CATCH-UP Vaccines: Engaged Approaches to Testing in Community/Healthcare Settings for the Underserved (COVID-19): Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for CATCH-UP Vaccines: Engaged Approaches to Testing in Community/Healthcare Settings for the Underserved (COVID-19) (NCT05236270), a randomized na study. The primary outcome is Percentage of Participants Who Plan to Receive a COVID-19 Vaccine Post-intervention.

2. Clinical Question

The primary outcome concerns stated intention, not verified vaccination. The survey was scheduled from baseline to post-intervention, approximately 15-30 minutes. The registry also lists self-reported vaccine uptake as a secondary outcome, but does not post its results. The posted comparisons therefore address the intention measure only and should not be treated as direct estimates of vaccination behavior.

3. Trial Design

NCT05236270 is a completed, randomized, unmasked, factorial behavioral study sponsored by the University of Oklahoma. The registry reports enrollment of 312 participants and lists COVID-19 and vaccine refusal as conditions. The listed start date is 2022-03-25, and the primary completion date is 2023-06-28. The study is classified as not applicable for phase.

Eight conditions combine the intervention components. The combinations vary whether participants receive a text notifying them about vaccine availability, motivational interviewing or standard interpretation of serology results with a vaccination discussion, and an educational message about COVID-19 vaccines or an attention-control message. Condition 1, which combines no text, standard interpretation and discussion, and attention control, is the reference group in the posted comparisons. The posted results compare each of the other conditions separately with this reference.

Randomization and the factorial label describe the study’s broad design, but details of sequence generation, allocation concealment, blocking, and stratification are not reported in the registry record. Masking is listed as none. The registry does not describe the delivery personnel, adherence to each intervention, or whether participants received all assigned components. It also does not report the trial’s original sample-size justification or its planned statistical analysis.

The primary outcome is a count of participants reporting plans to receive a COVID-19 vaccine after intervention. The registry describes comparison of intention among those receiving interventions with those not receiving the intervention. However, the posted numerical results are organized as comparisons of each condition against Condition 1; no factorial main-effect or interaction estimates are reported. The registry lists one secondary outcome, self-reported vaccine uptake, without posted numerical results.

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.

Before assignment: After participants provided informed consent and HIPAA authorization, participants were randomized to a treatment condition.

Only the first 6 of 8 groups are shown.

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.

Only the first 6 of 9 groups are shown.

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Participants Who Plan to Receive a COVID-19 Vaccine Post-intervention, measured over Baseline to Post-Intervention Survey (approximately 15-30 minutes).

The primary endpoint is the number of participants who plan to receive a COVID-19 vaccine after the intervention. It is measured with survey questions asking how likely participants are to get an approved vaccine, and, for people who have received the specified initial doses, how likely they are to get an approved booster shot. The timeframe is baseline to post-intervention survey, approximately 15-30 minutes.

This endpoint captures stated intention soon after the intervention. Intention is relevant to the study’s aim of addressing vaccine hesitancy, but it is not the same outcome as actual vaccine uptake. The registry lists self-reported uptake separately as a secondary outcome; its result is not reported in the registry record. Consequently, the primary endpoint can describe participants’ expressed plans at the survey, but cannot establish whether those plans were later carried out.

The reported arm values are counts, not percentages. For example, the reference condition has 19 participants with the endpoint among 42 participants, and Condition 2 has 24 among 42. The registry does not report a detailed scoring rule for combining the vaccine and booster questions, how unanswered questions were treated, or whether the outcome was assessed in every randomized participant. Such details matter when interpreting exactly what qualifies as an endpoint event.

7. Results Posted in the Registry

• Condition 2: 57.1% (24/42) versus 45.2% (19/42) on control; risk difference 11.9 percentage points (95% CI -9.3 to 33.1), risk ratio 1.26 (0.83–1.93); unadjusted two-proportion z-test p = 0.275.
• Condition 3: 54.3% (19/35) versus 45.2% (19/42) on control; risk difference 9.0 percentage points (95% CI -13.3 to 31.4), risk ratio 1.20 (0.76–1.88); unadjusted two-proportion z-test p = 0.429.
• Condition 4: 60.0% (21/35) versus 45.2% (19/42) on control; risk difference 14.8 percentage points (95% CI -7.4 to 36.9), risk ratio 1.33 (0.86–2.04); unadjusted two-proportion z-test p = 0.197.
• Condition 5: 57.1% (24/42) versus 45.2% (19/42) on control; risk difference 11.9 percentage points (95% CI -9.3 to 33.1), risk ratio 1.26 (0.83–1.93); unadjusted two-proportion z-test p = 0.275.

8. Statistical Interpretation

9. Statistical Methods Explained

The posted comparisons can be understood as simple unadjusted comparisons of each condition’s endpoint count with the count in Condition 1. The registry record does not report an official sponsor analysis, a prespecified model, or a statistical analysis plan. The numerical comparisons should therefore be understood as recalculations from posted summary statistics rather than attributed to an undocumented analysis by the investigators.

What does a risk difference describe?

A risk difference is the difference between the observed proportions with the endpoint in two groups. Here, the posted estimates compare a condition with Condition 1. A positive estimate means that the observed proportion in the named condition is higher; a negative estimate means that it is lower. The measure is on the proportion scale, so 0 represents no observed difference. For example, the estimate for Condition 2 is 0.119, while the estimate for Condition 8 is -0.089. The estimates describe the posted data and do not alone establish causality.

The associated intervals express uncertainty around each estimate. An interval that crosses zero includes the possibility of no difference under the method used to construct it. In these comparisons every risk-difference interval crosses zero. That does not demonstrate equality: the intervals also cover values on both sides of zero, and several cover a broad range. The registry record does not specify the interval construction method or confidence level, so neither should be assumed when interpreting the posted intervals.

How should the p-values be read?

A p-value describes how unusual data at least as discrepant as the observed data would be under a specified null model, assuming that model and its analysis conditions are appropriate. It is not the probability that the null hypothesis is true, and it does not quantify the size or importance of a difference. The posted p-values are unadjusted, and no multiplicity correction is reported. They should be considered alongside the estimated risk differences and their intervals, rather than used as a stand-alone decision rule.

The p-values and intervals are recalculations from the posted counts, not a reported sponsor analysis. The registry lists zero analyses. The registry record does not identify a fitted model, adjustment variables, covariates, or a method for handling dependence among comparisons. The randomized design supports comparisons in principle, but the exact analysis choices and their relationship to the original protocol are not reported.

What does the factorial label tell the reader?

In general, a factorial design assigns combinations of intervention components so that component effects and, when planned and analyzed, interactions can be studied. The eight conditions vary the text message, the approach to discussing serology results and vaccination, and the educational versus attention-control message. However, the posted results compare whole conditions with Condition 1. They do not report separate effects for each component, an interaction estimate, or an analysis that pools conditions across levels of a component. It is therefore not possible to use the posted comparisons to isolate the contribution of text messaging, motivational interviewing, or educational content.

The registry describes randomization but does not report its implementation details. It lists no masking, which means that masking is not part of the recorded design. The record also does not state whether outcome assessors were masked, whether intervention delivery was standardized, or whether adherence was measured. These details would inform assessment of potential bias, but should not be inferred from the factorial or randomized labels.

What can the counts establish?

The endpoint is a count of participants reporting intention, and the posted arm values are counts rather than model-adjusted estimates. The displayed risk differences, risk ratios, odds ratios, intervals, and p-values characterize the pairwise comparisons using those counts. The registry does not report an analysis population, missing-data procedure, imputation, or sensitivity analysis. It also does not explain how responses to the vaccine and booster questions were combined into the endpoint or whether all enrolled participants completed the post-intervention survey.

In general, a confidence interval is a range of estimates compatible with the observed data and the method’s assumptions; narrower intervals indicate greater precision. A p-value addresses evidence against a particular null model, not the magnitude of an effect. Neither concept removes the need to consider the study’s outcome definition, group sizes, multiple comparisons, and missing information. Here, all intervals include zero, and the p-values are unadjusted. The result is a set of uncertain pairwise descriptions, not a reported factorial conclusion or a definitive test of each intervention component.

The trial’s own statistical plan, sample-size justification, interim analyses, stopping rules, prespecified contrasts, and adjustments are not reported in the registry record. Those omissions limit what can be said about whether the posted comparisons correspond to the intended confirmatory analysis. The posted figures should be interpreted as the numerical information available for the reported endpoint, not as a substitute for an unreported protocol or analysis plan.

10. Confidence Intervals and Precision

Precision concerns how tightly the data locate a comparison estimate. The posted risk-difference intervals are broad, and every one includes zero. For Condition 2 the estimate is 0.119, with an interval from -0.093 to 0.331. For Condition 6 the estimate is 0.161, with an interval from -0.068 to 0.389. For Condition 8 the estimate is -0.089, with an interval from -0.296 to 0.118. The remaining intervals also extend across zero.

These ranges allow for uncertainty in both directions: a condition’s observed proportion could be lower or higher than the reference in values covered by its interval. Thus, the estimates do not precisely distinguish a direction of difference. A result whose interval includes zero is not proof that the groups are the same; it can reflect limited information as well as a small or absent difference. The arm counts are modest, and the registry does not report a formal confidence level or interval method, so those details cannot be added to the interpretation.

The risk ratios and odds ratios also have reported intervals that include 1 for every comparison, the value corresponding to no relative difference. These alternate scales do not remove the uncertainty evident on the risk-difference scale. Together, the posted intervals indicate that the comparisons are not precise enough to support a narrow estimate of how the conditions differ in stated intention.

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 sample sizes or total events. They are illustrations based on the observed effect, not the original trial’s design justification. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation; each planning scenario asks what sample size would correspond to detecting the stated fraction of the observed effect.

The 80% power column gives the illustrative number of participants per arm for an 80% chance of detecting the assumed effect under those planning assumptions, if that effect and the calculation assumptions applied. The 90% power column gives the corresponding number per arm for a 90% chance. These are planning quantities, not observed characteristics of the completed trial. They do not tell whether the trial did or did not have adequate power for its original research question.

Rows for 100% of the observed effect use the full observed comparison as the assumed effect. Rows for 75% and 50% use progressively smaller effects. Smaller effects require more participants per arm to detect at the same stated power, which is why the illustrative sample sizes rise as the assumed fraction falls. The figures differ across comparisons because the observed effects differ, including their direction and size.

The trial’s own sample-size justification, target power, and design assumptions are not reported in the registry record. The displayed figures are not observed power and should not be interpreted as evidence about the probability that this completed study detected an effect. They are planning illustrations based on recalculated observed effects, under the stated assumptions.

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 = 0.452; treatment proportion p₁ = 0.571; average p̄ = 0.512.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7069 = 1.3855.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.7019 = 0.5907.
4. n = (1.3855 + 0.5907)² / (0.1190)² = 275.6, rounded up to 276 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 <- 0.452380952380952                          # control proportion
p1 <- 0.571428571428571                          # 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(5236270)
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 = 276 (80% power), 369 (90% power)
 75% of observed effect: n per arm = 492 (80% power), 657 (90% power)
 50% of observed effect: n per arm = 1,106 (80% power), 1,479 (90% power)
power.prop.test: 275.6 
Simulated power at n = 276 per arm: 0.796 (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 posts seven pairwise comparisons, each contrasting a condition with Condition 1. Looking at several comparisons creates a multiplicity issue: when many tests are considered together, the chance of at least one apparently unusual result can increase. The posted p-values are unadjusted, and the registry record does not report a correction for multiple comparisons, a hierarchy of contrasts, or a prespecified strategy for interpreting them. The pairwise results are best treated as exploratory descriptions, not as a single confirmatory test of the factorial intervention.

Although the design is labeled factorial, the record does not report main-effect estimates for the intervention components or interaction tests. Nor does it report which contrasts were planned before data were collected. Without those details, the posted comparisons cannot establish which component, if any, accounts for the observed arm differences.

Missing-data handling and analysis populations are also not reported in the registry record. The record does not state how many randomized participants completed the post-intervention survey, whether all randomized participants were included in the outcome analysis, how unanswered items were classified, or whether any imputation or sensitivity analyses were performed. The arm counts give the observed group sizes for the posted comparisons, but do not explain losses, exclusions, or the relationship between enrollment and the analyzed endpoint.

The registry lists zero analyses and does not describe an intention-to-treat, per-protocol, or other analysis population. It also does not report interim analyses or stopping rules. These absences matter because they prevent readers from assessing whether the posted comparisons match a prespecified plan or how missing responses and repeated statistical testing were addressed. They do not change the arithmetic of the posted counts, but they limit claims about the confirmatory status and robustness of the comparisons.

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 zero serious adverse events affecting participants in each of the eight conditions, and zero deaths in each condition. It also reports zero participants affected by other adverse events in every condition. The number at risk is listed separately by condition: 42, 42, 35, 35, 42, 31, 41, and 44 participants.

These figures describe what was recorded in the registry and do not establish that no unrecorded events occurred. The registry record does not report detailed adverse-event definitions, collection procedures, follow-up duration, or event-specific descriptions. It also does not provide a serious-event comparison or statistical analysis. With no affected participants recorded, the posted safety counts cannot characterize the range of possible event rates or compare the conditions with precision.

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

The primary result concerns participants’ stated plans to receive an approved COVID-19 vaccine or booster shortly after intervention. The posted counts vary across the eight conditions, and the unadjusted risk-difference estimates range from -0.089 for Condition 8 to 0.161 for Condition 6. All reported risk-difference intervals include zero, and the unadjusted p-values do not provide strong evidence against a no-difference model under the posted pairwise comparisons.

This does not establish that the conditions are equivalent or that the interventions have no effect. The intervals are broad and allow for differences in either direction. Conversely, positive point estimates in several conditions do not establish that those intervention combinations caused greater intention. The posted recalculations are unadjusted comparisons, and the registry does not report the sponsor’s statistical analysis or a factorial analysis that separates the intervention components.

The endpoint also has a limited scope. It measures intention at a post-intervention survey approximately 15-30 minutes after baseline, not subsequent vaccine receipt. Although the registry lists self-reported vaccine uptake as a secondary outcome, its result is not reported in the registry record. Thus, the primary outcome cannot establish whether intention persisted or translated into uptake.

The results are specific to the participants enrolled in this Oklahoma community and healthcare setting study. The registry does not report detailed participant characteristics or the results of subgroup analyses, so the comparisons do not establish how responses might differ across demographic groups or settings. Overall, the record supports cautious description of observed short-term intention counts and their uncertainty, not a conclusion about later behavior, individual response, or the effect of any one component.

16. Why This Trial Matters Statistically

This trial raises a useful statistical distinction between studying a combination of behavioral components and estimating the effect of each component. Its eight randomized conditions vary text notification, the style of serology and vaccination discussion, and the educational message. That structure can support component-level questions when the analysis is designed to estimate them. The posted results, however, compare each complete condition with a single reference condition and do not report main effects or interactions.

That difference in estimand matters. A pairwise contrast between two combinations answers a condition-level question; it does not isolate whether a text message, motivational interviewing, or educational content accounts for a contrast. A factorial label alone does not supply those estimates. The registry’s lack of a reported statistical plan, prespecified contrasts, and multiplicity strategy makes it especially important to distinguish the posted comparisons from a formal factorial conclusion.

The outcome also illustrates the importance of matching measurement to the research question. Post-intervention intention is a behavioral surrogate for the listed goal of improving uptake, but the registry separately identifies self-reported uptake as a secondary outcome and does not post its result. Keeping intention distinct from uptake avoids treating an immediate survey response as documented behavior.

Finally, uncertainty is central to the interpretation. The small arm counts and broad intervals mean the observed differences are not precisely estimated. The planning illustrations can show how sample size needs change under specified assumptions, but they are not the trial’s original design rationale or evidence of observed power. Reading the design, endpoint, comparisons, and uncertainty together gives a more disciplined account than focusing on a point estimate or p-value alone.

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 leaves important design and analysis details unspecified. It does not report eligibility criteria, detailed participant characteristics, the method used to generate or conceal the randomization sequence, or whether randomization was stratified. Masking is listed as none, but the record does not state whether outcome assessment or data analysis was masked.

The primary endpoint description identifies survey questions and a short post-intervention timeframe, but does not explain how responses to the vaccine and booster questions were combined, how incomplete answers were handled, or whether the same participants answered both surveys. The registry does not report completion rates, missing-data procedures, exclusions, or analysis populations. These gaps make it difficult to assess the relationship between enrollment and the posted arm counts.

The numerical comparisons are unadjusted recalculations from posted summary counts. No sponsor analysis, confidence-interval method, confidence level, covariate adjustment, or factorial component analysis is reported. The seven comparisons also raise multiplicity considerations, with no reported correction or prespecified contrast plan. Consequently, the comparisons should not be treated as a definitive ranking of the eight conditions.

The posted endpoint measures intention shortly after intervention. Results for the listed secondary outcome of self-reported vaccine uptake are not reported in the registry record, so the record does not show whether intention corresponded to later uptake. The record also does not provide detailed safety-event definitions or follow-up information. These limitations restrict conclusions about component-specific effects, durability, generalizability, and safety beyond the recorded counts.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: CATCH-UP Vaccines: Engaged Approaches to Testing in Community/Healthcare Settings for the Underserved (COVID-19) — NCT05236270.
• Registry record last updated 2024-05-22; status last verified 2024-05.
• Results first posted 2024-04-23.
• 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

NCT05236270 is a completed, randomized, unmasked factorial behavioral study sponsored by the University of Oklahoma. The registry lists enrollment of 312, with a start date of 2022-03-25 and primary completion date of 2023-06-28. Its eight conditions combine text notification, motivational interviewing or standard interpretation of serology results with vaccination discussion, and educational or attention-control messages.

The primary endpoint is the count of participants reporting plans to receive a COVID-19 vaccine or booster after intervention. Posted pairwise comparisons use Condition 1 as the reference and show broad risk-difference intervals that all include zero. The registry does not report a sponsor analysis, factorial component estimates, missing-data procedures, or the trial’s original sample-size justification. It lists self-reported uptake as a secondary outcome without posting its results. The adverse-event record lists zero serious adverse events, other adverse events, and deaths affecting participants in each condition.