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
ACTG 388 was a completed randomized factorial trial in patients with HIV infections. The registry describes the study as a treatment trial with no masking and an enrollment of 240 participants.
| Feature | ACTG 388 |
|---|---|
| Study acronym | ACTG 388 |
| ClinicalTrials.gov identifier | NCT00014937 |
| Condition | HIV Infections |
| Study status | COMPLETED |
| Allocation | RANDOMIZED |
| Design model | FACTORIAL |
| Masking | NONE |
| Primary purpose | TREATMENT |
| Enrollment | 240 |
| Lead sponsor | National Institute of Allergy and Infectious Diseases (NIAID) |
| Sponsor type | NIH |
2. Clinical Question
The registry title describes ACTG 388 as a study of simplified drug regimens for HIV patients in ACTG 388 or patients who responded to a first potent combination regimen. The statistical structure is therefore centered on randomized comparisons within a factorial treatment design rather than a single two-arm comparison.
Population
Patients with HIV infections enrolled in ACTG 388, including patients described in the registry title as patients who responded to a first potent combination regimen.
Intervention set
The registry lists lopinavir/ritonavir, lamivudine/zidovudine, efavirenz, lamivudine, stavudine, zidovudine, and didanosine as study interventions.
Comparator structure
The registry identifies a randomized factorial design, but the available record does not specify the individual factorial cells or the exact intervention assigned to each cell.
Primary statistical question
Within a factorial randomized framework, the principal statistical task would be to compare the prespecified treatment factors and, where applicable, evaluate whether their effects interact.
3. Trial Design
ACTG 388 is identified as RANDOMIZED with a FACTORIAL design model. Factorial randomization assigns participants according to combinations of treatment factors, allowing more than one treatment question to be investigated within the same overall trial population.
Why the factorial label matters
A factorial design changes the statistical question. In a conventional two-arm trial, the principal contrast is treatment A versus treatment B. In a factorial trial, treatment combinations can allow investigators to estimate more than one main treatment effect within a shared randomized population. The design can also permit an interaction analysis asking whether the effect of one factor depends on the level of another factor.
The registry identifies the design as factorial but does not report the individual factorial cells or their allocation counts. Those details are therefore not specified here.
4. Intervention Framework
The registry lists seven drug interventions associated with ACTG 388:
| Intervention | Type |
|---|---|
| lopinavir/ritonavir | Drug |
| lamivudine/zidovudine | Drug |
| efavirenz | Drug |
| lamivudine | Drug |
| stavudine | Drug |
| zidovudine | Drug |
| didanosine | Drug |
5. Endpoints
The ClinicalTrials.gov record represented here does not report any registered primary endpoints. Consequently, there is no endpoint definition or time frame in the registry record from which a trial-specific numerical endpoint analysis can be presented.
| Endpoint information | Registry status |
|---|---|
| Registered primary endpoints | None reported in the ClinicalTrials.gov record |
| Endpoint definitions | Not reported in the registry record |
| Endpoint time frames | Not reported in the registry record |
This absence is statistically important. A treatment trial cannot be interpreted solely from its enrollment and design labels. The endpoint definition establishes what constitutes an event, when it is assessed, and which treatment contrast the analysis is intended to estimate.
6. Statistical Methodology
Randomization as the foundation of the comparison
Randomization is the principal design mechanism supporting causal treatment comparisons. If participants are randomly assigned to treatment conditions, systematic differences in observed outcomes can be attributed to treatment assignment under the assumptions of the randomized design rather than to baseline selection alone.
For ACTG 388, the registry identifies randomization but does not provide the individual randomization cells or allocation counts. The statistical interpretation therefore remains at the level of the factorial design rather than a reconstructed treatment contrast.
Factorial main effects
Suppose a factorial trial contains two treatment factors, conventionally represented as A and B. A main effect for A compares outcomes across the levels of A while averaging over the levels of B. Likewise, the main effect for B compares the levels of B while averaging over A.
Here, βA and βB represent main effects, while βAB represents the interaction between the factors.
This formulation is conceptual rather than a claim about the exact analysis model used in ACTG 388. The registry identifies the design as factorial but does not report a posted statistical analysis specifying a particular regression model.
Interaction
The interaction term is central to factorial interpretation. If the effect of factor A is materially different depending on whether factor B is present or absent, the two factors do not behave independently on the selected statistical scale.
Without an interaction assessment, averaging over one factor can conceal an important difference between treatment combinations. Conversely, an interaction estimate should be interpreted with appropriate attention to its uncertainty rather than simply comparing the apparent treatment effects within individual cells.
Analysis population
The registry record represented here does not identify an intention-to-treat population, per-protocol population, modified intention-to-treat population, or safety analysis population. Those distinctions matter because treatment-effect estimates can change when participants are excluded after randomization or classified according to treatment actually received.
Multiplicity
Factorial designs can involve several related statistical questions: multiple main effects, interaction effects, and potentially multiple endpoints. Each additional formal hypothesis increases the importance of a prespecified multiplicity strategy. The registry information represented here does not report an alpha-allocation or multiplicity procedure.
7. Planned Analysis
The registry identifies ACTG 388 as a randomized factorial treatment trial, but it does not post registered primary endpoints or statistical analyses. For a factorial trial of this type, the statistical analysis would ordinarily begin by defining the outcome and its measurement time frame, then estimating the prespecified treatment-factor effects while accounting for the factorial structure.
Main treatment effects
For each prespecified factor, the analysis would ordinarily compare outcomes across its randomized levels while accounting for the other factor in the factorial design.
Interaction
If clinically and statistically relevant, an interaction term would assess whether the effect of one treatment factor changes according to the level of another factor.
Outcome-specific model
The appropriate model depends on whether the endpoint is binary, continuous, count-based, longitudinal, or time-to-event. The registry does not report the endpoint definitions needed to select one trial-specific model.
Uncertainty
Effect estimates should ordinarily be accompanied by confidence intervals so that the magnitude and precision of treatment differences can be evaluated together.
8. Statistical Methods Explained
Why use a factorial design?
A factorial design can answer more than one treatment question within the same randomized population. Rather than conducting separate trials for separate factors, the design can evaluate multiple factors simultaneously and use the resulting combinations to estimate their effects efficiently.
What is a main effect?
A main effect describes the average effect of one treatment factor across the levels of another factor. For example, if factors A and B are randomized, the main effect of A compares the A levels while averaging across B.
What is an interaction?
An interaction occurs when the effect of one factor depends on the level of another factor. In practical terms, the treatment effect of A might differ when B is present compared with when B is absent.
Why is interaction different from a subgroup comparison?
Comparing treatment effects separately within factorial cells is not equivalent to testing whether those effects differ. The formal interaction term directly addresses the difference between effects. This distinction helps prevent the common error of concluding that effects differ merely because one comparison appears stronger or more statistically convincing than another.
Why are endpoints essential before selecting a statistical test?
The outcome scale determines the statistical model. A binary endpoint can naturally lead to a risk-based or odds-based analysis, a continuous endpoint may use a mean-difference model, and a time-to-event endpoint requires methods that account for censoring and event timing. Without a defined endpoint, the appropriate analysis cannot be identified at the trial-specific level.
Why does randomization matter if the trial is factorial?
Factorial structure does not replace randomization. Random assignment remains the mechanism that balances prognostic characteristics in expectation and supports causal interpretation of the treatment-factor comparisons.
Why should interaction be considered before interpreting individual treatment combinations?
If there is substantial interaction, the average main effect can hide clinically important differences between treatment combinations. Conversely, if interaction is not supported, averaging across the other factor can provide a more precise estimate of the main treatment effect.
9. Interpreting Factorial Treatment Effects
A main-effect estimate describes the average contrast associated with one randomized factor across the levels of the other factor. It does not necessarily describe the effect of every individual factorial combination.
An interaction estimate asks a different question: whether the treatment effect associated with one factor changes according to the level of another factor. This is a test of effect modification within the factorial structure.
A confidence interval communicates the statistical precision of an estimated treatment effect. A narrow interval indicates greater precision than a wide interval, although the clinical importance of the effect still depends on the outcome scale and context.
A p-value addresses the compatibility of the observed data with a specified null hypothesis under the statistical model. It does not measure the magnitude of a treatment effect, the probability that a treatment is effective, or the probability that the null hypothesis is true.
10. Open-Label Design and Statistical Interpretation
The registry identifies masking as NONE. In an unmasked trial, participants and investigators are not blinded to treatment assignment under the registry's design classification.
Objective outcomes
For outcomes measured using relatively objective criteria, lack of masking may have less influence on the measurement itself than on treatment behavior, adherence, co-interventions, or decisions made during follow-up.
Subjective outcomes
For subjective or behavior-dependent outcomes, knowledge of treatment assignment can influence reporting or assessment and therefore deserves explicit consideration in the interpretation.
Randomization and masking address different sources of bias. Randomization establishes the treatment-allocation framework, while masking can reduce certain forms of performance and assessment bias after allocation.
11. Sample Size and Statistical Precision
The registry reports an enrollment of 240. In a factorial trial, the effective precision for an individual main effect depends not only on the overall enrollment but also on how participants are distributed across factorial cells and on the variability and event frequency of the endpoint.
The precision of a treatment estimate also depends on allocation balance, endpoint variability or event frequency, missing observations, and the statistical model used.
Because the registry record represented here does not report the individual factorial allocation counts or endpoint definitions, the statistical precision of any particular treatment contrast cannot be quantified from the registry information alone.
12. Missing Data and Analysis Population
The ClinicalTrials.gov record represented here does not report a missing-data strategy or identify the formal analysis populations used for efficacy and safety. These details are important because incomplete follow-up can affect both the estimate and its precision.
For a factorial randomized trial, the preferred analysis framework ordinarily begins with preservation of the randomized groups. If participants are excluded because of treatment discontinuation, protocol deviations, or missing outcomes, the resulting population may no longer retain all of the protection provided by randomization.
The appropriate missing-data approach also depends on the endpoint. A continuous endpoint, repeated measurement, binary outcome, and time-to-event endpoint each create different patterns of missingness and require different assumptions.
13. Safety Analysis Considerations
The registry record represented here does not provide serious adverse-event counts by treatment arm. It therefore does not support an arm-specific safety table or a quantitative comparison of serious adverse events.
| Safety information | Registry status |
|---|---|
| Serious adverse events by arm | Not reported in the available registry record |
| Arm-specific adverse-event rates | Not reported in the available registry record |
| Safety analysis population | Not reported in the available registry record |
In a factorial treatment trial, safety interpretation can be more complex than in a simple two-arm comparison because adverse-event rates may be associated with more than one treatment factor. If treatment factors are combined, the analysis should distinguish main effects from possible interactions when the safety data and design support that assessment.
14. Limitations and Interpretation Issues
- No registered primary endpoints are reported: the registry record does not provide endpoint definitions or assessment time frames needed for trial-specific efficacy analysis.
- No posted statistical analyses: ClinicalTrials.gov does not provide formal statistical analyses for the record represented here.
- Factorial cell structure is not reported: the record identifies a factorial design but does not specify the individual factorial combinations or their sample sizes.
- Intervention assignment is not fully described: seven drug interventions are listed, but the registry information represented here does not specify how they map to the factorial treatment cells.
- No analysis populations are reported: the record does not identify the formal efficacy, per-protocol, or safety populations.
- No missing-data strategy is reported: the record does not describe how incomplete outcome information was handled.
- No multiplicity strategy is reported: the registry does not specify how multiple treatment factors, interactions, or endpoints were incorporated into type I error control.
- Unmasked design: the absence of masking can matter particularly for subjective outcomes, treatment behavior, and assessment decisions.
- Total enrollment does not define precision: the enrollment of 240 must be interpreted alongside the allocation structure and the endpoint-specific information available for analysis.
15. Why This Trial Matters Statistically
ACTG 388 is a useful teaching example because its registry design label illustrates an important distinction in clinical-trial statistics: a factorial randomized trial is not simply a larger collection of ordinary two-arm comparisons. Its statistical interpretation depends on understanding the treatment factors, their combinations, and the possibility of interaction.
| Concept | How it appears in ACTG 388 |
|---|---|
| Randomization | The registry identifies randomized allocation. |
| Factorial design | The design model is explicitly listed as FACTORIAL. |
| Main effects | Factorial analysis ordinarily estimates treatment-factor effects across levels of other factors. |
| Interaction | The factorial structure permits assessment of whether one factor's effect depends on another factor. |
| Masking | The registry identifies no masking. |
| Endpoint specification | No registered primary endpoints are reported in the available registry record. |
| Multiplicity | Multiple factorial treatment questions make prespecified error control important, but no registry strategy is reported. |
| Analysis populations | The registry record does not identify formal efficacy or safety analysis populations. |
| Missing data | No missing-data or imputation strategy is reported. |
| Sample size | Enrollment was 240. |
16. Related Tutorials
Learn more about the methods used in this trial:
17. Related Calculators
18. Sources
- ClinicalTrials.gov: ACTG 388, NCT00014937.
- PubMed: PubMed record associated with the ACTG 388 evidence base.
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19. Record Summary
ACTG 388 is recorded as a completed, randomized, unmasked, factorial treatment trial enrolling 240 participants with HIV infections. The registry lists seven drug interventions and identifies the study as a factorial design, making the concepts of main effects and treatment interaction central to its statistical interpretation. At the same time, the ClinicalTrials.gov record represented here does not report registered primary endpoints, endpoint time frames, factorial cell assignments, formal analysis populations, missing-data procedures, or posted statistical analyses. The resulting statistical lesson is therefore primarily about how factorial randomization structures a clinical-trial analysis: treatment effects must be defined in relation to the factorial factors, potential interactions must be distinguished from ordinary treatment contrasts, and endpoint-specific methods must be selected only after the outcome has been clearly specified.