Impact of E-cigarette Characteristics on Reinforcement and Tobacco Use Patterns: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Impact of E-cigarette Characteristics on Reinforcement and Tobacco Use Patterns (NCT03844672), a randomized na study. The primary outcome is Mean Cigarettes Per Day.

2. Clinical Question

The study was designed to examine how e-cigarette characteristics relate to their likeability and use among current smokers who tried e-cigarettes. Participants were to complete daily electronic diaries and weekly laboratory visits during the three-week sampling period. The primary outcome captures reported cigarette consumption at the end of that period. The registry record also lists average e-cigarette puffing episodes and choices to smoke a cigarette in a laboratory setting as secondary outcomes.

The central statistical question is about the pattern of group means and the precision of the comparisons, not whether a particular device or liquid should be used. The posted arm summaries describe the observed week-3 outcomes, while the comparisons quantify differences from the designated reference group. The registry record does not report a separate formal test of the factorial effects of concentration and power or of their interaction.

3. Trial Design

The registry describes a randomized, double-masked, factorial study sponsored by the Medical University of South Carolina. Its title is “Impact of E-cigarette Characteristics on Reinforcement and Tobacco Use Patterns.” The record lists the phase as NA and the study status as completed. Enrollment is reported as 93. The recorded start date is 2019-04-01 and the primary completion date is 2023-10-17.

The factorial treatment structure combined two nicotine-concentration levels with two device-power levels. Participants were assigned to low concentration/low power, low concentration/high power, high concentration/low power, or high concentration/high power. Each arm description specifies that the participant received the assigned e-liquid and e-cigarette for three weeks. The brief summary describes daily electronic diaries and weekly laboratory visits during that interval.

For the primary endpoint, the registry posts separate week-3 summaries for the four combinations. The arm counts are 18, 21, 17, and 17, respectively, in the order. These endpoint counts need not be assumed to represent every analysis population: the registry record does not explain the relationship between enrollment and the counts available for each outcome, or identify an analysis population such as intention-to-treat or per-protocol.

The registry record does not report the original sample-size justification, the detailed randomization procedure, how masking was implemented, the planned statistical model, or whether interim analyses were planned. It also does not post a reported analysis for either secondary outcome. Those gaps limit what can be inferred about the prespecified analysis strategy; the randomized and masked design labels alone do not establish how the comparisons were carried out.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

5. Baseline Characteristics

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

6. Primary Endpoint

The registry lists the primary outcome as Mean Cigarettes Per Day, measured over Week 3.

The primary endpoint is mean cigarettes per day at week 3, reported as a continuous measure with a standard deviation. Participants completed weekly surveys reporting the number of cigarettes smoked each day on a continuous scale. The endpoint therefore summarizes cigarette consumption during the final reported week of the three-week e-cigarette sampling period.

A mean gives a group-level measure of typical reported cigarette consumption, while the standard deviation describes how much individual observations vary around that mean. Considering both is useful: groups can have similar averages while individual responses vary substantially. The endpoint makes it possible to describe and compare cigarette consumption across the four combinations of nicotine concentration and device power.

In the registry record, the low-concentration/low-power arm is the reference for the posted comparisons. Each comparison is expressed as a mean difference between another combination and that reference, with a confidence interval. This contrast addresses differences between particular combinations; it does not, by itself, isolate an overall concentration effect, an overall power effect, or an interaction between those factors. The registry record does not report a specific rationale for selecting this endpoint as primary or define a clinically important difference in cigarettes per day.

7. Results Posted in the Registry

• Low Concentration / High Power: mean 12.60 versus 13.10 on control; difference -0.50 (95% CI -5.50 to 4.50), standardized effect d = -0.06; z-test p = 0.845.
• High Concentration / Low Power: mean 12.20 versus 13.10 on control; difference -0.90 (95% CI -6.90 to 5.10), standardized effect d = -0.10; z-test p = 0.769.
• High Concentration / High Power: mean 13.30 versus 13.10 on control; difference 0.20 (95% CI -5.96 to 6.36), standardized effect d = 0.02; z-test p = 0.949.

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.

Average Number of E-cigarette Puffing Episodes

• 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:
  • Low Concentration / High Power: mean 6.50 versus 4.80 on control; difference 1.70 (95% CI -3.51 to 6.91), standardized effect d = 0.20; z-test p = 0.523.
  • High Concentration / Low Power: mean 5.40 versus 4.80 on control; difference 0.60 (95% CI -2.69 to 3.89), standardized effect d = 0.12; z-test p = 0.720.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Low Concentration / High Power and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between High Concentration / Low Power and the control. The interval gives the range of effects that remain compatible with the data.

Number of Choices to Smoke Cigarette in Lab Setting

• 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 errors.
• Result:
  • Low Concentration / High Power: mean 2.40 versus 1.70 on control; difference 0.70 (95% CI -0.41 to 1.81), standardized effect d = 0.40; z-test p = 0.216.
  • High Concentration / Low Power: mean 1.80 versus 1.70 on control; difference 0.10 (95% CI -1.09 to 1.29), standardized effect d = 0.06; z-test p = 0.869.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Low Concentration / High Power and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between High Concentration / Low Power and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes add two different perspectives on the same three-week sampling period: average e-cigarette puffing episodes and choices to smoke a cigarette in a laboratory setting. Their posted arm summaries show that the relative ordering of the groups is not the same across both outcomes. This variation cautions against treating any single outcome as a complete account of tobacco or e-cigarette use patterns.

Across the secondary comparisons, the intervals are broad and include zero, and the posted p-values are unadjusted. The results therefore do not provide clear, precise pairwise evidence of differences against the reference arm. The registry record lists no reported analyses for either secondary outcome. The arm summaries and recalculated comparisons are not equivalent to a reported analysis under a specified model or a prespecified testing plan.

The secondary pattern does not overturn or independently confirm the primary result. The primary endpoint measures cigarettes per day, whereas puffing episodes and laboratory choices capture other behaviors and settings. Outcomes can differ because they measure different aspects of behavior; agreement is not required, and disagreement does not by itself identify an error. The registry record does not report a joint analysis linking the secondary measures to the primary endpoint.

Unadjusted secondary results carry less weight because several outcomes and contrasts create more opportunities for chance findings. General statistical context: when many hypotheses are examined, a strategy for controlling or interpreting multiplicity can help prevent overemphasis on an isolated result. The registry record does not report such a strategy. The cautious reading is therefore to consider the secondary findings exploratory descriptions, not independent confirmation of a treatment effect or definitive evidence of no difference.

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

• Average Number of E-cigarette Puffing Episodes
• Number of Choices to Smoke Cigarette in Lab Setting

9. Statistical Interpretation

10. Statistical Methods Explained

The registry posts continuous outcomes as means with a measure of spread, and the primary outcome is reported with standard deviations. The comparisons are unadjusted mean differences against the low-concentration/low-power arm. The following statistical explanations are general; they clarify how to read these summaries but do not establish which analysis model the trial originally planned or used.

What does a mean difference represent?

A mean difference is the mean in one arm minus the mean in the reference arm. A negative estimate means the posted mean in the compared arm is lower; a positive estimate means it is higher. For example, the primary comparison for low concentration/high power is reported as -0.5, with means of 12.6 and 13.1 in the compared and reference arms. The estimate is an absolute difference in the outcome’s units, cigarettes per day. It is not a percentage change or a standardized effect.

These are pairwise contrasts. In a factorial design, it can also be useful to estimate an average effect of concentration across power levels, an average effect of power across concentration levels, and an interaction that asks whether the effect of one factor varies according to the level of the other. The registry record does not report those estimates or a factorial model. Pairwise comparisons alone cannot substitute for those analyses.

What do the standard deviations describe?

A standard deviation describes the dispersion of individual observations around a group mean. It is not the same as the standard error, which describes uncertainty in an estimated mean. Larger standard deviations indicate more variability among observed values. For the primary endpoint, the posted standard deviations range from 6.0 to 9.3; this variability is relevant when interpreting group means and the precision of their differences.

General statistical context: if an outcome has a skewed distribution, a small number of unusually high observations can affect the mean and standard deviation. The registry record does not report the outcome distributions, checks of model assumptions, or any alternative summaries, so the effect of distribution shape cannot be assessed from the posted summaries alone.

How should the confidence intervals and p-values be read?

A confidence interval describes a range of estimates compatible with the observed data under the analysis assumptions and method used. It communicates uncertainty and direction as well as the central estimate. For a difference, an interval spanning zero includes the possibility of no difference. The primary confidence intervals all span zero and are broad, so they do not tightly determine the size or direction of the differences.

A p-value measures how unusual data at least as incompatible with a specified null hypothesis would be under that hypothesis and the test assumptions. It is not the probability that the null hypothesis is true, nor does it measure clinical importance. The posted values are labelled unadjusted. The registry record does not explain the statistical test, its assumptions, or any multiplicity strategy, so the values should be interpreted as posted rather than as a complete account of the trial’s analysis plan.

Why does variability affect precision?

General statistical context: estimates become less precise when the outcome varies greatly within groups relative to the size of the groups. Greater variation makes the group means less certain; larger samples can generally reduce that uncertainty when other conditions are comparable. In this trial, the outcome counts per arm are modest in the posted primary summaries, and the standard deviations are sizeable relative to the estimated differences. The confidence intervals reflect that uncertainty.

Over-dispersion is a general concern when data vary more than a chosen statistical model assumes. If a model underestimates variability, its standard errors may be too small and its intervals too narrow. The primary outcome is reported as a continuous measure, and the registry record does not report a fitted model, residual diagnostics, or an over-dispersion assessment. It would therefore be inappropriate to claim that over-dispersion was present or that a particular correction was used.

What is not established by the posted analysis?

The record does not report adjusted analyses, a model specification, a missing-data method, or an analysis population. It also does not report secondary-outcome analyses, despite posting arm summaries and recalculated comparisons for those outcomes. In general, adjusted models may account for design features or prespecified covariates, and explicit missing-data methods can clarify how incomplete outcomes were handled. Whether either was part of the original study analysis is not reported in the registry record. The posted summary comparisons should not be mistaken for a complete analysis protocol.

11. Confidence Intervals and Precision

The primary mean-difference intervals are -5.504 to 4.504 for low concentration/high power versus the reference, -6.896 to 5.096 for high concentration/low power, and -5.965 to 6.365 for high concentration/high power. Every interval includes zero. Thus, each comparison is compatible with no difference, but also with potentially meaningful differences in either direction. The intervals are wide relative to the estimates, so the point estimates alone give an incomplete picture of uncertainty.

A confidence interval is not a guarantee that the true difference lies within its limits. General statistical interpretation is conditional on the sampling and analysis assumptions behind the interval. The registry record does not report enough detail about the analysis method or assumptions to independently evaluate their validity. The intervals are presented as posted unadjusted comparisons, and they should be read in that context.

The arm-level standard deviations are 9.3, 6.0, 8.8, and 9.3. They indicate considerable individual variability in cigarettes per day within each group. With such variability and the posted arm counts, estimates can remain uncertain even when the observed means are close. The intervals therefore do not establish equivalence; doing so would require a prespecified equivalence margin and an analysis designed to assess it, neither of which is reported in the registry record.

Precision is distinct from statistical significance and from practical importance. A narrow interval can provide a precise estimate that may still be unimportant, while a broad interval may leave both small and larger effects plausible. The registry record does not define a clinically important difference for this endpoint. Accordingly, the intervals characterize the uncertainty in the posted mean differences but do not decide whether any particular difference would matter clinically or behaviorally.

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 figures are planning illustrations based on the observed effects, not the original trial’s sample-size justification. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two compared arms, and a normal approximation. Each planning row considers detecting the full observed effect or a stated fraction of that effect. The registry record does not report the original design assumptions or justification.

For these continuous-outcome comparisons, the figures mean participants per arm, not total events. The 80% power column gives the number of participants per arm estimated to yield an 80% chance of detecting the specified effect under the listed assumptions if that effect is the true one. The 90% power column gives the corresponding planning number for a 90% chance. Higher target power generally requires more participants for the same assumed effect.

Rows planning for 75% or 50% of the observed effect use smaller target effects than the row based on 100% of the observed effect. Smaller effects are harder to distinguish from random variation, so they require more participants under these assumptions. The resulting values can be very large, particularly when the observed contrast is small relative to variability. They illustrate how sensitive sample-size requirements are to the effect size chosen for planning.

These calculations are not observed power. They do not describe the probability that the completed study’s result is true, and they should not be used to convert a non-significant result into evidence for no effect. They are conditional planning illustrations, not replacements for a prespecified design calculation using an independently justified target effect.

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, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -0.500; pooled standard deviation σ = 7.694.
2. n = 2 × (1.96 + 0.842)² × 7.694² / -0.500² = 3,717.1, rounded up to 3,718 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
delta <- -0.5                      # observed difference in means
sigma <- 7.6940306802162                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^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 (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(3844672)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 3,718 (80% power), 4,977 (90% power)
 75% of observed effect: n per arm = 6,609 (80% power), 8,847 (90% power)
 50% of observed effect: n per arm = 14,869 (80% power), 19,905 (90% power)
power.t.test: 3718.1 
Simulated power at n = 3,718 per arm: 0.805 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-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 record posts three pairwise comparisons for the primary endpoint, each using the low-concentration/low-power arm as the reference. It also lists two secondary outcomes and posts pairwise comparisons for them. The associated p-values are unadjusted. A familywise error procedure, false-discovery approach, hierarchical testing plan, or other multiplicity strategy is not reported in the registry record. Without that information, the p-values should not be interpreted as if a particular adjustment had been applied.

The record lists three outcome measures and zero reported analyses in its analysis count, while also posting summary results and recalculated pairwise contrasts. No reported secondary analyses are listed. The registry record does not explain this difference in reporting or identify which analyses were prespecified. It also does not state whether outcomes or contrasts were selected after the data were reviewed. These omissions limit assessment of selective reporting and the intended inferential priority among endpoints.

Missing-data details are not reported in the registry record. In particular, the reasons that primary-outcome counts differ across the posted arms, the amount and pattern of missing outcome data, and the method used to handle missingness are not stated. The registry record also does not identify an analysis population, such as all randomized participants or participants with complete week-3 observations. Enrollment is reported as 93, but the record does not explain how that total relates to the endpoint-specific counts.

General statistical context: missing outcomes can affect estimates if the participants without measurements differ systematically from those with measurements. The direction and extent of any such effect cannot be established from the information recorded. Similarly, randomization protects comparisons at assignment, but it does not by itself resolve bias from incomplete follow-up or post-randomization exclusions. Details of interim analyses, protocol deviations, and sensitivity analyses are not reported in 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 5% reporting threshold.

Most frequent non-serious adverse event terms

The registry reports serious adverse event counts of zero in each of the four groups. The number at risk for serious adverse events is listed as 23, 24, 23, and 23 across the low-concentration/low-power, low-concentration/high-power, high-concentration/low-power, and high-concentration/high-power groups, respectively. Deaths are reported as zero in every group. No serious adverse event terms are listed.

These figures describe the counts recorded in the registry; they do not establish that no other adverse experiences occurred. For other adverse events, the record lists 12, 13, 12, and 11 participants affected across the same four groups, with the corresponding at-risk counts. The record does not provide event descriptions or additional detail about severity, timing, or attribution in the registry record reported for these groups.

Safety counts should be read alongside the study’s limited enrollment and three-week e-cigarette sampling period. The figures are descriptive, and the registry record does not report a comparative safety analysis. Zero recorded serious events should not be interpreted as proof that serious events cannot occur, while the other-event counts alone do not establish a difference among groups.

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

The posted primary endpoint describes cigarettes smoked per day at week 3 among participants assigned to four combinations of nicotine concentration and device power. The group means are relatively close, and the unadjusted pairwise estimates are small in relation to the uncertainty shown by their confidence intervals. This is a description of the observed summaries, not a conclusion that the treatment combinations are identical or that one is superior.

The intervals leave a range of possible differences in both directions. Because they are broad and include zero, the results do not provide precise evidence about the magnitude of pairwise differences. The registry record does not report an equivalence or non-inferiority design, a clinically important threshold, or a formal factorial analysis. Those omissions matter: similarity of point estimates is not equivalent to proof of equivalence, and pairwise comparisons do not establish separate concentration and power effects.

The study’s aim was to understand how different e-cigarette characteristics influence likeability and use among current smokers trying e-cigarettes. The posted primary measure concerns cigarette consumption, while the secondary outcomes address puffing episodes and laboratory choices. Together, these outcomes offer distinct descriptions, but the record does not report analyses linking them or a comprehensive model of tobacco-use patterns.

The randomized, double-masked labels describe important design features, but interpretation also depends on the analysis population, missing-data handling, model, and multiplicity plan. These are not reported in the registry record. The available summaries therefore support cautious description of the observed groups and uncertainty, not a broad causal claim about e-cigarettes, a prediction for people outside the study, or medical advice. The findings are limited to the outcomes, arms, and follow-up period recorded.

17. Why This Trial Matters Statistically

This trial illustrates a common statistical challenge in factorial studies: the treatment structure has more than one characteristic, but the posted result is organized as comparisons among combinations. That makes it important to distinguish pairwise arm contrasts from estimated main effects and interactions. Without a reported factorial model, the comparisons cannot answer whether concentration or power has an overall effect across the other factor, or whether the factors modify one another.

It also demonstrates why point estimates and uncertainty need to be read together. The primary group means appear close, but the confidence intervals span a range of differences in both directions. A non-significant unadjusted p-value is not proof of no effect; broad intervals can reflect limited precision. Conversely, an observed difference is not automatically important simply because it is numerically nonzero. The registry record does not supply a threshold for practical importance.

The gap between total enrollment and endpoint-specific arm counts, without an explanation of analysis populations or missingness, highlights how reporting completeness affects interpretation. Readers need to know who contributed outcome data and how incomplete observations were handled to assess the reach of the estimates.

Finally, the secondary outcomes and multiple pairwise comparisons show why an analysis plan matters. Unadjusted results across several outcomes should be interpreted with caution, especially when no multiplicity strategy or reported secondary analysis is documented. These are general lessons in reading clinical trial statistics, rather than evidence that a particular unreported analysis was performed.

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 registry record leaves several trial-specific uncertainties. It does not describe participant eligibility in detail or report baseline characteristics, so the extent to which the enrolled sample represents a broader population of current smokers cannot be evaluated. Although the brief summary describes a three-week sampling period with daily diaries and weekly laboratory visits, adherence and completeness of those measurements are not reported.

The posted primary-outcome counts are 18, 21, 17, and 17, while overall enrollment is 93. The record does not explain the difference, identify an analysis population, or describe missing observations, exclusions, or the handling of incomplete data. These details are needed to understand which participants inform the estimates and whether the comparisons may be affected by follow-up or selection.

The registry record does not report the original sample-size justification, the planned statistical model, the randomization procedure, masking implementation, or a prespecified multiplicity strategy. It also does not report formal analyses for the secondary outcomes, although summary values and recalculated comparisons are posted. The factorial structure makes the absence of reported main-effect and interaction estimates especially relevant to interpretation.

The outcome is self-reported cigarette consumption, and the record describes diary and survey collection, but it does not report validation or measurement-quality information. Follow-up for the primary measure is at week 3; longer-term results are not reported in the registry record. Thus, the posted results cannot establish whether the observed patterns persisted after the sampling period. The record also does not define a clinically meaningful difference, limiting conclusions about practical importance.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Impact of E-cigarette Characteristics on Reinforcement and Tobacco Use Patterns — NCT03844672.
• Registry record last updated 2024-10-24; status last verified 2024-10.
• Results first posted 2024-10-24.
• 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

NCT03844672 is a completed, randomized, double-masked factorial study sponsored by the Medical University of South Carolina, with enrollment reported as 93. It compared e-cigarette combinations of low or high nicotine concentration and low or high device power over three weeks among current smokers trying e-cigarettes.

The primary outcome was mean cigarettes per day at week 3. Posted pairwise mean differences versus the low-concentration/low-power group had unadjusted confidence intervals that included zero and were broad. The record also lists average e-cigarette puffing episodes and laboratory choices to smoke a cigarette as secondary outcomes; reported analyses for these outcomes are not listed.

The registry record does not report the original sample-size justification, analysis model, analysis populations, missing-data methods, or multiplicity plan. These gaps, together with the uncertainty in the posted estimates, support a cautious, descriptive interpretation.