A Study of the Safety and Efficacy of Two Different Regimens of Mipomersen in Patients With Familial Hypercholesterolemia and Inadequately Controlled Low-Density Lipoprotein Cholesterol: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study of the Safety and Efficacy of Two Different Regimens of Mipomersen in Patients With Familial Hypercholesterolemia and Inadequately Controlled Low-Density Lipoprotein Cholesterol (NCT01475825), a randomized phase 3 study. The primary outcome is Percent Change From Baseline To Primary Endpoint Visit (PET) In LDL-C In Cohort 1.

2. Clinical Question

3. Trial Design

The registry describes a Phase 3, randomized, double-blind, placebo-controlled, parallel-group study, followed by an open-label continuation period. Masking is recorded as quadruple. The study is marked completed, with enrollment of 309. The registry identifies Kastle Therapeutics, LLC as the sponsor. The recorded start is 2011-12 and primary completion is 2015-12-29.

The randomized portion compared two dosing schedules. Regimen A used subcutaneous mipomersen 200 mg once weekly or matching once-weekly placebo. Regimen B used subcutaneous mipomersen 70 mg thrice weekly or matching thrice-weekly placebo. Thus each active regimen has a schedule-matched control, and the reported primary comparisons are against those controls.

The primary endpoint concerns Cohort 1, defined by severe HeFH and specified LDL-C and CHD/risk-equivalent criteria. Cohort 2 is described in the study summary as participants with HeFH, LDL-C of at least 160 mg/dL and less than 200 mg/dL, and CHD or risk equivalents. Secondary outcomes include lipid measures in both cohorts. The primary endpoint population was a full analysis set with additional requirements for study-drug exposure and LDL-C measurements; it should not be assumed to include every enrolled participant.

The registry record does not report the original sample-size justification, the planned allocation ratio, the detailed conduct of the continuation period, or the statistical analysis plan. The record identifies randomization and masking but does not provide enough detail to reconstruct how those procedures were implemented. The existence of a reported endpoint and comparison does not fill in those unreported design details.

4. Participant Flow and Accounting

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

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The full analysis set for Cohort 1 included all randomized participants who took at least 1 dose of study drug in Cohort 1 (mipomersen or placebo), had a valid baseline LDL-C measurement, and had at least 1 post-baseline LDL-C measurement.

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 full analysis set included all randomized participants who took at least 1 dose of study drug (mipomersen or placebo), had a valid baseline low-density lipoprotein cholesterol (LDL-C) measurement, and had at least 1 post-baseline LDL-C measurement.

6. Primary Endpoint

The registry lists the primary outcome as Percent Change From Baseline To Primary Endpoint Visit (PET) In LDL-C In Cohort 1, measured over Baseline and Week 61.

The primary endpoint is percent change in LDL-C from baseline to the primary endpoint visit (PET) in Cohort 1. The registry gives the timeframe as baseline and Week 61. LDL-C is the lipid measure chosen for the primary question, and the endpoint expresses change relative to each participant’s baseline rather than reporting only an endpoint value. Negative changes represent decreases from baseline.

The registry reports least-squares means and standard errors for the four arms. A least-squares mean is a model-based mean, which can account for terms specified in the analysis model. The registry record does not describe the model terms or adjustment details, so the precise model behind those reported means is not reported in the registry record. The arm summaries are expressed in percent, and their standard errors describe uncertainty around the estimated arm means.

The population definition matters to interpretation: Cohort 1 is restricted to the severe HeFH criteria, and the full analysis set also requires at least one dose, a valid baseline LDL-C value, and at least one post-baseline LDL-C value. Findings for that endpoint population should not automatically be generalized to all participants with HeFH or to Cohort 2. LDL-C percent change is a measure of a lipid outcome; by itself it does not measure cardiovascular events, quality of life, or the balance of all benefits and harms.

7. Results Posted in the Registry

• Regimen A: Mipomersen, 200 mg, Once Weekly: mean -27.17 versus -6.77 on control; difference -20.40 (95% CI -37.65 to -3.15), standardized effect d = -0.46; z-test p = 0.020.
• Regimen B: Mipomersen, 70 mg, Thrice Weekly: mean -22.96 versus -6.77 on control; difference -16.19 (95% CI -33.08 to 0.70), standardized effect d = -0.38; z-test p = 0.060.
• Regimen B: Placebo, Thrice Weekly: mean -10.62 versus -6.77 on control; difference -3.85 (95% CI -21.25 to 13.55), standardized effect d = -0.11; z-test p = 0.664.

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 From Baseline To PET In LDL-C In Cohort 2

• 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:
  • Regimen A: Mipomersen, 200 mg, Once Weekly: mean -31.20 versus -9.25 on control; difference -21.95 (95% CI -49.14 to 5.24), standardized effect d = -0.43; z-test p = 0.114.
  • Regimen B: Mipomersen, 70 mg, Thrice Weekly: mean -43.60 versus -9.25 on control; difference -34.35 (95% CI -60.82 to -7.88), standardized effect d = -0.71; z-test p = 0.011.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Regimen A: Mipomersen, 200 mg, Once Weekly and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Regimen B: Mipomersen, 70 mg, Thrice Weekly than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percent Change From Baseline To PET In Apolipoprotein B (Apo B) In Cohort 1

• 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:
  • Regimen A: Mipomersen, 200 mg, Once Weekly: mean -24.14 versus -2.83 on control; difference -21.31 (95% CI -36.86 to -5.76), standardized effect d = -0.54; z-test p = 0.007.
  • Regimen B: Mipomersen, 70 mg, Thrice Weekly: mean -21.43 versus -2.83 on control; difference -18.60 (95% CI -33.91 to -3.29), standardized effect d = -0.49; z-test p = 0.017.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Regimen A: Mipomersen, 200 mg, Once Weekly than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Regimen B: Mipomersen, 70 mg, Thrice Weekly than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percent Change From Baseline To PET In Apolipoprotein B (Apo B) In Cohort 2

• 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:
  • Regimen A: Mipomersen, 200 mg, Once Weekly: mean -30.76 versus -6.67 on control; difference -24.09 (95% CI -46.45 to -1.73), standardized effect d = -0.57; z-test p = 0.035.
  • Regimen B: Mipomersen, 70 mg, Thrice Weekly: mean -36.34 versus -6.67 on control; difference -29.67 (95% CI -51.69 to -7.65), standardized effect d = -0.73; z-test p = 0.008.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Regimen A: Mipomersen, 200 mg, Once Weekly than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Regimen B: Mipomersen, 70 mg, Thrice Weekly than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary lipid summaries extend the primary question to Cohort 2 and to apolipoprotein B (Apo B) in Cohorts 1 and 2. Across these outcomes, the estimated changes in the mipomersen arms are generally more negative than those in the matching placebo arms. The intervals and unadjusted p values vary, so the strength and precision of the individual comparisons are not uniform. These findings provide context for the primary LDL-C result but do not replace it or establish that effects are the same across cohorts and measures.

LDL-C and Apo B are related but distinct lipid measures. Seeing a broadly similar direction across more than one measure can be informative, but correlated outcomes are not independent replications. The outcomes also involve different cohorts and analysis group sizes, and the registry record does not report a joint analysis that would quantify consistency across them. The results therefore support a descriptive account of the posted pattern, not a single combined estimate.

The registry lists lipoprotein (a) among the secondary outcomes, but numerical results for that measure are not reported in the registry record. Secondary outcomes that are listed without numerical results cannot contribute evidence to the interpretation of the observed pattern.

Unadjusted secondary comparisons carry less weight because several outcomes and comparisons can create opportunities for chance findings. The record does not report a multiplicity strategy or identify a hierarchy for interpreting the secondary endpoints. These comparisons are best treated as supplementary evidence: useful for understanding the range of reported lipid findings, but not a substitute for a prespecified primary analysis or a basis for strong claims from isolated p values.

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

• Percent Change From Baseline To PET In LDL-C In Cohort 2
• Percent Change From Baseline To PET In Apolipoprotein B (Apo B) In Cohort 1
• Percent Change From Baseline To PET In Apolipoprotein B (Apo B) In Cohort 2
• Percent Change From Baseline To PET in Lipoprotein (a) In Cohort 1
• Percent Change From Baseline To PET in Lipoprotein (a) In Cohort 2

9. Statistical Interpretation

10. Statistical Methods Explained

What does an arm mean represent?

The primary outcome is continuous: it is a percent change in LDL-C. For a continuous outcome, a mean summarizes the central value of the measurements in an analysis group. The registry labels the reported values as least-squares means and gives a standard error for each. Least-squares means are generally model-based estimates rather than simple arithmetic averages. Because the registry does not describe the model specification, readers cannot determine from the record which covariates or other terms were included, or how missing measurements entered that model.

The negative arm means indicate decreases from baseline on average. For example, the primary summaries report -27.17 for the once-weekly mipomersen arm and -6.77 for its matched placebo arm. These are arm-level estimates, not statements that every participant had that change. The standard errors are measures of uncertainty in the estimated means; they are not standard deviations of individual participant outcomes and do not describe the full spread of individual responses.

How should the treatment contrasts be read?

A mean difference compares the estimated mean in one arm with the estimated mean in its comparator. In the once-weekly primary comparison, the posted contrast is -20.4. The negative sign follows the direction of the underlying percent-change scale: the estimated change in the mipomersen group is more negative than the matched placebo estimate. A contrast is more directly relevant to the randomized comparison than either arm mean alone, because it expresses the estimated separation between the groups.

The registry also presents unadjusted p values and confidence intervals for comparison summaries. A p value measures how compatible the observed statistic is with a specified null hypothesis under the assumptions of the test; it is not the probability that the null hypothesis is true, nor a measure of the size or importance of an effect. A confidence interval gives a range of values compatible with the estimate and its statistical uncertainty under the method used. The detailed inferential method for the posted comparison summaries is not reported in the registry record.

What does unadjusted mean?

An unadjusted p value is reported without accounting for the fact that many outcomes or comparisons may have been examined. When multiple hypotheses are tested, the chance of at least one apparently unusual result can increase. Multiplicity procedures can change the threshold for interpretation or adjust estimates and tests. The registry record does not report whether a multiplicity procedure was planned or used. Therefore, the displayed p values should be interpreted as unadjusted summaries, not as evidence that a family-wide error rate was controlled.

Why can uncertainty be large?

Uncertainty depends on the amount and variability of information contributing to an estimate. Smaller analysis groups, more variable outcomes, and incomplete outcome data can all limit precision. The reported arm-specific standard errors differ, and the comparison intervals reflect uncertainty around contrasts. In this study, the Cohort 2 estimates are based on smaller analysis groups than the primary Cohort 1 summaries, so those secondary estimates warrant particular caution. That observation does not establish the reasons for the observed uncertainty; the record does not report a detailed missing-data account.

Over-dispersion is a general statistical issue that can occur when observed variability exceeds what a model assumes. If that assumption is wrong, standard errors and intervals can be too narrow or too wide. The registry record does not report enough about the analysis model to assess whether over-dispersion was relevant or how it was handled. This is a general point about model-based inference, not a finding that over-dispersion occurred in this trial.

What can summary-based comparisons establish?

Comparisons based on posted summary statistics can communicate the direction and approximate size of group differences, but they may not reproduce an analysis using participant-level data. They cannot reveal how repeated measurements, baseline covariates, protocol deviations, or missing observations were handled unless those details are reported. The trial’s original statistical analysis plan is not reported in the registry record. No reported analyses are listed for the secondary outcome entries in the registry data used for these summaries, so the comparisons should not be confused with a fully documented sponsor analysis.

General statistical interpretation also separates evidence about a mean from evidence about individual outcomes. A group mean does not show the distribution of responses, whether effects vary across participants, or the frequency of meaningful changes. Those questions require additional information not present in the posted endpoint summaries. The statistics describe group-level estimates, their uncertainty, and the comparison with matched placebo; they do not by themselves settle broader clinical or causal questions.

11. Confidence Intervals and Precision

Precision is about how tightly an estimate is determined by the available data and analysis. For the once-weekly primary contrast, the posted estimate is -20.4 and its confidence interval runs from -37.65 to -3.145. The interval is entirely below zero, so under the inferential method represented by that summary, the data are compatible with a negative contrast across the interval’s range. It does not mean that every plausible value is equally likely, nor does it give the probability that the true effect lies in the interval.

For the thrice-weekly primary contrast, the estimate is -16.19 and its interval runs from -33.08 to 0.704. This interval includes zero and includes values on either side of zero. The estimate is therefore less conclusive about the direction of the contrast than the once-weekly interval, while still allowing a range of negative differences. The confidence intervals should be read with their estimates, not as a measure of clinical importance.

The registry lists standard errors for the arm means, but does not provide confidence limits for those arm-level values in the posted endpoint record. The comparison intervals are the relevant posted uncertainty summaries for the regimen-versus-placebo contrasts. Their interpretation depends on the method used; the full method is not reported in the registry record. The displayed precision is also specific to Cohort 1’s analysis population and the endpoint timeframe.

Intervals do not account for every source of uncertainty. They do not automatically address multiplicity, possible bias from missing data, limitations in the analysis model, or whether the chosen endpoint captures clinical outcomes. The registry record does not report enough detail to evaluate those issues fully. Precision around a statistical contrast and certainty about broader implications 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 figures are illustrations based on the observed comparison summaries, not the trial’s original design calculations. The stated planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each scenario asks how many participants per arm would be needed to detect a stated fraction of the observed effect. These are participants per arm, not combined totals; the time-to-event convention of total events does not apply to these continuous outcomes.

The 80% power column describes a planning scenario in which the design would have an 80% chance of detecting the specified effect under the listed assumptions, if that effect and the model assumptions were correct. The 90% power column describes the corresponding scenario with a higher target chance of detection. Neither column is an observed result from this completed trial. The figures are not observed power.

Rows that plan for 100% of the observed effect use the full observed contrast as the target. Rows planning for 75% or 50% of the observed effect use progressively smaller target effects. Smaller effects are harder to distinguish from random variation, so they require more participants per arm under the same planning assumptions. The figures therefore illustrate how sample-size needs rise as the effect to be detected gets smaller.

The original sample-size justification, the assumptions used to design this trial, and any adjustments to those assumptions are not reported in the registry record. These planning illustrations should not be read as evidence that the trial was designed for a particular power, nor as a judgment that its observed enrollment was adequate or inadequate. They are conditional calculations using the observed comparison and stated planning assumptions.

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 = -20.400; pooled standard deviation σ = 44.086.
2. n = 2 × (1.96 + 0.842)² × 44.086² / -20.400² = 73.3, rounded up to 74 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 <- -20.4                      # observed difference in means
sigma <- 44.0863729512873                     # 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(1475825)
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 = 74 (80% power), 99 (90% power)
 75% of observed effect: n per arm = 131 (80% power), 175 (90% power)
 50% of observed effect: n per arm = 294 (80% power), 393 (90% power)
power.t.test: 74.3 
Simulated power at n = 74 per arm: 0.801 (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. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study evaluates multiple outcomes, groups, time points, or treatment contrasts. Each additional test creates another opportunity to observe a small p value by chance, even when no underlying difference exists. Possible approaches include adjusting significance thresholds, controlling a family-wise error rate, or specifying a hierarchy in which later tests are considered only after earlier criteria are met. These are general statistical approaches; the registry record does not say which, if any, were planned for this trial.

The record contains a primary endpoint in Cohort 1, two regimen-versus-placebo comparisons for that endpoint, and posted secondary summaries for other lipid measures and cohorts. It does not report the planned family of hypotheses, an adjustment method, a gatekeeping sequence, or whether the reported p values were prespecified. The comparison p values are identified as unadjusted. Accordingly, they should not be treated as multiplicity-controlled evidence.

Missing-data handling is another important element of interpretation. The primary analysis population required a valid baseline LDL-C measurement and at least one post-baseline measurement, in addition to randomization and at least one dose. This definition tells readers who qualified for the stated analysis set, but it does not explain how many measurements were missing, why they were missing, or how missing values were handled. The missing-data strategy and sensitivity analyses are not reported in the registry record.

The registry does not report whether the primary analysis followed intention-to-treat principles, how treatment discontinuation was handled, or whether observations after discontinuation were retained. It would be inappropriate to infer a particular approach from the name of the analysis set. The posted criteria indicate that participants without any post-baseline LDL-C measurement were outside the defined full analysis set.

The record also does not provide the original statistical analysis plan or describe model assumptions in sufficient detail to assess robustness. Without those details, readers cannot fully evaluate the risks of selective reporting, multiplicity, or sensitivity to analytic choices. These omissions do not negate the posted summaries, but they limit how confidently the inferential results can be interpreted beyond their stated endpoint and analysis population.

15. 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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: angina pectoris (7.8%), angina unstable (5.9%), acute coronary syndrome (3.9%), non-cardiac chest pain (3.9%), acute myocardial infarction (3.8%).

The registry reports serious adverse events and other adverse events as counts of affected participants among those at risk in each group. For serious adverse events, the listed affected counts are 17 among 104 at risk in the once-weekly mipomersen group, 13 among 51 in its placebo group, 23 among 102 in the thrice-weekly mipomersen group, and 11 among 52 in its placebo group. For other adverse events, the corresponding counts are 82 among 104, 27 among 51, 78 among 102, and 33 among 52. Deaths are listed as 1, 0, 1, and 0, respectively.

These are descriptive event summaries, not adjusted comparisons. The registry record does not provide event timing, duration, severity breakdowns beyond the serious-event classification, or a statistical comparison of groups in these summaries. The at-risk denominators differ from the primary endpoint analysis groups, so the safety counts should not be treated as if they arose from the same analysis population. The listed serious-event terms include several cardiac-related terms, with percentages reported in the record, but arm-specific attribution for those term percentages is not reported in the registry record. Counts alone do not establish whether events were caused by study treatment.

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

The posted primary results describe LDL-C percent change in the defined Cohort 1 analysis population. They show negative estimated changes in all four arms, with more negative arm estimates in each mipomersen group than in its schedule-matched placebo group. The once-weekly contrast has an interval that excludes zero, while the thrice-weekly contrast has an interval that includes zero. These results describe the estimates and their statistical uncertainty; they do not establish that the two regimens differ from each other.

A statistical result for a lipid endpoint is not the same as evidence about clinical events or overall health outcomes. The registry’s primary outcome is LDL-C percent change, not a cardiovascular event endpoint. No conclusion about a reduction or increase in clinical events can be drawn from this endpoint summary alone. Nor do the posted means show how individual participants responded or whether the pattern was consistent across all participants.

The safety data should be interpreted separately from the efficacy endpoint. They list affected participants and deaths by group, but do not provide a causal analysis or a complete account of timing and severity. The registry summaries alone cannot establish a full benefit-harm balance. They also do not report all protocol and analysis details that would be needed to evaluate every potential source of bias or uncertainty.

The secondary results broaden the descriptive picture but have less evidential weight than a prespecified primary analysis, particularly because the p values are unadjusted and a multiplicity plan is not reported. Some listed outcomes have no numerical results in the registry record. Taken together, the posted statistics support careful description of specific lipid estimates and safety counts, while leaving broader conclusions about clinical outcomes, comparative regimen effects, and the complete safety profile unresolved.

17. Why This Trial Matters Statistically

This trial is statistically informative because it reports two dosing schedules against schedule-matched placebo groups, rather than only a single active-treatment contrast. That design makes the primary summaries interpretable as separate regimen-versus-control comparisons. It also makes an important limitation visible: separate comparisons do not answer whether the active schedules differ from each other. A direct comparison and its uncertainty would be needed for that question, and it is not reported in the primary results.

The record also illustrates why effect estimates and uncertainty should be read together. The primary contrasts point in the same direction, but their confidence intervals differ in relation to zero. Looking only at point estimates would hide that difference in precision; looking only at p values would hide the size and range of compatible contrasts. Arm means, contrast estimates, confidence intervals, and unadjusted p values each convey different aspects of the results.

Another statistical lesson is that a completed randomized trial can still leave important interpretive details unavailable in its registry record. The analysis population is defined, but the missing-data methods, analysis model details, original sample-size justification, and multiplicity strategy are not reported. This limits independent assessment of how the posted comparisons were produced and how robust they are to analytic choices.

Finally, the secondary lipid results and safety counts show why outcome-specific interpretation matters. Correlated lipid measures do not constitute independent confirmation, and adverse-event counts are descriptive rather than causal. The registry record enables a focused reading of the reported estimates while making clear that statistical significance, precision, clinical meaning, and safety are separate questions.

18. Limitations

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

The registry record leaves several trial-specific limitations unresolved. The full statistical analysis plan is not reported, including the model specification underlying the least-squares means and comparison summaries. It is therefore not possible to evaluate which covariates were used, whether assumptions were checked, or whether the posted comparison method matches the planned primary analysis.

The primary analysis set required exposure to at least one dose and at least one post-baseline LDL-C measurement. The record does not report how many participants were excluded from this endpoint set, the reasons for missing measurements, or how discontinuation and missing values were handled. Without that information, the potential for bias related to missing outcome data cannot be assessed from the posted summaries.

The registry reports two regimen-versus-placebo comparisons but does not report a direct comparison between the active regimens. Their separate point estimates and intervals cannot be used to establish a difference between schedules. In addition, the posted p values are unadjusted, and the planned multiplicity approach is not reported. Secondary lipid measures are therefore best viewed as supplementary; numerical results for lipoprotein (a) are not reported in the registry record despite its listing among secondary outcomes.

The endpoint is a lipid measure and does not directly report clinical events. The safety summaries provide counts but not a complete account of timing, severity detail, or causal attribution. Details of the open-label continuation period are also not reported in the registry record. These limitations constrain the interpretation of the registry statistics and should not be filled in by assumptions about the protocol or analyses.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study of the Safety and Efficacy of Two Different Regimens of Mipomersen in Patients With Familial Hypercholesterolemia and Inadequately Controlled Low-Density Lipoprotein Cholesterol — NCT01475825.
• Registry record last updated 2019-03-26; status last verified 2019-03.
• Results first posted 2019-03-14.
• 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.

22. Record Summary

NCT01475825 is recorded as a completed Phase 3 randomized, double-blind, placebo-controlled, parallel-group study with an open-label continuation period. It compared subcutaneous mipomersen 200 mg once weekly and 70 mg thrice weekly with schedule-matched placebo regimens. The primary endpoint was LDL-C percent change in Cohort 1 at the primary endpoint visit, described as baseline and Week 61.

The posted primary summaries show negative mean changes in each arm. The once-weekly comparison interval excludes zero; the thrice-weekly comparison interval includes zero. These are unadjusted comparison summaries, and a multiplicity plan and detailed analysis methods are not reported in the registry record. Secondary lipid summaries and descriptive safety counts are also posted, with numerical lipoprotein (a) results not reported in the registry record. The statistics describe the listed outcomes and populations; they do not alone establish clinical outcomes or a direct difference between active regimens.