A Tailored, Peer-delivered Intervention to Reduce Recurring Opioid Overdoses: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Tailored, Peer-delivered Intervention to Reduce Recurring Opioid Overdoses (NCT02922959), a randomized na study. The primary outcome is Medication-Assisted Treatment Enrollment.

2. Clinical Question

3. Trial Design

The registry describes a randomized, parallel-group pilot trial with no masking. It was designed to compare tailored opioid-overdose prevention education and naloxone distribution alone with the same package plus peer intervention. The trial is listed as completed. Its phase is not reported in the registry record.

The planned enrollment was 80 participants. The posted primary-outcome groups each contain 40 participants. The study began on 2017-02-07, and primary completion is recorded as 2019-07-25. The registry names the University of Cincinnati as sponsor.

The control package, labeled PTOEND, included a naloxone nasal spray kit and personally tailored information about overdose and treatment. The PTOEND+PI arm added peer intervention. The registry summary describes the broader goal as testing whether the peer-delivered, tailored telephone intervention could facilitate entry into medication-assisted treatment after a non-fatal overdose. The full description of the peer component is not reported in the registry record.

Random allocation is intended to make the groups comparable on average, including for factors that were not measured, but it does not guarantee close balance in a small pilot. With no masking, participants and interventionists would know the assigned intervention; whether outcome assessors were masked is not reported in the registry record. The record does not report the allocation-generation procedure, concealment details, trial sites, or the original sample-size justification.

This design provides an initial randomized comparison of the added peer component against the education-and-naloxone package. It is a pilot trial, so its estimates are most useful for describing the observed outcomes and their uncertainty, and for informing future planning. The registry does not report whether the pilot was intended to make a definitive efficacy assessment.

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: 116 potential participants were consented and screened, and 80 were randomized to Personally-tailored opioid overdose education and naloxone distribution plus Peer Intervention (PTOEND+PI) (n=40) or PTOEND (n=40).

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 Medication-Assisted Treatment Enrollment, measured over 12 months.

The primary endpoint is medication-assisted treatment enrollment within the 12-month follow-up period. It is recorded as a count of participants who enrolled, and the description defines the outcome as whether or not each participant enrolled. This is a binary outcome: each participant is classified according to enrollment status during the stated period.

Enrollment is used because the registry describes the intervention’s purpose as facilitating entry into medication-assisted treatment after a non-fatal opioid overdose. The endpoint therefore reflects a step in treatment access, rather than treatment duration, adherence, or a change in opioid use. Those are different questions and are not captured by this primary measure.

In the posted results, 7 participants in the PTOEND group and 13 in the PTOEND+PI group enrolled. The corresponding reported proportions are 0.175 and 0.325. The unadjusted risk difference is 0.15, with a reported interval from -0.037 to 0.337. These summaries describe the observed enrollment outcome in the trial groups; they do not by themselves establish that the added peer intervention caused a difference.

The endpoint’s clinical meaning is also bounded by its definition and follow-up period. Enrollment does not show whether participants remained in treatment or what happened after the 12-month period. The registry record does not report additional detail about how enrollment was verified or how the outcome was handled for participants without an observed follow-up assessment.

7. Results Posted in the Registry

• PTOEND+PI: 32.5% (13/40) versus 17.5% (7/40) on control; risk difference 15.0 percentage points (95% CI -3.7 to 33.7), risk ratio 1.86 (0.83–4.16); unadjusted two-proportion z-test p = 0.121.

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.

Opioid Overdose Experience

• 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:
  • PTOEND+PI: 12.5% (5/40) versus 32.5% (13/40) on control; risk difference -20.0 percentage points (95% CI -37.8 to -2.2), risk ratio 0.38 (0.15–0.98); unadjusted two-proportion z-test p = 0.032.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for PTOEND+PI than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change in Opioid Use (Timeline Follow-back)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • PTOEND+PI: mean -8.70 versus -10.20 on control; difference 1.50 (95% CI -6.27 to 9.27), standardized effect d = 0.11; z-test p = 0.705.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between PTOEND+PI and the control. The interval gives the range of effects that remain compatible with the data.

Change in Opioid Use (Urine Drug Screen)

• 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:
  • PTOEND+PI: 87.0% (20/23) versus 85.7% (18/21) on control; risk difference 1.2 percentage points (95% CI -19.1 to 21.6), risk ratio 1.01 (0.80–1.28); unadjusted two-proportion z-test p = 0.905.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between PTOEND+PI and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes address related but distinct aspects of the intervention’s stated goals. They cover reported opioid overdose experience, change in opioid use measured by timeline follow-back, and change in opioid use assessed with a urine drug screen. Taken together, the observed directions are not uniform across every measure: the peer-intervention group had fewer reported overdose experiences, while the continuous opioid-use measure and urine drug screen summaries do not show a clearly separated pattern between groups.

This broad pattern can be considered alongside the primary result, which showed a higher observed enrollment proportion in the peer-intervention group. Enrollment, overdose experience, self-reported use, and urine drug screen findings are not interchangeable outcomes. A change in one does not establish a change in another, and variation in the secondary results does not resolve the uncertainty in the primary estimate. The outcomes may offer complementary descriptive information, but each has its own measurement and follow-up characteristics.

The secondary results warrant less weight than a single prespecified primary comparison when many outcomes are examined without a reported multiplicity adjustment. General: Testing multiple outcomes creates more opportunities for a seemingly unusual result to arise by chance. Without an adjustment strategy or a clearly reported hierarchy, an isolated unadjusted p-value should be treated cautiously and not as confirmatory evidence on its own. The registry does not report an adjustment for these comparisons or a statistical analysis plan that explains how they were prioritized.

The reported participant denominators also vary across outcomes. The reasons for those differences, and the handling of missing outcome data, are not reported in the registry record. This limits direct comparisons across endpoints and reinforces the value of treating the secondary findings as descriptive and exploratory rather than as a coordinated proof of effect across several measures.

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

• Opioid Overdose Experience
• Change in Opioid Use (Timeline Follow-back)
• Change in Opioid Use (Urine Drug Screen)

9. Statistical Interpretation

10. Statistical Methods Explained

The reported comparisons use different statistical summaries depending on whether the outcome is a participant count or a continuous measure. The primary outcome is binary, so the principal comparison is expressed as a risk difference. The registry also reports relative measures for the binary outcomes and a mean difference for the continuous outcome. The record does not describe the exact statistical procedures used to construct the reported intervals or p-values.

What does a risk difference describe?

General: A risk difference subtracts the event proportion in one group from the event proportion in the comparison group. It is expressed on the same scale as the proportions. A value of 0 indicates equal observed proportions; positive and negative values indicate which group had the higher proportion, according to the comparison’s direction. For medication-assisted treatment enrollment, the estimated risk difference is 0.15 for PTOEND+PI compared with PTOEND. The interval runs from -0.037 to 0.337, so it includes 0.

The risk ratio and odds ratio describe the binary comparison on relative scales rather than as an absolute difference. General: A risk ratio compares the event proportions directly, while an odds ratio compares the odds of an event. A value of 1 is the no-association value for each measure. For the primary outcome, the reported risk ratio is 1.857, with an interval from 0.828 to 4.165, and the odds ratio is 2.27, with an interval from 0.794 to 6.488. Their intervals include 1, so the reported relative estimates also leave substantial uncertainty about the direction and size of the difference.

What do the p-values tell the reader?

General: A p-value measures how unusual data at least as discrepant as those observed would be under a specified null model. It is not the probability that the null hypothesis is true, nor does it measure the probability that a result will replicate. The registry reports unadjusted p-values. For the primary endpoint, the unadjusted p-value is 0.121. A p-value should be interpreted alongside the estimated effect and its interval, rather than as a substitute for them. The registry record does not report an adjustment for multiple comparisons.

The secondary binary outcome of opioid overdose experience is also summarized with a risk difference, while the urine drug screen outcome uses a risk difference for its participant counts. Their estimates and uncertainty are reported in the secondary results. The timeline follow-back outcome is continuous: the registry reports mean changes in days for the groups and a mean difference of 1.5, with an interval from -6.272 to 9.272. Its unadjusted p-value is 0.705. The reported group standard deviations are 12.7 and 13.6. These summaries distinguish the average change from the spread of individual values around that average.

Why does variability matter?

General: For a continuous outcome, the standard deviation describes how dispersed individual observations are around the group mean. Greater variability makes a group mean less precise when the number of observations is unchanged. The standard error describes uncertainty in an estimated mean or contrast; it is not the same as the standard deviation. The registry reports standard errors of 2.771 and 2.836 for the two timeline follow-back group means.

For count outcomes based on whether participants experienced an event, the analysis compares event proportions. The record describes the outcomes as participant counts and reports their group denominators. It does not report a model for over-dispersion, and no such model should be inferred from these summary statistics. General: Over-dispersion matters when variation in count data exceeds what a chosen probability model expects; if present, a model that ignores it can understate uncertainty. That concept is relevant to count modeling generally, but the posted participant-event outcomes are not reported as rates or as over-dispersed counts.

What can randomization and masking contribute?

General: Random assignment can reduce systematic differences between groups in expectation, including differences in factors that are not measured. It does not eliminate chance imbalance, particularly in a small study. The registry classifies this study as randomized and parallel, with no masking. No allocation concealment details or masked outcome-assessment procedures are reported in the registry record. The posted comparisons should therefore be read as randomized group comparisons with the limitations of the reported design and analysis information.

These explanations describe statistical concepts in general terms. The registry does not identify the exact interval method, confidence level, covariate adjustment, or analysis population for the posted comparisons. Those details are not reported in the registry record and cannot be reconstructed from the group summaries alone.

11. Confidence Intervals and Precision

The primary risk-difference estimate is 0.15, with a reported interval from -0.037 to 0.337. Because the interval includes 0, it does not rule out no difference on this scale. The interval also extends to positive and negative values, leaving room for effects in either direction. Its breadth relative to the point estimate signals that the estimate is not precise enough to settle the size or direction of the difference.

General: A confidence interval conveys uncertainty around an estimate under the method used to construct it. A narrow interval indicates greater precision than a wide interval, while an interval that includes the null value does not establish that the true effect is exactly null. The confidence level associated with the registry’s intervals is not reported in the registry record, so it should not be assumed from the posted summaries.

The primary relative estimates are also uncertain: the risk-ratio interval is 0.828 to 4.165, and the odds-ratio interval is 0.794 to 6.488. Both include 1, their no-association value. The continuous timeline follow-back mean-difference interval ranges from -6.272 to 9.272, and the urine drug screen risk-difference interval ranges from -0.191 to 0.216. These intervals likewise include their respective no-difference values.

Intervals help distinguish an observed point estimate from the range of values still compatible with the data and analysis. They do not show the probability that the true value lies inside a particular interval, and they do not guarantee that future results will fall within it. The registry record does not report whether the intervals were adjusted for multiple comparisons.

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 planning figures are participants per arm, not total events. They are illustrations calculated from the observed effect, rather than the trial’s original design assumptions. The listed 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 the stated fraction of the observed effect. The original trial design assumptions and sample-size justification are not reported in the registry record.

For the primary medication-assisted treatment enrollment outcome, planning for 100% of the observed effect gives 130 participants per arm for 80% power and 174 participants per arm for 90% power. Planning for 75% of the observed effect gives 220 participants per arm for 80% power and 294 participants per arm for 90% power. Planning for 50% of the observed effect gives 466 participants per arm for 80% power and 624 participants per arm for 90% power.

General: Power is the chance that a study will meet its specified statistical criterion if a particular effect is truly present and the planning assumptions hold. The 80% column means a design aimed at an 80% chance of detecting the stated effect under those assumptions; the 90% column uses a higher detection probability. Smaller effects are harder to distinguish from random variation and therefore require more participants under otherwise unchanged assumptions. The figures are planning illustrations, not observed power. They do not describe what power the completed trial achieved, and the observed effect used as their starting point is itself uncertain.

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 = 0.175; treatment proportion p₁ = 0.325; average p̄ = 0.250.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6124 = 1.2002.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6031 = 0.5076.
4. n = (1.2002 + 0.5076)² / (0.1500)² = 129.6, rounded up to 130 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 <- 0.175                          # control proportion
p1 <- 0.325                          # 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(2922959)
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 = 130 (80% power), 174 (90% power)
 75% of observed effect: n per arm = 220 (80% power), 294 (90% power)
 50% of observed effect: n per arm = 466 (80% power), 624 (90% power)
power.prop.test: 129.6 
Simulated power at n = 130 per arm: 0.805 (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 lists one primary endpoint and three secondary outcomes. It reports unadjusted p-values for the posted comparisons, but does not report a multiplicity-adjustment method, a testing hierarchy, or an analysis plan specifying how conclusions across outcomes were to be controlled. When several outcomes are examined, the chance of at least one apparently notable result can increase. General: Multiplicity procedures address that issue by controlling the interpretation of a family of tests, though the appropriate approach depends on the study’s goals and prespecified plan. No particular procedure can be attributed to this trial from the registry record.

The record reports no analyses under its reported-analyses listing. The posted summary comparisons are therefore the available numerical results, but the registry does not say whether they correspond to a prespecified analysis, a final analysis, or a particular analysis population. The statistical model, covariate adjustments, and handling of protocol deviations are not reported in the registry record. An intention-to-treat or per-protocol analysis should not be assumed.

Missing-data handling is also not reported. The primary outcome summaries list 40 participants in each group, while some secondary summaries use different group denominators. The registry does not explain why the denominators differ, whether any outcome values were missing, how missing observations were classified, or whether sensitivity analyses were conducted. The differing denominators should therefore be treated as a feature of the posted outcome summaries, not as evidence of a particular reason for non-observation.

These omissions matter because analysis choices can affect estimates and uncertainty, especially when outcomes are incomplete or several comparisons are made. They do not demonstrate that the analyses were inappropriate; they mean that readers cannot assess those choices from the registry record. The reported estimates can be interpreted on their stated scales, but the degree to which they reflect prespecified methods, missing-data assumptions, or multiplicity control is not reported.

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 5% reporting threshold.

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: hospitalization for musculoskeletal problem / injury (7.5%), hospitalization related to pregnancy (5.0%), hospitalization for infection/abscess (5.0%), hospitalization for psychiatric problem (5.0%), hospitalization for multiple causes (5.0%).

The serious adverse event summaries list 9 participants affected in PTOEND and 7 in PTOEND+PI, with 40 participants at risk in each group. The registry reports 0 deaths in each group and 0 other adverse events in each group. These counts describe what the record posts for the stated groups; they are not, by themselves, a comparison adjusted for follow-up time or other factors.

The registry also lists serious-event terms with percentages: hospitalization for musculoskeletal problem or injury, 7.5%; hospitalization related to pregnancy, 5.0%; hospitalization for infection or abscess, 5.0%; hospitalization for psychiatric problem, 5.0%; hospitalization for multiple causes, 5.0%; and suicide attempt, 2.5%. The record does not assign these terms and percentages to specific arms in the listed summary, so arm-specific attribution should not be inferred.

General: Serious adverse event counts are descriptive safety information. A difference in observed counts does not establish that an intervention caused or prevented an event. The registry record does not report the event-collection procedures, exposure-adjusted rates, or a formal between-group safety analysis. The figures should be read as reported counts and terms, not as evidence of comparative safety.

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

The results answer a focused question: whether medication-assisted treatment enrollment during 12 months differed between participants assigned to tailored education and naloxone distribution and those assigned to that package plus peer intervention. The observed enrollment proportion was higher in the peer-intervention group, and the estimated risk difference was 0.15. The reported interval includes 0, leaving the size and direction of the underlying difference uncertain.

The trial does not establish that the peer-delivered intervention increases enrollment, nor does it establish that the groups are equivalent. An uncertain estimate is not the same as evidence of no effect. The pilot scale and breadth of the interval mean that the observed group difference should be interpreted as an estimate with substantial uncertainty rather than as a settled conclusion.

The primary endpoint measures enrollment, not continued participation in medication-assisted treatment, treatment adherence, overdose prevention, or broader recovery outcomes. The secondary outcomes add information about overdose experience and opioid use, but the registry does not report a multiplicity strategy or detailed analysis plan. These endpoints therefore do not combine into a single definitive conclusion about the intervention’s overall effect.

Randomization supports a comparison between the assigned intervention packages, but it does not eliminate chance imbalance or limitations arising from missing information. No masking was used, and the registry does not report outcome-assessment masking, missing-data procedures, or the exact statistical methods. These features should temper causal interpretation without negating the value of the randomized comparison.

Ultimately, the posted results describe what happened in the groups during the recorded follow-up. They offer an early estimate for a pilot study and a basis for considering further research. They do not establish effects beyond the measured outcomes, nor do they support medical advice or a claim that the intervention works or does not work.

17. Why This Trial Matters Statistically

This trial is statistically informative because it separates the point estimate from the uncertainty around it. The primary comparison shows a higher observed enrollment proportion in the peer-intervention group, but its risk-difference interval extends across 0. Reading only the direction of the point estimate would overstate what the data resolve; reading only the p-value would also discard the estimated magnitude and its uncertainty.

The study also illustrates why pilot results and future planning serve different purposes. A planning calculation based on an observed effect can show how strongly required sample size depends on the effect one aims to detect. It does not retrospectively establish the power of the completed study. Because pilot estimates are uncertain, planning directly around them can yield overly optimistic or unstable expectations for a later trial.

Multiple outcome types add another statistical lesson. Enrollment, overdose experience, timeline follow-back, and urine drug screen outcomes use different scales and do not necessarily move together. Each result needs interpretation in its own measurement framework, and several unadjusted comparisons should not be treated as if each were an independent confirmatory test.

Finally, the registry’s incomplete reporting of analysis details limits reproducibility and appraisal. Information about the analysis population, missing-data handling, multiplicity, and interval methods would help readers understand how the posted estimates were produced. The randomized pilot remains useful as an initial comparison, but its main statistical contribution is an uncertain estimate and a set of questions for more fully specified research, not a definitive answer.

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 study was a pilot with planned enrollment of 80 participants and 40 participants in each posted primary-outcome group. The primary estimate is accompanied by an interval that includes 0 and spans effects in either direction. This limits how precisely the trial can characterize the difference in medication-assisted treatment enrollment.

The registry reports no masking. That is relevant for a behavioral, peer-delivered intervention because participants and interventionists may know the assigned group. Whether outcome assessors were masked is not reported in the registry record. The record also does not provide details about allocation concealment, sites, eligibility criteria, or the full peer-intervention procedures.

Outcome denominators differ across the secondary measures. In particular, the timeline follow-back and urine drug screen summaries use fewer observations than the primary summaries, but the reason is not reported in the registry record. The record does not describe missing-data handling or sensitivity analyses. It is therefore not possible to determine from these summaries whether differences in observed denominators reflect missing assessments, endpoint-specific availability, or another reason.

The statistical analysis plan, analysis populations, covariate adjustments, confidence level, interval-construction method, and multiplicity procedures are not reported. The primary analysis is presented as an unadjusted comparison, and the secondary comparisons are also unadjusted. The registry does not report enough detail to assess whether the posted methods were prespecified or how results might change under alternative assumptions.

These limitations concern precision, interpretability, and reporting. They do not establish that the intervention had no effect or that the trial’s conduct was flawed. They indicate that the posted results should be treated as an early, limited comparison rather than a comprehensive assessment of all relevant outcomes.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Tailored, Peer-delivered Intervention to Reduce Recurring Opioid Overdoses — NCT02922959.
• Registry record last updated 2020-06-24; status last verified 2020-06.
• Results first posted 2020-06-24.
• 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

NCT02922959 is a completed randomized, parallel pilot trial sponsored by the University of Cincinnati. It compared tailored opioid-overdose prevention education and naloxone distribution with the same package plus peer intervention. The registry lists no masking and a planned enrollment of 80.

The primary outcome was medication-assisted treatment enrollment within 12 months. The posted groups recorded 7 enrollments in PTOEND and 13 in PTOEND+PI. The risk-difference estimate was 0.15, with a reported interval from -0.037 to 0.337 and an unadjusted p-value of 0.121.

Three secondary outcomes concern opioid overdose experience and opioid use measured by timeline follow-back and urine drug screen. The registry does not report the original sample-size justification, detailed statistical analysis plan, multiplicity handling, or missing-data methods. The observed primary difference is uncertain and should be interpreted as a pilot estimate, not a definitive conclusion about the intervention.