Pay-it-forward RCT for Gonorrhea and Chlamydia Testing: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Pay-it-forward RCT for Gonorrhea and Chlamydia Testing (NCT03741725), a randomized na study. The primary outcome is Number of Participants Who Received the Dual Gonorrhea/Chlamydia Testing.

2. Clinical Question

3. Trial Design

The registry record describes a randomized, cluster-randomized controlled trial with a parallel design and no masking. It compares three approaches: pay-it-forward testing, pay-what-you-want testing, and standard-of-care testing. The trial title identifies the setting as China and the population as men who have sex with men. Reported enrollment is 301.

The pay-it-forward approach offered free dual testing supported by donations from previous testers and asked participants whether they wished to donate. In the pay-what-you-want arm, participants could decide and pay any desired amount after receiving the test. The standard-of-care arm offered testing at the standard patient price. Each arm included a brief introduction to gonorrhea and chlamydia testing.

The registry record lists the trial as completed. The recorded start date is 2018-12-08, and primary completion is 2019-09-02. The primary outcome was assessed during the enrollment visit, described as lasting approximately 1.5 hours. The record does not report the cluster unit, how clusters were formed, the number of clusters, the allocation procedure, or whether any features of the design changed during conduct.

Randomization can reduce systematic differences between assigned groups, but cluster randomization assigns groups of people rather than necessarily assigning each person independently. That matters for interpretation because participants within a cluster may have more similar outcomes than participants in different clusters. The registry record does not give the details needed to assess how that feature was handled in the sponsor’s analysis.

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.

Population: Each arm had ten clusters of participants.

6. Primary Endpoint

The registry lists the primary outcome as Number of Participants Who Received the Dual Gonorrhea/Chlamydia Testing, measured over During enrollment visit, an approximate total of 1.5 hours.

The primary endpoint counts participants who received dual gonorrhea and chlamydia testing. The registry record says uptake was assessed from administrative records in each arm, during the enrollment visit. It is a count outcome: it concerns whether participants received the dual test, not whether testing identified an infection.

Uptake is relevant to a trial comparing different ways of offering and paying for testing because an offer may be followed by different levels of test receipt. The endpoint therefore addresses the immediate service-use question in the trial. It does not, by itself, measure test accuracy, infection prevalence, later testing, treatment, or health outcomes. Positive gonorrhea and chlamydia results are listed among the secondary outcomes, separate from the primary uptake outcome.

To interpret an uptake count, readers ordinarily need both the number who received testing and the relevant group denominator. A comparison also needs an analysis that reflects the randomized design. Neither primary-outcome counts nor an analysis result is reported in the registry record, so the observed uptake and the precision of any comparison cannot be described from this record.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

What does cluster randomization mean?

General statistical explanation: in an individually randomized trial, assignment is made separately for each participant. In a cluster-randomized trial, assignment is made to groups, or clusters, and people within a cluster receive the corresponding assignment. Members of the same cluster may resemble one another because they share a setting, contacts, or other features. Their outcomes can therefore be correlated rather than fully independent.

When observations within clusters are correlated, an analysis that treats every participant as independent can make the estimated uncertainty too small. Analyses of cluster-randomized trials generally need to account for the grouping, using a method appropriate to the design and outcome. The registry record does not state the cluster unit, cluster count, within-cluster correlation, or the statistical method used by the sponsor. It specifically cautions that a simple individual-level re-analysis would be misleading. No cluster-adjusted result is reported in the registry record.

How should a count endpoint be understood?

General statistical explanation: a count of participants who experienced an event records how many had the event, but its interpretation depends on the number eligible or assessed in each group. For a binary event such as receiving a test, readers often also consider the proportion of the group with the event. Comparisons can be expressed as differences or ratios, but those require observed group data and an analysis plan appropriate to the design. No primary count, group denominator for uptake, or comparison estimate is reported.

The registry labels the primary outcome as the number of participants who received dual testing and identifies administrative records as the assessment source. That description defines what was to be counted. It does not report the completeness of those records, how missing or ambiguous records were handled, or whether the count was analyzed as a count or converted into another scale for comparison.

What does a confidence interval contribute?

General statistical explanation: a confidence interval describes the uncertainty around an estimate under the assumptions of the statistical method. It gives a range of values compatible with the observed data and method, rather than proving that every value in the range is equally likely or that the true effect must lie within it. In a cluster trial, the interval should reflect the clustering as well as other relevant features of the design. No confidence interval or other uncertainty estimate for the primary outcome is reported in the registry record, so the precision of the comparison is unknown.

A p-value, when reported, assesses how compatible the data are with a specified statistical model and null hypothesis. It does not measure the size or clinical importance of an effect. A p-value also cannot repair an analysis that fails to account for the design. The registry record lists no analyses and reports no p-value for the primary endpoint. There is therefore no basis for describing statistical significance.

What do the planning illustrations mean?

General statistical explanation: a sample-size calculation estimates how much information a future study might need to detect a chosen effect under stated assumptions. It is a planning exercise, not a result from the completed trial and not a measure of the probability that this trial detected an effect. The registry’s planning description uses a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. It describes effect sizes as fractions of the observed effect. For time-to-event outcomes, the stated convention is total events; for other outcomes, it is participants per arm.

Those assumptions are not a description of the full three-arm cluster-randomized design. The registry record does not report a sample-size justification for the trial’s original design, and the planning description does not provide a result that can be assessed. In particular, the primary observed effect is not reported, so the planning illustrations cannot be interpreted as an estimate of the trial’s observed treatment contrast. Such calculations are not observed power.

Why does over-dispersion matter?

General statistical explanation: over-dispersion occurs when observed variability is greater than a statistical model expects. It can arise when observations share unmodeled characteristics, including similarities within clusters. A model that assumes less variation than the data contain can produce uncertainty estimates that are too narrow. Methods that allow for extra variation or explicitly model clustering can yield more appropriate uncertainty estimates. The registry record does not report a dispersion estimate or enough analytic detail to determine whether over-dispersion was assessed. No numerical conclusion about it is available.

10. Exact Estimates for Each Arm

No two-arm comparison is shown for this trial, but each arm's result can still be summarized with exact interval estimates computed from the posted counts. The Clopper-Pearson interval is exact and conservative; the Wilson interval is a common closer-to-nominal alternative; the Bayesian credible interval uses a uniform Beta(1, 1) prior. For small groups the three can differ noticeably.

R code and output

arm    <- c("Pay-it-forward", "Pay-what-you-want", "Standard-of-care")
n      <- c(101, 100, 100)
events <- c(57, 46, 18)
for (i in seq_along(n)) {
  ci <- binom.test(events[i], n[i])$conf.int          # Clopper-Pearson exact interval
  cr <- qbeta(c(0.025, 0.975), 1 + events[i], 1 + n[i] - events[i])   # Bayesian, uniform Beta(1,1) prior
  wi <- (function(x, n) { p <- x / n; z <- qnorm(0.975); d <- 1 + z^2 / n
    c((p + z^2 / (2 * n) - z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d, (p + z^2 / (2 * n) + z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d) })(events[i], n[i])
  cat(sprintf("ARM|%s|%d|%d|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f\n", arm[i], n[i], events[i], 100 * events[i] / n[i],
              100 * ci[1], 100 * ci[2], 100 * wi[1], 100 * wi[2], 100 * cr[1], 100 * cr[2]))
}

ARM  Pay-it-forward  101  57  56.4  46.2  66.3  46.7  65.7  46.7  65.7
ARM  Pay-what-you-want  100  46  46.0  36.0  56.3  36.6  55.7  36.5  55.8
ARM  Standard-of-care  100  18  18.0  11.0  26.9  11.7  26.7  11.7  26.7

11. Multiplicity, Missing Data and Analysis Populations

The registry lists secondary outcomes covering positive gonorrhea tests, positive chlamydia tests, cost per test, cost per diagnosis, community engagement activities, community connectedness, and social cohesion. These outcomes involve different measures and questions. Testing several outcomes can create a multiplicity issue: when many statistical tests are performed, the chance of at least one apparently notable result can increase. General statistical practice may address this through a prespecified hierarchy, adjusted testing, or careful emphasis on estimation, but the approach depends on the study plan.

A multiplicity strategy, prespecified outcome hierarchy, and analysis plan are not reported in the registry record. No secondary-outcome analysis results are reported either. It is therefore not possible to assess whether multiplicity was addressed or to distinguish planned analyses from any analyses conducted after examining the data.

Missing-data methods and the handling of incomplete outcome records are not reported in the registry record. The record does not state whether all randomized participants were included in the primary analysis, how exclusions were handled, or which analysis populations were used. It also does not describe missingness by arm or explain whether missing outcomes were imputed or otherwise addressed. These details matter because incomplete records can affect estimates, especially if completeness differs between groups.

The cluster unit and the sponsor’s analytic method are also not specified in the available record. Together with the absence of outcome results, these gaps limit assessment of the statistical conclusions. They do not establish that the trial had missing data, that multiplicity adjustments were absent in practice, or that the analysis was inappropriate; those matters are simply not reported in the registry record.

12. 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 lists serious adverse events, other adverse events, and deaths by arm. For pay-it-forward, the number at risk is 101 for serious adverse events and 101 for other adverse events; the recorded number affected is 0 in each category, and deaths are 0. For pay-what-you-want, the number at risk is 100 for each adverse-event category; the recorded number affected is 0 in each category, and deaths are 0. For standard-of-care, the number at risk is 100 for each adverse-event category; the recorded number affected is 0 in each category, and deaths are 0.

These are registry-reported counts for the listed categories. Zero recorded events does not establish that an intervention has no risk, and it does not show that all possible harms were assessed or captured. The safety observation window, ascertainment procedures, and definitions beyond the category labels are not reported in the registry record. The figures should be read as recorded events in the registry, not as a guarantee of safety or as a comparison demonstrating that one arm is safer.

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

The trial addresses a service-delivery question: whether different ways of offering and paying for dual gonorrhea and chlamydia testing are associated with test uptake among men who have sex with men in China. The pay-it-forward arm offered testing free to participants through donations from previous testers and invited participants to donate; the pay-what-you-want arm let participants choose an amount after testing; and the standard-of-care arm offered testing at the standard patient price.

The primary outcome was receipt of dual testing, assessed through administrative records during the enrollment visit. That outcome can describe whether participants proceeded to testing in the trial context. It is not a measure of infection reduction, later care, or health benefit. The secondary outcome list includes positive test results and other measures, but the registry record does not report results for them.

Most importantly, the primary uptake counts and statistical analysis are not reported in the registry record. It is not possible to say which approach had greater uptake, whether the arms differed, or how certain any comparison would be. The described interventions and completed status do not answer those questions without outcome estimates.

Even if an arm difference were reported, its meaning would depend on the design and analysis, including appropriate handling of cluster randomization. A statistical difference would not automatically establish that the approach is more important in other settings, and an absence of statistical significance would not establish equivalence. The record does not provide enough information to evaluate those possibilities. The findings also cannot be generalized beyond the studied context solely from the design description.

14. Why This Trial Matters Statistically

This trial is statistically noteworthy because it combines a three-arm comparison with cluster randomization and an uptake endpoint. Those features make the analysis more than a simple comparison of independent participant counts. If people within clusters have related outcomes, the statistical method needs to reflect that dependence when estimating uncertainty. The registry record itself warns that a simple individual-level re-analysis would be misleading.

The intervention arms also represent different payment arrangements, not just different test technologies. The primary endpoint focuses on whether a participant received dual testing. This makes uptake a direct measure of the service-use question, while positive test results and cost measures address distinct questions listed as secondary outcomes. Separating these outcomes helps prevent an uptake count from being mistaken for evidence about infection or costs.

The record illustrates why trial registration and outcome reporting are not interchangeable. It gives the design, interventions, primary endpoint, and several secondary outcomes, but it does not give the primary results or analysis. Without those results, readers cannot evaluate an effect estimate, its uncertainty, or the sponsor’s handling of clustering. The central statistical lesson is to interpret a trial from its reported estimates and design-aware analysis, not from its intervention descriptions or completion status alone.

15. 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 principal limitation for statistical interpretation is the absence of reported primary-outcome counts and analysis results. The registry record does not provide arm-specific test uptake, an effect estimate, an uncertainty interval, or a test result. It also lists no analyses. This prevents assessment of the magnitude, direction, and precision of the primary comparison.

Important design and analysis details are not reported in the registry record. These include the cluster unit and number of clusters, the allocation procedure, the method used to account for clustering, and the analysis populations. The record does not describe missing-data handling, outcome-record completeness, or a multiplicity strategy for the secondary outcomes. These omissions limit evaluation of whether the analysis appropriately matched the design, but they do not show what methods were or were not used outside the registry record.

The intervention descriptions distinguish the payment approaches but do not report the standard patient price, the amount of any donations, or participants’ payment behavior. The registry record also does not report the recruitment setting or enough eligibility detail to assess how representative the enrolled population may be. Consequently, the record alone cannot establish how findings would apply in other settings.

The safety record lists zero serious adverse events, zero other adverse events, and zero deaths in each arm, but the observation window and ascertainment methods are not reported. Those counts should not be interpreted as proof that no harms occurred beyond what was recorded. Finally, planning illustrations based on an observed effect do not substitute for a reported result or a documented original design justification.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Pay-it-forward RCT for Gonorrhea and Chlamydia Testing — NCT03741725.
• Registry record last updated 2020-07-07; status last verified 2020-01.
• Results first posted 2020-01-21.
• 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.

19. Record Summary

The registry record describes a completed, randomized, cluster-randomized, parallel trial with no masking and reported enrollment of 301. It compares pay-it-forward testing, pay-what-you-want testing, and standard-of-care testing among men who have sex with men in China. The primary endpoint is the number of participants who received dual gonorrhea and chlamydia testing, assessed using administrative records during the enrollment visit.

Secondary outcomes include positive gonorrhea and chlamydia results, cost measures, community engagement, community connectedness, and social cohesion. The safety listings record zero serious adverse events, zero other adverse events, and zero deaths in each arm. The registry record does not report primary or secondary outcome results, statistical analyses, or key details of the cluster-level analysis. As a result, the comparative effect and its precision cannot be assessed from the record.