Randomized Study to Evaluate the Effect of Obicetrapib on Top of Maximum Tolerated Lipid-Modifying Therapies: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Randomized Study to Evaluate the Effect of Obicetrapib on Top of Maximum Tolerated Lipid-Modifying Therapies (NCT05142722), a randomized phase 3 study. The primary outcome is Percent Change in Low-Density Lipoprotein Cholesterol (LDL-C) From Baseline to Day 84 [PUC].

2. Clinical Question

The primary endpoint focuses on a laboratory measure of LDL-C rather than a clinical event. The comparison asks whether average change from baseline differed between the randomized groups over the stated follow-up interval. The result is specific to this population, treatment context, measurement method, and time point. It does not by itself answer whether the difference changes the likelihood of cardiovascular events or other outcomes that matter directly to participants.

The registry also lists secondary lipid measures, including LDL-C at later time points and changes in apolipoprotein B and non-HDL-C. Those outcomes provide related measures of lipid response, but they do not replace the prespecified primary question. The registry record does not report the rationale for choosing the endpoint or the detailed eligibility criteria beyond the population description above.

3. Trial Design

This was a randomized, placebo-controlled, double-blind, phase 3 study with a parallel-group design. The registry identifies quadruple masking. Participants were assigned to obicetrapib 10mg or placebo, each taken once daily, on top of diet and maximally tolerated lipid-lowering therapy. The registry lists NewAmsterdam Pharma as sponsor.

The registry reports enrollment of 2530. The primary endpoint analysis is described for the baseline population, defined as all participants randomized, with baseline values taken from the last measurement before the first dose of study drug. The arm-level primary endpoint summaries include 842 participants in the placebo group and 1679 in the obicetrapib 10mg group. These analysis counts are not interchangeable with the registry’s enrollment figure: they describe the posted endpoint summaries, not necessarily every participant’s status at every stage of the study.

The study began on 2021-12-15, had primary completion on 2024-09-26, and is listed as completed. Its stated purpose was to evaluate efficacy, safety, and tolerability of obicetrapib as an adjunct to diet and maximally tolerated lipid-lowering therapy among participants with HeFH and/or ASCVD whose lipid-modifying therapies did not adequately control their condition.

The registry record does not report the original sample-size justification, the allocation ratio specified in the protocol, the detailed conduct of masking, or whether interim analyses were planned or performed. The planning illustrations associated with the posted results are not evidence of the original design assumptions. The reported analysis method for the primary endpoint is ANCOVA, with least-squares means; further model 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:

Analysis population for the primary outcome, as defined in the registry: Baseline Population is defined as all participants who were randomized in the study. Baseline values were defined as last measurement prior to the first dose of study drug. Missing values were imputed using a multiple imputation model assuming the data are missing not at random. Missing measurements of non-retrieved dropouts were modeled by known measurements from retrieved dropouts in the same treatment group.

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: Baseline Population is defined as all randomized participants.

6. Primary Endpoint

The registry lists the primary outcome as Percent Change in Low-Density Lipoprotein Cholesterol (LDL-C) From Baseline to Day 84 [PUC], measured over 84 Days.

The primary endpoint is percent change in LDL-C from baseline to Day 84, measured by preparative ultracentrifugation (PUC). It is reported as a least-squares mean for each group and as a between-group contrast. Percent change expresses how the endpoint measurement differs from the participant’s baseline relative to that baseline, while the between-group estimate compares the adjusted group averages.

LDL-C is a continuous laboratory measure, so the analysis estimates average change rather than counting participants who crossed a threshold. In general, continuous outcomes can retain information about the size of changes across the range of observed values. A group mean, however, does not show how much individuals vary or whether every participant’s outcome followed the group average.

The Day 84 time point makes the primary question specific to the early follow-up defined in the registry. PUC is the stated method for measuring LDL-C for this endpoint. The registry also lists other lipid measures and later time points as secondary outcomes, but they address distinct questions and should not be substituted for the primary endpoint.

The endpoint is a biomarker outcome, not a direct count of cardiovascular events, deaths, or symptoms. The registry record does not report a minimally important difference or another threshold for judging clinical importance. Statistical evidence about the group contrast therefore needs to be interpreted separately from claims about patient-important benefit.

7. Results Posted in the Registry

• Obicetrapib 10mg: mean -29.94 versus 2.70 on control; difference -32.64 (95% CI -36.40 to -28.88), standardized effect d = -0.72; z-test p = <0.001.

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.

Percent Change in Low Density Lipoprotein-Cholesterol (LDL-C) From Baseline to Day 180 [Martin/Hopkins]

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Obicetrapib 10mg: mean -29.09 versus 4.68 on control; difference -33.77 (95% CI -37.70 to -29.84), standardized effect d = -0.70; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Obicetrapib 10mg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percent Change in Low Density Lipoprotein-Cholesterol (LDL-C) From Baseline to Day 365 [PUC]

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Obicetrapib 10mg: mean -25.25 versus -1.27 on control; difference -23.98 (95% CI -28.54 to -19.42), standardized effect d = -0.41; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Obicetrapib 10mg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percent Change in Apolipoprotein B (ApoB) From Baseline to Day 84

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Obicetrapib 10mg: mean -17.84 versus 1.08 on control; difference -18.92 (95% CI -21.14 to -16.70), standardized effect d = -0.70; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Obicetrapib 10mg than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary outcomes cover LDL-C at later follow-up and other lipid measures, including apolipoprotein B and non-HDL-C. Across the reported secondary lipid results, the overall pattern is consistent with between-group differences in percent change favoring lower values in the obicetrapib group. These related measures provide context for the primary LDL-C finding, but they represent separate endpoints, measurement approaches, and follow-up times.

The registry lists 21 outcome measures, 13 analyses, and 19 posted secondary outcomes. The registry record does not report why not every listed outcome has a posted result, nor does it describe a complete hierarchy or confirmatory testing strategy for the secondary outcomes. The individual results should be interpreted according to their own estimates and uncertainty rather than treated as interchangeable measures of a single outcome.

Multiplicity matters when many outcomes are examined. In general, each additional statistical test creates another opportunity for a result to appear notable by chance. If analyses are not adjusted or otherwise organized into a prespecified testing strategy, nominal p-values may overstate the strength of evidence across the entire collection of outcomes. The registry record does not report whether multiplicity adjustments were used for these secondary analyses.

For that reason, the secondary findings can support a broad description of the observed lipid pattern, but they carry less weight than a primary endpoint analyzed as the main trial question. They should not be presented as multiple independent confirmations, and the posted secondary results do not establish effects on clinical events or outcomes outside the measured lipid endpoints.

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

• Percent Change in Low Density Lipoprotein-Cholesterol (LDL-C) From Baseline to Day 180 [Martin/Hopkins]
• Percent Change in Low Density Lipoprotein-Cholesterol (LDL-C) From Baseline to Day 365 [PUC]
• Percent Change in Apolipoprotein B (ApoB) From Baseline to Day 84
• Percent Change in Apolipoprotein B (ApoB) From Baseline to Day 180
• Percent Change in Apolipoprotein B (ApoB) From Baseline to Day 365
• Percent Change in Non-HDL-C From Baseline to Day 84
• Percent Change in Non-HDL-C From Baseline to Day 180
• Percent Change in Non-HDL-C From Baseline to Day 365

9. Statistical Interpretation

10. Statistical Methods Explained

The registry reports ANCOVA for the primary comparison, with least-squares (LS) means. ANCOVA is a general statistical method for comparing group averages while accounting for covariates included in the model. In general, adjustment can improve precision when the chosen covariates explain variation in the outcome, but the adjustment depends on the specific model. The registry record does not report the full covariate list or all model specifications, so the reported analysis cannot be reproduced from the record alone.

What does a least-squares mean represent?

In general, a least-squares mean is a model-based group average, sometimes described as an adjusted mean. It is not necessarily the same as a raw arithmetic average. The registry reports primary group values of 2.7 for placebo and -29.94 for obicetrapib 10mg, and reports the ANCOVA contrast separately. The treatment contrast is the central comparative estimate: it expresses how far apart the model-based group means are on the endpoint scale.

How does the recalculation differ?

The recalculated comparison uses posted arm-level means and standard errors to make a simple, unadjusted estimate of the difference and its uncertainty. Its standard deviation is approximate, as indicated in the posted calculation details. This approach is useful as a check on the scale and direction of the reported result, but it cannot reproduce an ANCOVA that uses participant-level data and model covariates. The recalculated primary contrast is -32.64, while the registry ANCOVA estimate is -32.65. Their closeness is reassuring as a numerical consistency check, but the registry’s own analysis remains the main analysis to interpret.

A p-value measures how incompatible the observed data are with a specified null hypothesis under the statistical model. In general, a small p-value is not the probability that the null hypothesis is true, nor is it a measure of the size or clinical importance of an effect. The registry reports p <.0001 for its primary ANCOVA analysis; the simple recalculation reports p <0.001. The differing notation reflects the posted results and should be retained as reported.

A confidence interval describes a range of values compatible with the data and analysis under repeated-sampling assumptions. In general, a 95% confidence procedure would cover the true parameter in 95% of repeated studies conducted under the same assumptions. For the primary registry analysis, the reported interval runs from -35.79 to -29.50. Its location relative to zero supports a nonzero average contrast under the stated model, while its width communicates uncertainty about the estimate. It does not describe the range of individual participant responses.

What does the standard error describe?

The registry identifies standard error as the dispersion measure for these continuous outcome summaries. In general, a standard error describes uncertainty in an estimated mean, rather than the spread of individual measurements. A standard error can be used to construct an interval when combined with an appropriate statistical distribution and analysis assumptions. It should not be read as though most individual outcomes lie within one standard error of the group mean.

Over-dispersion is a general concern for some statistical outcomes when observed variation exceeds what a model assumes. It is especially relevant to count outcomes, where simple models may underestimate uncertainty if event counts vary more than expected. The primary endpoint is continuous, and the registry reports a standard error; the record does not report an over-dispersion model or a reason to apply one to this endpoint. General warnings about over-dispersion should not be mistaken for a feature documented in this trial.

How should missing data be understood?

The primary endpoint description states that missing values were imputed using a multiple-imputation model assuming data were missing not at random. In general, multiple imputation creates several plausible completed datasets, analyzes each, and combines the estimates to reflect uncertainty from missing values. A missing-not-at-random assumption allows the probability of missingness to depend on unobserved values, but its implementation requires assumptions about how missing outcomes relate to observed information and unobserved outcomes. The registry record does not report the imputation model details, sensitivity analyses, or the amount and pattern of missingness.

Analysis populations matter because the participants contributing to an endpoint may differ from those randomized, depending on outcome availability and analysis rules. The registry defines its baseline population as all randomized participants and states that baseline values were the last measurement before first dose. The record does not provide a complete account of endpoint-specific exclusions or the numbers of missing values. For that reason, the arm counts in the endpoint summaries should be read as the posted analysis counts, not as a complete description of every participant’s follow-up.

11. Confidence Intervals and Precision

The registry’s primary ANCOVA estimate is -32.65 percentage points, with a 95% confidence interval from -35.79 to -29.50. The interval is entirely below zero, so the range of values compatible with this analysis points toward a lower average percent change in the obicetrapib group than in the placebo group. The interval also gives a sense of the uncertainty around the point estimate: it does not reduce the result to a single exact value.

The simple recalculation gives -32.64, with a confidence interval from -36.4 to -28.88. This interval is wider than the registry ANCOVA interval. The recalculation is based on posted summary statistics and an approximate pooled standard deviation; the registry analysis uses ANCOVA. Differences in method and available information can lead to different interval widths. The recalculated interval should therefore not be treated as a correction to the registry result.

In general, confidence intervals reflect sampling uncertainty under the analysis assumptions, not every source of uncertainty. They do not capture possible bias from measurement, missing-data assumptions, model specification, or limits in applying the result to other populations. Nor do they show the distribution of individual responses. A narrow interval can indicate comparatively precise estimation of a group contrast while leaving questions about clinical importance or generalizability unresolved.

The registry record does not report a clinical-importance threshold for this outcome. Thus, the interval can be interpreted statistically in relation to zero, but the record alone does not establish whether every value within it represents a meaningful difference to patients. The interval and the endpoint’s clinical meaning are related but distinct considerations.

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 give participants per arm for 80% power and participants per arm for 90% power. They are not totals across both groups and are not total event counts. The assumptions listed for these illustrations are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect.

In plain language, 80% power describes the chance of detecting an effect of the assumed size using the stated test if that effect is the true one, under the planning assumptions. The 90% power column uses a higher target chance of detection for the same assumed effect. For a given effect assumption, targeting greater power generally requires more participants per arm.

The rows for 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect illustrate how the planning estimate changes when the assumed effect is reduced. Smaller effects need more participants to detect with the same power and significance assumptions. The figures are planning illustrations based on the observed estimate, not the original trial’s documented design calculation. The original sample-size justification and design assumptions are not reported in the registry record. These figures are not observed power.

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

Worked calculation for this trial

1. Observed difference Δ = -32.640; pooled standard deviation σ = 45.354.
2. n = 2 × (1.96 + 0.842)² × 45.354² / -32.640² = 30.3, rounded up to 31 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 <- -32.64                      # observed difference in means
sigma <- 45.3538862171218                     # 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(5142722)
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 = 31 (80% power), 41 (90% power)
 75% of observed effect: n per arm = 54 (80% power), 73 (90% power)
 50% of observed effect: n per arm = 122 (80% power), 163 (90% power)
power.t.test: 31.3 
Simulated power at n = 31 per arm: 0.793 (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.

14. Analyses Reported by the Sponsor

The registry’s own primary analysis is an ANCOVA comparison of least-squares means. It reports a difference of -32.65, a 95% confidence interval from -35.79 to -29.50, and p <.0001. That is the result to prioritize when describing the trial’s reported analysis.

The recalculated result is a simple unadjusted estimate based on the posted group summaries. It is -32.64, with a confidence interval from -36.4 to -28.88 and p <0.001. The point estimate is close to the registry estimate, while the confidence interval differs. This is expected to be possible because a summary-statistic recalculation does not reproduce the ANCOVA model or all information from participant-level data.

The registry record does not report enough model detail to independently reconstruct the ANCOVA. The recalculation is therefore a supplementary check on direction and approximate magnitude, not a replacement for the analysis reported by the study. No discrepancy is flagged between the recalculation and the registry analysis.

15. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study tests more than one endpoint, time point, or comparison. In general, the more tests conducted, the greater the chance that at least one result will appear statistically notable even when there is no true effect for that particular test. Trials can address this through a prespecified hierarchy, adjusted significance thresholds, or other strategies. The registry record lists numerous outcome measures and analyses, but does not report whether multiplicity was controlled, what procedure was used, or whether the secondary analyses were intended to be confirmatory.

The primary endpoint has a reported ANCOVA estimate, confidence interval, and p-value. The registry labels the analysis as superiority, but the record does not provide a detailed testing hierarchy or all statistical-plan details. The primary finding should therefore be distinguished from the collection of secondary findings, rather than interpreting every nominal result as carrying the same evidential weight.

For missing data, the primary endpoint description states that missing values were imputed using a multiple-imputation model assuming data were missing not at random. This makes the result dependent in part on assumptions about unobserved outcomes. The registry record does not report the imputation model’s variables, the number or pattern of missing observations, the sensitivity analyses, or how conclusions changed under alternative missing-data assumptions.

The registry defines the baseline population as all randomized participants and defines baseline values as the last measurement before the first study-drug dose. However, the record does not fully describe the analysis population rules for each endpoint, all exclusions, or the handling of participants with no post-baseline measurement. These omissions limit independent assessment of missingness and of how closely the posted endpoint summaries represent the randomized population. They do not, on their own, show that the reported method was inappropriate; they identify details that are not reported in the registry record.

16. Safety Results

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

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 2% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: acute myocardial infarction (1.2%), angina unstable (1.0%), myocardial infarction (0.8%), pneumonia (0.7%), coronary artery disease (0.5%).

The registry reports serious adverse events for 117 of 843 participants in the placebo group and 211 of 1685 participants in the obicetrapib 10mg group. It reports other adverse events for 241 of 843 participants in the placebo group and 459 of 1685 participants in the obicetrapib 10mg group. Deaths are listed as 12 and 19, respectively. These are counts and denominators as posted, not adjusted comparisons.

The most frequently listed serious-event terms include acute myocardial infarction at 1.2%, unstable angina at 1.0%, myocardial infarction at 0.8%, pneumonia at 0.7%, coronary artery disease at 0.5%, and cardiac failure at 0.5%. A listed event term does not establish that the event was caused by the study treatment. Counts can also be influenced by follow-up duration, exposure, event ascertainment, and how events are coded.

The registry record does not report a comparative statistical analysis of these safety figures or a causal assessment. The figures describe recorded events and should not be treated as proof of equivalence or difference in safety.

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

The primary result describes a difference between randomized groups in average percent change in PUC-measured LDL-C through Day 84. The registry ANCOVA estimate is -32.65 percentage points, with a 95% confidence interval from -35.79 to -29.50. The interval is below zero, and the reported p-value is p <.0001. These statistics support a difference in the measured primary endpoint under the reported analysis.

That conclusion is narrower than a claim about overall health outcomes. LDL-C percent change is a laboratory measure, not a direct measure of cardiovascular events, symptoms, or survival. The registry record does not report a threshold for clinical importance, and the primary result alone cannot establish how the observed biomarker contrast translates into those outcomes.

A group average also does not imply that all participants experienced the same change. It does not describe the full distribution of individual responses, and it cannot show from the mean alone how many participants improved, worsened, or had no change. The confidence interval quantifies uncertainty in the estimated group contrast under the analysis assumptions; it is not an interval for individual outcomes.

The findings are tied to the study population and context: participants with HeFH and/or ASCVD whose lipid-modifying therapies were not adequately controlling their condition, with obicetrapib or placebo added to diet and maximally tolerated lipid-lowering therapy. The result should not be assumed to apply to other populations, interventions, background-treatment settings, endpoints, or time points without additional evidence.

Finally, the endpoint result does not resolve safety or missing-data questions. The registry separately lists adverse-event counts and describes multiple imputation for missing primary endpoint values, but detailed safety comparisons and sensitivity analyses are not reported in the registry record. Those issues require consideration alongside, rather than inference from, the primary lipid result.

18. Why This Trial Matters Statistically

This trial provides a clear statistical example of how a randomized comparison can estimate a difference in a continuous biomarker while preserving the distinction between a measured endpoint and a clinical outcome. The primary analysis reports a least-squares mean contrast with an interval, allowing readers to assess both the estimated direction and its uncertainty rather than relying on a p-value alone.

The registry also makes it possible to compare a reported ANCOVA estimate with a simple recalculation from arm-level summaries. The close point estimates offer a check on the numerical direction and scale, while the difference in interval width illustrates why a recalculation cannot substitute for a participant-level adjusted model. Clear separation of the two estimates helps prevent unadjusted checks from being mistaken for the trial’s own analysis.

The case also highlights interpretive limits that matter in clinical research: missing outcomes require assumptions, multiple endpoints raise multiplicity concerns, and safety counts require context. The registry provides some methods information, including the missing-not-at-random imputation assumption, but does not report enough detail to assess every assumption or analysis choice fully.

For biostatisticians and clinical researchers, the central lesson is to keep the estimand, analysis method, uncertainty, and clinical meaning connected but distinct. A statistically precise difference in a biomarker may be important evidence about that biomarker, while questions about clinical outcomes, applicability, safety, and robustness remain separate. The record’s level of detail determines how far those questions can be evaluated.

19. 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 report the full ANCOVA specification, including the complete set of covariates and other model details. This limits independent reproduction and makes it difficult to assess the role of adjustment in the reported interval. The unadjusted recalculation uses summary statistics and an approximate pooled standard deviation, so it cannot resolve that limitation.

The primary endpoint description identifies multiple imputation under a missing-not-at-random assumption, but the imputation model, amount and pattern of missingness, and sensitivity analyses are not reported in the registry record. Consequently, the robustness of the primary estimate to alternative missing-data assumptions cannot be assessed from these details alone. Endpoint analysis counts also do not provide a complete account of follow-up or exclusions.

The registry record does not report the original sample-size justification or the trial’s design assumptions. The planning illustrations are based on the observed effect and stated generic planning assumptions, not evidence of how the study was originally designed. The record also does not report whether interim analyses occurred, or the detailed multiplicity strategy for secondary outcomes.

The primary outcome is a lipid biomarker at a specified follow-up, so it does not directly establish effects on cardiovascular events or other patient-important outcomes. The registry lists safety counts but does not provide a comparative safety analysis or causal attribution. These limitations constrain interpretation; they do not establish that the trial’s analyses or results are invalid.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Randomized Study to Evaluate the Effect of Obicetrapib on Top of Maximum Tolerated Lipid-Modifying Therapies — NCT05142722.
• Registry record last updated 2025-09-19; status last verified 2025-02.
• Results first posted 2025-09-19.
• 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.

23. Record Summary

The registry describes a completed, randomized, placebo-controlled, double-blind, phase 3 parallel-group study of obicetrapib 10mg added to diet and maximally tolerated lipid-modifying therapy in participants with HeFH and/or ASCVD whose lipid levels were not adequately controlled. Enrollment is listed as 2530.

The primary endpoint was percent change in PUC-measured LDL-C from baseline to Day 84. The registry’s ANCOVA analysis reports a between-group difference of -32.65, with a 95% confidence interval from -35.79 to -29.50 and p <.0001. Secondary lipid outcomes show a broadly consistent pattern, but the registry record does not report whether multiplicity was addressed.

The primary endpoint description states that missing values were imputed under a missing-not-at-random assumption. Further model and sensitivity-analysis details are not reported in the registry record. Safety events are listed as counts, without a reported comparative causal analysis. The primary result concerns a lipid measure and does not by itself determine clinical outcomes or safety.