Zyban as an Effective Smoking Cessation Aid for Patients Following an Acute Coronary Syndrome: The ZESCA Trial: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Zyban as an Effective Smoking Cessation Aid for Patients Following an Acute Coronary Syndrome: The ZESCA Trial (NCT00689611), a randomized phase 3 study. The primary outcome is Smoking Abstinence.

2. Clinical Question

3. Trial Design

The ZESCA trial, registered as NCT00689611, was a Phase 3 randomized, quadruple-masked, parallel-group study of bupropion and placebo. The registry lists enrollment of 392 and a completed status. The start is listed as 2005-12 and primary completion as 2010-06. The registry identifies the sponsor as Mark Eisenberg. These record details describe the trial’s registration and status; they do not establish how many participants contributed to each posted outcome.

The intervention arms were bupropion HCl ER and placebo. Each arm description refers to 9 weeks of assigned treatment. The primary outcome was assessed at 12 months, after that treatment period. The primary outcome populations in the posted results were 194 for placebo and 183 for bupropion. The registry does not explain how those outcome denominators relate to the enrollment figure, nor does it report further information about treatment adherence or follow-up procedures beyond the outcome definition.

The endpoint was a binary classification of smoking abstinence. A participant met the definition if they reported abstinence in the week before the 12-month clinic visit and had an exhaled carbon monoxide measurement less than 11 ppm. The primary analysis was described as intention-to-treat. For participants who withdrew consent or were lost to follow-up, the stated assumption was that they had returned to smoking at their baseline rates. The registry says this assumption is common in smoking cessation trials.

A secondary outcome, composite major adverse cardiovascular events, is listed. Results for that outcome are not reported in the registry record. Detailed eligibility criteria, the original sample-size justification, allocation procedures, the exact masking roles, and additional operational details 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

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.

Population: All enrolled participants were included in the baseline analyses.

6. Primary Endpoint

The registry lists the primary outcome as Smoking Abstinence, measured over 12 months.

The primary endpoint was smoking abstinence at 12 months, expressed as a percentage of participants. It used a 7-day point-prevalence definition: self-reported abstinence during the week before the 12-month clinic visit, together with an exhaled carbon monoxide measurement less than 11 ppm. Requiring both a participant report and a carbon monoxide measure means the endpoint was not based on self-report alone.

Point-prevalence abstinence describes smoking status during a specified recent period, rather than continuous abstinence throughout the entire time since treatment. The outcome therefore answers a focused question about abstinence near the 12-month visit. It does not by itself describe the timing or duration of any earlier quit attempts or relapses.

The registry reports the primary analysis as intention-to-treat. It states that participants who withdrew consent or were lost to follow-up were assumed to have returned to smoking at their baseline rates. That assumption is part of how missing follow-up was handled in the stated analysis and affects how the abstinence outcome should be understood. The registry does not report additional details about the measurement process or how the carbon monoxide assessment was conducted.

7. Results Posted in the Registry

• Bupropion: 37.2% (68/183) versus 32.0% (62/194) on control; risk difference 5.2 percentage points (95% CI -4.4 to 14.8), risk ratio 1.16 (0.88–1.54); unadjusted two-proportion z-test p = 0.288.

8. Statistical Interpretation

9. Statistical Methods Explained

The primary endpoint is binary: each participant either met the stated abstinence definition at the 12-month visit or did not. For a binary outcome, a group percentage describes the proportion meeting the definition. The posted percentages are 32.0% for placebo and 37.2% for bupropion. The registry’s comparison expresses the contrast as a risk difference, with an estimate of 0.052. A risk difference is the difference between the two group proportions, expressed on the proportion scale; positive values in this comparison correspond to a larger abstinence proportion in the bupropion group.

What does the risk difference tell us?

The risk difference keeps the comparison on the absolute proportion scale. It describes how far apart the group outcome proportions are, rather than expressing one group’s outcome relative to the other as a ratio. The posted confidence interval for the risk difference runs from -0.044 to 0.148. Because that range includes zero, the data are compatible with no difference under the analysis and also with differences in either direction. The interval’s limits also show that the estimate is not highly precise.

How should the p-value be read?

The unadjusted p-value is 0.288. In general, a p-value measures how unusual data at least as incompatible with a specified null model would be if that model and the analysis assumptions were correct. 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 registry does not report additional detail about the testing procedure or whether the posted p-value was the prespecified primary test.

What does intention-to-treat mean in this record?

The primary endpoint description says that the analysis was on an intention-to-treat basis. In general, this approach seeks to analyze participants according to their assigned group rather than reclassifying them according to treatment received. The registry’s stated handling of missing follow-up is also important: participants who withdrew consent or were lost to follow-up were assumed to have returned to smoking at their baseline rates. The record does not describe other missing-data analyses or report how many participants were lost to follow-up or withdrew consent.

Why does the outcome definition matter?

The endpoint combines a participant’s report of abstinence during the week before the 12-month clinic visit with an exhaled carbon monoxide measurement less than 11 ppm. This rule sets a specific operational definition for the binary outcome. In general, a clearly defined endpoint reduces ambiguity about how participants are classified, but it does not make the endpoint equivalent to continuous abstinence over the full follow-up period. The registry does not report additional details about the assessment schedule or validation procedures.

What can be said about secondary outcomes?

Composite major adverse cardiovascular events is listed as a secondary outcome. Results for it are not reported in the registry record. The record lists one secondary outcome, but does not report its effect estimate, uncertainty, or analysis. It also does not report whether any multiplicity adjustment was planned or used. Without those details, the abstinence comparison should be read as the posted primary result rather than as evidence about every outcome considered in the study.

General statistical interpretation also depends on design, measurement, and analysis choices. Randomization can support a between-group comparison by balancing factors on average, but it does not remove uncertainty in a finite study or resolve questions about missing outcomes and outcome measurement. The registry identifies randomization, masking, and a parallel design, but details such as the allocation method, stratification, covariate adjustment, and model specification are not reported in the registry record.

10. Confidence Intervals and Precision

The risk-difference estimate for bupropion relative to placebo was 0.052, with a confidence interval from -0.044 to 0.148. The interval represents uncertainty around the estimated difference under the analysis. In general, a confidence interval is not a range containing a known true value with certainty; it is a measure of how much uncertainty remains when an estimate is calculated from finite data and a stated statistical procedure.

Here, the interval includes zero, the value corresponding to no risk difference. It also extends to values on both sides of zero. This means the estimate does not pin down the direction of the underlying contrast, and it allows a range of possible magnitudes. The point estimate alone should therefore not be read as a precise description of the difference that would necessarily be seen in another study.

The posted group percentages were 32.0% for placebo and 37.2% for bupropion, based on outcome group sizes of 194 and 183, respectively. The confidence interval is more informative about precision than the point estimate alone because it makes the uncertainty visible. The registry does not report a confidence level explicitly in the comparison description, and no alternative adjusted interval is reported.

In general, precision depends on the amount of information in the data and on variability in the outcome. A broader interval indicates less precision than a narrower one. The available estimate is also tied to the endpoint definition and the missing-follow-up assumption: participants who withdrew consent or were lost to follow-up were treated as having returned to smoking at their baseline rates. The record does not report sensitivity analyses showing how other assumptions might affect the interval.

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 planning figures are participants per arm, not total participants across both arms and not total events. They are illustrations based on the observed effect, rather than the trial’s original design calculation. The stated planning assumptions are a two-sided significance level of 0.05, equal allocation between the arms, and a normal approximation. The effect to detect is set to a stated fraction of the observed effect.

For planning for 100% of the observed effect, the figures are 1,312 participants per arm for 80% power and 1,756 participants per arm for 90% power. For planning for 75% of the observed effect, the figures are 2,313 participants per arm for 80% power and 3,095 participants per arm for 90% power. For planning for 50% of the observed effect, the figures are 5,154 participants per arm for 80% power and 6,900 participants per arm for 90% power.

In plain language, the 80% power column gives the planned number per arm for a hypothetical study with an 80% chance of detecting the specified effect under the stated assumptions and significance level. The 90% column gives the corresponding planned number per arm for a higher target chance of detection. These are planning targets, not observed power. They are not a retrospective measure of how likely this completed study was to produce a statistically significant result.

Smaller effects require more participants to detect under otherwise similar planning assumptions. The values are therefore sensitive to the effect chosen for planning; the observed effect may not be the effect a future study wishes to detect. The original trial’s own sample-size assumptions and justification are not reported in the registry record, so these illustrative figures should not be treated as the trial’s planned enrollment target.

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 = 0.320; treatment proportion p₁ = 0.372; average p̄ = 0.346.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6725 = 1.3182.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6715 = 0.5652.
4. n = (1.3182 + 0.5652)² / (0.0520)² = 1,311.9, rounded up to 1,312 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 <- 0.319587628865979                          # control proportion
p1 <- 0.371584699453552                          # 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(689611)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 1,312 (80% power), 1,756 (90% power)
 75% of observed effect: n per arm = 2,313 (80% power), 3,095 (90% power)
 50% of observed effect: n per arm = 5,154 (80% power), 6,900 (90% power)
power.prop.test: 1311.9 
Simulated power at n = 1,312 per arm: 0.798 (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 registry lists smoking abstinence as the primary outcome and composite major adverse cardiovascular events as a secondary outcome. Results for the secondary outcome are not reported in the registry record. The record does not report a multiplicity strategy, such as an adjustment to account for testing more than one outcome. Without that information, it is not possible to determine whether or how the reported p-value was situated within a broader family of planned tests.

The primary endpoint description states that the analysis was conducted on an intention-to-treat basis. It also specifies a missing-outcome assumption: participants who withdrew consent or were lost to follow-up were assumed to have returned to smoking at their baseline rates. The registry does not report the number of participants with missing outcome data, the reasons for missingness, or whether sensitivity analyses examined alternative assumptions. Consequently, the posted primary estimate should be interpreted in light of the stated assumption, without treating it as a direct observation of smoking status for every participant.

The record reports outcome denominators of 194 for placebo and 183 for bupropion. It separately lists enrollment of 392, but does not explain the relationship between enrollment and the primary-outcome denominators. No additional analysis population definitions are reported, such as a per-protocol population. The exact statistical model, any covariate adjustments, and any planned subgroup analyses are not reported in the registry record.

These reporting gaps do not invalidate the posted estimate, but they limit the detail available for assessing the analysis. In general, multiplicity procedures affect how false-positive risk is managed across multiple tests, while missing-data assumptions affect how participants without observed outcomes contribute to an analysis. The record’s stated intention-to-treat approach and missing-follow-up assumption can be described, but the unreported details should not be inferred.

14. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 4% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: other (8.5%), unstable angina (6.2%), death (4.7%), myocardial infarction (2.6%), psychiatric event (1.0%).

The registry reports serious adverse event counts for the placebo and bupropion groups. For serious adverse events, 37 of 200 participants were affected in the placebo group and 34 of 192 in the bupropion group. For other adverse events, the corresponding counts were 48 of 200 and 58 of 192. These are descriptive counts with the denominators listed for the safety groups; they are separate from the primary abstinence outcome denominators.

The registry also lists serious-event terms and percentages: Other, 8.5%; Unstable Angina, 6.2%; Death, 4.7%; Myocardial Infarction, 2.6%; Psychiatric event, 1.0%; and Stroke, 0.5%. The record does not specify in this listing whether these percentages are pooled or arm-specific, so they should not be assigned to one treatment group. Death counts by group are not reported in the registry record.

These counts do not by themselves establish whether there was a treatment-related difference in safety. The registry does not report comparative estimates, confidence intervals, or tests for these adverse event results. In general, safety comparisons require attention to group sizes, event definitions, follow-up, and the possibility of chance variation; the posted counts should be read as descriptive information rather than as a formal conclusion about relative risk.

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

The primary result describes smoking abstinence at a defined point in follow-up: the week before the 12-month clinic visit, accompanied by an exhaled carbon monoxide measurement less than 11 ppm. The posted percentages were 37.2% with bupropion and 32.0% with placebo. The estimated risk difference was 0.052, and its confidence interval ranged from -0.044 to 0.148. The interval includes no difference and values in either direction, so the estimate does not precisely identify the direction or size of the underlying contrast.

This result does not show that every participant assigned to either group stopped smoking or remained abstinent throughout the follow-up period. The endpoint is point-prevalence abstinence at the 12-month visit, not continuous abstinence since treatment began. It also does not, on its own, establish an effect on cardiovascular events. Although composite major adverse cardiovascular events is listed as a secondary outcome, results for it are not reported in the registry record.

The analysis used an intention-to-treat approach and treated those who withdrew consent or were lost to follow-up as having returned to smoking at their baseline rates. That assumption is part of the primary result’s interpretation. The registry does not report sensitivity analyses under other missing-data assumptions, so the extent to which alternative approaches would change the result is not known from this record.

The result should not be reduced to a binary claim based only on a p-value. The estimate and its interval communicate both the direction of the point estimate and the uncertainty around it. The p-value was 0.288, while the confidence interval spans zero. Neither statistic describes clinical importance by itself, and the registry does not provide a threshold for judging what difference would be clinically meaningful.

In general, trial findings apply most directly to the population, intervention, comparator, endpoint, and follow-up that were studied. Specific eligibility criteria and details of participant characteristics are not reported in the registry record. The available results therefore support a statistical description of this comparison, not a broader conclusion about all people who smoke after an acute coronary syndrome or about other treatment settings.

16. Why This Trial Matters Statistically

This trial raises a clear statistical question: how precisely can a randomized comparison estimate a difference in smoking abstinence at a defined follow-up time? Its binary endpoint is easy to state, but the result depends on an operational rule combining self-report and an exhaled carbon monoxide measurement. That specificity helps readers understand what the outcome represents and what it leaves outside the definition, including whether abstinence was continuous over the full follow-up period.

The contrast is reported as an absolute risk difference, alongside group percentages and a confidence interval. This makes the estimated separation between the groups interpretable on the same scale as the outcome proportions. The interval is important because the point estimate alone would conceal that the data remain compatible with no difference and estimates in either direction. The p-value adds information about compatibility with a null model, but it cannot replace the interval or indicate clinical importance.

The record also illustrates how missing-data assumptions are part of the statistical result. The stated intention-to-treat analysis treats participants lost to follow-up or withdrawing consent as having returned to smoking at their baseline rates. Readers need that detail to understand how incomplete outcome information was handled, while recognizing that the registry does not report sensitivity analyses under alternative assumptions.

Finally, the posted planning illustrations show how sample-size requirements depend on the effect selected for detection and the desired power. They are not the original design calculation, and the trial’s own sample-size justification is not reported. Keeping planning illustrations separate from observed trial results prevents confusion between a design target and an estimate generated by the trial data. Together, these points demonstrate why an effect estimate, its uncertainty, endpoint definition, and analysis assumptions should be considered 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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The registry record does not report detailed participant eligibility criteria or baseline characteristics, limiting assessment of how closely the enrolled group represents other populations. It also does not explain how the enrollment figure of 392 relates to the primary outcome denominators of 194 for placebo and 183 for bupropion. Those figures should be kept distinct rather than assumed to describe the same analysis population.

The primary outcome is a 7-day point-prevalence measure at 12 months, based on self-report and exhaled carbon monoxide. It does not measure continuous abstinence across the entire follow-up period. The stated missing-data approach classifies participants who withdrew consent or were lost to follow-up as having returned to smoking at their baseline rates, but the number of such participants and sensitivity analyses are not reported in the registry record.

The registry does not report the trial’s original sample-size justification, detailed statistical model, allocation method, covariate adjustments, or a multiplicity plan. The secondary outcome of composite major adverse cardiovascular events is listed, but its results are not reported in the registry record. Safety counts are posted, but comparative estimates and uncertainty measures are not reported. These limitations concern the detail available in the record and should not be filled in by assumption.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Zyban as an Effective Smoking Cessation Aid for Patients Following an Acute Coronary Syndrome: The ZESCA Trial — NCT00689611.
• Registry record last updated 2015-04-23; status last verified 2015-04.
• Results first posted 2015-04-23.
• 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

NCT00689611, the ZESCA trial, was a completed Phase 3 randomized, quadruple-masked, parallel-group comparison of bupropion HCl ER and placebo. The registry lists enrollment of 392. Assigned treatment was described as lasting 9 weeks, and the primary endpoint was smoking abstinence at 12 months.

The abstinence definition required self-reported abstinence in the week before the clinic visit and exhaled carbon monoxide less than 11 ppm. Posted percentages were 37.2% for bupropion and 32.0% for placebo. The risk-difference estimate was 0.052, with a confidence interval from -0.044 to 0.148 and an unadjusted p-value of 0.288. The registry reports an intention-to-treat analysis and a stated assumption for participants missing follow-up.

Composite major adverse cardiovascular events is listed as a secondary outcome, but results are not reported in the registry record. Serious and other adverse event counts are posted; the record does not provide comparative uncertainty estimates for them.