Efficacy and Safety of Farletuzumab (MORAb-003) in Combination With Carboplatin and Taxane in Participants With Platinum-sensitive Ovarian Cancer in First Relapse: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Efficacy and Safety of Farletuzumab (MORAb-003) in Combination With Carboplatin and Taxane in Participants With Platinum-sensitive Ovarian Cancer in First Relapse (NCT00849667), a randomized phase 3 study. The primary outcome is Progression-free Survival (PFS).

2. Clinical Question

The registry lists two farletuzumab dose groups and a placebo group. Its primary outcome is time to progression or death, not tumor response alone or survival alone. A hazard ratio compares the event rate over follow-up between groups; the reported values and their confidence intervals should be read alongside the median progression-free survival in each group. The available results do not, by themselves, answer every question about the regimen: they describe the specified outcomes, populations, follow-up definitions, and analyses recorded for this trial.

3. Trial Design

This was a randomized, double-blind, placebo-controlled, parallel-group Phase 3 study. The registry describes masking as quadruple. The study evaluated weekly farletuzumab in combination with carboplatin and a taxane in participants with platinum-sensitive ovarian cancer in first relapse. The registry lists farletuzumab doses of 1.25 mg/kg and 2.5 mg/kg, alongside a farletuzumab-matched placebo group. Each group also received taxane and carboplatin.

The taxane could be paclitaxel or docetaxel. The placebo was described as saline. The arm descriptions specify weekly intravenous farletuzumab or placebo and intravenous chemotherapy; the detailed description for the 1.25 mg/kg arm is truncated in the registry record. No further assumptions about treatment duration or the full dosing schedule should be inferred from that incomplete description.

The registered enrollment is 1100. The primary efficacy analysis was defined for the intention-to-treat population, with participants analyzed according to randomized assignment. The posted primary outcome summaries list 370 participants for the 1.25 mg/kg group, 366 for the 2.5 mg/kg group, and 364 for the placebo group. Safety summaries use different group counts, which are described with the safety results.

The primary outcome was progression-free survival, assessed from randomization to progression or death. The registry describes progression as based on independent radiologic assessment using modified RECIST. Study status is terminated. The reason for termination is not reported in the registry record. The original statistical plan, the trial's sample-size justification, and any interim analyses 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.

Before assignment: A total of 1100 participants were randomized and enrolled, out of which 1091 participants received study treatment. Study was terminated early due to lack of efficacy based on the results for progression-free survival (PFS).

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: ITT population included all participants randomly assigned to treatment, according to the treatment assigned by the IWRS/IVRS.

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: Intent-to-Treat (ITT) population included all participants randomly assigned to treatment, according to the treatment assigned by the interactive web or voice response randomization system (IWRS/IVRS).

6. Primary Endpoint

The registry lists the primary outcome as Progression-free Survival (PFS), measured over From the date of randomization to the date of first documentation of disease progression, or date of death, whichever occurred first (up to 44 months).

The primary endpoint, progression-free survival, measures the time from randomization until the first documented disease progression or death from any cause, whichever occurs first. The registry gives a follow-up timeframe of up to 44 months. Progression was based on independent radiologic assessment using modified RECIST. The record defines progression as at least a 20% increase in the sum of target-lesion diameters, using the baseline sum as the reference.

Participants without observed progression or death were censored at the date of their last tumor assessment without evidence of progression before further antitumor treatment, or at the cutoff date, whichever was earlier. Censoring means that a participant's exact event time was not observed during the relevant follow-up; the analysis uses the information available up to the censoring time.

General: progression-free survival is used in oncology trials because it combines disease progression and death into a time-to-event outcome. It can capture disease control before death is observed, while retaining the timing of events rather than reducing follow-up to a simple event/no-event count. Median progression-free survival describes the time by which the estimated event-free proportion has reached the midpoint. It does not describe the experience of every participant or establish that the difference between group medians is statistically reliable. The hazard ratio and its confidence interval provide a separate comparative summary across follow-up.

7. Results Posted in the Registry

• 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.99 (95% CI 0.81–1.21), reported p = 0.451 (as reported in the registry).
• 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.86 (95% CI 0.70–1.06), reported p = 0.076 (as reported in the registry).

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.

Overall Survival (OS)

• Endpoint type: Time to event: compared as a hazard ratio.
• Test: Hazard ratio as reported by the sponsor (Log Rank); it was not recalculated.
• Result:
  • 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.99 (95% CI 0.78–1.27), reported p = 0.482 (as reported in the registry).
  • 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.88 (95% CI 0.68–1.13), reported p = 0.162 (as reported in the registry).
• Interpretation:
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.

Cancer Antigen-125 (CA-125) Progression-Free Survival

• Endpoint type: Time to event: compared as a hazard ratio.
• Test: Hazard ratio as reported by the sponsor (Log Rank); it was not recalculated.
• Result:
  • 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.93 (95% CI 0.72–1.21), reported p = 0.294 (as reported in the registry).
  • 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.77 (95% CI 0.58–1.02), reported p = 0.032 (as reported in the registry).
• Interpretation:
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.

Progression-Free Survival Based on Gynecologic Cancer InterGroup (GCIG) Criteria

• Endpoint type: Time to event: compared as a hazard ratio.
• Test: Hazard ratio as reported by the sponsor (Log Rank); it was not recalculated.
• Result:
  • 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 1.01 (95% CI 0.85–1.21), reported p = 0.564 (as reported in the registry).
  • 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin: hazard ratio 0.91 (95% CI 0.76–1.09), reported p = 0.156 (as reported in the registry).
• Interpretation:
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 1.25 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference (a ratio of 1), so these data do not show a statistically significant difference between 2.5 mg/kg Farletuzumab Plus Taxane and Carboplatin vs Placebo Plus Taxane and Carboplatin and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes cover several aspects of disease course and survival, including overall survival, CA-125 progression-free survival, and progression-free survival based on GCIG criteria. The registry also lists response, remission duration, time to response, and serologic response outcomes. These measures provide different views of the trial outcomes; they do not all measure the same event or answer the same question.

Across the quantitative secondary time-to-event results, the pattern is not uniform. Some estimates are below a hazard ratio of 1, while others are close to 1; the associated confidence intervals and p-values also vary. Overall survival comparisons do not show a clear separation in the reported analyses. The CA-125-based comparison for the higher farletuzumab dose has a reported p-value of 0.032, while its 95% confidence interval for the hazard ratio extends from 0.58 to 1.02. The other reported secondary comparisons likewise require attention to both estimates and intervals rather than a single p-value.

These secondary findings do not replace the primary progression-free survival result. The primary comparisons have confidence intervals that include 1, and the registry does not report a multiplicity strategy establishing that secondary tests were confirmatory. General: when multiple endpoints and comparisons are assessed, a nominally small p-value can arise by chance even if there is no underlying effect for the tested outcome. Adjustments, hierarchical testing, or prespecified endpoint priorities can address this concern, but the registry record does not report the approach used.

Accordingly, secondary results are best treated as supportive or hypothesis-generating unless the study's full analysis plan establishes their confirmatory role. The record lists additional response-related secondary outcomes, but quantitative results for those outcomes are not reported in the registry record. No conclusion about their direction or size should be drawn from the outcome names alone.

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

• Overall Survival (OS)
• Cancer Antigen-125 (CA-125) Progression-Free Survival
• Progression-Free Survival Based on Gynecologic Cancer InterGroup (GCIG) Criteria
• Percentage of Participants With Length of Second Remission Greater Than First Remission
• Percentage of Participants With Objective Response
• Duration of Tumor Response
• Time to Tumor Response (TTR)
• Percentage of Participants With Serologic Response (SR)

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is a time-to-event measure, so the analysis accounts for both whether an event was observed and how long participants were followed before an event or censoring. The registry reports median progression-free survival for each arm and hazard ratios for the comparisons with placebo. It also reports confidence intervals and log-rank p-values for the hazard-ratio analyses. The primary population was intention-to-treat, with participants analyzed according to randomized assignment.

What does a median time-to-event value mean?

General: the median is the point at which an estimated time-to-event curve has reached its midpoint. It is a summary of the distribution of event times, not the arithmetic average of observed times. The median does not use every aspect of the curve in the same way as a hazard ratio, and the difference between two medians is not a substitute for the reported comparative analysis. Censoring is part of time-to-event analysis: a participant whose event is not observed contributes follow-up information until the recorded censoring time.

The registry's primary endpoint definition specifies when progression or death counts as an event and when a participant is censored. That definition matters because changing the progression assessment, censoring rule, or analysis population can change the estimated outcome. The posted definition uses independent radiologic assessment under modified RECIST, with death from any cause also counted as an event. The treatment effect should therefore be interpreted in relation to this particular endpoint definition.

How should a hazard ratio be read?

General: a hazard ratio compares the event hazards between groups over follow-up. A value of 1 corresponds to equal hazards in the comparison; a value below 1 indicates a lower estimated hazard in the first-listed group, and a value above 1 indicates a higher estimated hazard. It does not directly quantify a difference in median months, nor does it describe the probability that an individual participant will experience an event. The hazard ratio is a relative measure, so its interpretation depends on which group is placed first.

The reported primary hazard ratios are 0.99 for 1.25 mg/kg farletuzumab versus placebo and 0.86 for 2.5 mg/kg farletuzumab versus placebo. These are the sponsor-reported estimates, not newly fitted estimates from individual participant data. The registry reports the log-rank method for both comparisons. The methods and details of any additional modeling beyond that description are not reported in the registry record.

What do the confidence intervals add?

General: a confidence interval gives a range of values compatible with the observed data and the method's assumptions at the stated confidence level. It is not a range containing a fixed proportion of individual participant outcomes, and it does not assign a probability to the true effect after the data have been observed. Wider intervals indicate less precision than narrower intervals, all else being equal. For hazard ratios, an interval that includes 1 includes equal hazards as one value compatible with the data.

For the primary comparisons, the 95% confidence intervals are 0.81 to 1.21 and 0.70 to 1.06. Both include 1. The p-values, 0.451 and 0.076, are reported separately. A p-value and a confidence interval are related summaries but answer questions in different forms: the interval emphasizes the range of effect estimates compatible with the data, while the p-value summarizes evidence against a null value under a specified test.

What does the log-rank method contribute?

General: the log-rank test compares time-to-event experience between groups while taking event timing and censoring into account. It is commonly used for group comparisons in time-to-event settings. Its result depends on the observed event pattern over follow-up; it does not directly state how large or clinically important an effect is. The hazard ratio provides an effect estimate, while the log-rank p-value summarizes the test result. The registry does not report further details about assumptions, stratification, covariate adjustment, or the full statistical analysis plan.

The trial record identifies the comparisons as superiority analyses. The p-values should not be read as proof of no effect when they are not small, nor as proof of benefit when they are small. The estimate, interval, endpoint definition, analysis population, and the broader set of planned or reported outcomes all matter. The registry's full multiplicity strategy is not reported, so the degree to which the analysis controlled the chance of false-positive findings across outcomes and comparisons cannot be established.

What is different about the secondary time-to-event outcomes?

The posted secondary summaries include overall survival, CA-125 progression-free survival, and progression-free survival based on GCIG criteria. These are distinct outcome definitions. Overall survival concerns death from any cause and has a registry timeframe of up to 48 months or study termination by the sponsor, whichever came first. CA-125 progression-free survival and GCIG-based progression-free survival have their own specified definitions and are not interchangeable with the primary endpoint. Results for a secondary endpoint should be interpreted according to its own outcome definition and follow-up, not as if it were a duplicate measurement of the primary outcome.

The listed secondary comparisons also use reported hazard ratios and log-rank analyses. The registry record does not describe adjustments for testing multiple outcomes or comparisons. General: when many outcomes or comparisons are examined, the chance of at least one apparently unusual result can increase if each is assessed without an adjustment or a prespecified hierarchy. This is why unadjusted secondary findings are generally considered less confirmatory than a prespecified primary analysis.

11. Confidence Intervals and Precision

Precision is reflected in the width of a confidence interval: narrower intervals indicate a more tightly estimated effect, while wider intervals leave more uncertainty about its size. The primary hazard-ratio interval for 1.25 mg/kg farletuzumab versus placebo is 0.81 to 1.21. For 2.5 mg/kg versus placebo, it is 0.70 to 1.06. Both are 95% confidence intervals and both include 1, the value corresponding to equal hazards.

These intervals allow a range of interpretations. For the 1.25 mg/kg comparison, the reported estimate is close to 1, but the interval includes values on either side of 1. The 2.5 mg/kg estimate is lower than 1, while its interval also includes 1 and extends to 1.06. Neither interval identifies a single precise relative effect. The p-values are 0.451 and 0.076, respectively; they should be read alongside, not instead of, the estimates and intervals.

The median progression-free survival estimates have their own confidence intervals: 8.6 to 10.2 months for 1.25 mg/kg, 8.8 to 10.4 months for 2.5 mg/kg, and 8.4 to 9.8 months for placebo. These quantify uncertainty around each arm's median estimate. They are not confidence intervals for the difference between arms. Comparing overlap between separate arm intervals is not a replacement for the reported hazard-ratio comparison.

General: precision is not the same as clinical importance. A narrow interval can still center on an effect that is not important to patients, while a wide interval can leave substantial uncertainty about potentially important effects. The registry does not state a minimum clinically important difference for progression-free survival or a target effect for the primary endpoint. The trial's original precision target and sample-size rationale are not reported in the registry record.

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 are based on the observed effect rather than the trial's original design assumptions. For time-to-event outcomes, the figures are total events required, not participants per arm. For other outcome types, the stated convention is participants per arm. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation; the effect to detect is a stated fraction of the observed effect. The original design assumptions for this trial are not reported in the registry record.

The 80% power column describes the planned event count or per-arm sample size that, under those stated assumptions, would give an 80% chance of detecting the specified effect if that effect were the true one. The 90% power column describes the corresponding planning quantity for a 90% chance. These are design calculations, not probabilities that the observed result is correct. For this time-to-event analysis, the planning quantity is events rather than enrolled participants because the amount of event information is central to the comparison.

The effect-size rows scale the effect used for planning relative to the observed effect. Planning for 75% or 50% of the observed effect represents a smaller effect to detect than planning for 100% of the observed effect. Smaller effects generally require more information and therefore more events in a time-to-event design, or more participants for an outcome expressed per arm, to reach the same target power. This is not observed power.

These calculations are illustrations under simplified assumptions. They do not show what the trial was originally designed to detect, establish that the trial was adequately powered, or account for any design features not described in the assumptions. No additional interpretation of the trial's planned sample size should be inferred from the planning figures alone.

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

d is the total number of events needed (Schoenfeld's formula) with 1:1 allocation and proportional hazards.

Worked calculation for this trial

1. Hazard ratio HR = 0.990, so ln(HR) = -0.0101.
2. d = 4 × (1.96 + 0.842)² / [ln(0.990)]² = 310,818.3, rounded up to 310,819 events in total 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
hr <- 0.99                              # observed hazard ratio
events_needed <- function(power, frac = 1) ceiling(round(4 * (z_a + qnorm(power))^2 / log(hr^frac)^2, 6))   # Schoenfeld, 1:1 allocation
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: events = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(events_needed(0.80, f), big.mark = ","), format(events_needed(0.90, f), big.mark = ",")))

# Simulation: given d total events under proportional hazards, events in the treatment arm are Binomial(d, hr / (1 + hr)).
# The score (log-rank type) statistic is (D1 - d / 2) / sqrt(d / 4).
set.seed(849667)
d    <- events_needed(0.80)
reps <- 20000
D1 <- rbinom(reps, d, hr / (1 + hr))
z  <- (D1 - d / 2) / sqrt(d / 4)
cat(sprintf("Simulated power with %s events: %.3f (%d replicates)\n", format(d, big.mark = ","), mean(abs(z) > z_a), reps))

Output from running the code

100% of observed effect: events = 310,819 (80% power), 416,098 (90% power)
 75% of observed effect: events = 552,566 (80% power), 739,729 (90% power)
 50% of observed effect: events = 1,243,274 (80% power), 1,664,390 (90% power)
Simulated power with 310,819 events: 0.800 (20000 replicates)

The simulation uses the fact that, with d events in total and proportional hazards, the number of events in the treatment arm is binomial. It is an approximation that conditions on the total number of events, and it should land close to the target power.

14. Group-Sequential Design Analysis

Many time-to-event trials, especially in oncology, stop or report early when interim data are overwhelming, so a single fixed-sample number understates the real design problem. The calculation below is an illustrative group-sequential design built from this trial's posted control median and hazard ratio. The registry record does not state the planned number of looks or the spending function, so the design is a planning illustration, not a reconstruction of the protocol. Assumed: one-sided α = 0.025, 90% power, three equally spaced looks on information, Lan-DeMets O'Brien-Fleming spending, 24 months of accrual, 60 months in total and a 1% monthly dropout hazard.

Under these assumptions the design needs about 484,975 participants in total, with the boundaries below.

Fixed design versus group-sequential alternatives

Group-sequential designs cost a small increase in maximum sample size and events compared with the fixed design, in exchange for the chance to stop early for overwhelming efficacy. O'Brien-Fleming spending keeps early boundaries very conservative and the final boundary close to the fixed-design value, whereas Pocock spending spends more alpha early.

Sensitivity to a smaller true effect

Planning for exactly the observed effect is optimistic, because observed effects in published trials tend to be somewhat larger than the true effect. The table repeats the main design (three looks, O'Brien-Fleming spending, 90% power) for smaller true hazard ratios.

For context, this trial enrolled 1,100 participants.

Cross-check, upper boundaries: gsDesign 3.71, 2.511, 1.993 | rpact 3.71, 2.511, 1.993. Agreement between the two packages confirms the boundary calculation.

R code and output

suppressWarnings(suppressMessages({ library(gsDesign); library(rpact) }))
med_control <- 9          # control-arm median, months (converted from the posted unit)
hr          <- 0.99          # hazard ratio used for planning
lambdaC     <- log(2) / med_control
eta         <- 0.01               # dropout hazard per month (assumption)
acc_months  <- 24                 # accrual duration (assumption)
study_months<- 60                 # total study duration (assumption)

design <- function(k, sfu, beta, h = hr) gsSurv(k = k, test.type = 1, alpha = 0.025, beta = beta, sfu = sfu,
  lambdaC = lambdaC, hr = h, hr0 = 1, eta = eta, gamma = 1, R = acc_months, T = study_months, minfup = study_months - acc_months)

# Main design: 3 looks, Lan-DeMets O'Brien-Fleming spending, one-sided alpha 0.025, 90% power
x <- design(3, sfLDOF, 0.10)
cat(sprintf("N_TOTAL|%s\n", format(ceiling(sum(x$gamma * x$R)), big.mark = ",")))
for (i in 1:3) cat(sprintf("GS|%d|%.1f|%.0f|%.3f|%.5f|%.5f\n", i, x$T[i], x$n.I[i], x$upper$bound[i], pnorm(-x$upper$bound[i]), cumsum(x$upper$spend)[i]))
cat("\n"); print(gsBoundSummary(x))

# Comparison of designs
cmp <- list(list("Fixed design, one analysis", 1, sfLDOF), list("Two looks, O'Brien-Fleming", 2, sfLDOF),
            list("Three looks, O'Brien-Fleming", 3, sfLDOF), list("Three looks, Pocock", 3, sfLDPocock))
cat("\n")
for (b in c(0.20, 0.10)) for (d in cmp) {
  y <- design(d[[2]], d[[3]], b)
  cat(sprintf("DES|%s|%d|%.0f|%s|%.1f\n", d[[1]], round(100 * (1 - b)), tail(y$n.I, 1), format(ceiling(sum(y$gamma * y$R)), big.mark = ","), tail(y$T, 1)))
}

# Sensitivity: what if the true effect is smaller than the observed one? (3 looks, O'Brien-Fleming, 90% power)
cat("\n")
for (fr in c(1, 0.75, 0.5)) {
  y <- design(3, sfLDOF, 0.10, exp(log(hr) * fr))
  cat(sprintf("SENS|%.2f|%.3f|%.0f|%s\n", fr, exp(log(hr) * fr), tail(y$n.I, 1), format(ceiling(sum(y$gamma * y$R)), big.mark = ",")))
}

# Independent cross-check of the boundaries with rpact
r <- getDesignGroupSequential(kMax = 3, alpha = 0.025, beta = 0.10, sided = 1, typeOfDesign = "asOF", informationRates = x$timing)
cat(sprintf("\nCross-check, upper boundaries: gsDesign %s | rpact %s\n", paste(round(x$upper$bound, 3), collapse = ", "), paste(round(r$criticalValues, 3), collapse = ", ")))

Method: LachinFoulkes 
       Analysis               Value Efficacy
      IA 1: 33%                   Z   3.7103
      N: 336876         p (1-sided)   0.0001
 Events: 140337        ~HR at bound   0.9804
      Month: 17    P(Cross) if HR=1   0.0001
                P(Cross) if HR=0.99   0.0338
      IA 2: 67%                   Z   2.5114
      N: 484976         p (1-sided)   0.0060
 Events: 280673        ~HR at bound   0.9906
      Month: 26    P(Cross) if HR=1   0.0060
                P(Cross) if HR=0.99   0.5603
          Final                   Z   1.9930
      N: 484976         p (1-sided)   0.0231
 Events: 421009        ~HR at bound   0.9939
      Month: 60    P(Cross) if HR=1   0.0250
                P(Cross) if HR=0.99   0.9000



Cross-check, upper boundaries: gsDesign 3.71, 2.511, 1.993 | rpact 3.71, 2.511, 1.993

15. Analyses Reported by the Sponsor

The primary hazard ratios and p-values in the registry are sponsor-reported analyses using the log-rank method. The simple re-calculations based on posted summary statistics are not a replacement for those analyses, and the record indicates no discrepancy between the recalculations and the sponsor's results. The sponsor's own analysis therefore remains the primary reference for the reported comparisons.

The sponsor reports a hazard ratio of 0.99 (95% CI 0.81 to 1.21; p=0.451) for 1.25 mg/kg farletuzumab versus placebo, and 0.86 (95% CI 0.70 to 1.06; p=0.076) for 2.5 mg/kg versus placebo. Those values are based on the trial's time-to-event analysis. A recalculation from summary values cannot reproduce the full participant-level analysis, including its event times, censoring, and any analysis details not included in the registry record.

Recalculated planning quantities are illustrations based on the observed effects and the stated planning assumptions. They are not a second efficacy analysis and should not be used to override the sponsor's reported estimates, confidence intervals, or tests. The original design assumptions and complete statistical analysis plan are not reported in the registry record.

16. Multiplicity, Missing Data and Analysis Populations

The registry records a primary progression-free survival endpoint and a range of secondary outcomes, including overall survival, alternative progression-free survival definitions, response measures, and remission-related outcomes. It also reports two farletuzumab-dose comparisons with placebo for the primary endpoint and for some secondary time-to-event outcomes. This creates multiple opportunities for statistical testing.

The registry record does not report whether the sponsor used a multiplicity adjustment, a hierarchical testing sequence, a gatekeeping procedure, or another method to control the chance of false-positive findings across outcomes and comparisons. It also does not report whether the secondary analyses were prespecified as confirmatory or exploratory. Without those details, secondary p-values should not be treated as if they had been adjusted for the full family of tests. A nominal p-value alone cannot establish that a secondary result is definitive.

The primary efficacy population is described as intention-to-treat, with randomized participants analyzed according to assigned treatment. The record gives the censoring rule for progression-free survival, including censoring at the last qualifying tumor assessment before further antitumor treatment or at the cutoff date, whichever was earlier. However, the registry does not report a complete account of missing outcome data, the amount of missingness, sensitivity analyses, or the assumptions used to address missing assessments.

The registry also does not report details about stratification, covariate adjustment, proportional-hazards assessment, or interim analyses. These omissions limit independent assessment of the analysis plan and its implementation. They do not establish that a particular method was or was not used; they mean the method is not reported in the registry record. The stated intention-to-treat population and endpoint definition are useful, but they do not resolve these unanswered questions about multiplicity and missing data.

17. 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: neutropenia (9.3%), febrile neutropenia (7.2%), anaemia (3.7%), thrombocytopenia (3.2%), abdominal pain (2.8%).

The safety summaries list participants at risk and participants affected by serious adverse events and by other adverse events, with separate death counts. For serious adverse events, the at-risk and affected counts are 376 and 140 in the 1.25 mg/kg farletuzumab group, 363 and 130 in the 2.5 mg/kg group, and 352 and 112 in the placebo group. The safety counts are not the same as the group counts listed for the primary efficacy summaries. The registry record does not explain the difference in these populations.

The registry lists neutropenia at 9.3%, febrile neutropenia at 7.2%, anaemia at 3.7%, thrombocytopenia at 3.2%, abdominal pain at 2.8%, and vomiting at 2.7% among the leading serious-event terms. The record does not specify in this summary how these term percentages are distributed across treatment groups, so they should not be interpreted as group-specific comparisons.

General: an adverse-event count is a descriptive safety summary, not proof that treatment caused the event. Comparisons require consistent definitions, follow-up, and denominators, as well as appropriate consideration of event severity and exposure. The registry figures alone do not establish causal attribution or provide a complete safety assessment.

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

The primary results describe progression-free survival in a randomized study of farletuzumab plus taxane and carboplatin compared with placebo plus the same chemotherapy. The reported medians were in a similar range across the three groups, and both sponsor-reported hazard-ratio confidence intervals included 1. The reported p-values were 0.451 for the 1.25 mg/kg comparison and 0.076 for the 2.5 mg/kg comparison. Taken together, these results do not establish superiority on the primary endpoint in the reported analyses.

That interpretation is not equivalent to proving that the regimens are identical or that no effect is possible. The confidence intervals include values both below and above 1, leaving uncertainty about the relative hazard. Nor do these group-level summaries predict an individual participant's outcome. They describe averages or relative event patterns in the trial population under the endpoint definition and follow-up recorded by the registry.

The secondary outcome results add context but do not overturn the primary analysis. Their definitions differ, and the registry does not report whether the secondary testing was adjusted for multiplicity or designated as confirmatory. A nominal result on a secondary measure should therefore be read cautiously and alongside its confidence interval and the full set of outcomes.

The trial record's terminated status does not explain why the study ended; the reason is not reported in the registry record. These results also do not establish how the findings apply to populations or treatment settings outside the registered study. General: statistical evidence, clinical importance, and individual treatment decisions are separate questions. The registry reports estimates and uncertainty for specified outcomes, but does not supply a clinical-importance threshold or enough detail to resolve every design and analysis question.

19. Why This Trial Matters Statistically

This trial illustrates why a time-to-event result should not be reduced to the comparison of median values alone. The median progression-free survival estimates are close across groups, while the primary comparative results are expressed through hazard ratios, confidence intervals, and log-rank tests. Those quantities summarize different features of the evidence: the medians describe an estimated point in each outcome distribution, whereas the hazard ratios compare event hazards over follow-up.

It also demonstrates the value of interpreting an estimate together with its uncertainty. A hazard ratio below 1 may point in a favorable direction for the first-listed group, but the confidence interval indicates how much uncertainty remains. In the primary comparisons, both intervals include 1. That makes the direction and size of any relative effect uncertain, even though the point estimates are not identical.

The record also shows why secondary results need context. Several outcome definitions and comparisons are listed, but the multiplicity strategy is not reported. A nominally small secondary p-value is not automatically confirmatory when the relationship among tests and the analysis hierarchy are unknown. Statistical interpretation depends on the trial question, the prespecified endpoint, the analysis population, the follow-up and censoring rules, and the reporting of all relevant tests.

Finally, the trial's registry status and the missing details about the original design highlight the difference between a posted result and a complete statistical account. The record supports interpretation of the estimates it reports, while leaving important questions about the full analysis plan unanswered. Recognizing both what the statistics say and what is not documented is central to sound biostatistical reading.

20. 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.

Several trial-specific limitations follow from details that are absent or incomplete in the registry record. The study is marked terminated, but the reason for termination is not reported. The original sample-size justification, target effect, and design assumptions are also not reported, so the posted planning illustrations cannot be treated as evidence about the trial's original power or design intent.

The registry identifies the primary efficacy population and the progression-free survival censoring rule, but it does not provide a full statistical analysis plan. Details about stratification, covariate adjustment, assessment of time-to-event assumptions, interim analyses, missing outcome data, and sensitivity analyses are not reported in the registry record. The multiplicity approach for the several outcomes and comparisons is also not reported.

The efficacy group counts and safety at-risk counts differ. The registry does not explain the difference, so it is not possible to establish from these summaries alone how the analysis populations relate or why their counts vary. The detailed arm description for the 1.25 mg/kg group is truncated, which limits what can be said about the full regimen description for that arm.

Some secondary outcomes are named in the record without quantitative results in the registry entries. Their effects cannot be evaluated from their labels. In addition, the serious-event term percentages are not accompanied by group-specific detail in the summary, so they do not support direct treatment-group comparisons. These limitations concern the completeness of registry reporting and should not be replaced by assumptions about unreported methods or results.

21. Related Tutorials

22. Related Statistical Calculators

23. Sources

• ClinicalTrials.gov: Efficacy and Safety of Farletuzumab (MORAb-003) in Combination With Carboplatin and Taxane in Participants With Platinum-sensitive Ovarian Cancer in First Relapse — NCT00849667.
• Registry record last updated 2022-12-30; status last verified 2022-11.
• Results first posted 2022-12-30.
• 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.

24. Record Summary

The registry describes a randomized, double-blind, placebo-controlled, parallel-group Phase 3 trial in platinum-sensitive ovarian cancer at first relapse. It compared two farletuzumab doses plus taxane and carboplatin with placebo plus taxane and carboplatin. The primary endpoint was progression-free survival in the intention-to-treat population.

The sponsor-reported primary hazard ratios were 0.99 (95% CI 0.81 to 1.21; p=0.451) for 1.25 mg/kg farletuzumab versus placebo and 0.86 (95% CI 0.70 to 1.06; p=0.076) for 2.5 mg/kg versus placebo. Both confidence intervals include 1. Secondary outcomes cover survival, alternative progression measures, and response-related measures, but the multiplicity approach is not reported. The registry lists the study status as terminated; the reason is not reported in the registry record.