Partnering With Food Bank to Provide Tobacco Treatment to Underserved Smokers: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Partnering With Food Bank to Provide Tobacco Treatment to Underserved Smokers (NCT05004662), a randomized na study. The primary outcome is Self-Reported 7-Day Point-Prevalence Smoking Abstinence.

2. Clinical Question

3. Trial Design

The registry record describes a randomized, parallel-group trial with no masking. The two groups were Standard Treatment and Automated Treatment. The record lists enrollment of 502, while the primary outcome results list 249.0 participants in Standard Treatment and 250.0 in Automated Treatment. The reason these outcome group counts do not account for the full enrollment is not reported in the registry record.

Both groups received a 10-week supply of nicotine patches and lozenges. Standard Treatment participants were connected with their state's Quitline services. Automated Treatment included smartphone-delivered treatment, including 12 proactive video-based sessions delivered weekly and tailored to several participant characteristics. The description of the automated arm is incomplete in the registry record, so additional features should not be inferred from the partial text.

The study status is completed. The registry lists a start date of 2021-12-01 and a primary completion date of 2025-06-05. The phase is listed as NA. The registry record names H. Lee Moffitt Cancer Center and Research Institute as sponsor. It describes the study as evaluating the efficacy and economic impact of an interactive smartphone-based smoking cessation intervention, but posted outcome results in the record focus on participant counts for abstinence measures.

The primary outcome was assessed at the 12 Month Follow-Up. Three secondary outcomes are also posted for that timeframe. The registry record contains no reported analyses, and details such as the allocation procedure, original sample-size justification, or analysis populations are not reported in the registry record.

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 Self-Reported 7-Day Point-Prevalence Smoking Abstinence, measured over 12 Month Follow-Up.

The primary endpoint is Self-Reported 7-Day Point-Prevalence Smoking Abstinence at the 12 Month Follow-Up. It counts participants who report no smoking, not even a puff, within the past 7 days. This is a binary outcome: each participant either meets the stated abstinence definition or does not. The posted result is a count of participants in each group, rather than a continuous measure such as the number of cigarettes smoked.

A point-prevalence measure describes reported status during a defined recent period. It does not, by itself, establish that abstinence was continuous throughout the study or throughout the entire follow-up period. The endpoint is self-reported, so its classification depends on participant reports. The registry also lists biochemical verification via salivary cotinine as a secondary outcome; that is a separate measure and should not be conflated with the primary self-report outcome.

Self-reported 7-day abstinence offers a clearly stated outcome that can be compared between randomized groups at follow-up. The definition makes the endpoint interpretable, while its limited time window and reliance on self-report are important when considering what it can establish. The registry record does not report further details about how the endpoint was collected or how missing reports were handled.

7. Results Posted in the Registry

• Automated Treatment: 38.4% (96/250) versus 35.7% (89/249) on control; risk difference 2.7 percentage points (95% CI -5.8 to 11.1), risk ratio 1.07 (0.85–1.35); unadjusted two-proportion z-test p = 0.539.

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.

Biochemical Verification Via Salivary Cotinine

• 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:
  • Automated Treatment: 16.4% (41/250) versus 16.9% (42/249) on control; risk difference -0.5 percentage points (95% CI -7.0 to 6.1), risk ratio 0.97 (0.66–1.44); unadjusted two-proportion z-test p = 0.888.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Automated Treatment and the control. The interval gives the range of effects that remain compatible with the data.

Self-Reported 24-Hour Point-Prevalence Smoking Abstinence

• 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:
  • Automated Treatment: 46.4% (116/250) versus 45.4% (113/249) on control; risk difference 1.0 percentage points (95% CI -7.7 to 9.8), risk ratio 1.02 (0.85–1.24); unadjusted two-proportion z-test p = 0.819.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Automated Treatment and the control. The interval gives the range of effects that remain compatible with the data.

Self-Reported 30-Day Point-Prevalence Smoking Abstinence

• 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:
  • Automated Treatment: 31.6% (79/250) versus 30.9% (77/249) on control; risk difference 0.7 percentage points (95% CI -7.5 to 8.8), risk ratio 1.02 (0.79–1.33); unadjusted two-proportion z-test p = 0.871.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Automated Treatment and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover a biochemical verification measure and two additional self-reported abstinence windows. Across these measures, the posted group comparisons are close to no difference in their point estimates, and their confidence intervals include the corresponding null values. This broad pattern is consistent with the primary result in that none of the posted outcomes gives a clearly separated group comparison. It does not mean that the outcomes are identical or that the interventions are equivalent.

The outcomes are related but not interchangeable. The biochemical measure assesses a different aspect of abstinence from participant self-report, while the self-reported measures use different recent time windows. Agreement in the general direction of the pattern is informative descriptively, but it does not remove the uncertainty in each estimate or resolve possible differences in measurement. The registry record does not report analyses that combine these outcomes or assess agreement between self-report and biochemical verification.

Secondary outcomes usually carry less evidential weight when they are not designated as the primary endpoint or when many outcomes are assessed without a reported plan to address multiplicity. Testing multiple outcomes increases the opportunity for apparently notable results to arise by chance. The registry record lists no reported analyses and does not report an adjustment procedure, a hierarchy among secondary outcomes, or a prespecified multiplicity strategy. The secondary results should therefore be treated as additional descriptive evidence rather than as independent confirmation of a definitive conclusion.

The secondary outcomes also do not answer questions about persistence of abstinence beyond their stated follow-up assessment or about economic impact. The registry record identifies economic impact as part of the study's overall purpose but does not report economic results. Taken together, the secondary pattern is broadly in line with the primary comparison's uncertainty, while leaving the same important limitations around analysis methods, missing data, and outcome measurement.

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

• Biochemical Verification Via Salivary Cotinine
• Self-Reported 24-Hour Point-Prevalence Smoking Abstinence
• Self-Reported 30-Day Point-Prevalence Smoking Abstinence

9. Statistical Interpretation

10. Statistical Methods Explained

The primary comparison is between two randomized, parallel groups: Standard Treatment and Automated Treatment. Randomization is intended, in general, to make the groups comparable at baseline on average, including on characteristics that may not have been measured. It does not ensure that every characteristic is identical between groups, and it does not address differences that arise after randomization, such as missing outcome reports. The registry record says allocation was randomized, but does not describe the randomization sequence or concealment procedures.

What does a risk difference describe?

For a binary outcome, a risk difference expresses the difference between the proportion meeting the outcome in one group and the proportion in the comparison group. A value above zero in the posted direction indicates a higher observed proportion in Automated Treatment; a value below zero indicates a higher observed proportion in Standard Treatment. This measure is expressed on the probability scale and directly describes an absolute difference. The primary posted estimate is positive, but the confidence interval crosses zero, so the data remain compatible with differences in either direction.

The registry also posts a risk ratio and an odds ratio. A risk ratio compares the outcome proportions as a ratio, while an odds ratio compares the odds of the outcome. A ratio of one represents no relative difference under the relevant measure. These measures answer related but distinct questions and can look different numerically because they use different scales. They should be interpreted alongside the absolute risk difference, not as interchangeable quantities. The posted confidence intervals for the primary relative measures include one.

What does the p-value contribute?

The posted unadjusted p-value for the primary comparison is 0.539. In general, a p-value describes how compatible the observed data are with a specified null model, under the assumptions used for the test. It is not the probability that the null hypothesis is true, and it does not quantify the size or practical importance of an effect. A p-value alone also does not show the range of effects that are reasonably compatible with the data; that is why the estimate and its confidence interval matter. The registry record does not report the precise test or model used for the sponsor's analysis.

The value 0.05 appears in the sample-size planning assumptions as the two-sided significance level used for those illustrations. The registry record does not state that this was the threshold specified for the trial's original analysis, or report the original statistical analysis plan. The planning assumption should not be mistaken for a documented trial analysis rule.

Why are counts and proportions both relevant?

The endpoint is a participant count, with each participant classified according to whether they meet the abstinence definition. Counts show how many participants met the outcome; group sizes provide the context needed to compare the groups. Because the group sizes are similar but not identical, counts alone should not be treated as the comparison measure. The posted risk difference, risk ratio, and odds ratio are comparison summaries based on the group data. No additional calculation is needed to interpret the posted results, and no adjusted estimate is reported in the registry record.

What can be concluded about the analysis?

The registry record lists no reported analyses, despite posting outcome counts and comparison statistics. It does not state whether the analysis followed intention-to-treat principles, whether participants were analyzed according to randomized assignment, or how incomplete follow-up was handled. Those details affect how the results should be interpreted and cannot be filled in by assuming common practice.

The registry lists a total enrollment of 502, while the primary outcome groups list 249.0 and 250.0 participants. These are the reported figures; the reason for the difference between enrollment and the outcome group counts is not reported in the registry record. Without missing-data methods or an account of analysis populations, it is not possible to determine from the record how that difference arose or whether it reflects missing outcome measurements, exclusions, or another explanation.

In general, confidence intervals describe uncertainty around an estimate under the statistical method used. Their interpretation depends on the model and assumptions, which are not fully described for the trial's posted analysis. The sample-size illustrations use a normal approximation and equal allocation, but those are planning assumptions for the illustrations, not reported details of the trial's original design or analysis.

11. Confidence Intervals and Precision

The primary risk-difference confidence interval extends from a negative value to a positive value, crossing zero. This means the posted result is compatible with an advantage in either direction as well as with little or no difference. The interval is broad enough that the point estimate alone should not be treated as a precise measure of the difference between groups. The posted p-value, 0.539, is consistent with this uncertainty, but the interval is more informative about the range of differences compatible with the analysis.

In general, a confidence interval gives a range of values compatible with the data and the statistical method, rather than a guarantee that the true effect falls within a particular interval. Its width reflects precision: narrower intervals indicate less statistical uncertainty, while wider intervals indicate more. The registry record does not report the method used to construct the sponsor's interval, so its detailed assumptions cannot be assessed from the record.

The primary relative-effect intervals also include their null value: one for the risk ratio and one for the odds ratio. This is consistent with the risk-difference interval crossing zero. The different measures use different scales, so their intervals should be read according to their own null values and not compared by width alone.

The registry record posts no confidence intervals for participant counts themselves and no adjusted estimates. Precision is also affected by what is not reported: missing-data handling, analysis populations, and the analytic method are not described. These omissions limit how fully the posted uncertainty can be evaluated.

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 illustrations express sample size as participants per arm for 80% power and participants per arm for 90% power. They are based on equal allocation between the two arms, a two-sided significance level of 0.05, and a normal approximation. The calculation uses the observed effect as a reference and considers planning for 100%, 75%, or 50% of that observed effect. These are planning scenarios, not the sample-size assumptions for the completed trial; the original design assumptions are not reported in the registry record.

The 80% power column describes the planned number of participants per arm under which the calculation would have an 80% chance of detecting the stated effect, if the planning assumptions and effect were correct. The 90% power column describes the corresponding target for a 90% chance. These are prospective planning concepts, not guarantees about the result of a trial. Smaller effects require more participants to detect with the same planned power, which is why the rows planning for a smaller fraction of the observed effect have larger sample-size requirements.

The outcome planning figures are participants per arm, not total events. The general planning note distinguishes these from time-to-event illustrations, which would report total events, but the outcomes posted for this trial are binary participant outcomes. The displayed figures should therefore be read using the per-arm convention. They are not observed power: observed power is not an appropriate substitute for interpreting the estimated effect and its confidence interval after results are known.

The illustrations are based on the observed effect and should not be interpreted as evidence that the trial was designed with those targets. The registry record does not report the original sample-size justification or its assumed treatment difference. The displayed planning values are hypothetical illustrations for future design considerations, not a retrospective claim about what the completed study could establish.

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 = 0.357; treatment proportion p₁ = 0.384; average p̄ = 0.371.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6831 = 1.3388.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6828 = 0.5747.
4. n = (1.3388 + 0.5747)² / (0.0266)² = 5,186.0, rounded up to 5,187 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 <- 0.357429718875502                          # control proportion
p1 <- 0.384                          # 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(5004662)
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 = 5,187 (80% power), 6,943 (90% power)
 75% of observed effect: n per arm = 9,187 (80% power), 12,298 (90% power)
 50% of observed effect: n per arm = 20,591 (80% power), 27,565 (90% power)
power.prop.test: 5186 
Simulated power at n = 5,187 per arm: 0.801 (20000 replicates)

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

14. Multiplicity, Missing Data and Analysis Populations

The registry record lists one primary outcome and three secondary outcomes. It does not report whether the endpoint hierarchy, hypotheses, or analysis plan were prespecified, nor does it report a method for controlling multiplicity. In general, assessing several outcomes can increase the chance of at least one apparently unusual result if each is tested separately. A multiplicity strategy may address this issue by adjusting significance thresholds or by specifying an order in which outcomes are interpreted. Because no such strategy is reported, the posted secondary comparisons should be treated cautiously and should not be read as a set of independent confirmatory tests.

The registry record lists no reported analyses. Although it posts counts and comparison statistics, it does not describe the statistical test, covariate adjustment, model specification, or population included in the sponsor's analysis. It also does not state whether participants were analyzed according to their randomized group or whether any post-randomization exclusions were made. These analysis-population details are not reported in the registry record.

Missing-data handling is also not reported in the registry record. The primary outcome group counts are 249.0 and 250.0, while the total enrollment is 502. The record does not explain the difference or say whether missing reports were counted as not abstinent, excluded, imputed, or handled in another way. Those approaches can lead to different interpretations, so the observed counts cannot by themselves resolve the issue.

The record does not report interim analyses, stopping rules, or a data-monitoring plan. Their absence from the record should not be taken as evidence that any particular approach was or was not used. More complete analysis documentation would be needed to evaluate these features, missing outcomes, and the planned role of secondary endpoints.

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

The posted results show that 89 participants in Standard Treatment and 96 participants in Automated Treatment met the primary self-reported 7-day abstinence definition at the 12 Month Follow-Up. The estimated comparison is uncertain: its confidence interval includes values on either side of no difference, and the unadjusted p-value is 0.539. The results therefore do not establish a clear difference between the groups, but they also do not establish that the treatments are equivalent.

The primary outcome is a report of smoking status during the past 7 days. It should not be interpreted as evidence of continuous abstinence throughout the entire follow-up period. It is also not the same as biochemical verification. A separate secondary outcome concerns salivary cotinine, and the registry does not report an analysis combining biochemical and self-reported measures.

The findings apply to the randomized comparison described in the registry, not automatically to other populations, service systems, or versions of the intervention. The record's description identifies underserved smokers as the study focus, but detailed eligibility criteria and additional population characteristics are not reported. The intervention arm description is incomplete, which also limits the ability to characterize exactly what was delivered.

The registry summary mentions economic impact, but economic results are not reported in the registry record. The posted abstinence counts do not answer questions about costs or cost-effectiveness. Likewise, the listed adverse-event fields show zero values for the recorded at-risk, affected, and death counts in both groups, but the registry record gives no further safety context or detailed adverse-event terms. Those fields should not be treated as a complete assessment of safety.

Overall, the registry supports a cautious description of the observed counts and the uncertainty in the group comparison. It does not support a claim that one treatment is superior, that the treatments are equivalent, or that the findings settle questions about longer-term outcomes or economic impact.

16. Why This Trial Matters Statistically

This trial is statistically relevant because it compares a fully automated, smartphone-delivered treatment approach with a standard treatment that includes nicotine replacement and connection to Quitline services. Both groups received nicotine patches and lozenges, so the primary comparison concerns the added treatment approaches within the arms as described, rather than a comparison of medication with no medication.

The result also illustrates why an estimated difference and its precision should be read together. The primary point estimate is positive in the posted direction, but the confidence interval crosses zero and the unadjusted p-value is 0.539. A point estimate on its own can make a comparison seem more decisive than the data warrant. The interval shows that uncertainty remains about both the direction and size of the group difference.

The study includes a self-reported primary endpoint and a biochemical secondary outcome. Considering both types of measurement can be useful when interpreting abstinence outcomes, but the posted results remain uncertain and the record does not describe a method for combining or reconciling the measures. The three secondary endpoints also raise the general need to distinguish descriptive patterns from confirmatory inference when no multiplicity plan is reported.

Finally, the record makes visible the importance of complete reporting. The enrollment, outcome group counts, missing-data procedures, analysis populations, and original sample-size justification are not fully reconciled or described. Those details matter for assessing how the posted comparison was formed and how much confidence to place in it. The trial's statistical contribution is therefore best understood through the outcome definitions, effect estimates, uncertainty, and limits of the registry reporting together.

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 registry record does not describe eligibility criteria in enough detail to judge how closely the enrolled population represents other groups of smokers. It identifies underserved smokers as the study focus, but broader population characteristics and recruitment details are not reported. The automated arm description is incomplete, leaving some intervention features unspecified.

The primary endpoint relies on self-report and captures a defined recent period at follow-up. It does not measure continuous abstinence across the full follow-up period. Biochemical verification is posted as a secondary measure, but the registry does not report how the two measures were related or whether discordant results were evaluated.

There is also limited information about the analysis. The registry record does not report the analysis population, missing-data approach, statistical model, prespecified multiplicity procedure, or original sample-size justification. The difference between total enrollment and the listed primary outcome group counts is not explained. These gaps make it difficult to assess whether the posted counts represent all randomized participants and how incomplete follow-up may have affected the comparison.

The registry summary includes economic impact as an objective, but no economic outcomes are reported. Adverse-event fields contain zero values for the listed counts, yet the record provides little context for interpreting those entries. These reporting limits do not change the posted outcome values, but they constrain conclusions about implementation, cost, missing outcomes, and safety.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Partnering With Food Bank to Provide Tobacco Treatment to Underserved Smokers — NCT05004662.
• Registry record last updated 2026-09-24; status last verified 2026-09.
• Results first posted 2026-08-04.
• 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 describes a completed, randomized, parallel-group study comparing Standard Treatment with Automated Treatment, with no masking. The study focused on smoking cessation among underserved smokers and lists enrollment of 502. Both groups received a 10-week supply of nicotine patches and lozenges; Standard Treatment included connection to the state's Quitline services, while Automated Treatment included smartphone-delivered sessions.

The primary outcome was self-reported 7-day point-prevalence smoking abstinence at the 12 Month Follow-Up. The posted counts were 89 participants in Standard Treatment and 96 in Automated Treatment, with an unadjusted p-value of 0.539 and a confidence interval for the risk difference that crossed zero. Three secondary outcomes were also posted. The registry record does not report analyses, missing-data methods, the original sample-size justification, or economic results.