Post-Discharge Smoking Cessation Strategies: Helping HAND 4: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Post-Discharge Smoking Cessation Strategies: Helping HAND 4 (NCT03603496), a randomized phase 4 study. The primary outcome is Tobacco Abstinence, Biochemically Confirmed.

2. Clinical Question

3. Trial Design

The registry describes NCT03603496, titled Post-Discharge Smoking Cessation Strategies: Helping HAND 4, as a completed, randomized, parallel-group Phase 4 trial sponsored by Massachusetts General Hospital. The trial compared two post-discharge approaches among adult smokers admitted to 3 U.S. hospitals. The registry lists enrollment as 1416. The outcome group denominators for the primary comparison are 706 for TTCM and 703 for eReferral to the State Tobacco Quitline; the record does not explain the difference between the enrollment figure and those denominators.

In the TTCM group, the planned care model included nicotine replacement therapy at hospital discharge for 8 weeks and proactive automated contacts over 3 months. Participants could request follow-up from a hospital-based tobacco coach for counseling, medication advice, and coordination of care. In the QL group, the research team made a state quitline referral on each enrolled patient’s behalf. Quitline staff offered up to 5 proactive calls from a tobacco coach, and participants might have been eligible for a nicotine replacement therapy sample. These descriptions define the approaches being compared; they do not establish how much of each intervention was actually received.

The masking designation is single, but which people were masked is not reported in the registry record. Details of the randomization procedure, allocation concealment, site-specific enrollment, and adherence to assigned care are also not reported in the registry record. The record lists a start date of 2018-09-01, a primary completion date of 2020-11-17, and a completed status. It does not provide the original protocol’s statistical plan or sample-size justification. The comparison therefore needs to be read from the outcomes and analyses that the registry posts, without assuming unreported design or analysis details.

4. Participant Flow and Accounting

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

Before assignment: 708 patients were randomized to TTCM arm. 2 patients were excluded post-randomization but before hospital discharge and the start of the intervention and follow-up assessment due to not meeting eligibility criteria. 708 patients were randomized to QL arm. 5 patients were excluded post-randomization but before hospital discharge and the start of the intervention and follow-up assessment due to not meeting eligibility criteria.

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 Tobacco Abstinence, Biochemically Confirmed, measured over 6 months after hospital discharge.

The primary endpoint was the count of participants with biochemically confirmed tobacco abstinence 6 months after hospital discharge. To meet the endpoint, a participant had to report no use of any tobacco product during the past 7 days and have either a saliva cotinine concentration at or below 10 ng/ml or a breath carbon monoxide concentration at or below 9 ppm. The self-report and a qualifying biochemical result were both required; the endpoint was not defined by self-report alone.

Biochemical confirmation is used to add an objective check to a participant’s report of recent tobacco use. The combined definition makes the outcome specific to a short recent abstinence window at the 6-month follow-up, rather than evidence of uninterrupted abstinence throughout the entire period since discharge. The registry separately lists self-reported continuous abstinence since discharge as a secondary outcome, which is a different measure.

The posted counts were 110 of 706 participants in the TTCM group and 96 of 703 in the QL group. Those counts describe how many participants met the registry’s endpoint definition in each group. They do not by themselves show whether the observed difference is compatible with chance variation, how missing outcome measurements were handled, or whether the findings would generalize beyond the enrolled population. The registry record does not report the biochemical testing completion rate or the handling of participants without an evaluable result.

7. Results Posted in the Registry

• eReferral to State Tobacco Quitline (QL): 13.7% (96/703) versus 15.6% (110/706) on control; risk difference -1.9 percentage points (95% CI -5.6 to 1.8), risk ratio 0.88 (0.68–1.13); unadjusted two-proportion z-test p = 0.307.

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.

Tobacco Abstinence, Self-report

• 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:
  • eReferral to State Tobacco Quitline (QL): 35.0% (246/703) versus 41.2% (291/706) on control; risk difference -6.2 percentage points (95% CI -11.3 to -1.2), risk ratio 0.85 (0.74–0.97); unadjusted two-proportion z-test p = 0.016.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for eReferral to State Tobacco Quitline (QL) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Engagement in Cessation Treatment

• 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:
  • eReferral to State Tobacco Quitline (QL): 52.5% (369/703) versus 69.3% (489/706) on control; risk difference -16.8 percentage points (95% CI -21.8 to -11.8), risk ratio 0.76 (0.70–0.83); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for eReferral to State Tobacco Quitline (QL) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Self-reported Continuous Tobacco Abstinence Since Hospital Discharge

• 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:
  • eReferral to State Tobacco Quitline (QL): 12.9% (91/703) versus 17.0% (120/706) on control; risk difference -4.1 percentage points (95% CI -7.8 to -0.3), risk ratio 0.76 (0.59–0.98); unadjusted two-proportion z-test p = 0.033.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for eReferral to State Tobacco Quitline (QL) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary findings show a pattern that is not identical across outcomes. Some secondary comparisons favor TTCM on measures of self-reported abstinence and engagement in cessation treatment, while the primary endpoint of biochemically confirmed abstinence at 6 months does not show a clear group difference. The secondary measures broaden the picture by addressing different time points and aspects of cessation care, but they do not overturn or replace the primary result.

Self-reported outcomes differ from the primary endpoint because they do not use the same combined self-report and biochemical criterion. Continuous abstinence since discharge also asks about a different time span than abstinence during the 7 days before the primary follow-up. Engagement in cessation treatment measures participation in care rather than abstinence itself. Consequently, agreement or disagreement across these outcomes can reflect different constructs and ascertainment methods, not simply repeated estimates of one effect.

The registry lists six secondary outcomes and seven outcome measures and analyses. The record does not provide a multiplicity plan explaining how the chance of false-positive findings across multiple outcomes was addressed. When multiple outcomes are examined, an unadjusted p-value for one result does not account for the full set of comparisons. Secondary findings with unadjusted analyses therefore carry less confirmatory weight than a prespecified primary result, particularly when the analysis plan and hierarchy of outcomes are unavailable.

The secondary results can be viewed as context for the primary comparison and as potential evidence about treatment engagement or self-reported behavior. They should not be combined informally into a single overall conclusion. The record does not report a formal composite interpretation or a multiplicity-adjusted analysis. The overall pattern is therefore suggestive of differences on some secondary measures, alongside an uncertain primary comparison, rather than a definitive conclusion shared across all outcomes.

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

• Tobacco Abstinence, Self-report
• Tobacco Abstinence, Self-report
• Tobacco Abstinence, Self-report
• Engagement in Cessation Treatment
• Engagement in Cessation Treatment
• Self-reported Continuous Tobacco Abstinence Since Hospital Discharge

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary at the participant level: each participant either met the combined abstinence definition or did not. The registry reports counts for each group and a chi-squared analysis of a risk ratio. A separate unadjusted recalculation uses the same counts to express the difference as a risk difference. These are two ways to describe a comparison of group outcome probabilities, and their numerical scales and null values differ.

What do risk differences and risk ratios describe?

A risk difference subtracts the outcome probability in one group from the outcome probability in the other. A value of zero represents equal probabilities. The recalculation compares QL against TTCM, so its interval crossing zero indicates that the posted data are compatible with no difference under this unadjusted calculation. A risk ratio divides one group’s outcome probability by the other’s. A value of 1 represents equal probabilities. The sponsor reports the ratio for the two groups as 1.18, with an interval that includes 1. The direction of a ratio depends on which group is placed in the numerator; the sponsor’s result is reported as a comparison of the groups listed in the registry analysis.

Risk differences and risk ratios answer related but distinct questions. A risk difference describes an absolute contrast in outcome probability, while a risk ratio describes a relative contrast. Neither measure should be read without its group order and interval. The reported primary counts make the underlying event totals visible, while the inferential summaries describe the uncertainty around a group comparison. The recalculation is unadjusted: it does not account for any covariates or other features beyond the posted group counts.

What does the chi-squared analysis contribute?

A chi-squared test evaluates whether the observed distribution of binary outcomes across groups is compatible with equal outcome probabilities, under the assumptions of that test. The registry reports this method for its risk-ratio analyses. A p-value describes how unusual the observed data, or data more extreme, would be under the test’s null hypothesis and assumptions. It is not the probability that the null hypothesis is true, and it does not measure the size or practical importance of an effect. The sponsor’s primary p-value is .19. The unadjusted recalculation reports p = 0.307; the distinct values reflect different reported analysis approaches and should not be blended into a new result.

General statistical context: a confidence interval gives a range of estimates compatible with the data and the method used, at the stated confidence level. For a risk ratio, the null value is 1; for a risk difference, it is zero. If the interval contains the relevant null value, the corresponding analysis does not rule out no difference at the matching conventional two-sided threshold. An interval does not assign equal probability to every value within its limits, and it does not account for biases or analysis choices that are not represented in the calculation.

How should the outcome definition affect interpretation?

The primary endpoint combines a report of no tobacco use during the prior 7 days with a qualifying saliva or breath measurement. This definition makes it a binary outcome suitable for a comparison of counts and probabilities. It concerns recent abstinence at the 6-month follow-up, not necessarily sustained abstinence from discharge onward. The separate secondary measure of continuous self-reported abstinence addresses a different duration and relies on self-report rather than the combined primary definition. Differences in endpoint definition mean the measures should not be treated as interchangeable replications of the same outcome.

General statistical context: biochemical confirmation can reduce reliance on self-report alone, but no measurement removes all uncertainty. The registry does not report the proportion of participants who completed biochemical testing, how invalid or unavailable specimens were handled, or whether outcome assessors were masked. Those details matter because the completeness and consistency of outcome measurement can affect the validity of a group comparison.

What can the recalculation establish?

The recalculated comparison is a simple unadjusted analysis of the posted summary counts. It is useful for showing the direction and uncertainty of a raw group contrast, but it cannot reproduce analyses that might adjust for stratification factors, site, covariates, or other protocol details. Whether any such adjustments were planned or performed is not reported in the registry record. There is no reported discrepancy between the sponsor’s analysis and the recalculation flag in the record, but the two results still differ in measure and method. The sponsor’s reported analysis remains the registry’s own inferential result, and the recalculation should be read as an additional descriptive check rather than a substitute for the planned analysis.

General statistical context: binary outcomes are often analyzed using methods that compare proportions or model event probabilities. The choice among a risk difference, risk ratio, or another effect measure changes how the contrast is expressed, not the underlying participants or events. Interpretation is strongest when the estimand, analysis population, handling of missing outcomes, and statistical method are specified in advance. Those protocol details are not reported in the registry record.

11. Confidence Intervals and Precision

The sponsor’s primary estimate is a risk ratio of 1.18, and its 95% confidence interval extends from 0.92 to 1.50. The interval includes the no-difference value of 1. It also spans values on either side of that null, so the primary data do not precisely determine the direction of the relative contrast. The sponsor reports p = .19, consistent with an analysis that does not rule out no difference at a conventional threshold.

The unadjusted risk-difference recalculation also has an interval that crosses its no-difference value of zero. Its limits are fairly broad relative to the point estimate, so a range of underlying absolute contrasts remains compatible with that calculation. The recalculated p-value is 0.307. The two presentations use different scales, but both indicate uncertainty about whether the primary endpoint differs between groups.

General statistical context: a confidence interval describes sampling uncertainty under the model and analysis used. A narrower interval indicates greater precision in estimating the particular effect measure; it does not guarantee the estimate is unbiased or that the outcome was measured without error. The interval also does not cover uncertainties caused by unreported analysis choices, missing outcomes, or limited generalizability.

The secondary sponsor estimates have varying precision. For example, the reported self-reported abstinence ratio at 1 month has a 95% confidence interval from 1.08 to 1.35, while the continuous abstinence ratio at 6 months has a 95% confidence interval from 1.02 to 1.66. These intervals are reported for different outcomes and should not be compared as though they were estimates of a single common effect. The outcome counts and definitions, as well as the group contrasts, differ. Precision is therefore specific to each endpoint and analysis.

12. Planning a Similar Trial: Sample Size

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

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

The 80% power column gives the planned number of participants per arm under the stated assumptions for an analysis to have an 80% chance of detecting the specified effect if that effect were the true one. The 90% power column applies the same idea with a higher target chance of detection. These are design-planning quantities, not guarantees that a study of that size will reach a particular conclusion. The estimates depend on the effect size being planned for, the chosen significance level, allocation, and approximation.

The rows that plan for 100%, 75%, and 50% of the observed effect illustrate how the assumed effect changes the sample-size requirement. A smaller effect is harder to distinguish from chance variation, so detecting it at the same power generally requires more participants. This pattern is visible across the planning estimates for the primary and secondary binary outcomes. Because the observed effect is itself uncertain, the planning figures should not be treated as a definitive requirement for a future study.

This is not observed power. It is a planning illustration calculated from the observed effect under the stated assumptions, and it does not describe the probability that the completed trial’s result is correct or that a treatment effect exists. It also does not reconstruct the trial’s original design process, which is not reported in the registry record.

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 = 0.156; treatment proportion p₁ = 0.137; average p̄ = 0.146.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.4996 = 0.9792.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.4994 = 0.4203.
4. n = (0.9792 + 0.4203)² / (-0.0192)² = 5,286.3, rounded up to 5,287 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 <- 0.155807365439093                          # control proportion
p1 <- 0.136557610241821                          # 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(3603496)
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 = 5,287 (80% power), 7,077 (90% power)
 75% of observed effect: n per arm = 9,527 (80% power), 12,753 (90% power)
 50% of observed effect: n per arm = 21,722 (80% power), 29,079 (90% power)
power.prop.test: 5286.3 
Simulated power at n = 5,287 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. Analyses Reported by the Sponsor

The registry’s sponsor-reported primary analysis uses a chi-squared method and reports a risk ratio of 1.18, a 95% confidence interval from 0.92 to 1.50, and p = .19. That is the trial’s posted inferential analysis. The unadjusted recalculation instead compares the posted counts as a risk difference for QL relative to TTCM; its interval crosses zero and its p-value is 0.307. These analyses use different effect measures and presentation, but neither provides clear statistical evidence of a primary difference.

The recalculation is a direct, unadjusted analysis of the available group counts. It does not incorporate adjustments or other specifications that might be part of a protocol analysis. The registry record does not report such details, so the recalculation cannot be judged as a reproduction of the original analysis plan. The record indicates no discrepancy between the sponsor analysis and recalculation flag. That does not make their estimates numerically interchangeable; it means the posted comparison does not identify a stated conflict requiring the sponsor result to be displaced.

Accordingly, the sponsor analysis should be cited as the registry’s reported result, with the recalculation serving as a transparent complementary summary of the raw count comparison. Neither result should be turned into a claim of equivalence, because the confidence intervals allow effects in more than one direction. The sponsor’s analysis and the recalculation both leave uncertainty about the size and direction of the primary contrast.

15. Multiplicity, Missing Data and Analysis Populations

The registry posts a primary outcome and multiple secondary outcomes, including self-reported abstinence at different follow-up times, engagement in cessation treatment, and continuous abstinence since discharge. Testing multiple outcomes can increase the chance of at least one apparently notable result arising by chance, especially if each is interpreted using an unadjusted threshold. The record does not report whether the investigators prespecified a testing hierarchy, adjusted for multiplicity, or treated secondary analyses as exploratory. The posted secondary p-values should therefore be read cautiously and not as independent confirmation of a single overall effect.

Missing-data methods are also not reported in the registry record. The outcome denominators are listed for the posted comparisons, but the record does not explain whether all randomized participants were included in each analysis, how unavailable follow-up responses were handled, or whether a biochemical result was required for every analyzed participant. It does not report imputation, sensitivity analyses, or assumptions about participants with missing outcomes. Without those details, it is not possible to determine how strongly the analysis depends on the handling of unavailable measurements.

The analysis population is not reported in the registry record. In particular, the record does not specify whether the analysis followed initial random assignment regardless of intervention uptake or used another population definition. The analysis plan, any adjustments for sites or baseline characteristics, and the handling of protocol deviations are likewise not reported. These omissions limit assessment of whether the posted comparisons preserve the benefits of randomization and match the intended estimand.

The record does identify the trial as randomized and lists arm-level outcomes, but that alone does not resolve questions about missing observations or multiplicity. These are reporting limitations rather than evidence that a particular method was or was not used. A full interpretation of the posted p-values and intervals would benefit from a protocol or statistical analysis plan, together with a clear account of participant flow and outcome availability.

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

The primary registry result describes a comparison between two post-discharge tobacco-cessation strategies among participants in this randomized trial. The endpoint required self-reported absence of tobacco use in the prior 7 days plus biochemical confirmation at the specified threshold. The posted counts and sponsor analysis do not establish a clear statistical difference between the TTCM and QL groups on that endpoint at 6 months. The confidence interval nevertheless includes effects on either side of no difference, so the finding should not be interpreted as demonstrating that the strategies are identical.

The secondary analyses add information about self-reported abstinence and treatment engagement. Some show differences between groups, but they involve outcomes that are not the same as the primary endpoint and are not reported with a stated multiplicity adjustment. They may help describe the overall pattern in the registry record, but they do not provide a basis for replacing the primary result with a stronger claim.

The results do not establish why any group differences arose. The intervention descriptions identify distinct service models, but the record does not report uptake, fidelity, or the amount of support participants actually received. Nor do the outcome comparisons establish the effect of any one component, such as nicotine replacement therapy, coaching, automated contact, or quitline referral in isolation.

General statistical context: a nonsignificant result is not proof of no effect, and a statistically notable secondary result is not by itself proof of a robust or clinically important effect. Interpretation depends on the size and precision of the estimate, outcome definition, analysis plan, and quality of outcome ascertainment. The record’s missing-data and multiplicity details are not available, which limits conclusions beyond the posted comparisons.

The findings apply most directly to the participants and hospital context represented in the trial record. The registry describes adult smokers admitted to 3 U.S. hospitals, but gives limited information about participant characteristics and eligibility. The results therefore do not automatically generalize to other populations, health systems, or cessation programs. They describe the posted trial evidence, not an individualized treatment recommendation.

17. Why This Trial Matters Statistically

This trial is statistically informative because it places a relatively direct randomized comparison beside several distinct outcome definitions. The primary endpoint combines recent self-report with biochemical confirmation, while secondary outcomes include self-reported abstinence, continuous abstinence, and treatment engagement. Separating these measures helps prevent a common interpretive error: treating every tobacco-related outcome as if it measured the same construct over the same time period.

The primary comparison also illustrates why an observed difference in counts should be read together with an uncertainty interval. The group counts are not sufficient to establish a reliable group contrast. The sponsor’s risk-ratio interval includes its no-difference value, and the unadjusted risk-difference interval crosses zero. The result is thus more accurately described as uncertain than as evidence that the groups are equal.

The secondary pattern highlights the importance of outcome hierarchy and multiplicity. Results on engagement and self-reported measures may be relevant, but several analyses across different outcomes can produce a range of findings. Without a reported multiplicity strategy, individual secondary p-values have less confirmatory force. The distinction between a primary endpoint and secondary measures is central to reading the evidence responsibly.

Finally, the registry’s limited reporting of missing-data handling, analysis populations, and the original statistical plan illustrates how transparency affects statistical interpretation. Randomization is important, but readers also need to know who was analyzed, how unavailable outcomes were treated, and which comparisons were planned. Those details are not reported in the registry record, so the posted estimates are informative but cannot answer every methodological 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.

The registry record leaves important details about the trial population and conduct unspecified. It identifies adult smokers admitted to 3 U.S. hospitals, but further eligibility criteria and participant characteristics are not reported. The setting and enrollment description alone do not establish how representative the participants were of other hospitalized smokers or health systems.

The intervention descriptions state what the models were intended to offer, but actual receipt and engagement are not reported. For TTCM, the record does not state whether participants completed the planned contacts or received the full nicotine replacement therapy period. For QL, it does not state how many participants were reached, accepted coaching, completed calls, or received a sample. Without implementation measures, outcome differences cannot be attributed to a particular intervention component.

The registry lists enrollment as 1416 and primary outcome denominators of 706 and 703. It does not explain the relationship between these figures or give a participant-flow account that would clarify exclusions or unavailable outcomes. Missing-data procedures and the analysis population are not reported, so the potential influence of incomplete follow-up cannot be assessed from the record alone.

Although the masking designation is single, the identity of those masked is not reported. The record also does not report allocation concealment, the randomization procedure, site-specific analyses, or a full statistical analysis plan. Details about biochemical testing completion and handling of non-evaluable samples are absent. These reporting gaps limit assessment of measurement, analysis choices, and the robustness of the posted results.

The registry lists 19 deaths in the TTCM group and 24 in the QL group, while listing zero serious and zero other adverse events in the respective adverse-event categories. The record does not provide the context or attribution needed to interpret these entries, including the reporting period or whether deaths were considered related to an intervention. They should not be used to infer causation.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Post-Discharge Smoking Cessation Strategies: Helping HAND 4 — NCT03603496.
• Registry record last updated 2022-05-05; status last verified 2022-05.
• Results first posted 2022-05-05.
• 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

NCT03603496 is a completed randomized, parallel-group Phase 4 trial sponsored by Massachusetts General Hospital. It compared Transitional Tobacco Care Management with eReferral to a State Tobacco Quitline among adult smokers admitted to 3 U.S. hospitals. The registry lists enrollment of 1416 and a primary completion date of 2020-11-17.

The primary outcome was biochemically confirmed tobacco abstinence 6 months after discharge. The sponsor reported a risk ratio of 1.18, with a 95% confidence interval from 0.92 to 1.50 and p = .19. The unadjusted recalculation likewise does not provide clear evidence of a primary difference. Some secondary measures show group differences, but their interpretation is limited by the absence of a reported multiplicity plan and by their differing definitions.

The registry record does not report the original sample-size justification, detailed missing-data procedures, or analysis population. The posted findings describe the recorded comparisons but leave these methodological details unresolved.