Advancing Symptom Alleviation With Palliative Treatment: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Advancing Symptom Alleviation With Palliative Treatment (NCT02713347), a randomized na study. The primary outcome is Function Assessment of Chronic Illness Therapy-General (FACT-G).

2. Clinical Question

The trial addresses a practical question about adding structured symptom management, psychosocial care, and advance care planning support to usual care for people with serious cardiopulmonary illnesses. The registry describes these illnesses as common and burdensome and notes that people may have poor quality of life despite disease-specific care. It also identifies advance care planning as an area of interest because people with these illnesses may not engage in discussions about values and goals.

The clinical question is not simply whether participants report fewer symptoms. The principal outcome combines several areas of health-related quality of life, while the secondary outcomes cover symptom distress, depressive and anxiety symptoms, condition-specific quality of life, end-of-life quality of life, advance care planning, and mortality. These outcomes can help describe different aspects of participants’ experience, but the primary comparison remains the six-month FACT-G result.

The registry records a randomized, double-blind, parallel-group study comparing ADAPT with enhanced usual care. Randomization supports a comparison of assigned groups, but the posted summaries do not establish that all possible differences between the groups were eliminated. The results should therefore be read as estimates from the recorded trial, with attention to their uncertainty, available outcome data, and the incomplete reporting of analysis details.

3. Trial Design

The registry identifies NCT02713347 as a completed randomized, double-blind, parallel-group clinical trial sponsored by the VA Office of Research and Development. The listed enrollment is 306. The recorded conditions are heart failure, chronic obstructive pulmonary disease, emphysema, and pulmonary fibrosis or interstitial lung disease. The phase is recorded as NA. The registry lists a start date of 2016-09-01 and a primary completion date of 2020-12-31.

The parallel-group design assigns participants to ADAPT intervention or enhanced usual care. In the ADAPT description, a nurse uses structured algorithms for breathlessness, fatigue, and pain, and a social worker provides structured counseling concerning adjustment to illness, depression, and advance care planning. The description cuts off during its account of an additional component, so the complete intervention protocol is not reported in the registry record. Enhanced usual care permits care at providers’ discretion, including possible referrals and continuing care in several specialties. The control description likewise ends before all details are available.

The registry labels the study double-blind but does not report which groups of people were masked or how masking was maintained. The precise randomization procedure, allocation concealment, and any stratification factors are not reported in the registry record. The record also does not report the original sample-size justification or the trial’s original statistical analysis plan. These omissions limit how fully the design and analysis can be reconstructed from the posted record alone.

The primary outcome is FACT-G at 6 months, reported as a least-squares mean with standard error for each group. The outcome description says that the total score ranges from 0-108 and that higher scores indicate greater quality of life. The record cautions that the number contributing to this outcome may differ from participant flow because a participant could skip a survey and remain enrolled, or provide too many missing FACT-G items for analysis. This makes the outcome-specific denominators important: they describe the reported measure, not necessarily every enrolled participant.

The posted primary summaries include 153 participants in the ADAPT group and 147 in enhanced usual care. The broader record also describes an enrollment of 306. These figures are reported as recorded; no total should be inferred from the outcome-specific group counts. For secondary outcomes, denominators vary by measure, consistent with the registry’s warning that survey completion and evaluability may differ across outcomes.

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.

Baseline to 4 Months

4 Months to 6 Months

6 Months to 12 Months

Analysis population for the primary outcome, as defined in the registry: Total number reported here may be inconsistent from the participant flow number because (1) participants could skip completion of a survey and maintain enrollment in the study, and (2) FACT-G surveys may not have been completed enough for analysis (i.e., there were too many missing FACT-G items to score the measure).

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 Function Assessment of Chronic Illness Therapy-General (FACT-G), measured over 6 months.

The primary endpoint is the Function Assessment of Chronic Illness Therapy-General, or FACT-G, score at 6 months. It is a self-report measure of health-related quality of life with physical, social/family, emotional, and functional well-being domains. The registry describes it as widely used, valid, reliable, and responsive. Its total score range is 0-108, and a higher score means greater quality of life.

FACT-G is used because the intervention aims to address more than a single symptom. ADAPT combines symptom management with psychosocial support and advance care planning, while the trial’s stated purpose is to improve quality of life. A measure spanning several domains is therefore relevant to the broad outcome the intervention seeks to influence. The primary question compares six-month scores between the randomized groups, rather than focusing only on one symptom or one disease-specific measure.

The registry reports least-squares means and standard errors for the groups. It does not report the model specification or explain which covariates, if any, were included in those means. The record also warns that participants could skip surveys or have too many missing items for a FACT-G score to be analyzed. Consequently, the outcome counts can differ from participant flow, and the endpoint summaries apply to participants with an analyzable score rather than necessarily to everyone enrolled.

Although the scale’s direction is clear, the registry record does not state a threshold for a clinically important difference. The posted group contrast should therefore be interpreted as a difference in scores and its uncertainty, not as a direct determination of whether participants experienced a meaningful benefit.

7. Results Posted in the Registry

• ADAPT Intervention: mean 59.00 versus 54.00 on control; difference 5.00 (95% CI -6.09 to 16.09), standardized effect d = 0.10; z-test p = 0.377.

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.

General Symptom Distress Scale (GSDS)

• 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 errors.
• Result:
  • ADAPT Intervention: mean 5.56 versus 6.00 on control; difference -0.44 (95% CI -2.21 to 1.33), standardized effect d = -0.06; z-test p = 0.627.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between ADAPT Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Patient Health Questionnaire-8 (PHQ-8)

• 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 errors.
• Result:
  • ADAPT Intervention: mean 10.02 versus 11.58 on control; difference -1.56 (95% CI -5.51 to 2.39), standardized effect d = -0.09; z-test p = 0.439.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between ADAPT Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Kansas City Cardiomyopathy Questionnaire- Short Form (KCCQ-SF)

• 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 errors.
• Result:
  • ADAPT Intervention: mean 47.60 versus 43.05 on control; difference 4.55 (95% CI -5.14 to 14.24), standardized effect d = 0.18; z-test p = 0.357.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between ADAPT Intervention and the control. The interval gives the range of effects that remain compatible with the data.

The available numerical secondary summaries span symptom distress, depressive symptoms, and condition-specific quality of life. Across these measures, the estimated differences are not uniformly in the same direction, and each reported interval includes no difference. This broad pattern is consistent with uncertainty rather than a clear, consistent secondary-outcome signal. Since the scales differ in content and units, the estimates should not be pooled informally or judged by their signs alone.

These results complement the primary FACT-G comparison by addressing selected aspects of participants’ reported health and experience. They do not replace the primary outcome, which was designated as the principal quality-of-life measure. The secondary outcomes listed in the registry also cover anxiety, end-of-life quality of life, advance care planning, and mortality. Numerical results for those outcomes are not included in the available outcome summaries, so their directions and precision cannot be inferred.

Secondary findings generally carry less evidential weight when many outcomes are considered and no multiplicity strategy is reported. If each comparison is assessed without accounting for the number of questions, chance variation can produce apparently notable results. The registry record does not report whether secondary outcomes were arranged in a hierarchy, adjusted for multiplicity, or designated as exploratory. The unadjusted comparisons are therefore best treated as descriptive context rather than independent confirmatory tests.

The record indicates 15 secondary outcomes were posted, while the listed outcomes include multiple measures and domains. The complete analysis status and results for the other outcomes are not reported in the registry record. Missing numerical summaries should not be interpreted as evidence of no difference. Taken together, the available secondary measures neither establish a broad benefit across domains nor show that every domain was unaffected; they provide limited, uncertain evidence alongside the primary result.

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

• General Symptom Distress Scale (GSDS)
• Patient Health Questionnaire-8 (PHQ-8)
• Kansas City Cardiomyopathy Questionnaire- Short Form (KCCQ-SF)
• Clinical COPD Questionnaire (CCQ)
• Quality of Life at the End of Life (QUAL-EC)
• Advance Care Planning Communication and Documentation
• Mortality
• Generalized Anxiety Disorder Scale (GAD-7)

9. Statistical Interpretation

10. Statistical Methods Explained

For a continuous outcome such as FACT-G, a group mean describes the average score among participants included in that outcome summary. The registry reports least-squares means, which are model-based means in many trial reports. The registry does not identify the model or its adjustment variables, so the meaning of “least squares” cannot be made more specific from this record. The reported group values are 59.0 for ADAPT and 54.0 for enhanced usual care, each with a standard error of 4.0.

The mean difference expresses the contrast between groups on the outcome’s original scale. The posted recalculation is 5.0 points for FACT-G, with ADAPT as the group named first in the contrast. A positive estimate indicates a higher score in ADAPT for this measure; since higher FACT-G scores mean greater quality of life, that direction corresponds to a higher mean score. The interpretation still depends on uncertainty and the scale’s relevance, and the registry does not state a threshold for a clinically important difference.

A confidence interval gives a range of values compatible with the observed data under the assumptions used to construct it. It is not a guarantee that the true difference lies within that particular interval, nor is it a range of individual participant outcomes. The primary interval runs from -6.087 to 16.09. Because it crosses zero, the posted summary does not distinguish the observed result from no group difference at the conventional boundary used for a two-sided test. The record’s own planned significance threshold is not reported; the 0.05 value appears only among the assumptions for the separate planning illustration.

The p-value is a measure of how inconsistent the observed data are with a specified null model, under the test’s assumptions. It is not the probability that the null hypothesis is true, nor a measure of clinical importance. The primary unadjusted p-value is 0.377. The secondary unadjusted p-values are also reported with their estimates, but the registry does not report a multiplicity procedure. Thus, these values should not be read as if each outcome had been the only question asked.

The registry labels the group summaries as least-squares means and standard errors, but the comparison estimates and intervals in the posted calculations are unadjusted recalculations from summary statistics. The pooled standard deviations and standardized effects are also recalculated, and the pooled standard deviations are marked as approximate. A standard deviation describes the spread of individual scores; a standard error describes uncertainty in an estimated mean. They are not interchangeable. The standard deviations used for standardized comparisons are reconstructed from the reported standard errors and group sizes, rather than reported as observed outcome dispersions. That approximation is one reason to treat standardized effect sizes as descriptive aids, not as precise measures of the underlying score distribution.

Standardizing a mean difference places it in units related to the outcome’s spread, which can assist rough comparisons across scales. It does not make different clinical measures equivalent. The posted FACT-G standardized effect is 0.102; the secondary standardized effects are -0.056 for GSDS, -0.088 for PHQ-8, and 0.183 for KCCQ-SF. These values are small relative to the corresponding estimated spreads, but they inherit the approximations in the spread calculations and should not replace interpretation in the measures’ original units.

For continuous outcomes, over-dispersion means that data vary more than a model’s assumed distribution or variance structure allows. In general, underestimating variability can make standard errors too small and intervals too narrow, while allowing for over-dispersion can improve uncertainty estimates when the model is appropriate. This is a general statistical point; the registry record does not report an over-dispersion assessment or provide enough model detail to determine whether it was relevant to these analyses.

Randomization aims to make assigned groups comparable on average, including for characteristics not measured at baseline. It does not ensure that every realized group is identical, nor does it prevent missing outcome data or differences in adherence and care after assignment. The registry identifies randomization but does not report its procedure, allocation concealment details, or a full analysis population definition. These omissions make it difficult to assess the precise inferential framework used for the reported least-squares means.

The sample-size illustration uses an assumed two-sided significance level of 0.05, equal allocation, and a normal approximation. It considers effects equal to stated fractions of the observed effect. These are planning assumptions for hypothetical future studies, not a reconstruction of the original design. The registry record does not report the original trial’s sample-size justification, planned detectable effect, or power calculation. The illustration is not observed power, and it cannot establish whether the completed trial was adequately powered for its original question.

Finally, an analysis of multiple outcomes raises multiplicity concerns: when many hypotheses are examined, the opportunity for a chance finding can increase. General approaches include prespecifying a primary outcome, adjusting inference across selected comparisons, or treating secondary outcomes as exploratory. The registry identifies a primary outcome and lists secondary outcomes, but the procedure for multiplicity, the analysis hierarchy, and any adjustment are not reported. Consequently, the displayed unadjusted secondary comparisons deserve less weight than the primary question and should not be treated as confirmatory evidence on their own.

11. Confidence Intervals and Precision

The precision of the primary estimate is best judged from its confidence interval, not from the point estimate alone. The recalculated difference is 5.0 FACT-G points, but its interval extends from -6.087 to 16.09. This range includes no difference as well as estimates in either direction. It is therefore compatible with uncertainty about whether ADAPT had a higher or lower mean score at 6 months, and about the size of any difference.

The group standard errors are both 4.0. A standard error describes uncertainty in the estimated group mean, rather than the spread of individual scores. The registry does not provide the model details needed to reconstruct the sponsor’s uncertainty calculation, and the posted comparison is an unadjusted recalculation from summary statistics. The confidence interval should be understood in that context.

The three available secondary comparisons also have intervals crossing zero: GSDS, -2.214 to 1.334; PHQ-8, -5.51 to 2.39; and KCCQ-SF, -5.138 to 14.24. Their widths and units differ because the measures capture different constructs and are on different scales. A wider interval indicates less precision in the outcome’s original units, but width alone does not determine clinical importance.

For every measure, interval compatibility with no difference is not the same as proof of equivalence. Equivalence or non-inferiority requires a prespecified margin and a suitable design and analysis; such a margin is not reported in the registry record. The registry also does not report a clinically important difference threshold for FACT-G. As a result, precision can be described statistically, but the clinical importance of the full range of plausible values cannot be settled from these summaries alone.

Outcome availability is another aspect of precision and interpretation. The primary outcome description notes skipped surveys and records that some surveys may have too many missing items for analysis. The group counts for other outcomes vary. Without a full account of missingness and its handling, it is difficult to know whether the available-score summaries represent the randomized groups without bias. The intervals quantify sampling uncertainty under their calculation assumptions; they do not automatically account for uncertainty caused by missing data or unreported model choices.

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 planning illustrations based on the observed effect, not the original trial’s design calculation. The listed figures are participants per arm for continuous outcomes. They are not total participants across both arms and are not event counts. The registry’s assumptions for this illustration are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect.

The 80% power column describes the hypothetical number of participants per arm that would give an 80% chance of detecting the specified effect under those assumptions, if the assumptions and effect were correct. The 90% power column describes the corresponding planning target for a 90% chance. Power is a property of a planned design under specified assumptions, not a probability that the observed finding is true. These figures are not observed power.

The rows reduce the assumed effect from 100% of the observed effect to 75% and then 50%. A smaller effect is harder to distinguish from random variation, so the planning figures rise as the target effect becomes smaller. For the primary FACT-G comparison, the illustration gives 1,508 participants per arm for 80% power and 2,019 participants per arm for 90% power when planning for 100% of the observed effect. For 75% of the observed effect, the figures are 2,681 and 3,588; for 50%, they are 6,031 and 8,073.

These calculations use the observed estimate as their starting point. If that estimate is imprecise, the resulting planning figures are also uncertain and may not be good targets for a future study. The assumptions do not account for every possible design feature, including missing data or any adjustments that might be used in an actual analysis. The original trial’s own sample-size justification, target effect, and power assumptions are not reported in the registry record, so the illustration should not be mistaken for evidence about the adequacy of the completed trial’s 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 = 5.000; pooled standard deviation σ = 49.000.
2. n = 2 × (1.96 + 0.842)² × 49.000² / 5.000² = 1,507.6, rounded up to 1,508 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 <- 5                      # observed difference in means
sigma <- 48.9996575799568                     # 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(2713347)
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 = 1,508 (80% power), 2,019 (90% power)
 75% of observed effect: n per arm = 2,681 (80% power), 3,588 (90% power)
 50% of observed effect: n per arm = 6,031 (80% power), 8,073 (90% power)
power.t.test: 1508.6 
Simulated power at n = 1,508 per arm: 0.800 (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. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when a study examines several outcomes or comparisons. Even if every tested effect were absent, repeated testing can make at least one result appear unusual by chance. A prespecified primary endpoint helps identify the central question, while secondary outcomes can add context. To interpret secondary tests as confirmatory, readers also need to know whether comparisons were prioritized or adjusted to account for multiple testing.

The registry identifies FACT-G at 6 months as the primary outcome and lists secondary measures covering symptom distress, mood, condition-specific quality of life, end-of-life quality of life, advance care planning, and mortality. However, the multiplicity procedure, any testing hierarchy, and the status of secondary outcomes as confirmatory or exploratory are not reported in the registry record. The displayed secondary p-values are unadjusted. They should therefore be read cautiously and should not be treated as if each were the single prespecified test.

Missing data are also important. The primary outcome description explicitly notes that participants could skip a survey while remaining enrolled, and that some FACT-G surveys might not have enough completed items for analysis. Outcome-specific group counts differ, so the analyses do not all appear to use the same number of observations. The registry does not report the amount or pattern of missing data by reason, how missing items were handled, or whether sensitivity analyses were conducted.

In general, missingness can affect an estimate if the availability of a score is related to participants’ health, experience, or outcomes. Statistical methods can address some missing-data situations, but their suitability depends on assumptions about why data are missing and on the information collected. This is general methodological context; the record does not identify a missing-data method or provide enough information to assess its assumptions.

The analysis population is not fully defined in the available record. It does not state whether all randomized participants were included under their assigned groups for each outcome, how protocol deviations were handled, or whether any per-protocol analyses were planned. The registry also does not provide detailed analysis records. These reporting gaps limit assessment of whether the posted outcome summaries follow the original plan and how robust they are to missing outcomes or alternative analytic choices.

15. Safety Results

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

Deaths

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

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hospitalization (21.4%), mortality (3.9%).

The safety summaries report serious adverse events and other adverse events separately by assigned group. For serious adverse events, the registry lists 39 affected in the ADAPT group, with 154 at risk, and 31 affected in enhanced usual care, with 152 at risk. For other adverse events, it lists 70 affected in ADAPT, with 154 at risk, and 34 affected in enhanced usual care, with 152 at risk. These are the record’s group-level counts; they should not be combined with one another or treated as a measure of treatment benefit.

The registry lists 6 deaths in ADAPT and 5 in enhanced usual care. Among the top serious-event terms, hospitalization is listed at 21.4% and mortality at 3.9%. The record does not provide enough detail in these summaries to determine how event terms relate to individual participants, whether events could overlap, or how severity and timing were assessed. The top terms also do not provide a complete account of every reported event.

These counts are descriptive. A safety comparison would require attention to definitions, observation time, event recurrence, and the analysis plan, none of which is fully reported in the available record. Differences in counts alone do not establish that the intervention caused more or fewer events, and the summaries do not support a safety conclusion without fuller trial reporting.

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

The primary posted estimate is a 5.0-point higher mean FACT-G score for ADAPT than enhanced usual care at 6 months. Higher FACT-G scores indicate greater quality of life. However, the confidence interval extends from -6.087 to 16.09 and includes no difference. The result therefore does not provide a precise answer about the size or direction of the group difference. It should not be described as proof that ADAPT improves quality of life, and it should not be described as proof that the groups are identical.

The registry’s stated rationale is important for understanding the question: people with chronic heart failure, COPD, and interstitial lung disease may experience burdensome symptoms and impaired function despite disease-specific care, and advance care planning may be limited. The trial tests a structured approach that combines symptom and psychosocial support with advance care planning. The posted results address a broad quality-of-life score and selected secondary outcomes, but they do not establish whether each component of the intervention contributed to any observed difference.

The primary outcome is a self-reported measure with several domains. Its meaning is broader than a measure of breathlessness, depression, or disease-specific function alone. That breadth aligns with the intervention’s stated aims, but it also means a single summary score may not describe every aspect of participants’ experience. The registry does not report a clinically important difference threshold, so it is not possible to determine from this record whether any particular score difference would be meaningful to participants.

The estimates also need to be read alongside limitations in reporting. The available comparisons are unadjusted recalculations from summary statistics; model details, missing-data methods, and the multiplicity strategy are not reported. The registry notes that some participants may lack an analyzable FACT-G score, and outcome counts vary. The reported confidence interval reflects uncertainty under the calculation used, but does not by itself resolve possible bias from missing outcomes or unknown analysis choices.

The most accurate conclusion is bounded: the posted primary estimate is positive, but its uncertainty includes no difference and a range of values in both directions. The available secondary comparisons are also uncertain, and their unadjusted status limits their evidential weight. These findings describe what the registry record reports; they do not give medical advice, establish an individual’s likely response, or settle whether a broader implementation of the intervention would be beneficial.

17. Why This Trial Matters Statistically

This trial is statistically important because its target is a broad outcome that reflects several dimensions of life with serious illness. Evaluating quality of life can require more than measuring disease-specific events or a single symptom. FACT-G captures physical, social/family, emotional, and functional well-being, making it relevant to an intervention that combines symptom management, psychosocial care, and advance care planning.

The randomized parallel-group comparison gives a framework for estimating differences between assigned approaches. Yet the primary estimate illustrates why a point estimate alone is not enough: the reported difference is 5.0 points, while the interval ranges from -6.087 to 16.09. A reader must consider both the direction of the estimate and how much uncertainty surrounds it. The registry record does not provide a clinical-importance threshold that would translate that range into a judgment about meaningful change.

The trial also illustrates the interpretive challenges of multiple outcomes. The secondary measures cover distinct domains, but unadjusted comparisons across a set of outcomes can be misleading if treated as separate confirmatory findings. Distinguishing a primary question from secondary context, and knowing how multiplicity was handled, are central to responsible interpretation. The registry does not report that handling in detail.

Finally, the outcome-specific counts and the registry’s warning about skipped or incomplete surveys show why enrollment alone does not describe the information available for each endpoint. Missingness, analysis populations, and model details affect how confidently estimates can be understood. The record’s incomplete reporting of these methods makes transparent statistical interpretation especially important. The findings are useful as a description of the posted trial results, while also showing why estimates, uncertainty, data availability, and analysis plans must be read together.

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.

Several limitations are specific to the available record. The primary outcome description says some participants could skip a survey while remaining enrolled and that incomplete FACT-G responses might not be analyzable. It does not report how many scores were missing, why they were missing, or how missingness was addressed. The group counts also vary across measures, so the evidence base differs by outcome.

The analysis details are sparse. The registry reports least-squares means but not the model specification, covariates, handling of repeated observations, or full analysis population. The posted comparison estimates are recalculated and unadjusted, and the pooled standard deviations used for standardized effects are approximate. The registry record does not report a detailed sponsor analysis or provide enough information to independently assess whether the recalculation matches the trial’s intended analysis.

The protocol descriptions are incomplete in the available record. The ADAPT description ends during its account of an additional component, and the enhanced usual care description also cuts off. The registry labels the trial double-blind but does not say who was masked or how masking was maintained. The randomization procedure and allocation concealment are not reported.

The multiplicity approach, original sample-size justification, and planned statistical analysis are not reported in the registry record. The secondary outcome results available in numerical form cover only selected measures, while the registry lists additional outcomes. Missing results cannot be interpreted as null findings. The safety summaries also lack detail about event timing, recurrence, definitions, and participant-level overlap, limiting comparisons based on counts alone.

Finally, no clinically important difference threshold for FACT-G is stated. This prevents translating the statistical interval into a definite judgment about clinical importance. The confidence interval is informative about uncertainty under its calculation, but does not answer whether a possible difference would matter to participants.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Advancing Symptom Alleviation With Palliative Treatment — NCT02713347.
• Registry record last updated 2024-08-09; status last verified 2024-06.
• Results first posted 2024-08-09.
• 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

NCT02713347 is a completed randomized, double-blind, parallel-group trial sponsored by the VA Office of Research and Development. It compares ADAPT intervention with enhanced usual care for people with heart failure, COPD, emphysema, or interstitial lung disease. The registry lists enrollment of 306 and records a start date of 2016-09-01 and primary completion date of 2020-12-31.

The primary outcome is FACT-G at 6 months, a health-related quality-of-life score for which higher values indicate greater quality of life. The posted group means are 59.0 for ADAPT and 54.0 for enhanced usual care. The recalculated unadjusted difference is 5.0, with a confidence interval from -6.087 to 16.09 and unadjusted p-value 0.377. The interval includes no difference, so the estimate remains uncertain.

Selected secondary comparisons also have confidence intervals that include no difference. Their unadjusted status, the absence of a reported multiplicity strategy, varying outcome counts, and limited analysis detail call for cautious interpretation. The registry record does not report the original sample-size justification, detailed missing-data methods, or full statistical analysis plan.