Developing a Patient Navigation Intervention for PrEP Continuum of Care Among Young Latino MSM (PrEParate): Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Developing a Patient Navigation Intervention for PrEP Continuum of Care Among Young Latino MSM (PrEParate) (NCT04048382), a randomized na study. The primary outcome is Percent of Participants That Scheduled an Appointment for PrEP Consultation..

2. Clinical Question

3. Trial Design

This was a completed, randomized, single-masked, parallel-group study sponsored by San Diego State University. The registry classifies the study phase as NA. The study title is Developing a Patient Navigation Intervention for PrEP Continuum of Care Among Young Latino MSM (PrEParate). It was designed to develop and assess a patient-navigation approach intended to help young Latino gay and bisexual men access sexual-health information, including PrEP.

The registry lists 57 participants enrolled. The primary endpoint analysis includes 53 participants because of missing data: 26 in Patient Navigation and 27 in Usual Care. The randomization allocation ratio and the number assigned to each group at enrollment are not reported in the registry record. The outcome counts therefore describe the analyzed groups for this endpoint, not necessarily every participant enrolled.

The Patient Navigation intervention consisted of standardized educational materials and manualized sessions that could be matched to a participant’s stage in the PrEP continuum. Bilingual peer lay navigators and barrier-reduction strategies were also part of the intervention. Usual Care participants received the CDC’s 2-page PrEP Information Sheet in English or Spanish, based on their preferred language. The detailed content and delivery schedule beyond these descriptions are not reported in the registry record.

The study summary says participants were followed for 6 months. The primary endpoint was assessed at 3 months and asked whether a participant had scheduled an appointment for PrEP consultation. A response was to be confirmed through medical records. The precise follow-up schedule, procedures for confirming records, and definition of a completed appointment versus a scheduled appointment are not reported in the registry record.

The record gives a start date of 2019-08-05, a primary completion date of 2021-04-30, and a completed status. The identity of the person or people masked, and how masking was maintained in this behavioral intervention, are not reported in the registry record. The original design assumptions, including any planned allocation ratio or sample-size justification, are also not reported in the registry record.

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.

3-Month Follow Up

Reasons for not completing this period:

6-Month Follow up

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Number of participants analyzed= 53 due to missing data. This question was displayed contingent on answering "Yes" to "Before this study, had you ever heard of PrEP?"

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 Percent of Participants That Scheduled an Appointment for PrEP Consultation., measured over 3 months.

The primary endpoint is the number of participants who scheduled an appointment for a PrEP consultation by the 3-month assessment. Participants were asked about scheduling on a follow-up questionnaire, and the registry description says the response was to be confirmed by reviewing medical records. This is a binary, participant-level outcome: each analyzed participant contributes either a recorded event or no recorded event. The posted counts are 10 of 26 in Patient Navigation and 11 of 27 in Usual Care.

An appointment-scheduling endpoint captures a step in the PrEP continuum: movement from information or interest toward contact with a health service. It is distinct from later steps, such as attending the appointment, starting PrEP, or continuing it. The endpoint therefore addresses scheduling only; it does not establish whether an appointment occurred or what followed it. Those later outcomes are not reported in the registry record as secondary posted results.

Interpretation also depends on the eligibility of participants to answer the endpoint question. The registry states that the question was displayed only if a participant answered yes to having heard of PrEP before the study. Although the population description says 53 participants were analyzed because of missing data, the record does not separately report how many were eligible to receive this conditional question or how the conditional display was handled in the analysis. The recorded counts should be read in light of that limitation.

7. Results Posted in the Registry

• Patient Navigation (PN): 38.5% (10/26) versus 40.7% (11/27) on control; risk difference -2.3 percentage points (95% CI -28.6 to 24.0), risk ratio 0.94 (0.49–1.84); unadjusted two-proportion z-test p = 0.865.

8. Statistical Interpretation

9. Statistical Methods Explained

The primary endpoint is binary: a participant either had a reported and confirmed appointment scheduled or did not. For binary outcomes, counts and proportions describe how often the event was recorded in each group. The posted counts are 10 events among 26 analyzed PN participants and 11 among 27 analyzed UC participants. The comparison can be expressed on different scales, each answering a slightly different descriptive question.

What does a risk difference express?

A risk difference subtracts the event proportion in one group from the event proportion in the other. It is an absolute contrast, expressed on the probability scale. The recalculated estimate comparing PN with UC is -0.023. A value of zero corresponds to equal observed proportions on this scale; a negative value means the estimated event proportion is lower in the first group named in the comparison. The point estimate is only one estimate from the observed data and should be interpreted alongside its confidence interval.

What does logistic regression express?

Logistic regression models the odds of a binary outcome. An odds ratio compares the modeled odds between groups. An odds ratio of 1 corresponds to equal odds under the comparison being made; values on either side represent different odds. The registry reports a logistic-regression odds ratio of 1.1 with p = .88. It does not report a confidence interval for that odds ratio, nor the model covariates or other specification details. Odds are not the same as probabilities, so an odds ratio should not be read as an absolute difference in the chance of scheduling an appointment.

The unadjusted recalculation and the sponsor-reported regression result are not identical forms of analysis. The recalculation is derived directly from the posted group counts and does not adjust for other variables. Logistic regression can be used for a binary outcome and may incorporate covariates, but whether covariates were included in this analysis is not reported in the registry record. The reported odds ratio’s direction also depends on which group is the reference. The registry does not provide enough model detail to reconstruct that choice, so the odds ratio should be quoted as reported rather than reinterpreted as a particular adjusted comparison.

How should the p-value be read?

A p-value measures how unusual the observed data, or data more extreme, would be under a specified null model and its assumptions. Here, the sponsor reports p = .88 for the logistic-regression analysis. In general, a large p-value means the data do not provide strong evidence against the null model; it does not show that the null is true, nor does it measure the size or importance of an effect. A p-value also does not communicate the range of plausible effect sizes by itself. For that, an estimate and confidence interval are useful.

The recalculated unadjusted comparison reports the unadjusted p-value = 0.865 in the registry-derived statistics. It is a separate unadjusted result from the sponsor’s reported logistic-regression p-value of .88. The two values are close, but they arise from different reported analyses and should not be treated as interchangeable. No multiplicity adjustment is described for either result in the registry record.

What does a confidence interval contribute?

A confidence interval is a range produced by a statistical procedure that, under its assumptions and repeated use, has a stated coverage property. In practical reporting, it communicates uncertainty around an estimate more directly than a single point estimate. The recalculated risk difference has a 95% confidence interval from -0.286 to 0.240. Because this interval includes zero and spans negative and positive values, it does not isolate one direction of difference. Its width also signals limited precision. The interval is for the unadjusted risk difference, not for the sponsor’s logistic-regression odds ratio.

All such summaries depend on the data and analysis assumptions. The registry identifies a parallel randomized design, but does not report the full analysis plan, details of missing-data handling, covariates, or the approach used to account for the conditional endpoint question. The posted estimates should consequently be read as the results of the analyses described, rather than as a complete account of every analytic decision.

10. Confidence Intervals and Precision

The recalculated risk difference is -0.023, with a 95% confidence interval from -0.286 to 0.240. The interval includes zero and extends in both the negative and positive directions. Thus, the point estimate alone should not be taken as a definitive description of the direction of the underlying difference. Values in a range around the estimate remain compatible with the data under the calculation’s assumptions.

The interval is broad, indicating limited precision for this comparison. The analyzed groups contain 26 PN participants and 27 UC participants, and the posted event counts are 10 and 11, respectively. With few observations, estimates for binary outcomes can be sensitive to how a small number of participants are classified. The confidence interval makes that uncertainty visible; it is not a range containing a known fixed proportion of individual results.

The interval pertains to the simple unadjusted risk difference. It does not describe uncertainty in the sponsor-reported odds ratio of 1.1, for which the registry does not report a confidence interval. The sponsor’s p-value of .88 is not a substitute for that missing interval: a p-value and confidence interval convey related but distinct information.

Precision is also affected by the endpoint population and missing data. The analysis population is described as 53 due to missing data, and the endpoint question was displayed only after a yes response to prior awareness of PrEP. The number eligible for that question and the missing-data pattern are not reported in the registry record. The confidence interval does not account for uncertainty arising from any unreported selection or missingness process unless that process was incorporated into the analysis, which is not described.

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 sample-size figures are planning illustrations based on the observed effect, not the trial’s original design. For this binary endpoint, the listed figures are participants per arm, not total events. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set at the stated fraction of the observed effect. The trial’s own original sample-size assumptions and justification are not reported in the registry record.

The 80% power column gives the illustrative number of participants per arm for a design with an 80% chance of detecting the specified effect under the planning assumptions, if the effect and assumptions used for planning were true. The 90% power column gives the corresponding number per arm for a 90% chance. These are design-planning quantities, not results from the completed trial. This is not observed power.

The rows that plan for 100% of the observed effect require 7,227 participants per arm for 80% power and 9,674 for 90% power. Planning for 75% of the observed effect requires 12,880 participants per arm for 80% power and 17,242 for 90% power. Planning for 50% of the observed effect requires 29,050 participants per arm for 80% power and 38,889 for 90% power. These figures are conditional illustrations, not recommendations or a claim that those sample sizes would be appropriate for a future study.

Smaller effects are harder to distinguish from random variation, so detecting a smaller effect generally requires more participants when other assumptions are held constant. The very large illustrative requirements reflect the small observed risk-difference estimate used in this calculation. They should not be interpreted as evidence that the original study was designed to detect that effect; the original design assumptions are not reported in the registry record.

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 = 0.407; treatment proportion p₁ = 0.385; average p̄ = 0.396.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6916 = 1.3556.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6915 = 0.5819.
4. n = (1.3556 + 0.5819)² / (-0.0228)² = 7,226.7, rounded up to 7,227 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 <- 0.407407407407407                          # control proportion
p1 <- 0.384615384615385                          # 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(4048382)
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 = 7,227 (80% power), 9,674 (90% power)
 75% of observed effect: n per arm = 12,880 (80% power), 17,242 (90% power)
 50% of observed effect: n per arm = 29,050 (80% power), 38,889 (90% power)
power.prop.test: 7226.7 
Simulated power at n = 7,227 per arm: 0.801 (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. Analyses Reported by the Sponsor

The sponsor-reported primary analysis is logistic regression, with an odds ratio of 1.1 and p = .88. No confidence interval is reported for the odds ratio. The simple recalculation from the posted counts gives a risk difference of -0.023, with a 95% confidence interval from -0.286 to 0.240, and an unadjusted p-value of 0.865. These results use different effect scales and analysis approaches: the risk difference is an unadjusted absolute contrast, while logistic regression reports an odds-scale comparison.

The registry does not report the regression model’s covariates, reference-group coding, or other specification details. It is therefore not possible to reconstruct the sponsor’s analysis fully from the record or to explain any difference by attributing it to adjustment. The sponsor’s odds ratio is reported as 1.1 and should remain on its stated scale. It should not be converted into a risk difference using information that is not reported.

The registry’s discrepancy indicator is false. The posted sponsor result and the unadjusted recalculation are broadly close in their indication of little separation, but their estimates and p-values should be presented separately. The sponsor’s analysis is the trial’s reported analysis; the recalculation is a supplementary, simple unadjusted calculation from the posted summary statistics.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists 7 outcome measures and 6 analyses, while 0 secondary outcomes are posted. The available record does not establish how many outcomes were prespecified as primary, how the analyses correspond to particular outcomes, or whether any outcomes or analyses were selected after data review. A full protocol and detailed statistical analysis plan are not reported in the registry record.

When several outcomes or statistical tests are examined, the chance of at least one apparently notable result can increase. Multiplicity methods are sometimes used to control that risk, or investigators may designate a limited set of confirmatory tests in advance. Whether either approach was used in this trial is not reported in the registry record. No adjustment for multiple comparisons is described for the primary result, so the posted p-values should be read as the reported test results rather than as documented multiplicity-controlled evidence.

The primary endpoint analysis population is described as 53 because of missing data. The registry does not say how many values were missing in each group, why data were missing, whether missingness related to participant characteristics or outcomes, or how missing values were handled. It also does not state whether the analysis followed an intention-to-treat approach, a per-protocol approach, or another population definition. The number randomized to each arm is not reported separately from the analyzed endpoint counts.

The endpoint question itself was conditional: it was displayed only when a participant said they had heard of PrEP before the study. The registry does not report how many participants answered yes, whether all such participants posted endpoint data, or how participants who answered no were represented in the analysis. This affects interpretation of the stated analyzed population and the scope of the endpoint comparison.

These reporting gaps do not show that the analyses were inappropriate; they limit what can be assessed from the registry record. Without the analysis plan and missing-data details, readers cannot verify prespecification, evaluate sensitivity to missingness, or determine whether the reported result represents all randomized participants under a defined analysis strategy.

15. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: serious adverse event (3.4%).

The serious adverse event figures are reported separately from the primary appointment-scheduling endpoint. In Patient Navigation, 0 serious adverse events were recorded among 28 participants at risk. In Usual Care, 1 serious adverse event was recorded among 29 participants at risk. The registry lists the top serious adverse event term as Serious Adverse Event, with 3.4%. It reports 0 other adverse events and 0 deaths in both groups.

These figures are counts during the recorded safety assessment, not an adjusted comparison of risk. The registry record does not provide details about the event’s nature, timing, relatedness to study participation, severity criteria beyond the serious classification, or how long participants were monitored for adverse events. The at-risk counts differ from the analyzed primary-endpoint counts, and the record does not explain the relationship between those populations.

A single event in one group and none in the other should not be read as evidence that the study condition caused or prevented an event. The record reports the observed safety counts, but does not provide enough information for causal attribution or a precise comparative safety estimate.

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

The posted results describe a narrow, specific outcome: whether participants scheduled a PrEP consultation by the 3-month assessment. The counts were 10 of 26 in Patient Navigation and 11 of 27 in Usual Care. The unadjusted risk-difference estimate was -0.023, and its 95% confidence interval ranged from -0.286 to 0.240. The sponsor’s logistic analysis reported an odds ratio of 1.1 and p = .88. Together, these statistics do not demonstrate a clear separation in appointment scheduling in the analyzed groups, and the interval leaves substantial uncertainty about the direction and size of a difference.

This result does not establish that the groups are equivalent. A study can fail to provide clear evidence of a difference while still being too imprecise to rule out differences that may matter. The registry does not describe an equivalence or non-inferiority objective, and the sample-size illustration is not the trial’s original design justification. The observed endpoint counts should not be treated as proof of no effect.

The outcome is also only one step in a care continuum. Scheduling an appointment is not the same as attending a consultation, starting PrEP, or continuing its use. The primary endpoint does not answer those questions. Nor does it assess broader access to sexual-health information beyond this particular recorded outcome.

The endpoint question was shown only to participants who reported prior awareness of PrEP. The number eligible for the question is not reported, and missing-data details are limited. That means the result’s interpretation depends on an analysis population that is not fully described in the registry.

Finally, the study focused on developing a patient-navigation intervention among young Latino men who met the stated eligibility criteria. Its findings are specific to the enrolled and analyzed participants and the described study conditions. The record does not support assuming the same results in other populations, settings, or intervention implementations. The statistics characterize the posted data; they are not medical advice or a clinical recommendation.

17. Why This Trial Matters Statistically

This trial is statistically useful because its primary outcome is a concrete step in the PrEP continuum and is reported as participant counts in two randomized groups. Readers can see both the numerator and the analyzed group size: 10 of 26 in Patient Navigation and 11 of 27 in Usual Care. The unadjusted risk difference and its confidence interval put those counts into a comparative frame while retaining uncertainty about the size and direction of the contrast.

The comparison also illustrates why an estimate, interval, and p-value answer different questions. The risk-difference estimate describes an absolute contrast, its confidence interval shows the uncertainty around that contrast, and the sponsor-reported p-value summarizes compatibility with a specified null model under the logistic analysis. None alone captures the whole result. In particular, a non-small p-value is not evidence of equivalence, and a point estimate near zero does not eliminate uncertainty.

The registry’s conditional question and missing-data statement matter for understanding who contributed to the endpoint. The primary analysis includes 53 participants because of missing data, and the endpoint question depended on prior awareness of PrEP. Without a count of eligible respondents or a detailed missing-data account, the denominator is not fully interpretable. This is a reminder that outcome definitions and analysis populations are part of the statistical result, not administrative details.

The sample-size illustrations further show how planning calculations depend on the effect chosen for detection. They use the observed effect and explicit assumptions, rather than documenting the study’s original design. Distinguishing such planning illustrations from the original study rationale prevents a retrospective calculation from being mistaken for evidence about what the trial was designed to detect.

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.

The endpoint analysis includes 53 participants, while enrollment was 57. The registry attributes the smaller analysis population to missing data but does not describe the missingness by group, the reasons for missing values, or the method used to handle them. This prevents assessment of whether the analyzed participants differ systematically from those without endpoint data.

The endpoint question was conditional on a yes response to having heard of PrEP before the study. The number of participants who met that condition is not reported, nor does the record clarify how those who answered no were treated in the endpoint analysis. The outcome counts and analysis denominator therefore cannot be fully connected to the endpoint’s conditional display rule using the registry information alone.

The primary outcome records scheduling, not attendance or subsequent PrEP use. The description says medical records were to be reviewed for confirmation, but the registry does not report the confirmation procedures, completeness of records, or adjudication rules. The outcome’s 3-month timeframe is stated, while the overall follow-up is described as 6 months; further timing details are not reported.

The intervention description is incomplete in the registry record. It identifies educational materials, manualized sessions, bilingual peer lay navigators, and barrier-reduction strategies, but does not provide the full strategy description or enough detail to assess implementation consistency. The person or people masked are also not identified, and masking procedures for this behavioral intervention are not described.

The sponsor reports logistic regression, but the model specification, covariates, and confidence interval are not reported. The record also does not provide the original sample-size justification, full statistical analysis plan, handling of multiple analyses, or a clear definition of the analysis population. These gaps limit reproducibility and prevent a full assessment of prespecification and robustness.

Finally, the study is focused on a defined population and intervention-development setting. The registry record alone does not establish how findings would generalize to other populations or service contexts. These limitations qualify interpretation of the posted endpoint result without changing the counts and analyses that were reported.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Developing a Patient Navigation Intervention for PrEP Continuum of Care Among Young Latino MSM (PrEParate) — NCT04048382.
• Registry record last updated 2022-07-08; status last verified 2022-06.
• Results first posted 2022-07-08.
• 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

NCT04048382 is a completed, randomized, single-masked, parallel study sponsored by San Diego State University. It enrolled 57 participants and compared Patient Navigation with Usual Care among young Latino men meeting the stated eligibility criteria. Follow-up was described as 6 months.

The primary endpoint was the number scheduling a PrEP consultation by 3 months. The analyzed counts were 10 of 26 for Patient Navigation and 11 of 27 for Usual Care; the analysis population was described as 53 because of missing data. The unadjusted risk difference was -0.023, with a 95% confidence interval from -0.286 to 0.240. The sponsor reported a logistic-regression odds ratio of 1.1 and p = .88.

The endpoint question was conditional on prior awareness of PrEP. Further details about missing data, analysis populations, the regression specification, and original sample-size assumptions are not reported in the registry record.