Smoking Cessation E-Visit: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Smoking Cessation E-Visit (NCT04316260), a randomized na study. The primary outcome is Number of Participants Who Utilized Smoking Cessation Treatment.

2. Clinical Question

The stated hypothesis was that participants assigned to the e-visit would have higher treatment utilization and superior cessation-related outcomes than participants assigned to treatment as usual. That is a hypothesis, not a conclusion established by the result. The posted primary comparison concerns treatment utilization, not a direct measure of abstinence or whether a quit attempt occurred. The registry record describes the trial as feasibility testing intended to provide effect-size estimates for a larger randomized trial. It therefore offers an early estimate of a between-arm difference in reported treatment use, alongside a record of how many participants contributed to this outcome. It does not establish how the approach would perform in a larger or different setting.

3. Trial Design

The registry describes a randomized, parallel-group trial with no masking. It lists the study as a feasibility randomized controlled trial of a smoking-cessation e-visit compared with treatment as usual, delivered via primary care. Enrollment was 51. The listed status is completed; the study start was 2019-05-01 and primary completion was 2019-10-31. The registry lists the phase as not applicable.

In the e-visit arm, participants were sent an invitation to complete an electronic visit focused on cigarette smoking and an invitation for a follow-up e-visit one-month after the initial visit. In the treatment-as-usual arm, participants received information about the state quitline and the importance of quitting, and were advised to contact their primary-care provider to schedule a medical visit to discuss quitting. Because masking was listed as none, the record does not describe the trial as masked. The details of how randomization was generated or implemented are not reported in the registry record.

The brief study description frames the primary objective as obtaining feasibility information and effect-size estimates for a larger trial. Outcomes were described across feasibility, acceptability, satisfaction, cessation-treatment utilization, and cessation-related outcomes, including quit-attempt incidence and abstinence. The listed secondary outcomes include e-visit completion, provider fidelity, ease of use, and time to complete the e-visit. The registry lists 16 outcome measures, while four secondary outcomes are posted in the record.

The primary posted result is the number of participants who utilized smoking-cessation treatment since the last assessment, with a Month 1 timeframe. The outcome-specific analyzed groups are 28 in the e-visit arm and 17 in the treatment-as-usual arm. The registry explicitly notes that the number analyzed differs from each group’s total because not everyone completed the questionnaire assessing this information. The record does not give a complete account of participant flow or the reasons for missing questionnaires.

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

Analysis population for the primary outcome, as defined in the registry: The number of participants analyzed differs from the total participants in each group because not everyone completed the questionnaire that assessed this information.

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 Number of Participants Who Utilized Smoking Cessation Treatment, measured over Month 1.

The primary endpoint counts participants who reported using smoking-cessation treatment since the last assessment. Its timeframe is Month 1. The registry describes the measure as a count of participants, rather than a continuous score or a time-to-event measure. It does not specify in the posted primary result which individual treatments qualified beyond the general description of smoking-cessation treatment.

This outcome is relevant to the trial’s stated aim because the study description focuses on whether the e-visit is feasible and whether participants use evidence-based cessation treatments, including medications and counseling. Treatment utilization is also distinct from a cessation outcome: a participant may use treatment without the endpoint itself establishing whether they attempted to quit or achieved abstinence. The registry lists quit-attempt incidence and abstinence among the broader cessation-related outcomes, but the posted primary result is utilization.

Counts are useful for stating how many observed participants met the endpoint in each analyzed arm. By themselves, they do not account for differences in the number assessed in each arm. The comparison accompanying the counts expresses the between-arm result, while the outcome-specific group sizes show the amount of observed information behind it. In this trial, missing questionnaire completion matters because the primary endpoint depends on that questionnaire. The registry record does not report the missingness pattern in enough detail to determine whether questionnaire completion differed according to participants’ treatment use.

7. Results Posted in the Registry

• Smoking Cessation Electronic Visit (E-visit): 53.6% (15/28) versus 17.6% (3/17) on control; risk difference 35.9 percentage points (95% CI 10.0 to 61.8), risk ratio 3.04 (1.03–8.97); unadjusted two-proportion z-test p = 0.017.

8. Statistical Interpretation

9. Statistical Methods Explained

What does a count endpoint represent?

The primary endpoint is a count of participants who utilized smoking-cessation treatment since the last assessment. A count answers how many assessed participants met the endpoint. For an arm-to-arm comparison, the count is interpreted alongside the number assessed in that arm. Here the posted result gives both: 15 of 28 analyzed participants in the e-visit arm and 3 of 17 in the treatment-as-usual arm. The analysis population for this outcome is therefore not identical to the full enrolled population.

How should the relative measures be read?

The registry comparison reports a risk ratio and an odds ratio. General statistical explanation: a risk ratio compares the proportions experiencing an event in two groups, whereas an odds ratio compares the odds of the event. A value of 1 represents no relative difference. Values above 1 indicate a larger observed event proportion or odds in the intervention arm under the stated comparison direction. These measures describe the observed contrast; they do not by themselves identify its cause or establish its practical importance. Because the outcome is common enough that odds and proportions need not track one another closely, the odds ratio should not be read as if it were the risk ratio.

The posted risk ratio is 3.036, with a 95% confidence interval from 1.028 to 8.968. The odds ratio is 5.385, with a 95% confidence interval from 1.261 to 22.99. These estimates are based on the posted outcome counts. Their broad intervals indicate that the plausible relative contrast is not pinned down precisely. The registry record’s discrepancy indicator is false, so there is no recorded discrepancy that would require favoring a sponsor analysis over a recalculation.

What does the p-value tell the reader?

The unadjusted p-value is 0.017. General statistical explanation: a p-value assesses how incompatible the observed data are with a specified null model, assuming that model and its analysis assumptions apply. It is not the probability that there is no effect, nor the probability that chance alone produced the data. It does not measure the size or importance of a difference. “Unadjusted” indicates that the posted value is not presented as corrected for multiple comparisons. The record does not report a multiplicity adjustment or a complete statistical analysis plan, so the p-value should be read as the reported unadjusted result rather than as a family-wise conclusion across all outcomes.

Why does missing outcome information matter?

The registry states that the number analyzed differs from the total in each group because not everyone completed the questionnaire that assessed treatment use. General statistical explanation: when outcome data are missing, the observed participants may not fully represent all randomized participants. If missingness is related to treatment use or differs between arms, the observed comparison can be affected. That possibility does not show that bias occurred; the missingness reasons and patterns needed to evaluate it are not reported in the registry record. The record also does not report whether any imputation or other missing-data method was used.

What can randomization and lack of masking contribute?

General statistical explanation: random assignment is intended to make comparison groups similar on average at the start of a trial, reducing systematic differences in measured and unmeasured factors. It does not guarantee balance in a particular small trial, and it cannot resolve problems caused by missing outcome data. The registry identifies the allocation as randomized and the model as parallel, but the sequence-generation and concealment methods are not reported in the registry record. Masking is listed as none. In a behavioral intervention, participants and providers may know the assigned approach; the record does not detail which individuals knew allocation or how knowledge might have influenced questionnaire responses or treatment use.

What is not specified about the analysis?

The registry record does not report the exact statistical test or model used for the primary comparison, the method used to form the confidence intervals, or a prespecified primary analysis population beyond the endpoint-specific counts. It also does not report an interim analysis plan or a full analysis plan. Those details should not be inferred from the posted measures. The broad methods terms above explain how to read the reported quantities, not the unreported analysis procedures used by the investigators.

10. Confidence Intervals and Precision

The relative estimates are accompanied by broad confidence intervals: the risk ratio is 3.036, with a 95% confidence interval from 1.028 to 8.968, and the odds ratio is 5.385, with a 95% confidence interval from 1.261 to 22.99. Each interval gives a range of values compatible with the observed data under the interval method and its assumptions. General statistical explanation: a confidence interval is not a guarantee that the true value lies within the particular interval, nor does it assign a probability to each value inside it. Its width conveys how precisely the estimate is determined by the data and method.

Both relative intervals exclude 1, the value corresponding to no relative difference, but the intervals extend from values near 1 to much larger values. This means the posted data are compatible with a wide range of relative contrasts. The broad range is important: the point estimates alone can make the contrast seem more settled than the uncertainty supports. The outcome-specific analyzed groups were 28 and 17, and the registry notes that some participants did not complete the questionnaire. The limited amount of observed endpoint information and missing questionnaires are relevant to how much precision the result can offer.

The registry also provides a risk-difference estimate and interval, but their posted bounds use more than three decimal places. Those values are not repeated. The interval, rather than a single point estimate, is the more informative guide to uncertainty. Precision is not the same as validity: a narrow interval would not remove potential bias from missing data or other design limitations, and a broad interval does not prove there is no difference. The record does not report an adjusted interval or a multiplicity correction for this result.

11. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size figures are planning illustrations based on the observed effect, not the original design assumptions for this feasibility trial. The registry record does not report the trial’s own original sample-size justification. The planning calculation assumes a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect to detect set to the stated fraction of the observed effect. These assumptions describe the illustration, not necessarily the trial’s conduct or a future study’s design.

Each displayed figure is the number of participants per arm, not a total across arms or a total number of events. The 80% power column gives the planned participants per arm for a calculation that would have an 80% chance of detecting the specified effect under the stated assumptions, if that effect and those assumptions applied. The 90% power column gives the corresponding per-arm planning figure for a 90% chance. Power is a property of a planned design under assumptions; it is not a measure of the probability that the observed result is true.

The rows use 100%, 75%, and 50% of the observed effect as planning targets. The displayed per-arm values are 27 and 36 for planning at 100% of the observed effect; 46 and 60 for planning at 75%; and 94 and 126 for planning at 50%, with the 80% and 90% columns, respectively. Smaller effects require more participants to reach the same planned power under otherwise unchanged assumptions. These are illustrations, not observed power and not a recommendation or a guarantee that a future study will achieve its target.

The observed effect in a feasibility trial can be imprecise, so planning directly from it may give an unstable estimate of the effect a future study should target. The registry record does not report the original design assumptions for this trial; the assumptions above belong to the posted planning illustration only.

12. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.176; treatment proportion p₁ = 0.536; average p̄ = 0.356.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6772 = 1.3273.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6277 = 0.5283.
4. n = (1.3273 + 0.5283)² / (0.3592)² = 26.7, rounded up to 27 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 <- 0.176470588235294                          # control proportion
p1 <- 0.535714285714286                          # 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(4316260)
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 = 27 (80% power), 36 (90% power)
 75% of observed effect: n per arm = 46 (80% power), 60 (90% power)
 50% of observed effect: n per arm = 94 (80% power), 126 (90% power)
power.prop.test: 26.7 
Simulated power at n = 27 per arm: 0.812 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

13. Multiplicity, Missing Data and Analysis Populations

The record lists 16 outcome measures and reports four secondary outcomes. The study description discusses several outcome domains, including feasibility, acceptability, satisfaction, cessation-treatment utilization, and cessation-related outcomes. Considering multiple outcomes can increase the chance that at least one result appears noteworthy even when there is no true effect, particularly if results are interpreted without accounting for the full set of comparisons. General statistical explanation: multiplicity methods can adjust testing or interval interpretation when a family of outcomes is considered together.

The primary comparison is reported with an unadjusted p-value of 0.017. The record does not report a multiplicity adjustment, a hierarchy of outcome testing, or enough detail to establish how the set of outcomes was handled analytically. It lists zero analyses in the corresponding registry summary, but that does not substitute for a detailed analysis plan. The original statistical plan, including any prespecified approach to multiplicity, is not reported in the registry record. The unadjusted result should therefore not be presented as if it accounts for all outcomes.

Missing primary-outcome information is explicitly noted: not everyone completed the questionnaire assessing treatment utilization, and the analyzed number differs from the total in each arm. The record does not give the reasons for noncompletion, participant-level missingness patterns, or an analysis comparing participants with and without outcome data. It also does not report whether missing outcomes were imputed, whether sensitivity analyses were conducted, or how missingness was handled in the primary analysis. General statistical explanation: conclusions from a complete-case comparison can be sensitive to the relationship between missingness and the outcome, so the extent and handling of missing data are important to assess.

The endpoint-specific analyzed groups are reported, but a full analysis-population definition is not. The registry record does not state whether the primary comparison followed an intention-to-treat approach, how protocol deviations were handled, or whether additional populations were analyzed. The posted counts should be understood as results among the participants with outcome information used for this measure; a broader population interpretation requires analysis details not reported in the registry record.

14. 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 adverse-event record lists serious adverse events for 34 participants at risk in the e-visit group and 17 at risk in treatment as usual. The serious adverse event count was 0 in each group. The record lists other adverse events for 34 at risk in the e-visit group and 17 at risk in treatment as usual; the counts affected were 4 and 3, respectively. Deaths were recorded as 0 in both groups. No serious adverse event terms are listed.

These safety figures use the adverse-event groups and at-risk counts reported in the registry, which differ from the analyzed group sizes for the primary treatment-utilization endpoint. They should not be substituted for the primary outcome denominators. General statistical explanation: a count of zero observed serious events means none were recorded in the listed at-risk group during the reported period; it does not establish that an event is impossible or that the intervention has no safety concerns. The registry record does not provide event descriptions for the other adverse events or enough detail to assess their severity, timing, or relationship to study participation.

The figures are descriptive. They do not establish that either study approach caused or prevented an adverse event, and they do not support a comparative safety conclusion beyond reporting the observed counts.

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

The posted primary result shows that 15 participants in the e-visit arm and 3 in treatment as usual were recorded as having used smoking-cessation treatment since the last assessment. The analyzed group sizes were 28 and 17, and the registry notes that questionnaire noncompletion reduced the number assessed for this endpoint. The reported unadjusted relative comparisons favor a higher observed rate of treatment utilization in the e-visit arm within this analyzed sample. The confidence intervals are broad, so the magnitude of the contrast remains uncertain.

This finding is about reported treatment use at Month 1. It is not itself evidence that participants quit smoking, maintained abstinence, made a quit attempt, or experienced a particular health outcome. The registry describes those cessation-related domains as part of the broader study, but the primary result does not answer those questions. Nor does treatment utilization establish that the treatment was completed or effective for an individual participant.

The result also does not establish that the e-visit caused the difference. Randomization is an important design feature, but interpretation is limited by incomplete questionnaire data, the lack of masking, and the limited scale of a feasibility study. The record does not report enough detail to evaluate missing-data handling, all analysis-population choices, or multiplicity procedures. The unadjusted p-value should be interpreted alongside those limitations and the broad confidence intervals, not as a standalone verdict.

The trial description states that the study was intended to provide preliminary feasibility information and effect-size estimates for a larger randomized trial. The observed contrast may therefore be useful as an estimate to consider in future research, while remaining too uncertain to treat as a definitive measure of the difference that would occur in other populations or settings. The result neither establishes general effectiveness nor answers whether the e-visit is acceptable, easy to use, or feasible in routine practice. Those questions require their own outcome results and appropriate interpretation.

16. Why This Trial Matters Statistically

This trial is statistically informative because it illustrates the distinction between a feasibility study’s purpose and a definitive estimate of treatment effect. The registry describes the primary objective as providing effect-size estimates for a larger randomized trial. The posted utilization counts and relative estimates can inform what a future study might investigate, but the broad confidence intervals show why a point estimate alone is a weak basis for assuming the size of a future effect.

It also makes the analysis population visible. Enrollment was 51, while the primary outcome was analyzed among 28 participants in the e-visit arm and 17 in treatment as usual. The registry explains that not everyone completed the questionnaire. This difference matters statistically because the denominator used to interpret a count is outcome-specific, and incomplete outcome data can affect both precision and the validity of a comparison. The record does not supply enough information to determine whether missingness was related to treatment use.

The trial has multiple outcome domains, while the posted primary p-value is unadjusted. This highlights why readers should distinguish a single reported comparison from a prespecified strategy for interpreting a set of outcomes. The registry record does not describe such a strategy. Likewise, a randomized design helps structure comparison but does not make uncertainty, missingness, or multiplicity disappear.

Finally, the planning illustration translates the observed effect into possible per-arm sample sizes under stated assumptions and smaller-effect scenarios. It is useful as a planning exercise, not as an account of observed power or proof that a particular future sample size is adequate. Together, these features make the record a useful example of reading early trial estimates with attention to denominators, uncertainty, and the purpose of the study.

17. 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 primary endpoint depends on completion of a questionnaire, and the registry states that some participants did not complete it. The reasons for missingness and its distribution across outcome status are not reported in the registry record. This leaves uncertainty about whether the assessed participants represent all randomized participants for treatment utilization. No missing-data method or sensitivity analysis is described in the posted record.

The trial is described as feasibility testing, and the outcome-specific analyzed groups are limited in size. The relative estimates have broad confidence intervals, so the observed contrast is not estimated precisely. The registry describes the primary objective as generating effect-size estimates for a larger trial; this context argues against treating the point estimate as a settled value for future studies.

Masking is listed as none. The record does not specify who knew assignment during outcome assessment or whether knowledge of allocation affected reporting or behavior. The exact randomization procedures, allocation concealment details, statistical test, confidence-interval method, and full analysis populations are not reported in the registry record.

The study description includes multiple outcome domains and the record lists 16 outcome measures, but the multiplicity strategy is not reported. The primary p-value is explicitly unadjusted. The registry also does not provide, in the posted primary result, a detailed breakdown of the types of cessation treatment participants used. The primary endpoint does not itself measure abstinence or quit attempts, and those outcomes cannot be inferred from treatment-use counts.

Finally, the registry describes implementation in primary care or family medicine, but the posted information does not establish how well these findings would transfer to other settings or populations. The findings should be interpreted as an early, outcome-specific comparison within this feasibility trial, not as a complete assessment of efficacy, acceptability, implementation, or safety.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Smoking Cessation E-Visit — NCT04316260.
• Registry record last updated 2020-12-08; status last verified 2020-12.
• Results first posted 2020-12-08.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

21. Record Summary

The registry record describes a completed, randomized, parallel-group feasibility trial of a smoking-cessation e-visit compared with treatment as usual in primary care. It lists enrollment as 51, no masking, and a Month 1 primary outcome counting participants who used smoking-cessation treatment since the last assessment. The posted counts were 15 among 28 analyzed participants in the e-visit arm and 3 among 17 in treatment as usual; the record notes that not everyone completed the relevant questionnaire.

The unadjusted relative comparisons were a risk ratio of 3.036 and an odds ratio of 5.385, both with broad confidence intervals. The study description frames the trial as preliminary feasibility testing intended to provide effect-size estimates for a larger randomized trial. Missing questionnaire information, uncertainty in the estimates, and the absence of a reported multiplicity strategy limit how strongly the primary comparison can be interpreted.