An Integrated Care Model for Improving Hepatitis C Virus Patient Outcomes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for An Integrated Care Model for Improving Hepatitis C Virus Patient Outcomes (NCT00722423), a randomized na study. The primary outcome is Sustained Virologic Response Rates.

2. Clinical Question

3. Trial Design

This was a prospective randomized controlled trial with a parallel-group design. The registry lists allocation as randomized and masking as none, so the trial was open label. It lists enrollment as 364. The primary outcome results list 182 participants in the integrated care group and 181 in usual care; the registry does not explain the difference between the enrollment figure and the group sizes in the outcome results.

The two groups were assigned to different hepatitis C clinic models. In the integrated care group, an on-site mental health practitioner addressed psychiatric and substance use issues. The comparator was the usual care model. The registry summary states that both groups received current standard of care. The trial was sponsored by the US Department of Veterans Affairs and is listed as completed. The recorded start was 2009-05 and primary completion was 2013-07.

The study is listed as phase NA. The registry record does not report the randomization method, whether allocation was concealed, the detailed eligibility criteria, or the planned analysis population. It also does not report the original sample-size justification or the trial's own design assumptions. These details should not be inferred from the randomized label or from the planning illustrations associated with the posted results.

The main question concerns the number of participants with sustained virologic response, measured 12-24 weeks post-treatment. Antiviral treatment rate was listed as a secondary outcome. No analysis results are listed under the reported secondary analyses, and the registry lists zero analyses. The posted arm-level outcome counts nevertheless allow descriptive comparisons; they do not fill in the missing details of the prespecified statistical analysis plan.

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.

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 Sustained Virologic Response Rates, measured over 12-24 weeks post-treatment.

The primary endpoint is the number of participants with a sustained virologic response. The registry defines this as virus not detected by PCR assay, assessed 12-24 weeks post-treatment. It is a binary outcome at the participant level: a participant either met the stated definition or did not. The record reports counts in each group rather than a continuous measurement.

This endpoint is used to capture whether the virus was undetected at the stated post-treatment assessment. It is clinically relevant to the trial's stated goal because the integrated clinic model was intended to help patients address barriers to receiving antiviral therapy, and the registry identifies the number cured with antiviral therapy as the major study outcome. The endpoint therefore connects the clinic model to a treatment-related outcome, while not itself measuring the mental health practitioner's activities or the barriers addressed.

The timeframe is post-treatment, not a fixed time from randomization. The registry does not specify how the 12-24 week assessment window was handled in the analysis, how missing PCR results were classified, or whether additional confirmation was required. Those details are not reported in the registry record. The endpoint description also does not provide information about assay performance or how assessments were conducted across the two groups.

7. Results Posted in the Registry

• Integrated Care Model: 15.9% (29/182) versus 7.7% (14/181) on control; risk difference 8.2 percentage points (95% CI 1.6 to 14.8), risk ratio 2.06 (1.13–3.77); unadjusted two-proportion z-test p = 0.016.

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.

Antiviral Treatment Rate

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Integrated Care Model: 31.9% (58/182) versus 18.8% (34/181) on control; risk difference 13.1 percentage points (95% CI 4.2 to 21.9), risk ratio 1.70 (1.17–2.46); unadjusted two-proportion z-test p = 0.004.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Integrated Care Model than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The registry identifies antiviral treatment rate as a secondary outcome. Its posted estimates follow the same direction as the primary outcome: the integrated care group has the higher reported rate. This broad alignment is relevant to the trial's rationale, which links attention to psychiatric and substance use issues with enabling patients to undergo antiviral therapy, and then identifies sustained virologic response as the major outcome.

The outcomes address related but distinct steps. Antiviral treatment rate concerns receipt of treatment, while sustained virologic response concerns the post-treatment PCR outcome. A similar direction across them is consistent with the proposed pathway, but it does not establish that the secondary outcome caused the primary outcome difference. The registry does not report an analysis linking individual treatment receipt to response, or a mediation analysis.

The record lists no reported secondary analyses. That limits what can be concluded about how the secondary outcome was tested, whether it was prespecified in a detailed analysis plan, or whether its uncertainty was handled alongside the primary outcome. A nominal, unadjusted result for a secondary endpoint generally carries less evidential weight than a clearly prespecified primary endpoint, particularly when multiple outcomes may be examined. Without reported multiplicity procedures, the chance of at least one apparently notable finding across several tests cannot be evaluated from the record.

The pattern can therefore be described as directionally coherent, but not as independent confirmation or proof of the mechanism. The secondary outcome adds context to the primary result; it does not replace the primary endpoint or resolve the missing information about analysis planning and conduct.

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

• Antiviral Treatment Rate

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is binary: each participant is counted as having or not having a sustained virologic response, according to the registry definition. For this kind of outcome, the group risk is the proportion of participants with the event. The registry posts group estimates as well as counts and denominators. Comparing those risks gives several familiar effect measures, each of which answers a slightly different descriptive question.

The risk difference is the integrated care risk minus the usual care risk, expressed on an absolute scale. A value of zero corresponds to equal group risks. The posted estimate is 0.082. The 95% confidence interval runs from 0.016 to 0.148. A confidence interval describes a range of estimates compatible with the observed data and the interval method, under its assumptions. It is not a range containing a known true value with a probability that can be read directly from this one trial.

The risk ratio compares the two risks by division. The posted ratio is 2.06, with a confidence interval from 1.126 to 3.768. A ratio of one represents equal risks; values above one indicate a larger risk in the integrated care group for this outcome. The odds ratio compares odds rather than risks. The posted odds ratio is 2.261, with a confidence interval from 1.152 to 4.438. When outcomes are not rare, odds ratios and risk ratios can differ meaningfully in magnitude. They are alternative summaries of the same binary endpoint, not separate replications of the result.

The unadjusted p-value for the primary comparison is 0.016. A p-value is a measure of compatibility between the observed data and a statistical model that assumes no group difference, given the test's assumptions. It does not measure the size or importance of the effect, and it is not the probability that the finding arose by chance. The record labels the p-value unadjusted. The particular test, any covariate adjustment, and the full statistical analysis plan are not reported in the registry record.

For a general explanation, randomization can reduce systematic differences between treatment groups at assignment, while an unmasked design means participants and study staff were not blinded to group assignment. Lack of masking may matter for outcomes or behaviors affected by knowledge of assignment. The primary outcome is based on a PCR assay result, but the registry does not report who conducted or assessed the assay, or whether laboratory assessment was masked. No conclusions about the presence or size of any assessment bias can be drawn from that omission alone.

For a general explanation, confidence intervals reflect sampling variation and the method used to calculate them. Wider intervals indicate less precision than narrower intervals on the same scale. The registry does not state the confidence-interval method for the outcome comparison. It is therefore appropriate to interpret the posted interval as the registry's reported uncertainty range, without attributing it to an unreported calculation procedure.

Over-dispersion is a concern in some models for count data when variation exceeds what the model expects. It is not the central issue for the reported primary outcome, which is presented as participant counts with a binary event definition. More generally, statistical methods should match the outcome type and study design; the registry does not give enough analysis detail to evaluate whether any model-based assumptions were assessed.

The record also posts an antiviral treatment rate as a secondary outcome. Its estimates are a separate binary comparison, but the registry lists no reported secondary analyses. The reported information therefore supports descriptive interpretation of that outcome but does not establish how it was incorporated into a formal analysis plan. Adjustment for multiple outcomes, subgroup analyses, or covariates is not reported in the registry record.

Group counts, effect estimates, confidence intervals, and p-values each convey different aspects of a result. Counts show how many participants had the outcome. Effect estimates describe the contrast on a chosen scale. Intervals characterize uncertainty, and p-values describe compatibility with a specified null model. None alone answers questions about generalizability, mechanisms, or the quality of outcome ascertainment. Those judgments require design and analysis details that are not reported in the registry record.

11. Confidence Intervals and Precision

The primary risk-difference estimate is 0.082, and its 95% confidence interval extends from 0.016 to 0.148. The interval excludes zero, the value that would represent no absolute difference. Its lower and upper limits nevertheless span a range of possible effect sizes, so the point estimate should not be treated as exact. The interval communicates uncertainty about the size of the difference, not uncertainty about whether each individual participant had the outcome.

The risk-ratio interval is 1.126 to 3.768 around an estimate of 2.06. The odds-ratio interval is 1.152 to 4.438 around an estimate of 2.261. Both intervals exclude one, the no-association value for ratio measures. These intervals are on different scales from the risk-difference interval; their numerical widths cannot be compared directly. All three effect estimates use the same underlying group outcomes, so agreement in direction is not independent confirmation.

Precision is influenced by the amount of outcome information and by the method used to construct an interval. The registry reports the group sizes and outcome counts, but does not report the interval method, missing-data handling, or the analysis population in enough detail to assess their possible influence. Confidence intervals quantify statistical uncertainty under the analysis assumptions; they do not account automatically for unreported design limitations, measurement issues, or selective reporting.

The unadjusted p-value is 0.016, but a p-value does not replace the interval. The interval is more informative about the range of effects compatible with the data, while the p-value summarizes compatibility with a specific null model. Neither proves that the observed contrast will have the same size in another setting or population.

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 participants per arm, not total events. They are illustrations based on the observed effect, rather than the trial's original design calculations. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect to detect equal to the stated fraction of the observed effect. The trial's own sample-size assumptions and justification are not reported in the registry record.

For 100% of the observed effect, the illustration gives 243 participants per arm for 80% power and 325 participants per arm for 90% power. For 75% of the observed effect, it gives 400 participants per arm for 80% power and 534 participants per arm for 90% power. For 50% of the observed effect, it gives 824 participants per arm for 80% power and 1,102 participants per arm for 90% power.

In plain terms, the 80% power column estimates the per-arm sample size under which the assumed design would have an 80% chance of detecting the specified effect at the stated significance level, if the assumptions hold. The 90% column describes the corresponding planning target for a higher chance of detection. Smaller effects are harder to distinguish from random variation, so the illustrations require more participants as the assumed effect becomes smaller. These figures are not observed power.

Because the illustrations use the observed effect as their starting point, they are sensitive to how accurately that effect represents what might be expected in a future study. They should be read as conditional planning scenarios, not as evidence that the completed trial was adequately or inadequately sized. The original design calculation is not reported in the registry record.

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 = 0.077; treatment proportion p₁ = 0.159; average p̄ = 0.118.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.4568 = 0.8953.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.4531 = 0.3814.
4. n = (0.8953 + 0.3814)² / (0.0820)² = 242.5, rounded up to 243 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 <- 0.0773480662983425                          # control proportion
p1 <- 0.159340659340659                          # 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(722423)
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 = 243 (80% power), 325 (90% power)
 75% of observed effect: n per arm = 400 (80% power), 534 (90% power)
 50% of observed effect: n per arm = 824 (80% power), 1,102 (90% power)
power.prop.test: 242.5 
Simulated power at n = 243 per arm: 0.804 (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. Multiplicity, Missing Data and Analysis Populations

The registry record identifies a primary outcome and one secondary outcome, but it does not report a multiplicity adjustment, a hierarchy for testing outcomes, or a broader statistical analysis plan. The primary comparison has an unadjusted p-value. The secondary outcome has no reported analysis listed. As a result, the record does not show whether formal conclusions were intended to depend on one primary test alone or on a family of outcome tests.

For a general explanation, multiplicity arises when several statistical tests are considered together. If each test is interpreted without accounting for the others, the chance of at least one false-positive result can increase. The relevant family of tests depends on the prespecified analysis plan, which is not reported in the registry record. No adjustment should be assumed, and no adjusted result can be inferred from the posted estimates.

Missing-data methods are not reported in the registry record. The outcome counts and arm denominators are posted, but the record does not explain whether every randomized participant had an outcome assessment, how incomplete follow-up was handled, or whether any imputation or sensitivity analysis was used. It would be inappropriate to assume that participants without an outcome assessment were counted as failures, excluded, or handled in another particular way.

The analysis population is also not reported. The record does not state whether the primary comparison followed an intention-to-treat principle, included only participants who received treatment, or used another population definition. Random allocation does not by itself establish how participants were handled after randomization. The registry also does not report interim analyses, stopping rules, or subgroup-analysis plans. These omissions constrain assessment of the robustness and prespecification of the reported inference; they do not establish that any particular procedure was or was not used.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent serious adverse event terms

Serious events with the highest arm-level rate: hospitalization events (61.9%), death (4.4%), cardiac arrest (1.1%), cancer (1.1%), decompensated cirrhosis (1.1%).

The registry reports serious adverse event figures for both groups: 112 affected among 182 at risk in integrated care, and 125 affected among 181 at risk in usual care. These are group-level registry entries. The record lists no other adverse events in the corresponding at-risk or affected fields for either group. The deaths fields are null, which means a death count is not reported in those fields; it should not be read as a confirmed count of zero.

The registry also lists serious adverse event terms, including Hospitalization Events at 61.9% and Death at 4.4%, followed by Cardiac Arrest at 1.1%, Cancer at 1.1%, Decompensated Cirrhosis at 1.1%, and Medication Overdose at 0.5%. The denominator and the precise interpretation of these term-level percentages are not reported in the registry record. The Death term should not be treated as interchangeable with the null deaths fields.

These entries do not provide a comparative safety analysis, event timing, severity grading beyond the serious-event category, or attribution to the clinic model. They should be read as registry-reported safety information, not as evidence that the models caused or prevented particular events.

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

The posted primary outcome data show a higher observed sustained virologic response estimate in the integrated care group than in the usual care group. The absolute risk-difference estimate is positive, and its confidence interval excludes zero. The ratio estimates also have confidence intervals that exclude their no-difference value. These results describe the observed comparison and its statistical uncertainty.

The trial's rationale was that psychiatric and substance use issues can be barriers to hepatitis C treatment, and that on-site mental health support might help patients undergo antiviral therapy. The primary result is compatible with a difference between the clinic models on the stated post-treatment endpoint. It does not identify which component of integrated care mattered, demonstrate the proposed pathway, or prove that the same contrast would occur in other settings.

The registry does not report important details such as the analysis population, missing-outcome handling, allocation procedures, or full statistical plan. It also does not report baseline characteristics or a detailed description of usual care. Those omissions limit assessment of how the groups were analyzed and how broadly the observed result can be applied. The open-label design is recorded, but the registry does not describe the potential impact of awareness of assignment on treatment receipt, follow-up, or endpoint assessment.

A statistically notable result is not the same as proof of a causal mechanism, a guarantee of a particular outcome for an individual, or evidence that all relevant benefits and harms have been measured. The posted findings should be understood as an estimate from this randomized comparison, with uncertainty and reporting limitations. No clinical decision can be made from the registry summary alone.

17. Why This Trial Matters Statistically

This trial is statistically interesting because it randomizes a model of clinic organization rather than a specific antiviral treatment. The comparison connects an integrated service approach, including on-site attention to psychiatric and substance use issues, with a treatment-related primary outcome. It highlights how a behavioral or service-delivery intervention can be evaluated using a binary clinical endpoint.

The result also illustrates why effect measures should be read together. The risk difference gives an absolute contrast, while the risk ratio and odds ratio express relative contrasts on different scales. Confidence intervals convey uncertainty around each estimate, and the unadjusted p-value addresses compatibility with a no-difference model. None of these quantities alone describes every aspect of evidence strength or practical relevance.

The alignment between the primary outcome and the listed secondary outcome is consistent with the trial's stated rationale, but the registry does not provide reported secondary analyses or a multiplicity plan. This makes it important to distinguish a coherent pattern from independent confirmation. It also illustrates why a clear analysis plan and complete reporting of missing-data methods and analysis populations matter for interpreting randomized trials.

The registry's planning illustrations show how sample-size targets change under different assumed fractions of the observed effect. Because the original design assumptions are not reported, those illustrations cannot stand in for the trial's own design rationale. Overall, the record offers a useful case for reading effect estimates, intervals, outcome relationships, and incomplete analysis reporting together rather than reducing a trial to a single p-value.

18. 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 record leaves several trial-specific details unavailable for judging internal validity and generalizability. It does not provide eligibility criteria, baseline participant characteristics, the randomization procedure, allocation concealment, or a detailed account of the usual care model. It lists masking as none, but does not explain how the laboratory assessment or other outcome procedures were conducted in relation to group assignment.

The enrollment entry is 364, while the primary outcome denominators are 182 and 181. The record does not explain this difference. It also does not state the analysis population or how participants with missing post-treatment PCR results were handled. Without those details, the group counts alone do not establish whether all randomized participants were represented in the primary analysis.

The registry reports one secondary outcome but no reported secondary analyses. It does not describe multiplicity handling, the statistical analysis plan, interim analyses, or subgroup analyses. The method used for confidence intervals and p-values is not stated. The original sample-size justification and trial design assumptions are not reported; the posted planning illustrations instead use the observed effect and specified hypothetical assumptions.

Safety reporting also has limitations: serious event counts are listed, but the record does not provide event timing, attribution, or the denominator and interpretation for the term-level percentages. Deaths are null in the group fields. These gaps make it difficult to assess comparative safety from the registry entries alone.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: An Integrated Care Model for Improving Hepatitis C Virus Patient Outcomes — NCT00722423.
• Registry record last updated 2015-10-15; status last verified 2015-09.
• Results first posted 2015-10-15.
• 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.

22. Record Summary

NCT00722423 is a completed, randomized, open-label, parallel-group trial sponsored by the US Department of Veterans Affairs. It compared an integrated hepatitis C clinic model, with an on-site mental health practitioner, against usual care. The registry lists enrollment as 364 and records a start of 2009-05 and primary completion of 2013-07.

The primary outcome was sustained virologic response, defined as virus not detected by PCR assay at 12-24 weeks post-treatment. The posted outcome counts and estimates favor the integrated care group descriptively, with a positive risk-difference estimate and an unadjusted p-value of 0.016. Antiviral treatment rate was listed as a secondary outcome, but no secondary analyses are listed.

The registry does not report the full statistical analysis plan, original sample-size justification, analysis population, or missing-data methods. Its findings are best read as an estimate from the reported randomized comparison, with uncertainty and important reporting details unavailable.