This page separates the registered trial design from statistical interpretation. The ClinicalTrials.gov record identifies the primary endpoint and study design, but no formal statistical analyses are posted for the primary endpoint.
1. Trial at a Glance
DIG is a randomized, double-masked, phase 3 parallel-group trial evaluating digoxin versus placebo for the treatment of acute heart failure. The registry lists 500 participants and a primary endpoint measuring mortality and rehospitalization at 30 days.
| Feature | DIG |
|---|---|
| Phase | Phase 3 |
| Condition | Acute Heart Failure |
| Design | Randomized, double-masked, parallel-group |
| Allocation | Randomized |
| Primary purpose | Treatment |
| Enrollment | 500 |
| Number of arms | 2 |
| Interventions | Digoxin and placebo |
| Primary endpoint | 30 days mortality and rehospitalization rate |
| ClinicalTrials.gov | NCT02544815 |
| Lead sponsor | University of Monastir |
| Sponsor type | OTHER |
2. Clinical Question
The central question is whether short-term treatment with digoxin, compared with placebo, affects the registered composite outcome of mortality and rehospitalization over 30 days in patients with acute heart failure.
Population
Participants with acute heart failure.
Intervention
Digoxin.
Comparator
Placebo.
Primary question
How does randomized assignment to digoxin versus placebo relate to the 30 days mortality and rehospitalization rate?
3. Trial Design
Digoxin
- Digoxin is the registered drug intervention.
- Participants are assigned within the randomized parallel-group design.
- The primary endpoint is assessed at 30 days.
Placebo
- Placebo is the registered comparator.
- Participants are assigned within the randomized parallel-group design.
- The primary endpoint is assessed at 30 days.
The design has the basic structure needed for a randomized comparative treatment analysis: participants are allocated to one of two parallel groups, masking is used, and the outcome is evaluated over a prespecified 30-day time frame. Because the registry describes the study as double-masked, knowledge of treatment assignment is intended to be limited during the trial. That feature can reduce the opportunity for knowledge of assignment to influence treatment or outcome assessment.
4. Trial Timing and Registry Status
Study start
The registry lists May 1, 2023 as the study start date.
Primary completion
The registry lists July 31, 2025 as the primary completion date.
UNKNOWN
The ClinicalTrials.gov record currently lists the study status as UNKNOWN.
The registry's dates place the study start on May 1, 2023 and its primary completion date on July 31, 2025. The status of UNKNOWN is distinct from a conclusion about whether the intervention was effective or ineffective; it describes the status recorded by the registry.
5. Primary Endpoint
| Endpoint | Registry definition | Time frame |
|---|---|---|
| Primary endpoint | 30 days mortality and rehospitalization rate | 30 days |
The primary endpoint is expressed as a combined mortality and rehospitalization rate over 30 days. Statistically, this is important because the endpoint is not simply a mortality endpoint. The analysis needs to respect the fact that the registered outcome incorporates both mortality and rehospitalization.
The registry specifies the outcome and its 30-day time frame. It does not provide a numerical event rate, treatment effect estimate, confidence interval, or p-value.
6. Planned Analysis
No formal statistical analyses are posted to ClinicalTrials.gov. The registry does, however, provide enough information about the primary endpoint and trial design to explain the type of analysis that would ordinarily be appropriate.
Analysis of the primary endpoint
Because the primary endpoint is a 30-day mortality and rehospitalization rate, the first statistical task would ordinarily be to compare the proportion of participants experiencing the registered outcome between the randomized digoxin and placebo groups. A risk ratio, risk difference, or odds ratio could describe the between-group effect, depending on the prespecified statistical analysis plan.
The most important point is that the comparison should be anchored to randomized treatment assignment. With a randomized parallel-group design, an intention-to-treat analysis would generally preserve the treatment comparison created by randomization. The analysis population and handling of participants with incomplete 30-day follow-up would ordinarily be specified in the statistical analysis plan.
Why the 30-day time frame matters
The endpoint is explicitly defined at 30 days. An analysis of the 30-day outcome is therefore different from an analysis of unrestricted long-term follow-up. Restricting the outcome to the registered time frame creates a clearly defined assessment window and avoids silently changing the estimand to a different period.
Composite endpoints require care
The wording "mortality and rehospitalization rate" identifies two clinically distinct types of events. Mortality and rehospitalization do not necessarily have the same clinical importance or frequency. A composite analysis can provide a useful overall comparison, but interpretation should consider what contributes to the combined endpoint.
7. Statistical Methodology
Randomization
Randomization is the central design feature supporting causal comparison between the digoxin and placebo groups. In expectation, randomization balances both measured and unmeasured prognostic factors across treatment groups. Any observed difference in the prespecified outcome can therefore be evaluated as a treatment-assignment comparison rather than merely an association between treatment and outcome.
Double masking
The trial is registered as double-masked. Masking is particularly relevant when post-randomization decisions or outcome assessment could be influenced by knowledge of treatment assignment. By limiting knowledge of assignment, masking can reduce some sources of differential behavior or assessment.
Parallel-group comparison
The registered design model is parallel. Each participant is assigned to one of the two intervention groups and remains part of that randomized comparison. This differs from a crossover design, in which the same participant would receive multiple interventions in sequence.
Binary 30-day outcome analysis
If the registered mortality and rehospitalization endpoint is operationalized as whether a participant experiences the defined outcome during the 30-day window, the primary comparison would ordinarily be a comparison of two proportions. Several effect measures are possible.
A risk difference describes the absolute difference in event probability, while a risk ratio describes the relative event probability. Neither measure is available from the registry record.
Confidence intervals
A treatment-effect estimate should normally be accompanied by a confidence interval. The interval describes statistical uncertainty around the estimated effect under the chosen model and sampling framework. It is not a range containing the individual treatment effects experienced by participants.
Hypothesis testing
A formal comparison could use a two-group hypothesis test appropriate to the endpoint definition, with the exact test, test statistic, and significance level determined by the prespecified statistical analysis plan. A p-value would address evidence against the null hypothesis under the specified testing framework; it would not measure the magnitude or clinical importance of an effect.
8. Statistical Methods Explained
Why does randomization matter statistically?
Randomization creates the primary basis for a causal comparison. Without randomization, differences between participants who receive digoxin and those who do not could reflect why they received a particular treatment. Random allocation reduces that concern by determining treatment assignment through the trial design rather than clinical selection.
Why is the 30-day window important?
The registered endpoint is explicitly defined over 30 days. The time window determines which events contribute to the primary analysis. Changing the window can change the estimand and potentially the treatment effect, so the registered 30-day frame should remain central to interpretation.
Why use a risk difference?
A risk difference directly describes the absolute separation between two event probabilities. If one group had an event probability of 20% and another had 30%, the risk difference would be −10 percentage points when calculated as the first group minus the second. This is often intuitive clinically, although the registry does not report the event probabilities for DIG.
Why also consider a risk ratio?
A risk ratio places the treatment-group event probability relative to the comparator-group probability. A value below 1 indicates a lower observed event probability in the numerator group, while a value above 1 indicates a higher probability. The ratio does not describe the absolute number of events avoided or caused.
Why can a composite endpoint be difficult to interpret?
Mortality and rehospitalization represent different outcomes. If most events in a composite are rehospitalizations, the overall composite result may be driven largely by rehospitalization rather than mortality. Conversely, a relatively uncommon mortality component could still be clinically important even if it contributes fewer total events.
Why is an intention-to-treat analysis generally useful?
Analyzing participants according to their randomized assignment preserves the principal advantage of randomization. Removing participants after randomization based on adherence, treatment receipt, or subsequent events can create differences between groups that were not generated by the original random allocation.
Why does a p-value not measure treatment effect size?
A p-value addresses compatibility of the observed data with a specified null hypothesis under a particular statistical model. It does not tell the reader how large the treatment effect is. Effect size and precision are better conveyed by an appropriate effect estimate together with its confidence interval and, where useful, absolute event rates.
9. Analysis Population and Missing Data Considerations
The registry identifies a randomized trial with 500 planned participants but does not provide an analysis population, numbers analyzed by arm, treatment exposure information, or missing-data results.
For a 30-day endpoint, incomplete follow-up is particularly relevant. A participant who does not have a fully observed 30-day period may require a prespecified rule for determining whether the primary outcome occurred. The statistical analysis plan would ordinarily specify how such observations are handled rather than allowing the analyst to make post hoc decisions.
Complete outcome observation
Participants with a fully observed 30-day outcome can be classified according to the prespecified endpoint definition.
Incomplete follow-up
Participants without complete information require a prespecified approach to missing outcome data.
Randomized population
The randomized population is the natural foundation for an intention-to-treat comparison.
Interpretive sensitivity
When missing outcomes are present, conclusions can depend on the assumptions used to handle those observations.
10. Composite Endpoint: Mortality and Rehospitalization
The primary endpoint combines mortality and rehospitalization within the 30-day period. From a statistical perspective, the composite should be treated as a prespecified endpoint rather than automatically interpreted as if it were mortality alone.
| Component | Role in the registered endpoint | Statistical consideration |
|---|---|---|
| Mortality | One component of the 30 days mortality and rehospitalization rate | Clinically distinct from rehospitalization and ordinarily examined separately when component results are available |
| Rehospitalization | One component of the 30 days mortality and rehospitalization rate | May occur more frequently than mortality and can therefore influence the composite substantially |
| Combined endpoint | Registered primary endpoint | Primary inference should correspond to the prespecified composite definition and 30-day time frame |
The statistical meaning of the primary endpoint therefore depends on the exact operational definition of "mortality and rehospitalization rate." A complete numerical analysis would require the observed outcome data and the prespecified rules governing participants who experience more than one component.
11. Design Features That Shape the Analysis
Several common design features are notably absent from the registry information presented here. The record does not specify a non-inferiority margin, crossover procedure, factorial structure, interim-analysis plan, multiplicity strategy, stratification factors, or Bayesian methodology. These should not be assumed simply because they are common features of other clinical trials.
12. What a Complete Numerical Analysis Would Report
For this trial's registered primary endpoint, a statistically complete results presentation would ordinarily place the randomized treatment groups side by side and report the observed 30-day outcome in each group.
| Result element | Purpose | Registry record |
|---|---|---|
| Number randomized by arm | Defines the randomized comparison | Not reported by arm |
| Primary endpoint events by arm | Describes the observed outcome | Not reported |
| 30-day event rate by arm | Provides absolute outcome frequency | Not reported |
| Treatment effect estimate | Quantifies the between-group difference or ratio | Not reported |
| Confidence interval | Quantifies statistical uncertainty | Not reported |
| P-value | Quantifies evidence under a specified null hypothesis | Not reported |
The absence of these numerical elements means that the registry record currently supports analysis of the design and planned endpoint, rather than an evidence-based estimate of the treatment effect.
13. Interpreting a Future Treatment Effect
A risk ratio below 1 would indicate a lower observed probability of the registered 30-day endpoint in the digoxin group relative to placebo. It would not mean that the same proportional reduction applies to every participant, nor would it by itself establish the clinical importance of the difference.
A negative risk difference, when calculated as digoxin minus placebo, would indicate a lower absolute event probability in the digoxin group. The magnitude would be expressed in probability units or percentage points and would provide an absolute perspective that a relative measure does not.
The interval would quantify uncertainty around the treatment-effect estimate under the specified analysis. A narrow interval would indicate greater statistical precision than a wide interval, but precision and clinical importance remain separate concepts.
The p-value would describe the evidence against the prespecified null hypothesis. It would not measure the size of the treatment effect, the probability that digoxin works, or the probability that the null hypothesis is true.
14. Safety Considerations
The registry information identifies digoxin and placebo as the two drug interventions but does not provide serious adverse-event counts, adverse-event rates, discontinuations, or other numerical safety outcomes.
For a randomized treatment study, safety would ordinarily be summarized separately from the efficacy endpoint. Serious adverse events by randomized group would provide an important assessment of potential treatment-related harms, while overall adverse events and treatment discontinuations could provide additional context.
15. What the Registry Does and Does Not Establish
| Question | What the record establishes |
|---|---|
| Was the study randomized? | Yes. Allocation is listed as randomized. |
| Was the study masked? | Yes. Masking is listed as double. |
| Was the study parallel-group? | Yes. The design model is parallel. |
| How many participants were enrolled? | 500. |
| What was the primary endpoint? | 30 days mortality and rehospitalization rate. |
| What was the endpoint time frame? | 30 days. |
| Were formal statistical analyses posted? | No. |
| What was the treatment effect? | The registry does not report a treatment-effect estimate. |
| What was the p-value? | The registry does not report a p-value. |
| What was the confidence interval? | The registry does not report a confidence interval. |
This distinction is important. A registered endpoint tells the reader what the study set out to measure; it does not by itself demonstrate the direction, magnitude, precision, or statistical significance of an eventual treatment effect.
16. Limitations
- No posted statistical analyses: the ClinicalTrials.gov record does not provide a numerical treatment-effect estimate, confidence interval, or p-value for the primary endpoint.
- No arm-specific enrollment: the record identifies 500 participants and two arms but does not provide the number assigned to each arm.
- No observed event counts: the registry does not report the number of mortality or rehospitalization events.
- No numerical safety results: serious adverse events and other safety outcomes are not reported in the available record information.
- Composite endpoint: mortality and rehospitalization are clinically different outcomes, so the interpretation of a combined endpoint depends on the contribution of its components.
- Missing analysis details: the registry information does not specify the statistical model, analysis population, missing-data method, or multiplicity procedure for the primary endpoint.
- Registry status: the study status is listed as UNKNOWN, which limits interpretation of the study's current administrative status.
- Generalizability: the ClinicalTrials.gov record identifies acute heart failure as the condition but does not provide detailed eligibility or baseline-characteristic information for assessing applicability to specific patient populations.
17. Why This Trial Matters Statistically
DIG is a useful teaching case because its design illustrates a fundamental clinical-trial problem: translating a clearly defined randomized comparison into an interpretable short-term endpoint.
| Concept | How it appears in DIG |
|---|---|
| Randomization | Participants are randomly allocated to digoxin or placebo. |
| Parallel-group design | The two interventions form separate randomized treatment groups. |
| Double masking | The registry identifies the study as double-masked. |
| Binary outcome comparison | The registered 30-day endpoint can ordinarily be represented as an outcome proportion once event data are available. |
| Composite endpoint | Mortality and rehospitalization are combined into the primary endpoint. |
| Absolute effect | A risk difference can describe the absolute separation between treatment groups. |
| Relative effect | A risk ratio can describe the relative event probability between treatment groups. |
| Confidence interval | An interval would quantify uncertainty around the estimated treatment effect. |
| Hypothesis testing | A prespecified statistical test would evaluate evidence against the null hypothesis. |
| Missing data | Incomplete 30-day outcome information requires prespecified handling. |
The key statistical lesson is that a randomized design and a prespecified endpoint establish the framework for inference, but they do not substitute for observed outcome data. Effect estimates, uncertainty intervals, and formal hypothesis tests are necessary to describe what happened in the randomized comparison.
18. Planned Statistical Workflow
A rigorous analysis would begin by locking down the exact endpoint definition, then classifying participants according to the prespecified rules. The treatment groups would be compared according to randomized assignment, followed by estimation of an appropriate effect measure and its confidence interval. A formal hypothesis test could then be interpreted in the context of the prespecified significance level and analysis plan.
For a composite endpoint, the workflow should also distinguish the overall endpoint from its individual components. This allows the reader to understand whether the observed treatment difference is broad across components or concentrated in one component.
19. Statistical Interpretation vs Clinical Interpretation
Statistical interpretation
The trial provides a randomized comparison of digoxin versus placebo for a registered 30-day mortality and rehospitalization endpoint. The registry does not report the numerical treatment effect or formal statistical analysis.
Clinical interpretation
The clinical meaning of the intervention would depend on the observed mortality and rehospitalization outcomes, their component-specific results, the magnitude and precision of any treatment effect, and the safety profile.
Keeping these two levels separate is important. Statistical significance, if eventually demonstrated, would address evidence under a specified hypothesis-testing framework. Clinical interpretation would additionally require consideration of absolute effects, the importance of mortality versus rehospitalization, treatment harms, and the population represented by the study.
20. A Practical Guide to Reading the Primary Result
When a numerical result becomes available, readers can approach the 30-day endpoint in a structured sequence.
- Start with the event rates. Determine how frequently the primary endpoint occurred in each randomized group.
- Examine the absolute difference. This shows the magnitude of separation in percentage-point terms.
- Examine the relative effect. A risk ratio can show how the event probability compares proportionally between groups.
- Read the confidence interval. Assess how precisely the treatment effect has been estimated.
- Read the p-value in context. Treat it as a hypothesis-testing quantity, not as a measure of treatment magnitude.
- Inspect the composite components. Determine how mortality and rehospitalization each contribute to the overall endpoint.
- Review safety separately. Efficacy and safety are distinct dimensions of the randomized comparison.
This sequence prevents a common statistical mistake: reducing an entire clinical-trial result to a single p-value. The most informative presentation combines the observed event rates, an effect estimate, its uncertainty, and the clinical components that make up the endpoint.
21. Sources
- ClinicalTrials.gov: DIG — NCT02544815.
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22. Record Summary
DIG is a randomized, double-masked, phase 3 parallel-group trial of digoxin versus placebo in acute heart failure, with 500 participants and a registered primary endpoint of 30 days mortality and rehospitalization rate. The study began on May 1, 2023 and has a registered primary completion date of July 31, 2025. The ClinicalTrials.gov record currently lists the study status as UNKNOWN.
From a statistical perspective, the trial illustrates how randomization, masking, a defined follow-up window, and a composite endpoint establish the framework for a treatment comparison. The registry does not provide the observed event rates or formal statistical analyses, so the available record supports interpretation of the trial design and planned endpoint rather than a numerical conclusion about the treatment effect.