Double-blind, Randomized, Placebo-controlled, Prospective Phase III Study Evaluating Efficacy and Safety of Panzyga in Primary Infection Prophylaxis in Patients With Chronic Lymphocytic Leukemia ("PRO-SID" Study): Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Double-blind, Randomized, Placebo-controlled, Prospective Phase III Study Evaluating Efficacy and Safety of Panzyga in Primary Infection Prophylaxis in Patients With Chronic Lymphocytic Leukemia ("PRO-SID" Study) (NCT04502030), a randomized phase 3 study. The primary outcome is Occurrence of Major Infections.

2. Clinical Question

The clinical question concerns prevention of major infections in the trial population, rather than treatment of an infection after it occurs. The endpoint counted participants who experienced at least one qualifying major infection. The definition included bacterial or viral infections resulting in death; documented infections requiring anti-infective treatment; and fever of unknown origin requiring hospitalization or hospitalization prolongation. Some respiratory, urinary, skin and oral infections qualified as major only when treatment with anti-infectives and hospitalization or hospitalization prolongation were both required.

The registry describes a Phase III study with a placebo comparator. Its posted primary result therefore addresses the relative occurrence of this defined outcome in the two randomized groups over the study follow-up. It does not answer every question about infections, such as whether all infections were prevented or whether the result applies to people outside the enrolled population. Those broader questions require evidence beyond this endpoint and record.

3. Trial Design

PRO-SID is registered as a randomized, quadruple-masked, parallel-group Phase III study of Panzyga versus placebo. The registry lists an enrollment of 247. Octapharma is the sponsor. The study is marked completed, with a start date of 2020-10-05 and a primary completion date of 2025-09-19.

The study evaluated Panzyga for primary infection prophylaxis in patients with chronic lymphocytic leukemia. Hypogammaglobulinemia is also listed among the conditions. The arm descriptions do not add further detail about dosing or treatment procedures. Randomized allocation means assignment to the groups was intended to be determined by a random process; quadruple masking indicates masking across four roles, but the identities of those roles are not reported in the registry record.

The primary outcome was the occurrence of major infections, measured as the number of participants with major infections. Its timeframe ran from first treatment to a major infection or the end-of-study visit. For participants completing the study, the median duration was 366 days. The record notes that 3 participants with delayed end-of-study visits remained in the study for 407 days or more.

Secondary outcomes included overall infection rate, frequency of prophylaxis with anti-infectives, and duration of prophylaxis with anti-infectives. The posted secondary results include measures for overall infection rate and frequency of prophylaxis; a result for duration of prophylaxis is not among the posted outcomes described in the record. The registry does not report the detailed original design assumptions, the allocation sequence procedure, or the masking roles.

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: Of 336 participants screened, 247 participants were randomized to treatment. The other participants were excluded due to screening failures.

Overall Study

Reasons for not completing this period:

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Occurrence of Major Infections, measured over From first treatment to the occurrence of a major infection or to the end of study visit. The median study duration of participants completing the study was 366 days. For delayed end of study visits 3 participants stayed in the study for 407 days or more..

The primary endpoint counted participants who experienced a major infection during follow-up. It was not a count of every infection: each participant was counted as having or not having the qualifying outcome. The definition covered bacterial or viral infections resulting in death, documented bacterial or viral infections requiring anti-infective treatment, and fever of unknown origin requiring hospitalization or hospitalization prolongation. Certain common infection types, including upper respiratory tract infections, bronchitis, lower urinary tract infections, localized skin infections and stomatitis, were considered major only if they required anti-infectives and hospitalization or hospitalization prolongation.

This endpoint is used to focus the primary comparison on infections judged clinically important under the trial definition. In general, a participant-level binary endpoint makes the comparison about whether an individual experienced at least one qualifying event during the specified follow-up, rather than about the total burden of repeated events. It therefore does not capture the number of infections beyond the yes-or-no classification, nor does it describe severity differences among qualifying infections.

The observation period began at first treatment and ended at the occurrence of a major infection or the end-of-study visit. Follow-up duration was not identical for every participant: the median duration among participants completing the study was 366 days, and delayed visits extended beyond that for some participants. The registry does not report further detail about how incomplete follow-up was handled in the primary analysis.

7. Results Posted in the Registry

• Panzyga: 11.3% (14/124) versus 22.0% (27/123) on control; risk difference -10.7 percentage points (95% CI -19.9 to -1.5), risk ratio 0.51 (0.28–0.93); unadjusted two-proportion z-test p = 0.024.

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 Infection Rate

• Endpoint type: Event rate: events per unit of follow-up, compared as a rate ratio.
• Test: Poisson rate ratio, based on the estimated number of events and follow-up time.
• Result:
  • Panzyga: 1.15 versus 1.65 (number of infections per person year) on control; rate ratio 0.70 (95% CI 0.56–0.86, approximate, Poisson model); p ≈ <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference (a ratio of 1), so the event rate is statistically lower for Panzyga than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Frequency of Prophylaxis With Anti-infectives

• 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 deviations.
• Result:
  • Panzyga: mean 1.90 versus 1.90 on control; difference 0.00 (95% CI -0.26 to 0.26), standardized effect d = 0.00; z-test p = 1.000.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Panzyga and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes broaden the view beyond the primary yes-or-no measure of whether a participant experienced a major infection. Overall infection rate addresses the frequency of infection events over person-time, while frequency of prophylaxis with anti-infectives concerns medication use. The posted results therefore cover different aspects of infection burden and its management rather than repeated versions of the same endpoint.

As a broad pattern, the unadjusted rate comparison favors a lower observed overall infection rate in the Panzyga group, while the reported mean frequency of anti-infective prophylaxis is the same in both groups. The sponsor's rate analysis and the unadjusted rate recalculation differ in their reported statistical evidence. This means the secondary findings should be interpreted with attention to the method used, not collapsed into a single conclusion.

These outcomes complement but do not replace the primary result. Overall infection rate counts events over time, whereas the primary endpoint counts participants with at least one major infection. The prophylaxis-frequency outcome measures medication use and is not itself an infection outcome. The registry lists duration of prophylaxis with anti-infectives as a secondary outcome, but a result for that outcome is not reported in the registry record.

Unadjusted secondary results carry less weight than a prespecified primary comparison because multiple outcomes and analyses create more opportunities for apparently noteworthy findings. The registry record does not report a multiplicity adjustment or the full hierarchy of planned analyses. Accordingly, the secondary results are best treated as supportive or exploratory descriptions rather than independent confirmation of the primary finding.

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

• Overall Infection Rate
• Frequency of Prophylaxis With Anti-infectives
• Duration of Prophylaxis With Anti-infectives

9. Statistical Interpretation

10. Statistical Methods Explained

What does the primary comparison estimate?

The primary recalculation uses a risk difference: the observed risk in the Panzyga group minus the observed risk in the placebo group. A negative estimate means the event risk was lower in the Panzyga group on that scale. The estimate is unadjusted, meaning it is based on the posted group summaries rather than a model that adjusts for other participant characteristics. The reported risk difference is -0.107. Its confidence interval excludes zero, the value corresponding to no difference on this scale, and the unadjusted p-value is 0.024.

General statistical explanation: a risk difference gives an absolute contrast between groups, while a risk ratio or odds ratio expresses a relative contrast. These measures can describe the same binary outcome but have different interpretations and numerical scales. The record also includes recalculated relative measures, but the risk difference is the primary recalculated measure. The relative measures should not be confused with the sponsor's reported net mean difference.

What does the confidence interval convey?

In general, a confidence interval describes the range of estimates compatible with the observed data under the method's assumptions. For the risk difference, an interval entirely below zero is consistent with a lower observed risk in the Panzyga group on the unadjusted scale. An interval is not a probability statement that the true value lies within its bounds, and it does not account for uncertainties omitted by the analysis, such as unreported missing-data handling or deviations from the analysis assumptions.

How should the p-value be read?

The unadjusted p-value of 0.024 measures how unusual a result at least as incompatible with a no-difference hypothesis would be under that hypothesis and the test's assumptions. It is not the probability that the null hypothesis is true, nor does it quantify clinical importance. It also does not automatically account for examining multiple outcomes or analyses. The registry's primary reported analysis uses a one-sided z-test and gives p = 0.011; the unadjusted recalculation and the registry analysis are distinct analyses.

How do the secondary outcome methods differ?

Overall infection rate is expressed as infections per person-year. A rate ratio compares the event rates between groups; a value below one indicates a lower observed rate in the Panzyga group relative to placebo. The unadjusted recalculation reports a rate ratio of 0.697 and p < 0.001. Its calculation uses the posted event rates and person-time summaries. The record also reports a sponsor analysis using linear regression, with a mean rate ratio of 0.70, an upper confidence limit of 1.06 and p = 0.046. The methods and results are not numerically identical, and the registry does not include the full details needed to reproduce the sponsor model.

General statistical explanation: rate analyses account for time at risk and can be useful when participants contribute different follow-up durations or experience repeated events. A Poisson model is a common framework for event-rate data. It relies on assumptions about how event counts vary around the expected rate; over-dispersion, where counts vary more than the model expects, can make standard errors too small if it is not addressed. The registry's recalculation is flagged as Poisson, but a dispersion estimate is not reported. The sponsor's listed method is linear regression, so it should not be described as the same analysis.

Frequency of prophylaxis with anti-infectives is reported as a mean number of medications per participant, with standard deviations. The posted group means are both 1.9. The unadjusted mean difference is 0.0, with an interval that spans zero and an unadjusted p-value of 1.0. General statistical explanation: a mean difference compares group averages; the standard deviation describes variation among individual observations, not uncertainty in the mean itself. The registry does not list a sponsor analysis for this outcome.

What cannot be established from the summary?

The registry does not report details of covariate adjustment, analysis populations, missing-data procedures, interim analyses, or a multiplicity plan. It is therefore not possible to infer whether the reported sponsor results used additional adjustments or how all planned analyses were controlled. The summary statistics support interpretation of the posted contrasts, but they do not allow a complete reconstruction of every analysis decision.

11. Confidence Intervals and Precision

Precision is reflected in the width of a confidence interval: narrower intervals indicate less statistical uncertainty about the estimate under the analysis assumptions, while wider intervals leave more values compatible with the data. For the primary unadjusted risk difference, the reported interval lies below zero. It does not include the no-difference value, which is consistent with the unadjusted p-value of 0.024. This statistical pattern does not establish the importance of the difference for every patient or setting.

The primary groups had 14 and 27 participants with major infections, respectively, against group sizes of 124 and 123. Those counts provide the observed basis for the binary comparison, but the interval also reflects the uncertainty associated with the group sizes and event frequencies. The sponsor's analysis reports a 97.5% confidence interval with an upper limit of -1.9; its lower limit is not reported in the registry record. Its reported p-value is 0.011.

For overall infection rate, the unadjusted rate-ratio interval is on one side of the no-difference value, while the sponsor's reported interval has an upper limit of 1.06. The analyses therefore differ in their interval-based presentation. The registry record does not report the sponsor interval's lower limit. This difference is a reason to distinguish the sponsor analysis from the simple recalculation rather than presenting them as one estimate.

Precision is not the same as freedom from bias. Confidence intervals quantify sampling uncertainty under a method; they do not resolve uncertainties about missing data, outcome classification, analysis choices, or whether results generalize beyond the enrolled population. Those issues are not fully documented 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 figures are illustrations based on the observed effect, not the trial's original design assumptions. The registry record does not report the original sample-size justification. For these illustrations, the stated assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. Each planning scenario asks how many participants per arm would be needed to detect a stated fraction of the observed effect. For time-to-event outcomes, the convention would instead be a total number of events; the outcomes addressed by these figures are not time-to-event outcomes, so the figures are participants per arm.

For the primary outcome, planning for 100% of the observed effect gives 191 participants per arm for 80% power and 255 participants per arm for 90% power. Planning for 75% of the observed effect gives 361 participants per arm for 80% power and 483 participants per arm for 90% power. Planning for 50% of the observed effect gives 859 participants per arm for 80% power and 1,150 participants per arm for 90% power.

In plain terms, the 80% column describes a design that would have an 80% chance of detecting the specified effect under the planning assumptions if that effect were real; the 90% column describes the corresponding design with a higher chance of detection. Detecting a smaller effect generally requires more participants, which is why the planning counts increase as the assumed fraction of the observed effect decreases. These are planning illustrations, not observed power. They do not show the probability that the trial's observed finding is true, and they do not replace the unreported original design rationale.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.220; treatment proportion p₁ = 0.113; average p̄ = 0.166.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5265 = 1.0319.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5210 = 0.4385.
4. n = (1.0319 + 0.4385)² / (-0.1066)² = 190.2, rounded up to 191 participants per arm for 80% power.

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

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.219512195121951                          # control proportion
p1 <- 0.112903225806452                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(4502030)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 191 (80% power), 255 (90% power)
 75% of observed effect: n per arm = 361 (80% power), 483 (90% power)
 50% of observed effect: n per arm = 859 (80% power), 1,150 (90% power)
power.prop.test: 190.2 
Simulated power at n = 191 per arm: 0.809 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The registry reports a sponsor primary analysis using a one-sided z-test. It gives a net mean difference of -13.2, a 97.5% confidence interval with an upper limit of -1.9, and p = 0.011. The unadjusted recalculation from the posted group counts instead expresses the contrast as a risk difference and reports p = 0.024. Both analyses point in the same direction, but their estimates are expressed on different scales and should not be treated as interchangeable values.

For overall infection rate, the sponsor's linear-regression analysis reports a mean rate ratio of 0.70, an upper confidence limit of 1.06, and p = 0.046. The unadjusted recalculation uses the posted event-rate summaries and gives a rate ratio of 0.697 with p < 0.001. This difference underscores that the sponsor model and the summary-based recalculation are not the same procedure. The sponsor method may reflect analysis choices not fully described in the registry summary, but those details are not reported in the registry record.

The registry does not flag a discrepancy for these results. The recalculations are simple, unadjusted analyses from posted summary statistics; they are useful as a transparent comparison, not as replacements for the sponsor's stated analysis. Where a result depends on model specification or other details that are not available in the record, the summary does not permit a complete explanation of the numerical difference.

15. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study evaluates several outcomes, comparisons, time points, or analysis methods. Even when each individual test uses a conventional significance threshold, looking across many tests increases the chance that at least one result appears statistically notable by chance. Common approaches include adjusting the significance threshold, controlling a family-wise error rate, or treating secondary analyses as exploratory. Which approach was prespecified matters for interpreting the strength of evidence.

The registry lists a primary outcome and multiple secondary outcomes. It reports two analyses for the primary outcome and sponsor analyses for overall infection rate, while a secondary medication-use result has no sponsor analysis listed. A multiplicity procedure or hierarchy for these analyses is not reported in the registry record. The unadjusted p-values should therefore be read as individual test results, not as evidence that a study-wide error rate was controlled.

Missing-data methods and analysis populations are also not reported in the registry record. The primary timeframe varies with the occurrence of a major infection or an end-of-study visit, and the record notes delayed visits for some participants. It does not explain whether all randomized participants were included in each analysis, how participants without complete follow-up were treated, or whether outcome data were missing. These omissions limit assessment of the analysis set and the robustness of the estimates to incomplete observations.

The registry record also does not report interim analyses, stopping rules, or the original statistical analysis plan. Their absence from the record should not be interpreted as proof that no such procedures existed; it means their details cannot be assessed from the posted summary. These uncertainties do not erase the reported group results, but they constrain how confidently they can be interpreted beyond the methods and information explicitly recorded.

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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: pneumonia (7.3%), covid-19 pneumonia (4.0%), covid-19 (2.4%), cardiac arrest (1.6%), lower respiratory tract infection (1.6%).

The safety summaries report serious adverse events affecting 34 of 124 participants in the Panzyga group and 43 of 123 participants in the placebo group. Other adverse events affected 70 of 124 and 69 of 123 participants, respectively. Death counts are reported as 9 in the Panzyga group and 7 in the placebo group. These are descriptive counts; the registry summary does not provide an adjusted comparison or enough detail to attribute events to treatment.

The listed serious adverse-event terms include pneumonia (7.3%), COVID-19 pneumonia (4.0%), COVID-19 (2.4%), cardiac arrest (1.6%), lower respiratory tract infection (1.6%), and infection (1.6%). These entries describe terms reported in the registry, not a complete account of each participant's clinical course. The summary does not specify the causal relationship of each event to the intervention or provide a statistical comparison for these terms.

Safety counts should be interpreted alongside the study's follow-up and reporting definitions. The registry record does not report further details about event severity grading, relatedness assessments, or the handling of repeated events for these summaries. The counts alone do not establish that one group had greater or lesser treatment-related harm.

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

The posted primary results show fewer participants with a major infection in the Panzyga group than in the placebo group, and the unadjusted risk-difference interval excludes zero. The sponsor's primary analysis also reports a result in the same direction. These findings describe the comparison in this randomized trial under its definition of major infection and its recorded follow-up; they do not establish that every infection was prevented or that every participant had the same experience.

The primary endpoint classifies participants according to whether they had a qualifying major infection. It does not measure the total number of infections per participant, time to first infection, or the severity of every event in a way that can be recovered from the group counts. The secondary rate outcome addresses event frequency, but its sponsor analysis and unadjusted recalculation differ. The medication-use outcome answers another question and does not itself demonstrate an infection benefit.

Statistical evidence is not equivalent to clinical importance. A confidence interval and p-value describe the data in relation to a statistical model and a null comparison; they do not determine whether the estimated contrast matters to an individual patient. The record does not report a threshold for a clinically important difference or enough detail about participant characteristics and treatment procedures to assess applicability across different clinical contexts.

The findings should also be read with the documentation limits in mind. The registry summary does not report the original sample-size justification, multiplicity plan, analysis populations, or missing-data methods. Safety counts are descriptive, and they do not establish causal attribution. The result is therefore a trial-specific statistical comparison, not a guarantee of benefit or harm for any individual.

18. Why This Trial Matters Statistically

This trial illustrates why endpoint definition is central to clinical interpretation. The primary outcome is not simply any infection; it is a participant-level count based on a specified definition of major infection. Some infection types qualify only under additional treatment and hospitalization conditions. That distinction shapes what the primary result can and cannot represent.

The results also illustrate the value of separating absolute and relative measures. The primary recalculation expresses the group contrast as a risk difference, while the overall infection-rate outcome uses a rate ratio. The former describes a difference in participant risks; the latter compares event rates over person-time. They answer related but distinct questions, and their numerical values cannot be compared as though they shared a scale.

Another statistical lesson is that sponsor analyses and summary-based recalculations may use different methods. In PRO-SID, the sponsor's reported primary test and rate analysis are not identical to the unadjusted recalculations based on posted summaries. Reporting the method alongside the estimate helps readers recognize when conclusions depend on analysis choices. The record does not supply enough detail to reconstruct every modeling decision.

Finally, the secondary outcomes and safety summaries show why a primary result is only one part of a trial's evidence. Infection frequency, medication use, serious adverse events, and deaths are different outcomes, with different measurement properties. Without reported multiplicity procedures, missing-data methods, and analysis populations, their evidential weight is harder to judge. Careful statistical reading keeps each result connected to the question it actually measures.

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 summary leaves several trial-specific uncertainties. It does not report detailed eligibility criteria, baseline characteristics, dosing procedures, or the roles covered by quadruple masking. These details would help readers assess how closely the enrolled population and study conduct match other clinical settings.

The primary outcome is a participant count, so it does not distinguish one major infection from repeated major infections in the same participant. The endpoint definition also combines several types of events, some of which require hospitalization or hospitalization prolongation under the stated criteria. The summary does not give event counts by infection type or explain how borderline cases were adjudicated.

Follow-up was not identical across participants. The record reports a median study duration of 366 days among participants completing the study and notes delayed end-of-study visits for 3 participants lasting 407 days or more. It does not report detailed handling of varying follow-up, incomplete observations, or missing outcome data. This limits assessment of whether all participants contributed comparable opportunities for outcome ascertainment.

The registry does not report the original sample-size justification, multiplicity strategy, analysis populations, interim analyses, or full statistical analysis plan. For the sponsor's secondary rate analysis, it gives a method and summary result but not the model details needed for full reproduction. These reporting gaps restrict evaluation of prespecification and robustness. The posted summaries remain interpretable as reported, but they do not provide a complete account of trial conduct and statistical decision-making.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Double-blind, Randomized, Placebo-controlled, Prospective Phase III Study Evaluating Efficacy and Safety of Panzyga in Primary Infection Prophylaxis in Patients With Chronic Lymphocytic Leukemia ("PRO-SID" Study) — NCT04502030.
• Registry record last updated 2026-11-02; status last verified 2026-09.
• Results first posted 2026-11-02.
• 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

PRO-SID (NCT04502030) is a completed, randomized, quadruple-masked, parallel-group Phase III study sponsored by Octapharma. It evaluated Panzyga versus placebo for primary infection prophylaxis in patients with chronic lymphocytic leukemia. The registry lists enrollment of 247 and dates the study from 2020-10-05 to primary completion on 2025-09-19.

The primary endpoint was the occurrence of major infections, measured as participants with at least one qualifying event. The posted counts were 14 of 124 in the Panzyga group and 27 of 123 in the placebo group. The unadjusted recalculation and sponsor analysis both report results favoring a lower observed primary outcome in the Panzyga group, though their methods and scales differ.

Secondary results cover overall infection rate and frequency of prophylaxis with anti-infectives; the record also lists duration of prophylaxis as a secondary outcome without a result reported. Safety summaries include serious and other adverse-event counts and deaths. The registry does not report key statistical details including the multiplicity plan, missing-data methods, analysis populations, and original sample-size justification.