Examining How a Facilitated Self-Sampling Intervention and Testing Navigation Intervention Influences COVID-19 Testing: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Examining How a Facilitated Self-Sampling Intervention and Testing Navigation Intervention Influences COVID-19 Testing (NCT05606016), a randomized na study. The primary outcome is Number of Participants Who Complete Testing (Antigen or PCR).

2. Clinical Question

3. Trial Design

The registry describes a completed, randomized, parallel-group study of COVID-19 testing interventions. The intervention groups were FSSI and TNI, with a control group receiving CDC-designed testing brochures. Masking is listed as none, and the phase is listed as not applicable. The sponsor is The University of Texas Health Science Center, Houston. The recorded enrollment is 1232.

FSSI consisted of testing education and provision of rapid antigen tests. TNI consisted of testing education and navigation to PCR testing sites. Both intervention groups received pre and post surveys. Control participants received brochures and the same survey schedule, according to the arm descriptions. The study summary says the research addressed access to and use of antigen testing among vulnerable populations, including whether interventions designed for low health literacy could affect testing decisions and behaviors.

The primary outcome is completion of antigen or PCR testing during the interval between baseline and 2 months after the intervention. The record posts results for the primary outcome and several secondary outcomes. The primary outcome denominators differ across the three groups, and the outcome results should be read using those posted denominators rather than assuming equal group sizes.

The registry gives the study start date as 2023-03-30 and primary completion date as 2024-08-14, with status completed. Details about random sequence generation, allocation concealment, recruitment locations, eligibility criteria, and the trial's original sample-size justification are not reported in the registry record. The record also does not report a direct head-to-head statistical comparison of FSSI and TNI.

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 Who Complete Testing (Antigen or PCR), measured over between baseline and 2 months after the intervention.

The primary endpoint is the number of participants who completed testing with either an antigen test or PCR test between baseline and 2 months after the intervention. The registry describes the measure as the percent of participants in each study arm who completed testing. Its purpose is to capture whether testing occurred during the stated follow-up period, rather than to measure test accuracy, infection severity, or the effect of testing on health outcomes.

As a binary outcome, each participant is classified according to whether testing was completed during the specified interval. The posted counts are 38 of 204 participants in FSSI, 41 of 249 in TNI, and 46 of 397 in control. The registry's comparison estimates express differences in the proportion with testing completion between each intervention group and control. The outcome definition combines antigen and PCR testing, so it does not by itself distinguish which type of test participants completed.

This endpoint is relevant to the study's stated focus on access to and use of rapid COVID-19 testing. It can describe testing behavior over the specified interval, but it does not establish why a participant did or did not test. The registry record does not report a separate primary result for antigen testing versus PCR testing, nor does it describe a more detailed operational definition of completed testing.

7. Results Posted in the Registry

• Facilitated Self-Sampling Intervention (FSSI): 18.6% (38/204) versus 11.6% (46/397) on control; risk difference 7.0 percentage points (95% CI 0.8 to 13.2), risk ratio 1.61 (1.08–2.39); unadjusted two-proportion z-test p = 0.018.
• Testing Navigation Intervention (TNI).: 16.5% (41/249) versus 11.6% (46/397) on control; risk difference 4.9 percentage points (95% CI -0.7 to 10.5), risk ratio 1.42 (0.96–2.10); unadjusted two-proportion z-test p = 0.077.

8. Secondary Efficacy Endpoints

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

Number of Participants Who Appropriately Used Antigen Test as Assessed by Survey

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Facilitated Self-Sampling Intervention (FSSI): 4.7% (15/320) versus 2.2% (13/579) on control; risk difference 2.4 percentage points (95% CI -0.2 to 5.1), risk ratio 2.09 (1.01–4.33); unadjusted two-proportion z-test p = 0.044.
  • Testing Navigation Intervention (TNI).: 4.5% (15/333) versus 2.2% (13/579) on control; risk difference 2.3 percentage points (95% CI -0.3 to 4.8), risk ratio 2.01 (0.97–4.16); unadjusted two-proportion z-test p = 0.057.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Facilitated Self-Sampling Intervention (FSSI) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Testing Navigation Intervention (TNI). and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants Who Share Antigen Tests With Close Contacts Who Need to Test as Assessed by Survey

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Facilitated Self-Sampling Intervention (FSSI): 10.9% (35/320) versus 9.0% (52/579) on control; risk difference 2.0 percentage points (95% CI -2.2 to 6.1), risk ratio 1.22 (0.81–1.83); unadjusted two-proportion z-test p = 0.342.
  • Testing Navigation Intervention (TNI).: 9.0% (30/333) versus 9.0% (52/579) on control; risk difference 0.0 percentage points (95% CI -3.8 to 3.9), risk ratio 1.00 (0.65–1.54); unadjusted two-proportion z-test p = 0.989.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Facilitated Self-Sampling Intervention (FSSI) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Testing Navigation Intervention (TNI). and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants Who Tested Positive for COVID-19 in the Past 30 Days

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Facilitated Self-Sampling Intervention (FSSI): 44.6% (91/204) versus 53.4% (212/397) on control; risk difference -8.8 percentage points (95% CI -17.2 to -0.4), risk ratio 0.84 (0.70–1.00); unadjusted two-proportion z-test p = 0.041.
  • Testing Navigation Intervention (TNI).: 51.8% (129/249) versus 53.4% (212/397) on control; risk difference -1.6 percentage points (95% CI -9.5 to 6.3), risk ratio 0.97 (0.83–1.13); unadjusted two-proportion z-test p = 0.693.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Facilitated Self-Sampling Intervention (FSSI) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Testing Navigation Intervention (TNI). and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcome entries cover testing-related behaviors and experiences, including appropriate antigen-test use, sharing tests with close contacts, mitigation measures following a positive test, recent positive tests, and vaccination status. The posted secondary data include baseline measures, and the registry contains repeated outcome titles. This range of topics can help describe the study context, but it does not form a single coherent measure of the primary testing-completion question.

Across the available secondary comparisons, the estimates are not uniform: some point toward higher outcome proportions in an intervention group, while others are close to no difference or point in the opposite direction. These patterns should be interpreted as exploratory descriptions rather than as a consistent confirmation of the primary result. Baseline measures are especially different in purpose from the primary outcome, which concerns testing between baseline and 2 months after intervention.

The secondary findings relate to the primary result by describing other behaviors that may accompany testing access or use. They do not establish that the interventions caused changes in those behaviors, nor do they explain the mechanism behind the primary outcome. In particular, a survey measure of prior or baseline behavior is not interchangeable with testing completion during follow-up.

Unadjusted secondary results carry less weight because the registry record does not report a multiplicity strategy, a hierarchy of secondary hypotheses, or detailed analysis methods. With multiple outcomes and comparisons, isolated p-values can overstate the strength of evidence if read without accounting for the broader set of analyses. The record also lists no reported analysis records. The secondary results are therefore most useful as context and as questions for further study, rather than as definitive evidence supporting a particular intervention.

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

• Number of Participants Who Appropriately Used Antigen Test as Assessed by Survey
• Number of Participants Who Appropriately Used Antigen Test as Assessed by Survey
• Number of Participants With a Positive Test Who Took Mitigation Measures as Assessed by Survey
• Number of Participants With a Positive Test Who Took Mitigation Measures as Assessed by Survey
• Number of Participants Who Share Antigen Tests With Close Contacts Who Need to Test as Assessed by Survey
• Number of Participants Who Share Antigen Tests With Close Contacts Who Need to Test as Assessed by Survey
• Number of Participants Who Tested Positive for COVID-19 in the Past 30 Days
• Number of Participants Vaccinated Against COVID-19

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary: participants either completed antigen or PCR testing during the specified period or did not. For a binary outcome, a risk difference is the difference between the proportions with the outcome in two groups. A positive estimate in the posted comparison direction means the intervention group had a higher observed proportion than control; a negative estimate means it had a lower observed proportion. The risk difference retains the outcome's absolute scale and is distinct from a relative measure such as a risk ratio or odds ratio.

What do the estimates and intervals represent?

The primary risk-difference estimates are 0.070 for FSSI versus control and 0.049 for TNI versus control. Each is accompanied by a 95% confidence interval. A confidence interval describes the range of values reasonably compatible with the data and the method used, under its assumptions. It communicates uncertainty about the estimated difference; it is not a range containing a fixed share of individual participant outcomes. For FSSI, the interval runs from 0.008 to 0.132. For TNI, it runs from -0.007 to 0.105. The latter includes zero, which represents no difference on the risk-difference scale.

The p-values are unadjusted: 0.018 for FSSI versus control and 0.077 for TNI versus control. A p-value describes how incompatible the observed data are with a specified null hypothesis under the statistical method. It is not the probability that the null hypothesis is true, nor a measure of clinical importance. The registry record does not report the sponsor's analysis method or a prespecified threshold, so these p-values should be treated as unadjusted descriptive evidence rather than as a complete account of the trial's planned inference.

Why distinguish recalculated results from reported analyses?

The registry lists no reported analysis records. The comparisons described for the primary endpoint are simple unadjusted recalculations from the posted group summaries. They do not incorporate adjustment for baseline characteristics, stratification, clustering, or other modeling unless such information is explicitly reported; no such analysis details are reported in the registry record. These calculations provide a transparent comparison of posted counts, but they should not be mistaken for a fully documented sponsor analysis.

The data are presented as participant counts for a binary endpoint. A comparison based on counts and denominators can be informative, but its validity depends on the outcome definitions, completeness of follow-up, and how participants were assigned to analysis groups. The record does not report missing-data procedures, an intention-to-treat or per-protocol population, or whether participants with unavailable follow-up were excluded. Those omissions limit what can be inferred about the analysis population and potential bias.

What do the relative measures add?

The comparison records also include risk ratios and odds ratios. A risk ratio expresses the relative proportion with the outcome between groups, whereas an odds ratio compares odds. Both are relative measures, in contrast with the absolute risk difference. Odds ratios can be harder to interpret directly as changes in probability, particularly when outcomes are not rare. The record does not state that either relative measure was the trial's primary analysis; they are best read as complementary descriptions, not replacements for the primary risk-difference comparison.

All these measures are based on the posted binary outcomes and remain subject to uncertainty. When an interval for an effect estimate includes the no-difference value for that measure, the data do not rule out no difference at the corresponding interval level. For risk differences the no-difference value is zero; for ratios it is one. A point estimate alone does not convey that uncertainty.

Why does multiplicity matter?

The record contains a primary outcome and multiple secondary outcome entries, with intervention comparisons against control. When many outcomes or comparisons are examined, the chance of obtaining an apparently unusual result somewhere among them can increase. A multiplicity plan may specify which comparisons are confirmatory and how inference is adjusted or prioritized. Such a plan is not reported in the registry record, so the posted secondary p-values should not be treated as if they account for all outcomes and comparisons.

Finally, the primary planning illustrations use the observed effect and stated planning assumptions; they are not the trial's original design calculation. The record does not report the original sample-size justification or its assumptions. The planning illustrations therefore help show how sample requirements change under specified hypothetical effects, but they do not establish the adequacy of the trial's original design or the probability that this trial would detect an effect.

11. Confidence Intervals and Precision

Precision is reflected in the width of an estimate's confidence interval: narrower intervals indicate less uncertainty under the method used, while wider intervals allow a broader range of effects. For FSSI versus control, the risk-difference estimate is 0.070 and its 95% confidence interval is 0.008 to 0.132. The interval is above zero, but it still spans effects of different magnitudes. The point estimate should therefore not be read as a uniquely determined effect size.

For TNI versus control, the risk-difference estimate is 0.049 and its 95% confidence interval is -0.007 to 0.105. This interval crosses zero and includes both a small negative difference and positive differences. The posted data consequently leave more uncertainty about the direction and size of the TNI comparison than a single point estimate suggests.

The group denominators for the primary outcome are 204 for FSSI, 249 for TNI, and 397 for control. The unequal denominators and the number of observed outcomes contribute to the precision of comparisons. The registry record does not report a confidence interval for a direct comparison between FSSI and TNI, so the separate intervention-versus-control intervals cannot be used as a substitute for that head-to-head analysis.

Confidence intervals quantify sampling uncertainty under the calculation's assumptions; they do not account automatically for missing outcomes, deviations from the protocol, measurement error, or multiplicity. Because the registry record does not describe the analysis population or missing-data approach, the intervals should be understood as precision measures for the posted recalculations, not as a complete summary of every source of uncertainty.

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 participants per arm, not total sample sizes or total events. The registry's planning assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. Each planning scenario uses a stated fraction of the observed effect as the effect to detect. These are planning illustrations based on the observed effect, not the trial's original design assumptions; the original design assumptions are not reported in the registry record.

The 80% power column gives the modeled number of participants per arm for a hypothetical study to have an 80% chance of detecting the specified effect under those assumptions. The 90% power column gives the corresponding number for a 90% chance. These are design calculations for hypothetical future studies, not evidence about the probability that the completed trial detected an effect. This is not observed power.

Rows for 100%, 75%, and 50% of the observed effect show how the planning requirement changes when the target effect is smaller than the observed estimate. Smaller effects need more participants per arm to detect under the same assumptions. In the TNI-versus-control comparison, the observed risk difference is close to zero, so planning based on that estimate produces very large sample-size illustrations. Such figures can be unstable when the observed estimate is small and should not be taken as reliable forecasts of the effect in another study.

The curves and figures should therefore be read as conditional planning aids: they depend on the assumed effect, significance level, allocation, and approximation. They do not provide a retrospective verdict on the trial's design, and the registry record does not report the trial's original sample-size justification.

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 = 0.116; treatment proportion p₁ = 0.186; average p̄ = 0.151.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5065 = 0.9926.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5040 = 0.4242.
4. n = (0.9926 + 0.4242)² / (0.0704)² = 405.0, rounded up to 405 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 <- 0.115869017632242                          # control proportion
p1 <- 0.186274509803922                          # 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(5606016)
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 = 405 (80% power), 542 (90% power)
 75% of observed effect: n per arm = 686 (80% power), 918 (90% power)
 50% of observed effect: n per arm = 1,464 (80% power), 1,960 (90% power)
power.prop.test: 405 
Simulated power at n = 405 per arm: 0.807 (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

The registry record lists a primary outcome alongside multiple secondary outcome entries and intervention-versus-control comparisons. It does not report a multiplicity strategy, such as a testing hierarchy or adjustment for the number of outcomes and comparisons. Without such a strategy, unadjusted p-values should be treated as descriptive evidence. The chance of at least one apparently notable finding can rise when many tests are examined, even if each test is considered separately.

The record does not describe the handling of missing outcome data. It does not report how many participants lacked follow-up information, whether missingness differed by group, or whether any imputation or sensitivity analysis was used. The posted outcome denominators are available, but the registry record does not explain how those denominators relate to the randomized population or why they differ across outcomes.

The analysis population is also not specified. The registry record does not say whether the primary comparison followed an intention-to-treat principle, included only participants who completed follow-up, or used another population definition. It does not report a protocol-deviation approach or a detailed account of participant flow through the analysis. Consequently, the recalculated comparisons from posted counts cannot resolve whether exclusions or incomplete follow-up might affect interpretation.

Further details not reported in the registry record include the prespecified statistical model, any adjustment for baseline variables, and whether there were interim analyses. The absence of these details does not show that a particular procedure was or was not used; it means that the registry record does not document it. A complete interpretation would require the analysis plan and a fuller account of outcome availability and population definitions.

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.

The registry posts serious adverse event figures separately for the three study groups. For FSSI, 320 participants were listed at risk, with 0 serious events and 0 deaths. For TNI, 333 participants were listed at risk, with 0 serious events and 0 deaths. For control, 579 participants were listed at risk, with 0 serious events and 0 deaths. The figures for other adverse events are also 0 in each group.

These are registry counts, not evidence that an adverse event could not occur. The numbers at risk for these summaries differ from the denominators posted for the primary outcome, and the registry record does not explain the difference. It also does not provide detailed event definitions, monitoring procedures, duration of safety follow-up, or a narrative account of adverse-event collection. The posted figures should therefore be read narrowly as reported counts, rather than as a complete assessment of safety or as proof of no risk.

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

The primary outcome describes whether participants completed antigen or PCR testing during the interval between baseline and 2 months after intervention. In the posted counts, testing completion was observed in 38 of 204 FSSI participants, 41 of 249 TNI participants, and 46 of 397 control participants. The unadjusted recalculated risk-difference estimates point toward higher completion in each intervention comparison with control. The FSSI interval excludes zero, while the TNI interval includes zero.

These results do not establish that either intervention works or does not work. They are estimates from the posted outcome data, with uncertainty expressed by their confidence intervals. The registry record does not report a sponsor analysis, a prespecified statistical plan, or a direct FSSI-versus-TNI comparison. The unadjusted comparisons should therefore not be treated as a definitive test of all study objectives.

The findings also do not show whether participants completed antigen testing or PCR testing separately, whether testing changed health outcomes, or what factors led participants to test. The primary endpoint combines test types and concerns completion behavior over a specified period. It does not measure access alone, test accuracy, infection prevention, or the effect of any test result on subsequent outcomes.

The secondary outcomes add context about testing-related behaviors, but several are recorded at baseline and the registry does not report a multiplicity plan. They cannot be used to make a stronger causal claim than the primary outcome supports. The safety record reports zero serious and other adverse events in the listed groups, but limited registry detail means those counts should not be interpreted as proof that no risk exists.

In short, the posted evidence describes observed group outcomes and unadjusted comparisons. It can inform questions about testing completion and the precision of the estimates, while leaving important questions about analysis methods, missing data, comparative effectiveness between interventions, and broader consequences unanswered.

17. Why This Trial Matters Statistically

This trial illustrates why a clinical research question needs a clearly defined outcome and follow-up period. The primary measure is testing completion with either antigen or PCR testing between baseline and 2 months after intervention. That definition makes the outcome interpretable as a behavior during a specified interval, while also limiting what it can say about the type of test used or the consequences of testing.

The comparison also shows why point estimates should be read with their confidence intervals. The FSSI estimate is positive and its interval excludes zero, whereas the TNI estimate is positive but its interval includes zero. The difference between those interval patterns is a question of uncertainty, not proof that the interventions differ from one another. A direct comparison would be needed to answer that head-to-head question, and it is not reported in the registry record.

The registry's planning illustrations provide another useful statistical lesson: sample requirements depend strongly on the effect chosen for planning. Smaller target effects require more participants per arm under the stated assumptions. When the observed estimate is small, planning based on it can yield very large figures, underscoring the uncertainty of using a single observed effect as a future planning target.

Finally, this record highlights the importance of transparent analysis documentation. The registry lists no reported analyses and does not report missing-data methods, analysis populations, or a multiplicity strategy. Those details affect how confidently readers can interpret unadjusted estimates across several outcomes. Clear reporting allows clinical researchers to distinguish the observed data from assumptions and from conclusions that the data cannot establish.

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.

Several trial-specific limitations follow from details that are not reported in the registry record. The record does not give the eligibility criteria or enough detail to characterize the vulnerable populations enrolled. That makes it difficult to assess how closely the participants represented the groups the interventions were intended to reach or how broadly the results may apply.

The primary outcome combines antigen and PCR testing. The registry does not provide separate completion results by test type, nor does it describe a more detailed definition of completion. Consequently, the posted endpoint cannot distinguish whether observed testing involved rapid antigen tests, PCR testing, or both across participants.

The outcome denominators differ across groups, and the registry record does not explain the participant flow or how missing follow-up was handled. The analysis population and any exclusions are not reported. These omissions limit assessment of selection and attrition effects. The record also does not report a direct FSSI-versus-TNI comparison, despite describing comparison of those intervention approaches among the study objectives.

The registry lists no reported analysis records and does not describe the statistical model, multiplicity approach, or any adjustments. Several secondary outcomes are recorded at baseline, which limits their use as evidence about post-intervention change. Repeated outcome titles also make the secondary outcome listing less clear. The original sample-size justification and interim-analysis details are not reported in the registry record.

Finally, the serious adverse-event counts are zeros, but event definitions, monitoring details, and safety follow-up procedures are not reported. These limitations concern the information available in the registry record; they do not establish that unreported methods or procedures were absent from the study itself.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Examining How a Facilitated Self-Sampling Intervention and Testing Navigation Intervention Influences COVID-19 Testing — NCT05606016.
• Registry record last updated 2026-08-26; status last verified 2026-06.
• Results first posted 2026-08-26.
• 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

The registry describes NCT05606016 as a completed, randomized, parallel-group study of interventions intended to influence COVID-19 testing in vulnerable populations. The study compared facilitated self-sampling, testing navigation, and a control condition. The primary outcome was completion of antigen or PCR testing between baseline and 2 months after intervention.

The posted primary counts are 38 of 204 for FSSI, 41 of 249 for TNI, and 46 of 397 for control. Unadjusted recalculated risk differences are positive for both intervention-versus-control comparisons; the confidence interval excludes zero for FSSI and includes zero for TNI. The registry lists no reported analysis records. Analysis populations, missing-data procedures, multiplicity handling, and the original sample-size justification are not reported in the registry record.