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
PRECISION, formally titled PRostate Evaluation for Clinically Important Disease: Sampling Using Image-guidance Or Not?, was a completed phase 3 randomized trial in prostate neoplasm. The study compared biopsy strategies involving MRI and MRI-targeted biopsy with TRUS-biopsy, with diagnostic detection of clinically significant disease as the registered primary endpoint.
| Feature | PRECISION |
|---|---|
| Study acronym | PRECISION |
| Full title | PRostate Evaluation for Clinically Important Disease: Sampling Using Image-guidance Or Not? |
| Phase | Phase 3 |
| Status | COMPLETED |
| Condition | Prostate Neoplasm |
| Enrollment | 500 |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Single |
| Primary purpose | Diagnostic |
| Number of arms | 2 |
| Lead sponsor | University College, London |
| Start date | 2016-01 |
| Primary completion date | 2017-12-31 |
| ClinicalTrials.gov | NCT02380027 |
2. Clinical Question
The central diagnostic question was whether an imaging-guided biopsy strategy could identify clinically significant disease in men with prostate neoplasm differently from TRUS-biopsy. The registered primary endpoint measures the proportion of men with clinically significant detected disease when histology results become available.
Population
Men enrolled in a phase 3 diagnostic trial concerning prostate neoplasm.
Intervention
MRI and MRI-targeted biopsy are listed among the study interventions.
Comparator
TRUS-biopsy is listed as a study procedure.
Primary question
What proportion of men have clinically significant disease detected after the assigned biopsy strategy?
3. Trial Design
PRECISION was randomized, used a parallel design, and had two arms. The registry identifies the study as phase 3, single-masked, and diagnostic in purpose. Enrollment was 500.
MRI-guided strategy
- MRI is listed as a study device intervention.
- MRI-targeted biopsy is listed as a procedure intervention.
- The diagnostic endpoint is assessed after histology results become available.
TRUS-biopsy
- TRUS-biopsy is listed as a study procedure.
- The study uses randomized allocation to compare biopsy strategies.
- The registered primary endpoint is a proportion rather than a time-to-event outcome.
4. Trial Timeline and Registry Status
Study start
The registry records the study start as January 2016.
Primary completion
The registry records December 31, 2017 as the primary completion date.
Registry status
The ClinicalTrials.gov record identifies PRECISION as completed.
5. Primary Endpoint
| Endpoint | Registry definition | Time frame |
|---|---|---|
| Proportion of men with clinically significant detected | Proportion of men with clinically significant detected | When histology results available, at an expected average of 30 days post-biopsy |
The endpoint is a binary diagnostic outcome summarized as a proportion. Each participant contributes according to whether clinically significant disease was detected, and the resulting proportion can be compared between randomized groups.
For a randomized diagnostic comparison, the principal quantity is the difference or ratio between the detection proportions in the two assigned strategies. The exact estimand and inferential procedure should follow the prespecified statistical analysis plan.
6. Planned Analysis
The registry identifies the primary outcome as the proportion of men with clinically significant detected disease, assessed when histology results are available at an expected average of 30 days post-biopsy. No formal statistical analyses are posted in the ClinicalTrials.gov record.
How an endpoint of this type is typically analysed
A binary diagnostic endpoint is commonly summarized separately for each randomized group as a proportion. The primary comparison can then be expressed as an absolute difference in proportions, a risk ratio, an odds ratio, or another prespecified measure of diagnostic effect.
Absolute difference
The difference between the detection proportions directly describes how much the observed proportion differs between the two biopsy strategies.
Risk ratio
The ratio of the two detection proportions describes the relative frequency of clinically significant detection between the randomized strategies.
Odds ratio
The odds ratio compares the odds of detection between the strategies. It is mathematically different from a risk ratio and should not be described as though the two measures were interchangeable.
Confidence interval
A confidence interval quantifies uncertainty around the estimated treatment or diagnostic-strategy contrast under the selected statistical model.
7. Statistical Methodology
Analysis of a binary proportion
The primary endpoint is expressed as a proportion because each participant's outcome can be classified according to whether clinically significant disease was detected. If \(x\) participants among \(n\) evaluated participants have the condition detected, the estimated proportion is:
The proportion is a descriptive estimate. Statistical inference concerns uncertainty around that estimate and, for a randomized comparison, uncertainty around the difference or ratio between the two groups.
Difference in proportions
One natural comparison for a randomized diagnostic trial is the absolute difference in detection proportions:
An absolute difference is expressed in proportion units. Multiplying by 100 expresses the same quantity in percentage points.
The advantage of an absolute difference is interpretability: it directly describes how far apart the two observed detection proportions are. Its precision depends on the number of participants and the observed proportions.
Risk ratio
A relative comparison can instead be expressed as a risk ratio:
A ratio of 1 indicates equal observed proportions. A ratio above or below 1 indicates that the numerator group's observed detection proportion is respectively higher or lower than the comparator's.
Odds ratio
The odds ratio compares the odds of detection rather than the probabilities themselves:
The odds ratio and risk ratio coincide only under particular circumstances. When the outcome is common, an odds ratio can differ materially from the corresponding risk ratio.
Confidence intervals for diagnostic effects
A confidence interval should accompany the principal effect estimate. For a difference in proportions, the interval describes uncertainty around the estimated absolute contrast. For a ratio such as a risk ratio or odds ratio, the interval is commonly interpreted on the logarithmic scale and then transformed back to the ratio scale.
Hypothesis testing
A conventional hypothesis test for equality of proportions asks whether the observed difference is compatible with a null hypothesis of no difference under the specified statistical assumptions. The choice of test depends on the prespecified analysis plan, sample sizes, and any stratification or adjustment factors specified for the trial.
8. Statistical Methods Explained
What does the primary endpoint measure?
It measures the proportion of men in whom clinically significant disease was detected. Unlike a continuous endpoint, the outcome is naturally summarized as a percentage or proportion. Unlike a survival endpoint, there is no need to model time until death or progression for the primary endpoint itself.
Why is randomization important for a diagnostic comparison?
Randomization assigns participants to study strategies according to the trial's allocation mechanism rather than allowing the investigator or participant to select the biopsy strategy. This supports a causal comparison of the randomized strategies, provided the analysis preserves the randomized comparison and other trial assumptions are appropriate.
Why use an absolute difference in proportions?
An absolute difference answers a direct question: how far apart are the observed detection proportions? This can be easier to interpret than a relative measure because the magnitude is expressed directly in proportion units.
Why might a risk ratio also be useful?
A risk ratio places the two proportions on a relative scale. It answers a different question from an absolute difference, so both measures can be complementary when the analysis plan supports them.
Why is an odds ratio not the same as a risk ratio?
Odds are calculated as \(p/(1-p)\), whereas a risk or probability is simply \(p\). Because the transformation is nonlinear, the odds ratio and risk ratio can differ, especially when the outcome is not rare.
Why does the timing of histology matter?
The registry defines the endpoint assessment as occurring when histology results are available, at an expected average of 30 days post-biopsy. The timing therefore identifies when the diagnostic outcome becomes observable rather than representing a survival follow-up period.
Why is the analysis population important?
For a randomized comparison, the denominator used for the primary endpoint must be defined carefully. Excluding participants after randomization can change the composition of the groups and potentially weaken the protection provided by randomization. The appropriate analysis population and handling of unavailable endpoint assessments should therefore follow the prespecified statistical plan.
9. Diagnostic Effect Measures
Several effect measures can describe the same binary endpoint, but they communicate different aspects of the comparison.
| Measure | Question answered | Scale |
|---|---|---|
| Detection proportion | What fraction of participants had clinically significant disease detected? | 0 to 1 |
| Absolute difference | How far apart are the two detection proportions? | Proportion units |
| Risk ratio | How large is one detection proportion relative to the other? | Ratio |
| Odds ratio | How do the odds of detection compare? | Ratio |
No single effect measure is universally sufficient. An absolute effect can communicate the practical difference in detection proportions, while a relative measure can describe the same comparison from another perspective. The most informative presentation generally makes the estimand explicit rather than allowing the reader to infer it from a generic label such as "effect."
10. Statistical Interpretation of a Proportion
If a group has an estimated detection proportion of \(p̂\), that value describes the fraction of evaluated participants whose clinically significant disease was detected under the study's endpoint definition.
A detection proportion is not itself a measure of survival, treatment benefit, or the probability that every participant with the disease will be detected. It is tied specifically to the endpoint definition and the participants evaluated.
The same observed proportion can arise from very different sample sizes. A larger amount of information generally produces a narrower confidence interval when other statistical conditions are comparable, whereas limited information produces greater uncertainty.
A p-value describes compatibility with a specified null hypothesis under the statistical testing framework. It does not tell the reader how large the difference between biopsy strategies is. That requires an effect estimate such as an absolute difference, risk ratio, or odds ratio.
11. Analysis of Missing or Unavailable Histology Results
The primary endpoint becomes available when histology results are available. Consequently, endpoint completeness is an important statistical consideration for this type of study.
Complete assessment
When the endpoint is observed for all participants included in the relevant analysis population, the detection proportion is calculated directly from the observed outcomes.
Unavailable assessment
When an endpoint cannot be determined because histology information is unavailable, the analysis denominator and the treatment of that participant must follow the prespecified statistical plan.
Why this matters
Different approaches to missing outcomes can produce different estimates, particularly if the availability of histology is related to assigned strategy or participant characteristics.
Interpretation
Missing-data assumptions should be distinguished from observed diagnostic performance. A statistical adjustment is not itself evidence that a disease was or was not detected.
12. Randomization and Causal Interpretation
Randomization is a central feature of PRECISION because it establishes the comparison between the assigned diagnostic strategies before the primary outcome is observed. In principle, this reduces systematic differences in participant characteristics between randomized groups that could otherwise confound the comparison.
Randomization does not guarantee that every baseline characteristic will be numerically identical between groups. Its principal statistical value is that, under appropriate implementation, assignment is not determined by prognostic characteristics or the eventual endpoint.
The causal interpretation concerns the strategies as randomized. It does not automatically generalize to every possible diagnostic setting, population, operator, or implementation of those procedures.
13. Single Masking and Diagnostic Assessment
The registry identifies PRECISION as a single-masked study. Masking can be relevant in diagnostic trials because knowledge of assigned strategy may influence aspects of assessment, interpretation, reporting, or subsequent clinical decisions.
The statistical importance of masking depends on which participants or investigators were masked and how the endpoint was determined. A registry designation of single masking does not, by itself, establish which individuals were masked or quantify the effect of masking on the primary endpoint.
14. Parallel Design
PRECISION uses a parallel design. Participants are randomized to study strategies and remain associated with their assigned strategy for the comparison rather than each participant serving as his own randomized control.
| Design feature | Statistical implication |
|---|---|
| Randomized | Supports comparison of outcomes according to assigned strategy. |
| Parallel | The randomized groups are compared across participants rather than through within-person crossover comparisons. |
| Two arms | The primary diagnostic comparison is between two randomized groups. |
| Single-masked | Some trial participants or personnel were masked according to the registry's designation. |
| Diagnostic purpose | The principal endpoint concerns detection rather than a time-to-event clinical outcome. |
15. What a Formal Primary Analysis Would Need to Establish
A complete inferential analysis of the primary endpoint would need to specify the exact estimand, analysis population, denominator, comparison measure, confidence level, hypothesis-testing framework, and treatment of any participants whose histology result is unavailable.
Estimand
Define precisely what contrast between randomized biopsy strategies is being estimated.
Analysis population
Define which randomized participants contribute to the primary endpoint analysis and how endpoint availability is handled.
Effect measure
Specify whether the primary comparison is an absolute difference, ratio, odds-based measure, or another prespecified contrast.
Uncertainty
Provide a confidence interval appropriate to the chosen effect measure and statistical model.
These choices are not interchangeable. For example, an absolute difference and a risk ratio can lead to different interpretations even when calculated from exactly the same two group proportions.
16. Sample Size and Statistical Information
The registry records 500 enrolled participants and 2 arms. Those quantities establish the scale of the randomized comparison but do not, by themselves, determine the precision of the primary endpoint estimate.
Precision depends on the number of participants contributing an evaluable endpoint, the distribution of outcomes, and the statistical method used for the comparison. A sample-size statement also does not reveal the magnitude of the observed diagnostic difference.
17. No Posted Statistical Results
The ClinicalTrials.gov record identifies the primary endpoint and its assessment time frame, but it does not contain posted statistical analyses for the endpoint. Accordingly, there is no registry-posted effect estimate, confidence interval, or p-value to interpret here.
The absence of a posted analysis is distinct from the registry's designation of the study as completed. A completed study can have a registry record that describes its design and endpoints without containing formal statistical result estimates.
18. Limitations
- No posted statistical analysis: the ClinicalTrials.gov record does not provide an effect estimate, confidence interval, or p-value for the registered primary endpoint.
- Endpoint definition: the registered endpoint is stated as "Proportion of men with clinically significant detected," so interpretation must remain tied to that registry wording.
- Histology-dependent assessment: the endpoint is assessed when histology results are available, making endpoint availability an important component of the analysis.
- Analysis population: the enrollment total of 500 does not establish how many participants would ultimately contribute to a formal primary endpoint analysis.
- Effect measure: the registry information does not identify a posted effect estimate for the primary endpoint, so the relative importance of an absolute difference versus a ratio cannot be established from the registry alone.
- Masking detail: the registry identifies the trial as single-masked, but that designation alone does not specify which individuals were masked or how masking was implemented.
- Generalizability: results from a randomized diagnostic strategy comparison apply most directly to the population and procedures represented in the study and may not automatically generalize to other settings.
19. Why This Trial Matters Statistically
PRECISION is a useful teaching case because it illustrates how clinical-trial statistics change when the principal outcome is a diagnostic detection proportion rather than a survival endpoint.
| Concept | How it appears in PRECISION |
|---|---|
| Randomization | Participants were randomized to two study arms. |
| Parallel design | The trial used a parallel design rather than a crossover design. |
| Diagnostic endpoint | The primary endpoint is the proportion of men with clinically significant disease detected. |
| Binary outcome | The primary endpoint naturally reduces to detection versus non-detection for each participant. |
| Proportion estimation | The endpoint can be summarized as a proportion within each randomized group. |
| Absolute effect | A difference in detection proportions describes the absolute contrast between strategies. |
| Relative effect | A risk ratio or odds ratio can describe the comparison on a relative scale when prespecified. |
| Confidence intervals | Uncertainty around the diagnostic effect is distinct from the magnitude of the point estimate. |
| Endpoint timing | The primary endpoint is assessed when histology results are available, at an expected average of 30 days post-biopsy. |
| Masking | The registry identifies the trial as single-masked. |
| Analysis population | The number enrolled is not necessarily identical to the number with an evaluable endpoint. |
20. Statistical Methods Explained: A Practical Reading Framework
Before interpreting a percentage, identify exactly what event is being counted, who is in the denominator, when the outcome is assessed, and which randomized strategies are being compared.
An absolute difference describes the separation between proportions directly. A ratio describes their relative relationship. The two measures can provide different but complementary information.
The confidence interval should be interpreted alongside the effect estimate. A narrow interval indicates greater statistical precision than a wide interval under the same inferential framework.
Randomization, masking, endpoint availability, analysis population, and endpoint definition all affect how confidently a statistical estimate can be connected to the randomized diagnostic strategies.
21. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The registered primary outcome is a binary diagnostic detection measure summarized as a proportion. A formal analysis would compare the proportions between the randomized strategies and quantify uncertainty around the selected effect measure.
Clinical interpretation
The clinical meaning of the comparison depends on what constitutes clinically significant disease and on how detection under each biopsy strategy affects the diagnostic pathway. The statistical estimate alone does not establish those broader consequences.
22. Understanding the Primary Endpoint at 30 Days
The registry specifies that the primary endpoint is assessed when histology results are available, at an expected average of 30 days post-biopsy. This is best understood as an endpoint-assessment window rather than a 30-day survival endpoint.
The distinction matters statistically. In a survival analysis, a 30-day time frame could describe follow-up for an event such as death or progression. Here, the time frame identifies when the diagnostic classification is expected to become available.
The statistical outcome is the detection classification obtained after histologic assessment, not the amount of time itself.
23. What the Registry Establishes
| Registry element | Recorded information |
|---|---|
| Study identity | PRECISION; NCT02380027 |
| Study title | PRostate Evaluation for Clinically Important Disease: Sampling Using Image-guidance Or Not? |
| Phase | Phase 3 |
| Status | COMPLETED |
| Condition | Prostate Neoplasm |
| Enrollment | 500 |
| Allocation | Randomized |
| Design model | Parallel |
| Masking | Single |
| Primary purpose | Diagnostic |
| Primary endpoint | Proportion of men with clinically significant detected |
| Endpoint time frame | When histology results available, at an expected average of 30 days post-biopsy |
| Study start | 2016-01 |
| Primary completion | 2017-12-31 |
24. Interpretation Checklist for This Trial
- Identify the unit of analysis: the primary endpoint is participant-level disease detection summarized as a proportion.
- Identify the denominator: determine which participants are considered evaluable for the endpoint under the prespecified analysis.
- Preserve randomization: interpret the principal comparison according to randomized assignment when the estimand calls for a randomized-group comparison.
- Use an explicit effect measure: distinguish absolute differences from risk ratios and odds ratios.
- Report uncertainty: pair an effect estimate with an appropriate confidence interval.
- Respect endpoint timing: the registry's expected average of 30 days post-biopsy refers to histology-based endpoint availability.
- Separate statistical from clinical conclusions: a statistical difference in detection is not automatically equivalent to a conclusion about the broader clinical value of a diagnostic strategy.
25. Sources
- ClinicalTrials.gov: PRECISION, NCT02380027.
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Clinical trial results can be studied by connecting the design, endpoint, estimand, effect measure, and statistical method used to evaluate the randomized comparison.
26. Record Summary
PRECISION is a completed phase 3 randomized, parallel diagnostic trial with 500 enrolled participants and two arms. Its registered primary endpoint is the proportion of men with clinically significant detected disease, assessed when histology results become available at an expected average of 30 days post-biopsy. Statistically, the trial provides a useful framework for understanding how randomized diagnostic comparisons differ from time-to-event trials: the principal outcome is a binary detection measure, naturally summarized as a proportion and potentially compared using an absolute difference, risk ratio, odds ratio, or another prespecified estimand.
The most important statistical considerations are therefore the definition of the endpoint, the population contributing to the denominator, preservation of randomized assignment, the selected effect measure, and the uncertainty around that measure. The registry records the study as completed but does not post formal statistical analyses for the primary endpoint, so the ClinicalTrials.gov record establishes the design and endpoint framework without providing a registry-posted numerical treatment effect.