Enhancing Caregiver Support for Heart Failure Patients: the CarePartner Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Enhancing Caregiver Support for Heart Failure Patients: the CarePartner Study (NCT00555360), a randomized na study. The primary outcome is Heart Failure-specific Quality of Life.

2. Clinical Question

The study title identifies it as the CarePartner Study. Its stated context is the possible role of informal caregivers, assisted by health information technology, in filling gaps in VA heart failure care management. The registry record does not detail how caregivers participated, what technology was used, or how the intervention was delivered. Those limits matter when interpreting the clinical question: the posted data compare the named study arms, but do not fully define the practical content of the support being compared.

3. Trial Design

The CarePartner Study is listed as a randomized, parallel-group trial with no masking. The registry identifies the sponsor as the US Department of Veterans Affairs and gives enrollment as 372. The record lists the study start as 2009-06, primary completion as 2013-01, and status as completed. The phase is listed as NA.

Both arm descriptions refer to veterans with heart failure who could identify an out-of-home informal caregiver. The reported outcome groups are named Standard mHealth and mHealth+Carepartner. The intervention list separately names HITCM only and HITCM+CP. The registry does not give further details about the interventions or clarify how the listed intervention names correspond to specific delivery procedures, beyond the arm labels used in the results.

The primary outcome is a continuous measure: the mean score on the Minnesota Living with Heart Failure Questionnaire at twelve-month follow-up. The posted group summaries include 156 participants in Standard mHealth and 153 in mHealth+Carepartner. The registry’s overall enrollment figure and the outcome-specific group counts are different quantities; an explanation of the outcome analysis population or any exclusions is not reported in the registry record.

Random allocation is relevant because it is intended to make groups comparable at the outset, while a parallel design assigns participants to one study arm rather than having them cross between arms. However, details of the randomization procedure, allocation concealment, stratification, and analysis populations are not reported in the registry record. The record also does not report the trial’s original sample-size justification or the assumptions used in its own design. The planning illustrations based on observed results are separate from those original design decisions.

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

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 Heart Failure-specific Quality of Life, measured over twelve-month followup.

The primary endpoint is heart failure-specific quality of life at twelve-month follow-up, measured with the Minnesota Living with Heart Failure Questionnaire (MLHFQ). The registry describes a 21-item instrument, with answer choices ranging from 0 to 5, and an overall score range from 0 to 105. Lower scores indicate better functioning.

The endpoint is reported as a mean and standard deviation in each arm. A mean gives the average score in a group, while the standard deviation describes how dispersed individual scores are around that group average. Comparing group means therefore summarizes the average outcome, but does not show every participant’s experience or the full distribution of scores.

Quality of life is relevant to the study’s stated focus on support for people with heart failure. The brief summary describes possible support for adherence, behavior changes, and symptom monitoring; the primary endpoint captures a broader patient-reported aspect of functioning rather than measuring any one of those processes directly. The registry record does not report additional details about the questionnaire’s administration, scoring procedures beyond the stated range and direction, or the handling of incomplete questionnaires.

The scores at twelve months are endpoint summaries. They should be interpreted alongside the difference estimate and its confidence interval, which describe the size and uncertainty of the group comparison. A mean difference near zero does not establish that groups are identical, and an interval spanning zero indicates that the posted data are compatible with differences in either direction.

7. Results Posted in the Registry

• mHealth+Carepartner: mean 38.60 versus 37.00 on control; difference 1.60 (95% CI -4.03 to 7.23), standardized effect d = 0.06; z-test p = 0.578.

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.

Revised Heart Failure Self-Care Behavior Scale (HFSCB)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • mHealth+Carepartner: mean 90.50 versus 92.10 on control; difference -1.60 (95% CI -5.58 to 2.38), standardized effect d = -0.09; z-test p = 0.431.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between mHealth+Carepartner and the control. The interval gives the range of effects that remain compatible with the data.

The registry names two secondary outcomes: the Revised Heart Failure Self-Care Behavior Scale and adherence to heart failure medication. The posted secondary results include a comparison for the self-care scale. A numerical result for medication adherence is not reported in the registry record. The secondary evidence is therefore incomplete across the outcomes named in the record, and the available material does not allow a balanced numerical comparison of all listed secondary outcomes.

At a broad level, the available secondary scale result does not offer a clear, precise counterpoint to the primary quality-of-life result. Both are continuous outcomes for which the posted comparisons are uncertain. This general pattern should not be interpreted as proof that all outcomes were unchanged, especially because a result for medication adherence is absent from the posted secondary results. Detailed behavioral or adherence conclusions cannot be drawn from the information recorded.

Secondary outcomes can help describe whether observed patterns extend beyond the primary endpoint, but they usually carry less confirmatory weight when many outcomes are examined. Testing multiple outcomes increases the chance that at least one result may appear notable through chance alone. Whether the trial prespecified a multiplicity adjustment, prioritized particular secondary outcomes, or used a hierarchy of tests is not reported in the registry record. The posted secondary p values should therefore be understood as unadjusted results unless the registry explicitly states otherwise.

The secondary outcomes are related to the study’s stated rationale, which mentions treatment adherence, behavior changes, and symptom monitoring. They are not interchangeable with the primary quality-of-life measure: each captures a different aspect of care or self-management. Without the full analysis plan and complete outcome reporting, the secondary findings are best treated as supportive and exploratory context rather than as independent confirmation of the primary comparison.

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

• Revised Heart Failure Self-Care Behavior Scale (HFSCB)
• Adherent to Heart Failure Medication

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous, so the posted summaries use group means and standard deviations. A mean difference puts the two group averages on the same scale: in the recalculation, the comparison is mHealth+Carepartner minus Standard mHealth. A positive estimate means the mHealth+Carepartner group’s average score is numerically higher; for this questionnaire, lower scores indicate better functioning. This arithmetic direction describes the scale, not a conclusion about the clinical value of either arm.

What does the standard deviation describe?

A standard deviation describes variation among participant scores within a group. It is not the uncertainty in the estimated group mean. The uncertainty in a mean depends both on the spread of values and on how many observations contribute to the estimate. The posted primary group standard deviations are 25.7 and 24.8. These values describe substantial individual-level spread relative to the means, so a group average does not imply that participants all had similar scores.

What is a confidence interval?

A confidence interval gives a range of values compatible with the data and the calculation’s assumptions. For the recalculated mean difference, the 95% interval runs from -4.031 to 7.231. Since zero lies within this interval, the result does not sharply separate a difference in one direction from a difference in the other direction. The interval is not a range containing 95% of individual participants’ scores, and it does not state the probability that the true difference lies in the particular interval.

How should the p value be understood?

The recalculated unadjusted p value is 0.578. In general, a p value describes how unusual data at least as inconsistent with a specified null model would be if that model and the analysis assumptions held. It is not the probability that the null hypothesis is true, nor is it a measure of the clinical importance of an estimate. The registry record does not report the trial’s original prespecified statistical plan or the decision threshold used for its sponsor analysis. The p value attached to the recalculation is explicitly unadjusted.

Why can sponsor and recalculated estimates differ?

The sponsor-reported analysis is labeled “Regression, Logistic,” while the recalculation is a simple, unadjusted comparison from the posted summary statistics. These approaches need not return the same estimate or uncertainty. The record does not describe the model details, covariates, analysis population, or other choices that could explain the difference. The registry does not mark a discrepancy, but it also does not provide enough methodological detail to reconcile the two results. The sponsor’s reported estimate should therefore be presented as the registry’s own analysis, alongside—not substituted silently for—the summary-statistic recalculation.

Why can over-dispersion matter in general?

Over-dispersion is a general issue in analyses where observed variability is greater than a model assumes. If a model understates the variability, its uncertainty estimates can be too narrow and its tests can appear more decisive than warranted. The primary endpoint is summarized as a continuous score with standard deviations; the registry record does not report an over-dispersion assessment or establish that it applies to the sponsor analysis. It should not be assumed to explain the difference between estimates.

What do the planning illustrations represent?

The sample-size illustrations use the observed effect as a planning reference, then consider fractions of that effect. They use a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. The columns for 80% and 90% power express different planning targets: under the stated assumptions, the 90% target seeks a higher chance of detecting the specified effect than the 80% target and therefore requires more participants per arm. These are planning calculations, not a reconstruction of the trial’s original design. The original sample-size assumptions and justification are not reported in the registry record.

All such calculations depend on assumptions about the effect to detect, variability, allocation, and analysis. They do not show the probability that the observed trial result is true, and they do not establish the trial’s power after it was completed. The posted sample-size illustrations are not observed power.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data estimate a group difference. For the primary outcome, the recalculated mean difference is 1.6, and its 95% confidence interval extends from -4.031 to 7.231. The interval includes zero, as well as values on either side of zero. This means the posted summary data leave uncertainty about both the direction and the size of the group difference.

The interval is relatively broad compared with the point estimate. It therefore does not support a fine-grained claim that the groups had almost exactly the same mean, nor does it pin down a specific difference. A point estimate is the center of the calculation, not a guarantee that the true group difference equals that value. The confidence interval communicates uncertainty that the point estimate alone would conceal.

The registry’s sponsor-reported result has a 95% confidence interval from -4.62 to 4.77 around a reported mean difference of .74. That interval also includes zero. Its width and center differ from the recalculated interval, and the registry does not explain the methodological reason. The two intervals should be read as results from different analyses, not combined into one interval or used as though they came from the same calculation.

Confidence intervals address statistical uncertainty under the methods used. They do not account automatically for missing outcomes, incomplete reporting, measurement limitations, or differences between the enrolled participants and other populations. Those issues require design and analysis information that is not reported in the registry record. The posted intervals are useful for judging how much uncertainty accompanies the estimates, but they do not resolve those other limitations.

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 sample-size figures are participants per arm, not total event counts. They are planning illustrations based on the observed effect, rather than the trial’s own original design assumptions. The stated calculation assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. For each planning scenario, the effect to detect is the stated fraction of the observed effect.

The 80% power column gives the number of participants per arm associated with an 80% planning target under those assumptions. The 90% power column gives the number per arm for a higher planning target. A higher target generally requires more participants for the same assumed effect, which is reflected in the listed figures. The rows consider planning for 100%, 75%, and 50% of the observed effect. Smaller effects are harder to distinguish from random variation, so planning to detect a smaller effect requires more participants per arm.

For example, the row planning for 100% of the observed primary effect lists 3,913 participants per arm for 80% power and 5,238 participants per arm for 90% power. These are illustrative calculations, not the actual enrollment or outcome counts. The trial’s original sample-size justification and assumptions are not reported in the registry record.

The figures are not observed power. They do not measure the probability that the completed trial’s result is correct, and they do not change the interpretation of the confidence interval. The sample-size curve should likewise be read as a planning aid: its values depend on the chosen effect size and stated assumptions, rather than describing participant outcomes or the trial’s original design.

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, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 1.600; pooled standard deviation σ = 25.258.
2. n = 2 × (1.96 + 0.842)² × 25.258² / 1.600² = 3,912.1, rounded up to 3,913 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
delta <- 1.6                      # observed difference in means
sigma <- 25.2584058959371                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^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 (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(555360)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 3,913 (80% power), 5,238 (90% power)
 75% of observed effect: n per arm = 6,955 (80% power), 9,311 (90% power)
 50% of observed effect: n per arm = 15,649 (80% power), 20,949 (90% power)
power.t.test: 3913.1 
Simulated power at n = 3,913 per arm: 0.805 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-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. Analyses Reported by the Sponsor

The registry reports the sponsor’s primary analysis as “Regression, Logistic,” with a mean difference in final values of .74, a 95% confidence interval from -4.62 to 4.77, and p=.975. The registry separately permits a simple recalculation from the posted group means and standard deviations: that estimate is 1.6, with a 95% confidence interval from -4.031 to 7.231 and an unadjusted p value of 0.578.

The two sets of figures are not identical. The recalculation is an unadjusted comparison of posted summary statistics, whereas the sponsor result is identified as a regression analysis. The registry does not give enough detail about the regression specification, covariate adjustment, or analysis population to explain the difference. It does not report a discrepancy flag, but that does not supply a methodological reconciliation.

Accordingly, the sponsor’s analysis is the registry’s own reported analysis and should be described as such. The recalculation is supplementary and makes transparent what follows from the displayed group summaries under a simple comparison. Neither result should be relabeled as the other, and the available information does not support attributing their difference to a particular modeling choice.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists three outcome measures and three analyses. It identifies heart failure-specific quality of life as the primary endpoint and names self-care behavior and medication adherence among the secondary outcomes. However, the statistical plan, including any approach to multiplicity, is not reported in the registry record. It is therefore not possible to determine whether the trial used a prespecified testing hierarchy, adjusted secondary p values, or treated any secondary findings as confirmatory.

Multiplicity matters because interpreting many statistical tests independently can make chance findings more likely across the set of outcomes. An adjustment can change the threshold or interpretation of those tests, but no adjustment should be assumed. The recalculated primary p value is explicitly unadjusted. The secondary findings also require caution because their multiplicity handling is not described.

Missing-data procedures and analysis populations are not reported in the registry record. The primary group summaries include 156 participants in Standard mHealth and 153 in mHealth+Carepartner, while overall enrollment is listed as 372. The record does not state whether the outcome counts reflect participants with observed twelve-month scores, a particular analysis population, exclusions, or another process. It does not explain how incomplete questionnaires or missing follow-up were handled.

The record also does not report whether the primary analysis followed intention-to-treat principles, whether any participants were analyzed according to treatment received, or whether sensitivity analyses addressed missing outcomes. These details can affect both the interpretation and generalizability of an estimate. Their absence does not establish that a particular method was or was not used; it means that readers cannot evaluate those choices from the registry record.

The registry reports the sponsor analysis method as “Regression, Logistic,” but does not provide enough detail to assess its specification. Covariates, model diagnostics, and handling of repeated or incomplete measurements are not reported in the registry record. These reporting gaps limit assessment of how robust the primary and secondary results are to alternative analysis choices.

16. 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.

The registry reports zero serious adverse events among 183 participants at risk in Standard mHealth and zero among 189 participants at risk in mHealth+Carepartner. It also reports zero other adverse events among the same respective at-risk counts. No serious adverse event terms are listed.

The at-risk counts differ from the group counts in the primary outcome summaries. The registry does not explain the difference or define the period of risk for these safety summaries. The figures should therefore be read with their stated denominators, not assumed to describe exactly the same analysis population as the quality-of-life results.

Deaths are not reported in the registry record. The absence of listed events is a statement about the posted safety fields; it does not establish that no events occurred outside the reporting definitions or observation process. The record does not give further information about event collection, severity grading, relatedness, or follow-up duration for safety outcomes, so those details cannot be assessed from these figures alone.

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

The posted primary results describe average MLHFQ scores at twelve-month follow-up for two randomized groups of veterans with heart failure who could identify an out-of-home informal caregiver. The recalculated mean difference is small relative to the uncertainty expressed by its confidence interval, which spans values on either side of zero. The sponsor-reported analysis also has an interval spanning zero. These data do not precisely establish the direction or size of a difference in the primary endpoint.

That interpretation is not equivalent to saying the groups were identical. A nonzero difference may remain compatible with the observed data, and the interval also allows values in the opposite direction. Nor does an uncertain group comparison prove that the intervention has no effect. It means the posted estimates are not precise enough to support a definite conclusion about the size or direction of the primary difference.

The result applies to the outcome, follow-up, and participants represented in the registry summaries. It does not directly answer questions about every aspect of caregiver support, adherence, symptom monitoring, health service use, or other clinical outcomes. Medication-adherence results are not reported in the registry record, and the available secondary findings do not fill that gap. Details needed to assess missing data and the analysis population are also absent.

The random allocation is an important design feature for comparing study arms, but the registry does not provide all details needed to evaluate implementation and analysis. The difference between the sponsor analysis and the summary-statistic recalculation is not explained. In addition, the intervention components and delivery are not fully described, limiting the ability to relate the result to a specific support program.

These statistical results are not medical advice and do not determine what care an individual should receive. They describe the posted trial data and the uncertainty around the reported group comparison; they do not establish a universal effect in other populations or settings.

18. Why This Trial Matters Statistically

This trial illustrates why a mean difference should be read together with its uncertainty. The primary point estimate alone gives a single summary of the group contrast, while the confidence interval shows that a wide range of differences remains compatible with the recalculation. In clinical research, that distinction helps prevent overinterpretation of a point estimate as a definitive finding.

It also demonstrates that a sponsor-reported analysis and a simple recalculation from group summaries can differ. The registry gives a method label for the sponsor analysis but not enough detail to reconcile the estimates. Transparent reporting of model specifications, analysis populations, and missing-data procedures would make it easier for readers to understand how estimates relate and which assumptions matter.

The study’s stated rationale connects caregiver support and health information technology with treatment adherence, behavior changes, and symptom monitoring. Its primary measure instead captures heart failure-specific quality of life. That distinction is statistically important: a measure of quality of life can provide a broad patient-centered endpoint, but it cannot stand in for every proposed mechanism or secondary outcome.

Finally, the sample-size illustrations are useful for understanding how planning depends on the effect one wants to detect. The required participants per arm increase as the assumed effect becomes smaller, and the illustrations depend on explicit assumptions. They are not the original trial design calculations and are not observed power. Keeping these planning concepts separate from the completed trial’s estimates supports more careful interpretation of both design and results.

19. 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.

Several limitations specific to this record constrain interpretation. First, the registry does not provide detailed descriptions of the two intervention arms, beyond their names and the broad rationale involving informal caregivers and health information technology. The practical content, intensity, and delivery of the support are not reported in the registry record. This limits assessment of what the comparison represents in operational terms.

Second, the sponsor analysis and the summary-statistic recalculation yield different mean differences, confidence intervals, and p values. The record does not explain the relationship between them or provide enough model detail to identify the reason. The sponsor analysis is the registry’s own reported result; the recalculation is a separate unadjusted comparison. Their differences make it especially important not to treat them as one estimate.

Third, the registry does not report the original sample-size justification, randomization procedures, allocation concealment, analysis population, missing-data methods, or multiplicity plan. Overall enrollment is listed as 372, while the primary outcome summaries contain smaller group counts. The reason for those outcome counts is not given. The adverse-event denominators also differ from the primary outcome counts, with no explanation in the record.

Fourth, medication adherence is listed as a secondary outcome, but its numerical result is not reported in the registry record. The record offers limited information for evaluating the full pattern across the named secondary outcomes. Safety reporting lists no events in the specified fields, but deaths and additional collection details are not reported.

Finally, the eligibility description is limited to veterans with heart failure who could identify an out-of-home informal caregiver. Further participant characteristics and detailed eligibility criteria are not reported in the registry record. These gaps limit assessment of how directly the results apply beyond the participants and setting represented in the record.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Enhancing Caregiver Support for Heart Failure Patients: the CarePartner Study — NCT00555360.
• Registry record last updated 2015-09-15; status last verified 2015-08.
• Results first posted 2015-09-15.
• 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.

23. Record Summary

The CarePartner Study is a completed, randomized, parallel-group trial sponsored by the US Department of Veterans Affairs. The registry lists enrollment of 372 and compares Standard mHealth with mHealth+Carepartner among veterans with heart failure who could identify an out-of-home informal caregiver. The study start is listed as 2009-06 and primary completion as 2013-01.

The primary endpoint is twelve-month heart failure-specific quality of life measured with the Minnesota Living with Heart Failure Questionnaire, for which lower scores indicate better functioning. The posted group summaries and recalculated comparison show an uncertain mean difference; the sponsor-reported analysis also has a confidence interval spanning zero. The registry does not explain the difference between the two analyses.

Secondary outcomes include self-care behavior and medication adherence, although a numerical medication-adherence result is not reported in the registry record. The record also lacks details on the original sample-size plan, missing-data handling, analysis populations, multiplicity, and intervention delivery. These reporting limits should accompany interpretation of the posted estimates.