 3.867. The odds ratio instead compares odds, where odds are the event probability relative to the probability of no event. The reported odds ratio is 2.499, with an interval from 1.361 to 4.589.

Risk ratios and odds ratios may be numerically different, even when they describe the same groups and outcome. An odds ratio is not generally a direct statement about how many times as common an event is. Here, the event proportions are reported alongside both relative measures, which helps keep the comparison grounded in the observed group frequencies. The registry record does not state which effect measure was designated as the principal inferential measure beyond naming the primary outcome as a proportion and reporting the risk difference comparison.

What does the p value say?

In general, a p value is calculated under a specified statistical model and a null hypothesis, and it indicates how unusual data at least as incompatible with that null would be if the model and null were correct. It is not the probability that chance alone caused the result, nor is it the probability that a treatment or program is effective. The reported p value is unadjusted and equals 0.002. The statistical test, its exact null formulation, and the assumptions used are not reported in the registry record.

A p value does not describe the size of a difference or its practical importance. It should be considered alongside the point estimate, interval, outcome definition, and study conduct. The interval for the risk difference presents a range of values compatible with the data under the interval method used; the method and confidence level are not reported in the registry record. No claim about a particular confidence level can therefore be made from this record.

What does randomization contribute?

In general, random allocation aims to distribute participant characteristics across comparison groups without systematically choosing who receives each condition. This can reduce confounding in expectation, making a group contrast more interpretable. It does not guarantee that groups are identical, ensure complete outcome data, or prevent bias arising after assignment. The study is listed as randomized, but sequence generation, allocation concealment, baseline characteristics, and participant flow are not reported in the registry record.

Masking is listed as none. In general, lack of masking can affect behavior, reporting, or assessment, depending on the outcome and who knows the assignment. The primary outcome is engagement with an online program, but the record does not specify who recorded engagement or whether the measure was automatically captured. It would be speculative to assign a particular direction or size to any potential influence.

What analysis details remain unavailable?

The registry record reports 0 analyses while also posting arm summaries and a comparison for the primary endpoint. The exact analysis procedure underlying the posted comparison is not reported in the registry record. The record also does not report covariate adjustment, missing-data methods, analysis population definitions, prespecified subgroup analyses, interim analyses, or a detailed statistical analysis plan. Their absence from the registry record does not establish that these procedures were or were not performed; it means they cannot be described from the registry information.

In general, analysis choices can affect both estimates and uncertainty. For example, adjustment for prognostic variables may alter a comparison, and missing-data assumptions can matter when outcome availability differs between groups. These are general considerations, not claims about this study. The available estimates should be interpreted as the posted summaries, with the unadjusted status of the reported p value kept explicit.

10. Confidence Intervals and Precision

The risk-difference estimate is 0.094, and its reported interval extends from 0.033 to 0.154. The interval gives a range of values that are compatible with the observed data under the interval procedure used. Its lower and upper limits are both above zero, so the reported interval does not include a zero risk difference. The registry record does not report the confidence level or the method used to construct this interval, so neither should be assumed.

Intervals express uncertainty about an estimate; they do not mean that the true value has a specified probability of lying inside the particular interval. In general, a narrower interval indicates greater precision under the same interval method, while a wider interval indicates more uncertainty. Precision depends on such factors as the amount of information in the data, the event frequency, and the analysis method. These general principles do not identify which factor accounts for the width of this trial’s interval.

The relative estimates also have reported intervals: 1.304 to 3.867 for the risk ratio and 1.361 to 4.589 for the odds ratio. These intervals are on different scales from the risk-difference interval, so their widths should not be compared as if they represented identical quantities. The registry record does not provide confidence levels or interval methods for these measures either.

The interval and p value are not substitutes for the study’s design details. They do not account for possible bias, incomplete outcome data, multiplicity, or deviations from planned analyses unless those are incorporated in the analysis procedure. Those details are not reported in the registry record. Precision should therefore be read as uncertainty around the posted comparison, not as a complete measure of all uncertainty about the result.

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 expressed as participants per arm, not as total events. They are illustrative calculations based on the observed effect and use the assumptions stated for the planning exercise: a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is specified as a fraction of the observed effect. These assumptions describe the planning illustration, not the trial’s original design; the original sample-size assumptions are not reported in the registry record.

For planning for 100% of the observed effect, the figures are 191 participants per arm for 80% power and 255 participants per arm for 90% power. For planning for 75% of the observed effect, they are 311 participants per arm for 80% power and 416 participants per arm for 90% power. For planning for 50% of the observed effect, they are 635 participants per arm for 80% power and 849 participants per arm for 90% power.

In plain terms, the 80% power column gives the planned participants per arm for a design with an 80% chance of detecting the specified effect under the stated assumptions. The 90% power column expresses the analogous planning target at 90%. Power is conditional on the assumptions and effect used in the calculation; it does not indicate whether the observed result is reliable or important. Smaller effects require more participants to detect under otherwise unchanged planning assumptions.

These values are planning illustrations, not observed power. They are not the trial’s actual enrollment target or a retrospective measure of what the completed study achieved. The registry record does not report the original sample-size justification, so the illustrative figures should not be treated as evidence about the study’s design rationale.

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 = 0.075; treatment proportion p₁ = 0.169; average p̄ = 0.122.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.4630 = 0.9074.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.4582 = 0.3857.
4. n = (0.9074 + 0.3857)² / (0.0937)² = 190.3, rounded up to 191 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 <- 0.0752212389380531                          # control proportion
p1 <- 0.168949771689498                          # 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(5183217)
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 = 191 (80% power), 255 (90% power)
 75% of observed effect: n per arm = 311 (80% power), 416 (90% power)
 50% of observed effect: n per arm = 635 (80% power), 849 (90% power)
power.prop.test: 190.3 
Simulated power at n = 191 per arm: 0.813 (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

The registry lists 21 outcome measures and 13 posted secondary outcomes in addition to the primary engagement outcome. The secondary outcomes span behavior change, self-efficacy, health action process responses, care-seeking barriers, and urinary and bowel incontinence measures. Considering multiple outcomes can raise the chance of finding apparently notable results by chance when many statistical tests are conducted. In general, multiplicity procedures can address this issue, but the appropriate approach depends on the prespecified questions and analysis plan.

The reported p value for the primary comparison is identified as unadjusted. The registry record does not report whether any multiplicity strategy was planned or used across the primary and secondary outcomes. It also does not report the hierarchy among outcomes, whether any outcomes were designated as exploratory, or whether secondary results were adjusted for multiple testing. The unadjusted p value should therefore not be described as multiplicity-adjusted evidence.

The registry record does not report how missing primary or secondary outcome data were handled, whether missingness differed between groups, or whether sensitivity analyses were conducted. The posted arm denominators are 226 for the group without tailoring and 219 for the group with tailoring, but participant flow and the relationship between enrollment and endpoint availability are not described. It is not possible to infer from the record why any denominator differs across outcome summaries.

Analysis populations are also not reported in detail. The record does not specify whether results include everyone as randomized, only participants with available outcomes, participants who engaged with the program, or another defined population. In general, these choices can change the interpretation of a randomized comparison. Missing-data handling and analysis population definitions should be considered unresolved reporting details, rather than assumed to follow a particular convention.

The registry lists 0 analyses while posting arm-level results and a primary comparison. A detailed statistical analysis plan, adjustments, interim analyses, and sensitivity analyses are not reported in the registry record. Consequently, the inferential details beyond the reported unadjusted comparison cannot be reconstructed from the listed information.

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 adverse-event summaries list 0 serious adverse events in the group without tailoring, among 225 participants at risk, and 0 in the group with tailoring, among 219 participants at risk. Other adverse events are also listed as 0 in both groups, using the same respective at-risk figures. Deaths are listed as 0 in both groups. No serious adverse-event terms are listed.

These are the counts recorded in the registry’s safety summaries. The at-risk figures for the group without tailoring differ from its primary outcome arm count of 226; the registry record does not explain this difference. It would not be appropriate to assume why the safety denominator differs or to substitute the primary outcome denominator for it.

A reported zero means that no event was recorded in that summary; it does not prove that an event could not occur or that the program has no risk. The registry record does not provide further information about how adverse events were defined, collected, assessed, or followed over time. The safety figures should therefore be read narrowly as the reported counts and denominators, rather than as a comprehensive assessment of safety.

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

The primary results describe engagement with online MOM at least 4 times during weeks 0-4. The posted proportion is 0.075 without tailoring and 0.169 with tailoring. The reported risk difference is 0.094, with an interval from 0.033 to 0.154, and the unadjusted p value is 0.002. Together, these figures describe a higher observed engagement proportion in the group assigned to the tailored program during the specified period.

That finding is limited to the measured engagement outcome. It does not establish that participants changed their pelvic floor muscle exercise behavior, improved self-efficacy, experienced fewer symptoms, or maintained engagement beyond weeks 0-4. Those questions require their own outcome results and interpretation. Although the registry lists secondary measures related to these topics, those results are not included in the primary comparison described by the posted figures.

Randomization supports comparison of the assigned groups, but the record does not provide all information needed to assess the conduct and analysis fully. Important details about allocation, missing outcomes, analysis populations, multiplicity, and the statistical methods are not reported in the registry record. The unmasked design is also part of the context for interpreting a behavioral engagement outcome.

The estimates should not be converted into a broader claim about clinical benefit. An online program may be accessed without producing health changes, and an engagement difference alone cannot establish symptom improvement. Similarly, the zero adverse-event counts are limited registry summaries and do not demonstrate that no risk exists. The defensible interpretation remains specific to the comparison and endpoint: engagement meeting the stated threshold was more frequent in the tailored group in the posted results, with statistical uncertainty described by the reported interval.

16. Why This Trial Matters Statistically

This trial raises a statistical question about implementation as well as outcome measurement: whether tailoring is associated with participants interacting with an online behavioral program at a specified level. Engagement is distinct from health benefit, but it can be an important intermediate measure when a program’s intended content depends on participant interaction. Keeping those outcomes separate prevents an engagement result from being mistaken for evidence about symptoms or behavior change.

The binary endpoint turns engagement into a clear participant-level event, enabling comparison of group proportions. Reporting the arm counts and proportions alongside risk difference, risk ratio, and odds ratio makes visible that the same contrast can be represented on absolute and relative scales. The risk difference directly describes separation between proportions, while ratios describe relative contrasts. These distinctions matter for communicating results without conflating effect measures.

The registry also identifies a range of secondary measures relevant to engagement pathways, self-efficacy, barriers, and continence outcomes. Such breadth can help frame further questions, while also making transparent reporting of outcome priority and multiplicity important. The record’s unadjusted p value and lack of reported multiplicity procedures make it necessary to avoid treating a single p value as a complete summary of all uncertainty.

Finally, the planning illustration emphasizes that sample-size requirements depend on the effect a study is designed to detect. Under the stated planning assumptions, the participant requirements increase as the target effect becomes smaller. This is a general design principle, not a retrospective assessment of the completed trial. The original sample-size rationale and detailed statistical plan are not reported in the registry record, making the distinction between the trial’s design and the post-result planning illustration especially important.

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 available registry information leaves several trial-specific questions unresolved. The intervention descriptions identify online programs with and without tailoring, but do not explain what participant information drove tailoring, what content changed, or whether participants received the intended version. Such details are relevant to understanding what contrast was actually implemented, but are not reported in the registry record.

The record identifies the study as randomized and unmasked, but does not report the sequence-generation method, allocation concealment, baseline characteristics, recruitment flow, or reasons for any withdrawals. It also does not specify who captured the engagement measure or how program interactions were recorded. These omissions limit assessment of selection, measurement, and attrition concerns without establishing that any particular bias occurred.

The outcome is restricted to reaching the at-least-4-times engagement threshold during weeks 0-4. The record does not report whether frequency was analyzed in other ways, how missing interaction data were handled, or whether the threshold was prespecified. The engagement measure does not by itself indicate the content viewed, engagement quality, health behavior uptake, or clinical change.

The statistical analysis plan, original sample-size assumptions, multiplicity methods, analysis populations, interval methods, and confidence level are not reported in the registry record. The posted p value is unadjusted, and the registry lists 0 analyses despite posting an arm comparison. These features limit evaluation of the full inferential process. The safety summaries also use an at-risk denominator that differs from the group’s primary endpoint count, without an explanation in the record.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Tailoring Online Continence Promotion — NCT05183217.
• Registry record last updated 2025-05-25; status last verified 2025-05.
• Results first posted 2025-05-25.
• 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

The registry record describes a completed randomized, unmasked, parallel-group behavioral study of an online continence promotion program for women. It compares a program with tailoring against a program without tailoring. Enrollment is listed as 445; the primary outcome timeframe is 0-4 weeks.

The primary outcome is engagement with online MOM at least 4 times. The posted proportions are 0.075 without tailoring and 0.169 with tailoring. The reported risk difference is 0.094, with an interval from 0.033 to 0.154, and the unadjusted p value is 0.002. The registry lists 21 outcome measures and 13 posted secondary outcomes. Further analysis details, including the original sample-size rationale, missing-data methods, analysis populations, and multiplicity procedures, are not reported in the registry record.