This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
PROSEVA was a completed, randomized, parallel-group clinical trial evaluating prone positioning in patients with severe and persistent acute respiratory distress syndrome. The registry lists 474 participants and a primary endpoint of 28-day mortality from all causes.
| Feature | PROSEVA |
|---|---|
| Trial acronym | PROSEVA |
| ClinicalTrials.gov identifier | NCT00527813 |
| Status | Completed |
| Phase | Not applicable |
| Condition | Acute Respiratory Distress Syndrome |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | None |
| Primary purpose | Supportive care |
| Enrollment | 474 |
| Number of arms | 2 |
| Intervention | Prone position (procedure) |
| Lead sponsor | Hospices Civils de Lyon |
2. Clinical Question
The clinical question is whether prone positioning affects mortality among patients with severe and persistent acute respiratory distress syndrome. The registry identifies prone position as the study intervention and 28-day mortality from all causes as the primary endpoint.
Population
Patients with severe and persistent acute respiratory distress syndrome.
Intervention
Prone position, recorded in the registry as a procedure.
Comparator
The registry identifies 2 study arms but does not provide an arm-specific comparator description in the available record.
Primary question
How does the randomized treatment comparison relate to 28-day mortality from all causes?
3. Trial Design
Participants were allocated using a randomized design.
The study used 2 parallel treatment arms.
The registry identifies the trial as unmasked.
The registry classifies the primary purpose as supportive care.
A randomized parallel-group design establishes the basic statistical structure for comparing outcomes between treatment assignments. Randomization is particularly important because the treatment groups can be compared on the basis of their assigned intervention rather than relying on an observational association between prone positioning and mortality.
4. Endpoints
| Endpoint | Registry definition | Time frame |
|---|---|---|
| 28-day mortality from all causes | Mortality from all causes | 28 days |
Why the 28-day time frame matters
A fixed 28-day mortality endpoint converts the outcome into a prespecified binary event assessment over a defined period. For each participant, the statistical outcome can be represented as whether death from any cause occurred during the 28-day window.
This differs from a time-to-event endpoint in which the exact time of death and censoring time are modeled. A fixed-time mortality proportion instead focuses on the cumulative occurrence of death by the specified assessment time.
5. Planned Analysis
The registry identifies 28-day mortality from all causes as the primary endpoint, measured over 28 days. The ClinicalTrials.gov record does not post a formal statistical analysis for this endpoint.
Primary statistical question
Compare the proportion of participants who die from any cause by day 28 between the randomized treatment groups.
For an endpoint defined as mortality by a fixed time point, a conventional analysis compares the event proportions between randomized groups. The treatment effect can be expressed as an absolute risk difference, a relative risk, or an odds ratio, with an appropriate confidence interval. A hypothesis test can then assess whether the observed difference is compatible with a null treatment effect.
The corresponding control-group proportion is calculated in the same way. The absolute risk difference is pT − pC.
Because the endpoint is defined at 28 days, the most direct descriptive quantity is the proportion of randomized participants who have died by that time. The randomized design then permits a comparison of those proportions across the treatment assignments.
Absolute risk difference
The difference between the 28-day mortality proportions directly describes the absolute separation between the randomized groups.
Relative risk
The ratio of the two mortality proportions describes how the observed probability of death by 28 days compares between groups.
Odds ratio
The ratio of the mortality odds between randomized groups provides another relative effect measure, although it is not the same as a relative risk.
Confidence interval
An interval around the treatment effect quantifies statistical uncertainty associated with the estimated difference or ratio.
6. Statistical Methodology
Randomization and causal comparison
The central statistical feature of PROSEVA is randomization. When participants are randomly assigned to treatment groups, the comparison of outcomes is anchored to treatment assignment. This provides the principal basis for interpreting a difference in 28-day mortality as a treatment-group comparison rather than simply a difference between patients who happened to receive different care.
Randomization does not guarantee that every baseline characteristic will be numerically identical between groups. Its role is instead to establish a probability-based allocation mechanism that, under the usual assumptions, balances measured and unmeasured prognostic factors in expectation.
Binary endpoint analysis
Twenty-eight-day all-cause mortality can be represented as a binary outcome: death by day 28 versus no death by day 28. The basic comparison therefore concerns two proportions.
The null hypothesis states that the probability of death by 28 days is the same in the two randomized groups.
Depending on the prespecified statistical analysis plan, possible approaches for a binary endpoint include a two-sample comparison of proportions, a chi-square test, Fisher's exact test when cell counts require it, or regression-based methods that produce an adjusted treatment effect. The registry does not identify which of these approaches was prespecified.
Confidence intervals
A confidence interval is essential because a point estimate alone does not describe its statistical precision. For example, an estimated risk difference summarizes the observed separation between groups, while its confidence interval describes the uncertainty surrounding that estimate under the chosen statistical framework.
The interval should not be interpreted as a range containing the individual treatment effects experienced by patients. It describes uncertainty about the population-level treatment effect being estimated.
Hypothesis testing
A hypothesis test asks whether the observed data provide evidence against a specified null hypothesis. For a mortality comparison, a p-value is associated with the compatibility of the observed data with the null hypothesis under the selected test.
A p-value is not a measure of the magnitude of the treatment effect. A small p-value can occur with a modest effect in a large study, while a clinically meaningful effect can fail to produce a small p-value when the estimate is imprecise.
Intention-to-treat principle
For a randomized efficacy comparison, analyzing participants according to their randomized treatment assignment is the standard intention-to-treat framework. This preserves the treatment comparison established by randomization and avoids defining the primary comparison solely according to treatment actually received.
The registry record available here does not specify the analysis population used for the primary endpoint, so no particular efficacy analysis population is attributed to the registry beyond the randomized trial structure itself.
7. Statistical Methods Explained
Why is 28-day mortality a binary endpoint?
The endpoint asks whether mortality from all causes occurred within a defined 28-day period. That produces a yes/no outcome for each participant at the specified assessment point. The principal analysis therefore compares the resulting mortality proportions between randomized groups.
Why report an absolute risk difference?
An absolute risk difference preserves the original probability scale. If two groups had mortality probabilities of pT and pC, the difference pT − pC would describe the absolute separation between those groups. This is often easier to interpret clinically than a ratio alone.
What does a relative risk measure?
A relative risk is the ratio pT/pC. A value below 1 would indicate a lower observed probability of death by 28 days in the treatment group, while a value above 1 would indicate a higher probability. The relative risk should not be confused with a hazard ratio.
Why is a confidence interval important?
The point estimate is only one estimate of the underlying treatment effect. A confidence interval communicates the degree of statistical precision. A narrow interval indicates greater precision than a wide interval, all else being equal.
Why doesn't the p-value measure treatment benefit?
The p-value addresses a hypothesis-testing question rather than an effect-size question. It does not tell the reader how large the mortality difference is, whether it is clinically important, or how likely a particular individual is to benefit.
Could a time-to-event method be used instead?
Potentially, but the registry-defined primary endpoint is specifically mortality at 28 days. A time-to-event analysis would use information about when death occurred and would address a somewhat different statistical representation of the endpoint. If such a method were used, its assumptions and censoring rules would need to be prespecified.
8. Interpreting a Fixed-Time Mortality Endpoint
A 28-day mortality proportion describes the fraction of participants who died from any cause by the specified 28-day assessment. It is an absolute probability over a defined period, rather than a continuous measure of instantaneous mortality.
A 28-day mortality rate does not describe survival beyond 28 days. It also does not, by itself, describe the timing of deaths within the 28-day period or distinguish early from late deaths.
A relative effect can appear large even when the absolute difference is small, particularly when baseline event risk is low. Conversely, a modest relative change can correspond to a substantial absolute difference when baseline risk is high. Reporting both perspectives is therefore useful.
The value of the mortality comparison comes not simply from observing different death proportions, but from comparing those outcomes after randomized allocation. Randomization provides the design framework for attributing differences in outcome to the treatment comparison, subject to the usual assumptions of randomized-trial inference.
9. Limitations
- Results availability: the ClinicalTrials.gov record does not post formal statistical analyses for the primary endpoint, so no mortality estimate, confidence interval, or p-value is reported here.
- Limited endpoint information: the registry identifies one primary endpoint, 28-day mortality from all causes, but does not provide additional endpoint definitions in the available record.
- Statistical-analysis details: the registry does not specify the statistical test or effect measure used for the primary mortality comparison.
- Analysis population: the available record does not identify a specific primary efficacy analysis population beyond the randomized trial structure.
- No masking: the trial is recorded as having no masking. This is an inherent design feature that should be considered when interpreting outcomes, although all-cause mortality is relatively objective compared with subjective clinical assessments.
- Fixed follow-up window: the primary endpoint is defined at 28 days. That endpoint alone does not characterize mortality after the 28-day assessment.
- Unreported allocation details: the record identifies randomized allocation and 2 arms but does not provide the allocation ratio or arm-specific enrollment counts in the available information.
10. Why This Trial Matters Statistically
PROSEVA provides a useful teaching example because its primary question can be expressed using a relatively direct randomized comparison: whether all-cause mortality by a fixed time point differs between treatment assignments.
| Statistical concept | How it appears in PROSEVA |
|---|---|
| Randomization | Participants were allocated using a randomized design. |
| Parallel-group design | The trial used 2 parallel arms. |
| Binary outcome | 28-day mortality can be represented as death by day 28 versus no death by day 28. |
| Absolute effect | A risk difference directly describes separation in mortality probabilities. |
| Relative effect | A relative risk or odds ratio can express the mortality comparison on a ratio scale. |
| Confidence interval | Uncertainty around the treatment-effect estimate should accompany the point estimate. |
| Hypothesis testing | A statistical test can assess the compatibility of the observed mortality comparison with a null hypothesis. |
| Fixed-time analysis | The primary endpoint is defined specifically at 28 days. |
| Open-label design | The registry records no masking. |
| Supportive care | The registry classifies the primary purpose as supportive care. |
11. Trial Timeline
Study start
The ClinicalTrials.gov record lists January 2008 as the study start date.
Primary completion
The registry lists August 2011 as the primary completion date.
Registry status
The current registry record identifies the study status as completed.
12. What a Complete Statistical Result Would Contain
For a randomized trial with 28-day all-cause mortality as its primary endpoint, a statistically informative result would normally present the number and proportion of deaths in each randomized group together with an effect estimate and its confidence interval. The statistical test and its p-value would provide the corresponding hypothesis-testing result.
Event counts
The number of participants who died by 28 days in each randomized arm establishes the observed event totals.
Event proportions
Dividing the event count by the appropriate analysis population produces the 28-day mortality proportion.
Effect estimate
A risk difference, relative risk, or odds ratio provides a quantitative comparison between randomized groups.
Uncertainty and testing
A confidence interval and prespecified hypothesis test complete the basic statistical description of the primary comparison.
The registry does not post those formal statistical analyses for the primary endpoint. Consequently, the ClinicalTrials.gov record supports interpretation of the trial's design and endpoint, but not a numerical estimate of the treatment effect from the registry record itself.
13. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The primary endpoint is a fixed-time binary mortality outcome evaluated after randomized allocation. The appropriate statistical comparison concerns the probability of death from any cause by 28 days between the treatment groups.
Clinical interpretation
The endpoint addresses an outcome that is directly relevant to patients with severe and persistent acute respiratory distress syndrome. Its 28-day time frame defines a specific period of mortality rather than a complete description of long-term survival.
Keeping these two levels separate is important. Statistical analysis determines how the observed treatment-group difference is quantified and how uncertainty is assessed. Clinical interpretation determines what that quantified difference means in the context of the population, intervention, endpoint, and duration of follow-up.
14. Sources
- ClinicalTrials.gov: NCT00527813 — PROSEVA.
Continue through Clinical Biostats
Explore additional clinical trial analyses and statistical methods for understanding randomized evidence, endpoints, effect measures, and uncertainty.
15. Record Summary
PROSEVA is a completed randomized, parallel-group trial of prone positioning in patients with severe and persistent acute respiratory distress syndrome. The ClinicalTrials.gov record identifies 474 enrolled participants, 2 study arms, no masking, and a supportive-care primary purpose. Its registered primary endpoint is 28-day mortality from all causes, measured over 28 days.
From a statistical perspective, the primary endpoint is naturally understood as a fixed-time binary outcome. The central comparison is therefore the mortality probability by day 28 between randomized treatment groups, with an absolute risk difference and relative effect measure providing complementary descriptions of treatment-group separation. Confidence intervals quantify uncertainty, while hypothesis testing addresses compatibility with a prespecified null hypothesis.