Efficacy of LCQ908 on Cardiovascular Risk: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Efficacy of LCQ908 on Cardiovascular Risk (NCT01474434), a randomized phase 2 study. The primary outcome is Change From Baseline in Myocardial Perfusion Reserve Index (MPRi) Overall Mean (Part A, Cohort 1).

2. Clinical Question

3. Trial Design

This was a Phase 2 randomized, triple-masked, placebo-controlled crossover study of pradigastat (LCQ908). The registry lists coronary artery disease and hypertriglyceridemia as conditions and states that the study was designed to evaluate the potential for pradigastat to affect cardiovascular risk. Enrollment was 41. The study began in 2011-12, and primary completion was recorded as 2014-06. Its status is terminated; the reason for termination is not reported in the registry record.

In a crossover design, participants receive more than one treatment condition in a sequence, so a participant can contribute observations under both treatment conditions. The two sequences listed were pradigastat followed by placebo and placebo followed by pradigastat. The registry describes a 30-day washout between the treatment periods. The pradigastat course included an 80 mg daily loading dose for three days followed by 20 mg daily for two days; the placebo course lasted five days. The record does not explain the rationale for the treatment duration or washout period.

The primary endpoint was limited to Part A, Cohort 1. The efficacy summaries include 13 participants for the pradigastat condition and 12 for placebo. These are the analysis counts reported for the primary outcome, rather than the enrollment figure. The registry defines the efficacy analysis set using study-drug adherence, protocol deviations, and availability of MPRi assessments at baseline and after treatment. The specific allocation procedure, sequence-generation method, and details of how the crossover data were analyzed are not reported in the registry record.

The listed secondary outcomes cover adverse events in Part A Cohorts 1 and 2, postprandial triglycerides, pradigastat plasma concentration, other lipid parameters, interleukin-6, and C-reactive protein. The registry lists 14 outcome measures and 9 posted secondary outcomes. No analysis count is posted. The record does not describe why the trial was terminated or whether all planned analyses were completed.

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.

Treatment Period 1 (5 Days [Day 1 - 5])

Reasons for not completing this period:

Treatment Period 2(5 Days[Day 36 - 40])

Analysis population for the primary outcome, as defined in the registry: Efficacy analysis set - Patients who took \> 80% of study drug as assessed by drug accountability, had no major protocol deviations, and had a valid assessment of MPRi at both baseline and post-treatment visits. Patients who had no baseline or post-treatment MPRi data in 1 of 2 periods were excluded from the analysis.

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: The safety analysis set included all patients who received at least one dose of study drug.

6. Primary Endpoint

The registry lists the primary outcome as Change From Baseline in Myocardial Perfusion Reserve Index (MPRi) Overall Mean (Part A, Cohort 1), measured over Baseline, and on day 5 of each of the two treatment periods.

The primary endpoint was change from baseline in the overall mean myocardial perfusion reserve index (MPRi) for Part A, Cohort 1. MPRi is intended to represent coronary microvascular function. The registry describes cardiac imaging at rest and during pharmacological stress induced with adenosine. An independent central reader assessed the images and calculated MPRi as global stress myocardial blood flow divided by resting blood flow. The stated direction is that a higher or increased index indicates improved flow and a better outcome.

Using a physiological measure gives the study an endpoint related to blood-flow function rather than a direct count of cardiovascular events. The registry’s timeframe was baseline and day 5 of each of the two treatment periods. The posted outcome parameter is a least-squares mean, with standard error as the reported measure of dispersion. The record does not provide further detail about the least-squares model or covariates. The endpoint was specified only for Part A, Cohort 1; it should not be read as a primary result for all enrolled participants or for both cohorts.

The reported mean change was -0.22 in the pradigastat condition and 0.32 in the placebo condition. Because MPRi is a continuous measure, the comparison concerns the difference in average change, not the number of participants who experienced a particular clinical event. A physiological change in MPRi, by itself, does not establish a change in cardiovascular outcomes.

7. Results Posted in the Registry

• Part A, Cohort 1: Pradigastat (LCQ908): mean -0.22 versus 0.32 on control; difference -0.54 (95% CI -1.21 to 0.13), standardized effect d = -0.64; z-test p = 0.112.

8. Statistical Interpretation

9. Statistical Methods Explained

The endpoint is continuous: MPRi can take a range of values rather than representing only whether an event occurred. A mean summarizes the average change for a treatment condition, while a mean difference expresses the contrast between the two reported condition means. Here, the posted difference is -0.54 for pradigastat compared with placebo. The sign depends on the comparison order; a negative value means the reported pradigastat mean is lower than the placebo mean. This is a description of the numerical contrast, not a conclusion about clinical benefit or harm.

What does a least-squares mean represent?

In general, a least-squares mean is a model-based estimate of an average. It may account for features specified in the analysis model, unlike a simple arithmetic average. The registry labels the primary endpoint values as least-squares means but does not report the model formula, included terms, or adjustment variables. It is therefore not possible to identify from the record which factors, if any, were incorporated into those estimates. The posted values should be described using the registry’s label rather than assuming a particular modeling approach.

How should standard errors be read?

The standard error describes the estimated precision of a mean, not the spread of individual participant measurements. The reported standard error is 0.24 for each treatment condition. In general, a smaller standard error indicates a more precisely estimated mean, while a larger one indicates more sampling uncertainty. Standard errors do not show the range of individual MPRi changes, and they should not be interpreted as standard deviations. The registry does not provide raw observations or additional distributional summaries for the primary endpoint.

Why does the crossover design affect analysis?

In general, a crossover design compares treatments within participants as well as across treatment sequences. Measurements from one participant may be more alike than measurements from different participants. A paired analysis can account for this dependence and may use the within-person contrast. The analysis may also need to consider treatment period and sequence. Washout periods are intended to separate treatment periods, but the presence of a washout does not by itself demonstrate that carryover is absent. The registry lists a 30-day washout but does not report how period, sequence, within-person dependence, or carryover were addressed statistically.

The posted comparison includes condition means, a mean difference, a confidence interval, an unadjusted p-value, and a standardized effect-size estimate. The record marks the pooled standard deviation as approximate, with the approximation based on the reported standard errors. Because the crossover analysis details are not reported, the posted summary should not be treated as a reconstruction of a paired model or as a substitute for the sponsor’s complete analysis. The discrepancy flag is false, so no discrepancy between the posted figures and a sponsor analysis is identified in the record; the sponsor’s full analysis is nevertheless not reported in the registry record.

What does the p-value address?

In general, a p-value measures how unusual data at least as extreme as those observed would be under a specified statistical model and null hypothesis. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect. The posted unadjusted p-value is 0.112. The record does not state whether this p-value was produced by the sponsor’s planned analysis or by another method, and it does not provide an adjusted p-value. Interpretation is limited by the absence of the underlying model and analysis plan.

What is a standardized effect size?

A standardized effect size expresses a difference in units of an estimated standard deviation, which can help compare effects measured on different scales. It is not in the original MPRi units and can be sensitive to how the standard deviation is estimated. The record gives a standardized effect-size estimate, but it is not necessary to use that estimate to interpret the original-scale mean difference. Since the pooled spread is marked approximate and the analysis is a crossover, the standardized summary should be treated as an approximate planning input rather than a definitive description of the treatment effect.

Statistical methods are useful only in relation to the question and the assumptions used. For this endpoint, the main limitations in judging the method are the unreported crossover model, unreported handling of missing measurements, and absence of the statistical analysis plan. Those omissions prevent a reader from checking whether the posted model aligns with the study design or whether any planned adjustments were applied.

10. Confidence Intervals and Precision

The confidence interval around the posted mean difference represents a range of estimates compatible with the data under the assumptions of the interval method. It spans values on both sides of no difference. Thus, the observed point estimate of -0.54 is not precise enough to rule out a difference in the opposite direction or a small difference near no effect. This uncertainty should be considered alongside the unadjusted p-value of 0.112.

A confidence interval is not a guarantee that the true effect lies within its bounds, nor does it assign probability to particular values after the data have been observed. In general, its width reflects both the variability in the data and the amount of information available, together with the method used to calculate it. The registry reports standard errors of 0.24 for the two condition means, but it does not provide the interval method or enough model detail to evaluate the calculation independently.

The primary analysis counts were 13 for pradigastat and 12 for placebo. The original enrollment was 41, but the primary analysis population was defined by treatment adherence, protocol deviations, and availability of MPRi assessments in both periods. The record does not provide a full accounting of exclusions or missingness. The limits of precision therefore cannot be understood solely from enrollment; the analysis population, crossover structure, and model also matter.

The interval is compatible with more than one interpretation of the treatment contrast. It does not establish equivalence, and it does not show that the result is clinically unimportant. The registry does not report a clinically meaningful threshold for MPRi, so the clinical importance of values in the interval cannot be judged from the record.

11. Planning a Similar Trial: Sample Size

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

The sample-size illustrations are planning calculations based on the observed effect, not the trial’s original design assumptions. For this continuous outcome, the figures are participants per arm; in time-to-event settings, the stated convention is total events. The assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect to detect set at the stated fraction of the observed effect. The original sample-size justification is not reported in the registry record.

For planning for 100% of the observed effect, the illustration gives 39 participants per arm for 80% power and 52 participants per arm for 90% power. For planning for 75% of the observed effect, the corresponding figures are 70 and 93 participants per arm. For planning for 50% of the observed effect, they are 156 and 208 participants per arm.

In plain language, the 80% power column gives the number of participants per arm estimated to provide an 80% chance of detecting the specified effect under the stated assumptions, if that effect were the true one. The 90% power column applies the same idea at a higher target chance of detection. Smaller effects are harder to distinguish from random variation, so the illustrations require more participants per arm when the target effect is a smaller fraction of the observed effect. These values are planning illustrations, not observed power.

The calculations use the observed effect as their starting point, so they should not be read as a recommendation for a future study or as evidence that the original trial was designed around these assumptions. The registry does not report the original planning assumptions, including any planned allowance for crossover correlation, missing data, or other design features.

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

Worked calculation for this trial

1. Observed difference Δ = -0.540; pooled standard deviation σ = 0.849.
2. n = 2 × (1.96 + 0.842)² × 0.849² / -0.540² = 38.8, rounded up to 39 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- -0.54                      # observed difference in means
sigma <- 0.849265667452207                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(1474434)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 39 (80% power), 52 (90% power)
 75% of observed effect: n per arm = 70 (80% power), 93 (90% power)
 50% of observed effect: n per arm = 156 (80% power), 208 (90% power)
power.t.test: 39.8 
Simulated power at n = 39 per arm: 0.785 (20000 replicates)

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

13. Multiplicity, Missing Data and Analysis Populations

The registry lists 14 outcome measures and 9 posted secondary outcomes. The secondary outcomes include adverse events in Part A, Cohorts 1 and 2; postprandial triglycerides in both cohorts; plasma concentration of pradigastat; other related lipid parameters; interleukin-6; and C-reactive protein. The record does not report a multiplicity strategy, such as a procedure for controlling the chance of false-positive findings across multiple outcomes or comparisons. It is therefore not possible to determine whether the posted unadjusted p-value was intended to be interpreted alongside adjustments for other analyses.

The analysis count is listed as 0, and no detailed statistical analysis plan is reported in the registry record. This does not establish whether analyses were or were not conducted; it means the record does not provide a count of posted analyses in that field. The registry also does not report whether the primary endpoint or secondary analyses were changed after study initiation, or whether interim analyses were planned or performed.

The primary endpoint population was an efficacy analysis set. It included patients who took > 80% of study drug, had no major protocol deviations, and had valid baseline and post-treatment MPRi assessments. Patients without baseline or post-treatment MPRi data in one of the two periods were excluded. This definition is not the same as a simple statement that every enrolled participant was included in the primary analysis. The record does not give counts or reasons for each type of exclusion, and it does not describe imputation or other missing-data methods.

These reporting gaps matter because outcome selection, analysis populations, missing observations, and multiplicity can affect how estimates and p-values should be interpreted. Without the analysis plan or additional participant-flow information, readers cannot fully assess whether the posted result reflects the intended analysis or how robust it is to alternative handling of missing data and multiple outcomes.

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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: coronary artery disease (4.2%).

For Part A, Cohort 1, the registry lists 0 serious adverse events among 17 participants at risk in the pradigastat group and 0 among 17 at risk in the placebo group. For Part A, Cohort 2, it lists 0 serious adverse events among 24 at risk in the pradigastat group and 1 among 24 at risk in the placebo group. These are event counts and the associated at-risk group sizes as recorded; they are not a statistical comparison of safety between treatment conditions.

The registry’s top serious-event term is coronary artery disease, listed as 4.2%. The record does not specify in the posted summary how that term relates to the cohort-specific figures, so it should not be assigned to a particular treatment group from these data alone. Death counts are not reported in the registry record. The listed non-serious adverse-event counts were 16 for Cohort 1 pradigastat, 6 for Cohort 1 placebo, 22 for Cohort 2 pradigastat, and 13 for Cohort 2 placebo.

Adverse-event counts in a limited study do not by themselves characterize all possible safety outcomes. The registry does not provide event-level descriptions, timing, severity details beyond the serious-event classification, or a full safety analysis. The counts should be read as the posted record, not as a basis for a treatment recommendation.

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

The registry’s primary result is a comparison of mean change in MPRi in Part A, Cohort 1. The mean change was -0.22 for pradigastat and 0.32 for placebo, with a posted mean difference of -0.54. The unadjusted p-value was 0.112, and the confidence interval spans values on both sides of no difference. These results describe an uncertain estimate of a physiological measure; they do not establish a clear treatment contrast.

The endpoint is relevant to coronary microvascular function as defined in the registry, but it is not itself a count of cardiovascular events. A change in MPRi cannot be assumed to translate into a change in cardiovascular risk or clinical outcomes. The registry’s description that higher or increased MPRi indicates improved flow explains the direction of the measure, but it does not provide a threshold for clinical importance. Such a threshold is not reported in the registry record.

The results are specific to the efficacy analysis set for Part A, Cohort 1. They should not be generalized automatically to Part A, Cohort 2, all enrolled participants, or other populations. The registry does list adverse events and several secondary outcomes, but those outcomes do not replace the primary endpoint, and their detailed statistical results are not included in the posted primary comparison. The reason for the terminated status is also not reported.

In general, a non-significant p-value does not prove that there is no effect, just as a numerical difference does not prove that there is an effect. The uncertainty in the interval, the modest primary analysis counts, and the unreported details of the crossover model limit how strongly the result can be interpreted. The record supports describing the observed estimates and their uncertainty, but not a claim that pradigastat does or does not alter cardiovascular risk.

The sample-size illustrations are separate planning exercises based on the observed effect. They do not establish that the study had adequate or inadequate power under its original design, because the original design assumptions are not reported. Nor can those illustrations resolve the limitations in the endpoint analysis or indicate what the result would be in a different population or study.

16. Why This Trial Matters Statistically

This study illustrates why the design and the analysis population matter as much as the headline estimate. The trial used a crossover structure, but the registry does not report whether the posted comparison accounts for measurements from the same participant, treatment sequence, or period. Those details can be central to interpreting a crossover comparison. A reported mean difference alone does not reveal how those design features were handled.

It also shows the distinction between enrollment and the population contributing to a specific endpoint. Overall enrollment was 41, while the primary efficacy summaries report 13 participants for pradigastat and 12 for placebo. The registry defines the efficacy set using adherence, protocol deviation, and MPRi data availability. Without participant-flow details, the reader cannot determine how those criteria affected the analysis or the representativeness of the result.

The interval and p-value provide complementary information. The point estimate describes the direction and size of the posted contrast, while the confidence interval communicates uncertainty and the p-value summarizes compatibility with a specified null model. The interval includes values on either side of no difference, so a single point estimate should not be read as a settled finding. The p-value does not establish equivalence or measure clinical importance.

Finally, the registry includes sample-size planning illustrations based on the observed effect, but not the trial’s original assumptions. Keeping those two kinds of information separate prevents retrospective planning calculations from being mistaken for evidence about the study’s original design or its observed power. The missing analysis-plan and multiplicity details further demonstrate why registry summaries should be interpreted as a limited account of a trial rather than a complete statistical report.

17. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The primary endpoint results apply to Part A, Cohort 1 and the defined efficacy analysis set, not automatically to the full enrolled population. Although enrollment was 41, the primary summaries report 13 participants for pradigastat and 12 for placebo. The registry does not provide a full participant flow or counts for the different exclusion criteria, so the reasons for the difference between enrollment and the endpoint analysis cannot be fully evaluated.

The crossover design makes the unreported analysis details particularly important. The registry does not state how within-participant correlation, treatment period, sequence, or possible carryover was handled. It also does not report the model used to produce the least-squares means, the confidence-interval method, or any adjustment variables. The displayed comparison is therefore not enough to reconstruct or assess the intended crossover analysis.

The record does not report a statistical analysis plan, multiplicity procedure, missing-data method, or interim-analysis plan. It does not give a clinically meaningful threshold for MPRi, detailed reasons for termination, or a complete explanation of how the serious adverse-event term relates to cohort-specific counts. These omissions limit interpretation of the primary estimate, the secondary outcomes, and the safety summaries.

The primary measure is a physiological index rather than a clinical-event endpoint. The registry describes the direction of MPRi but does not establish how a change in this index corresponds to cardiovascular risk or clinical outcomes. The posted confidence interval indicates uncertainty, and the original sample-size assumptions are not reported. The planning illustrations based on the observed effect cannot fill those gaps.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Efficacy of LCQ908 on Cardiovascular Risk — NCT01474434.
• Registry record last updated 2016-04-14; status last verified 2016-03.
• Results first posted 2016-04-14.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

21. Record Summary

The registry record describes a Phase 2 randomized, triple-masked, placebo-controlled crossover study of pradigastat (LCQ908), with enrollment of 41 and a terminated status. The study began in 2011-12, and primary completion was recorded as 2014-06. The reason for termination is not reported in the registry record.

The primary endpoint was change from baseline in MPRi for Part A, Cohort 1. The posted mean changes were -0.22 for pradigastat and 0.32 for placebo; the mean difference was -0.54, with an unadjusted p-value of 0.112. The confidence interval spans values on either side of no difference. The registry reports the endpoint as a least-squares mean with standard error, and defines an efficacy analysis set requiring adherence, no major protocol deviations, and valid MPRi observations in both periods.

Interpretation is limited by the absence of detailed crossover-analysis methods, missing-data handling, multiplicity procedures, and the original sample-size assumptions. The record lists adverse events and secondary outcomes, but the primary comparison alone does not establish effects on cardiovascular events or risk.