Computer-based Intervention for Alcohol-using HIV/HCV+ Women: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Computer-based Intervention for Alcohol-using HIV/HCV+ Women (NCT03362476), a randomized na study. The primary outcome is Percentage of Women Who Test Ethyl Glucuronide (EtG) Negative.

2. Clinical Question

3. Trial Design

The registry describes a randomized, parallel-group trial with no masking. It lists enrollment as 200 and status as completed. The study dates are 2018-01-03 for start and 2022-02-28 for primary completion. The phase is recorded as NA. New York University is listed as sponsor.

One group received the computer-based alcohol-reduction intervention in addition to clinician-delivered brief MET counseling. The comparison group received clinician-delivered brief MET counseling and standard of care. The counseling and standard-of-care components therefore form the common background across the conditions as described in the arm records; the randomized contrast concerns the addition of the computer-based intervention.

The registry’s primary outcome is the percentage of women who test EtG negative, assessed over 3-, 6-, and 9-month post-baseline follow-ups. Urine samples were to be collected at baseline and follow-ups at point of care. The registry also lists secondary measures concerning PEth, HIV viral load, CD4 cell count, FibroTest, and liver stiffness. The posted secondary results include PEth, HIV viral load, and CD4 cell count.

Randomization can support a comparison between assigned groups, but the registry record does not report the method used to generate or conceal allocation. Masking is explicitly listed as none. Details about treatment delivery, adherence, participant flow, and the analysis population are not reported in the registry record. The original sample-size justification and the trial’s prespecified statistical analysis plan are also not reported in the registry record, so the planning illustrations based on observed effects should not be confused with the trial’s original design rationale.

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

Reasons for not completing this period:

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Women Who Test Ethyl Glucuronide (EtG) Negative, measured over 3-, 6-, and 9-month post-baseline.

The primary outcome is titled as the percentage of women who test ethyl glucuronide negative. EtG is measured in urine, and the registry defines a negative result as <500 ng/mL. The described follow-up points are 3-, 6-, and 9-month post-baseline. The registry says results across those visits are represented by a dichotomous score: women testing negative over the three time points are assigned 0, while women testing positive at any of the three follow-up points are assigned 1.

Urine EtG is used in this trial as an objective indicator related to recent alcohol exposure. As a general statistical point, a binary endpoint can make a group comparison easy to express as the probability of meeting a defined outcome, but it also compresses information: it does not describe the amount or pattern of alcohol use, and a threshold-based classification can treat observations close to the cutoff differently. The endpoint therefore addresses the registry’s specified EtG classification, not every aspect of alcohol use or health.

The posted primary values are counts, with 93 reported for the computer-based intervention condition and 99 for clinician-delivered counseling. The corresponding posted proportions are 0.949 and 0.971. The registry does not explain in detail how the count presentation maps onto the stated dichotomous coding, or how results at the separate follow-up visits were combined in the posted summary. Those details are not reported in the registry record, so the reported summary should be interpreted according to its stated endpoint rather than as a visit-by-visit account.

7. Results Posted in the Registry

• Clinican-delivered Brief MET Counseling: 97.1% (99/102) versus 94.9% (93/98) on control; risk difference 2.2 percentage points (95% CI -3.3 to 7.6), risk ratio 1.02 (0.97–1.08); unadjusted two-proportion z-test p = 0.436.

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.

Percentage of Women Who Test Phosphatidylethanol (PEth) Negative (<= 8 ng/mL)

• 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:
  • Clinician-delivered Brief MET Counseling: 42.6% (43/101) versus 44.9% (44/98) on control; risk difference -2.3 percentage points (95% CI -16.1 to 11.5), risk ratio 0.95 (0.69–1.30); unadjusted two-proportion z-test p = 0.741.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Clinician-delivered Brief MET Counseling and the control. The interval gives the range of effects that remain compatible with the data.

Percentage of Participants in the Intervention Group With an Undetectable HIV Viral Load Compared to the Percentage of Women in the Control Group With an Undetectable Viral Load

• 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:
  • Clinican-delivered Brief MET Counseling: 25.7% (26/101) versus 16.3% (16/98) on control; risk difference 9.4 percentage points (95% CI -1.8 to 20.7), risk ratio 1.58 (0.90–2.75); unadjusted two-proportion z-test p = 0.104.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Clinican-delivered Brief MET Counseling and the control. The interval gives the range of effects that remain compatible with the data.

CD4 Cell Count

• 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:
  • Clinician-delivered Brief MET Counseling: mean 502.90 versus 532.10 on control; difference -29.20 (95% CI -66.82 to 8.42), standardized effect d = -0.22; z-test p = 0.128.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Clinician-delivered Brief MET Counseling and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary outcomes cover different domains: PEth as a biomarker related to alcohol exposure, undetectable HIV viral load, and CD4 cell count. Their estimates do not form a consistent pattern of clear separation between the randomized conditions. The binary results differ in direction, and the continuous CD4 comparison also has an interval that includes no difference. Across these measures, the posted uncertainty is substantial enough that the estimates should not be read as definitive evidence of group differences.

These outcomes relate to the primary EtG result by adding complementary biological measures, but they do not measure the same thing. A biomarker result at a specified visit is not the same endpoint as the primary EtG classification described across follow-up visits. HIV viral load and CD4 cell count address different clinical domains, so their estimates cannot be used as substitutes for the primary alcohol-related outcome. The registry’s secondary outcomes also list FibroTest and liver stiffness, but results for those measures are not included among the posted secondary results.

The registry lists no reported analyses for these secondary outcomes. The posted comparisons are unadjusted and should therefore be treated cautiously, particularly when several outcomes are considered together. As a general statistical matter, examining many outcomes increases the opportunity for apparently notable findings to arise by chance. Multiplicity adjustment or a prespecified hierarchy can help address that issue, but whether either was used is not reported in the registry record. Unadjusted secondary results carry less weight than a prespecified primary analysis and are best viewed as exploratory rather than as independent confirmation.

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

• Percentage of Women Who Test Phosphatidylethanol (PEth) Negative (<= 8 ng/mL)
• Percentage of Participants in the Intervention Group With an Undetectable HIV Viral Load Compared to the Percentage of Women in the Control Group With an Undetectable Viral Load
• CD4 Cell Count
• Count of Women With a Severe FibroTest Score (3-4) at 9-month Post Baseline
• Liver Stiffness

9. Statistical Interpretation

10. Statistical Methods Explained

The posted comparisons are expressed as risk differences for binary outcomes and a mean difference for CD4 cell count. A risk difference subtracts the outcome proportion in one group from the proportion in the other group, with the comparison direction defined by the registry’s arm labels. For the primary outcome, the estimate is 0.022 for clinician-delivered counseling compared with the computer-based intervention condition. Positive values therefore indicate a higher reported outcome proportion in the counseling condition under that contrast’s direction. This is a descriptive contrast, not a measure of the probability that the intervention caused an outcome.

What does a risk difference describe?

For a binary measure, the risk difference gives the absolute separation between the groups’ observed outcome proportions. It is generally easier to interpret in absolute terms than a relative measure, but it depends on the event definition and the comparison direction. Here, the outcome is tied to the registry’s EtG classification. The reported proportions are 0.949 and 0.971, and the registry gives an estimated risk difference of 0.022. The confidence interval extends from -0.033 to 0.076, so it includes both a difference favoring either group and no difference.

A relative risk or odds ratio expresses the comparison on a different scale. Those measures are also posted for the primary outcome, but they do not replace the absolute risk difference. Relative measures can appear large or small depending on the baseline outcome frequency, while the risk difference directly describes absolute separation. The registry’s comparison also includes an unadjusted p-value of 0.436. A p-value measures how compatible the data are with a specified null model under the test’s assumptions; it is not an estimate of clinical importance, and it is not the probability that the null hypothesis is true.

What does a confidence interval add?

A confidence interval describes the uncertainty around an estimate under the method used to calculate it. A narrower interval indicates greater precision; a wider interval indicates less precision. An interval that includes zero for a difference is compatible with no difference under the interval method, but it can also include nonzero effects. It does not prove that the true effect is zero. The confidence level and the specific interval calculation are not reported in the registry record, so those aspects of the method cannot be verified from the record alone.

How should the CD4 comparison be read?

CD4 cell count is reported as a mean and standard deviation for each group. The mean difference is -29.2, with an interval from -66.82 to 8.416. The negative direction corresponds to the clinician-delivered counseling group having a lower posted mean than the computer-based intervention group under the comparison definition. A mean summarizes the center of a distribution, while the standard deviation describes variation among observations. The registry does not report an adjusted estimate; the posted comparison is an unadjusted summary-based calculation.

In general, a standard deviation describes variation among individual observations, whereas the standard error describes uncertainty in an estimated mean. A confidence interval for a mean difference reflects sampling uncertainty, not the range in which individual participants’ values fall. For binary outcomes, uncertainty also depends on the numbers of observations and the outcome frequencies in each group. The record does not state whether the trial’s own reported analysis used additional covariate adjustment, repeated-measures modeling, or other methods.

Why do analysis details matter?

The primary outcome refers to measurements across follow-up visits, but the registry does not report a detailed model for repeated observations or a separate estimate at each visit. General statistical practice may account for correlations among repeated measurements, since observations from the same participant are not independent. Whether such a method was used in the trial is not reported in the registry record. Likewise, the record does not report a missing-data method, the analysis population, or a prespecified approach for handling deviations from assigned treatment.

The comparisons are unadjusted recalculations from the posted summary statistics. They are useful for describing the summaries and their uncertainty, but they do not establish what the trial’s full analysis plan specified. Because the discrepancy indicator is false, there is no recorded conflict between these calculations and a sponsor analysis. The registry nevertheless lists no reported analyses, so no sponsor-reported adjusted analysis can be used to resolve questions about modeling or analysis populations.

11. Confidence Intervals and Precision

The primary risk-difference estimate is 0.022, with an interval from -0.033 to 0.076. The interval crosses zero, the value representing no difference on this scale. It also spans values in both directions, so the observed data are compatible with a modest advantage for either condition as well as little or no separation. The estimate alone should not be read without this uncertainty.

The interval is not especially narrow relative to the small point estimate. That means the exact size and direction of the difference are not determined precisely by the posted primary summary. The unadjusted p-value of 0.436 is consistent with that uncertainty, but the interval conveys more than a threshold-based decision: it shows a range of differences compatible with the data under the method used. The confidence level and interval method are not reported in the registry record.

Similar caution applies to the secondary comparisons. Their intervals cover a range of plausible differences, including zero. For CD4 cell count, the mean-difference interval runs from -66.82 to 8.416. For the binary secondary outcomes, the risk-difference intervals also include zero. These intervals indicate that the estimates do not precisely distinguish a nonzero group difference from no difference. They do not establish equivalence, which would require a defined acceptable range and a suitable design and analysis.

Precision should also be considered alongside the registry’s reporting gaps. The primary outcome is described across follow-up visits, but separate visit-specific estimates and the handling of missing observations are not reported in the registry record. Those omissions make it harder to assess how the posted precision relates to the full planned outcome and whether uncertainty could be affected by the analysis choices.

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 illustrations give participants per arm for 80% power and participants per arm for 90% power. They are planning calculations based on fractions of the observed effect, not a description of the trial’s original enrollment rationale. 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 set to the stated fraction of the observed effect. The trial’s own original design assumptions are not reported in the registry record.

For example, the binary primary comparison lists 1,297 participants per arm for 80% power when planning for 100% of the observed effect, and 1,736 participants per arm for 90% power at that same effect assumption. Planning for 75% of the observed effect gives 2,454 participants per arm for 80% power and 3,285 participants per arm for 90% power. Planning for 50% of the observed effect gives 5,854 participants per arm for 80% power and 7,836 participants per arm for 90% power. The other outcomes have their own estimates because their observed effects and variability differ.

In plain terms, 80% power means that, under the stated assumptions and if the assumed effect were real, the design would have an 80% chance of detecting it at the specified significance level. The 90% column represents a higher chance of detection under the same conditions and generally requires more participants. Smaller assumed effects need more participants because they are harder to distinguish from random variation. These are planning illustrations, not observed power; observed power is not a useful substitute for reading the estimated effects and their uncertainty.

The calculations for these outcomes are expressed as participants per arm. The general note about total events applies to time-to-event calculations, and no time-to-event outcome is listed among the posted comparisons. These sample-size estimates should not be treated as proof that the completed trial was adequately or inadequately powered: the record does not report its original assumptions or the rationale for enrollment.

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 = 0.949; treatment proportion p₁ = 0.971; average p̄ = 0.960.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.2778 = 0.5446.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.2774 = 0.2335.
4. n = (0.5446 + 0.2335)² / (0.0216)² = 1,296.5, rounded up to 1,297 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 <- 0.948979591836735                          # control proportion
p1 <- 0.970588235294118                          # 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(3362476)
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 = 1,297 (80% power), 1,736 (90% power)
 75% of observed effect: n per arm = 2,454 (80% power), 3,285 (90% power)
 50% of observed effect: n per arm = 5,854 (80% power), 7,836 (90% power)
power.prop.test: 1296.5 
Simulated power at n = 1,297 per arm: 0.810 (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 identifies a primary outcome and several secondary outcomes, with follow-up measurements at specified times. However, multiplicity procedures are not reported in the registry record. It is not stated whether the study adjusted for testing multiple secondary outcomes, multiple follow-up times, or multiple comparisons, or whether it used a prespecified sequence for interpreting results. As a general statistical matter, repeated testing can increase the chance of at least one apparently unusual result even when there is no underlying effect. Without the analysis plan, the intended interpretation of the collection of results cannot be confirmed.

Missing-data handling is also not reported in the registry record. The record does not explain how participants with no urine result or other missing measurements were treated, whether an imputation method was used, or how missing follow-up data affected the denominators. The posted counts and group sizes alone do not establish whether all randomized participants contributed to every outcome. Missingness can affect both the estimate and its precision, especially if the chance of missing a result is related to outcome status or treatment assignment.

The analysis population is not reported in the registry record. It is therefore unclear whether results were analyzed according to randomized assignment, according to treatment received, or under another population definition. The record also does not give a detailed plan for repeated observations across visits, baseline adjustment, or handling of protocol deviations. These details are relevant to judging whether the analysis preserves the advantages of randomization and how closely the posted estimate corresponds to the stated primary question.

The registry lists zero reported analyses. The summary comparisons can be described, but they do not substitute for a full statistical analysis plan or an account of the trial’s primary analysis. The original sample-size assumptions and any interim analyses are not reported in the registry record. Their absence does not demonstrate that such procedures were or were not used; it means their role cannot be evaluated from the registry record.

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 adverse-event record reports zero serious adverse events in the computer-based intervention condition among 98 at risk, and zero in the clinician-delivered counseling condition among 102 at risk. Other adverse events are also recorded as zero in both groups. Deaths are recorded as zero in both groups, and no serious adverse-event terms are listed.

These figures describe what the registry reports, not proof that no adverse events occurred outside the record or that the interventions have no risks. The registry does not provide narrative details about how events were collected, what period the risk window covered, or how event severity and relatedness were assessed. Zero recorded events should therefore be read as a finding of the posted safety data, with those reporting limits in mind. The record does not support broader conclusions about safety beyond the trial’s reported observations.

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

The posted results describe a randomized comparison of a computer-based alcohol-reduction intervention added to clinician-delivered brief MET counseling against clinician-delivered brief MET counseling alone. For the primary EtG-related outcome, the group proportions are close, and the estimated risk difference is 0.022. Its interval includes zero and extends in both directions. The data therefore do not establish a clear difference on the posted primary summary, while also leaving uncertainty about the size and direction of any underlying difference.

This finding does not show that the intervention is ineffective, nor does it establish equivalence between the conditions. The registry does not report an equivalence margin or an analysis designed to demonstrate equivalence. The results also do not answer whether there were differences at any one follow-up visit, since separate primary estimates for the visits are not reported. The primary outcome is a threshold-based EtG classification and should not be taken as a complete measure of alcohol use, health, or transmission risk.

The secondary results concern related but distinct outcomes, and they do not provide a consistent, precise pattern that would resolve the uncertainty in the primary result. Unadjusted secondary comparisons have lower evidential weight when multiple outcomes are considered and no multiplicity strategy is reported. The posted findings should therefore be understood as registry summaries, rather than a comprehensive account of the trial’s full analysis.

In general, statistical uncertainty and clinical importance are different questions. A statistically uncertain estimate may still include effects that matter to patients or care, while a statistically notable result would not by itself establish meaningful benefit. The registry does not report a threshold for clinical importance, and no medical recommendation follows from these summaries. The design and outcome definitions allow a focused comparison of assigned conditions, but the limitations in analysis detail constrain how broadly the results can be interpreted.

17. Why This Trial Matters Statistically

This trial is statistically interesting because it evaluates an intervention added to an active counseling and standard-of-care background. The randomized comparison is therefore about the additional contribution of the computer-based component, not about computer-based support compared with no care. Clear interpretation depends on keeping that contrast in view.

The endpoint also illustrates how outcome definition shapes an analysis. The primary measure uses a urine biomarker threshold and a classification spanning follow-up visits. Such an outcome can be useful for a defined comparison, but it compresses information about timing and degree of exposure. Separate visit estimates and a clear account of how the repeated measurements were combined would help readers understand what aspect of the response the summary represents; those details are not reported in the registry record.

The trial also demonstrates why effect estimates and intervals are more informative than a p-value alone. The primary estimate is small, but its interval includes values on either side of no difference. The result is therefore not a precise estimate of a single effect size. The secondary endpoints broaden the clinical domains examined, yet their unadjusted status and the missing multiplicity information limit how much they can add to the primary comparison.

Finally, the registry record shows the importance of transparent analysis reporting. Information about missing data, analysis populations, repeated-measures methods, multiplicity, and the original sample-size rationale is not reported. Those details do not change the numerical summaries, but they are necessary to judge how the results were produced and how firmly they answer the prespecified clinical question.

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 limitations specific to this trial affect interpretation. The primary endpoint is described as a dichotomous classification across 3-, 6-, and 9-month follow-up, yet the registry presents a single summary and does not report separate estimates by visit. The exact aggregation and coding of the posted counts are not explained in detail. This limits the ability to assess timing or understand how the posted summary corresponds to the endpoint description.

The record reports no masked treatment assignment, and the intervention includes a computer-based component alongside counseling. It does not report whether outcome assessors were masked or how delivery and adherence were monitored. These details are relevant to understanding measurement and implementation, but their absence from the registry record does not establish that particular procedures were or were not used.

The record does not report missing-data methods, analysis populations, a detailed statistical analysis plan, or adjustments for multiple outcomes and timepoints. It also does not provide results for the listed FibroTest and liver-stiffness outcomes among the posted secondary results. Separate reporting of outcome-specific denominators and participant flow would help assess whether the observed summaries represent comparable groups at each assessment.

The intervention was tailored for HIV/HCV-co-infected women who used alcohol in clinical care in Russia. The registry does not provide enough eligibility and setting detail to determine how broadly the findings might apply beyond the study population and care context. The planning illustrations are based on observed effects and do not supply the original design justification. These limitations call for a cautious reading of the posted comparisons rather than a broader claim about effectiveness or generalizability.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Computer-based Intervention for Alcohol-using HIV/HCV+ Women — NCT03362476.
• Registry record last updated 2025-01-09; status last verified 2022-09.
• Results first posted 2025-01-09.
• 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 a completed, randomized, unmasked, parallel-group trial sponsored by New York University, with enrollment of 200. It compared a computer-based alcohol-reduction intervention plus clinician-delivered brief MET counseling with clinician-delivered brief MET counseling and standard of care. The primary measure was the percentage of women testing EtG negative across 3-, 6-, and 9-month post-baseline follow-up.

The posted primary comparison has a risk difference of 0.022, an interval from -0.033 to 0.076, and an unadjusted p-value of 0.436. The interval includes no difference, leaving the magnitude and direction uncertain. Posted secondary comparisons also have intervals that include no difference. Details of missing-data handling, analysis populations, multiplicity procedures, and the original sample-size rationale are not reported in the registry record.