Pharmacist-led Group Medical Visits to Help With Diabetes Management: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Pharmacist-led Group Medical Visits to Help With Diabetes Management (NCT00554671), a randomized na study. The primary outcome is Hemoglobin A1c.

2. Clinical Question

3. Trial Design

The registry describes a randomized, open-label, parallel-group study. Participants were assigned to pharmacist-led group medical visits or usual care; masking was listed as none. A parallel design compares groups that receive different study approaches over the same general study period, rather than assigning each participant to each approach in sequence. Random allocation supports a comparison between assigned groups, although the registry entry does not report the sequence-generation or allocation-concealment procedures.

The planned enrollment was 250. The record lists the study as multisite and completed, with a start date of 2008-05 and primary completion in 2011-12. It identifies the VA Office of Research and Development as sponsor. The phase is listed as NA. These registry entries describe the study’s administrative design; they do not establish how closely participants followed the assigned approach or how representative the participants were of other settings.

The intervention combined group visits with medication titration and behavioral components, including monitoring, group support, and self-efficacy. The comparator was continued usual care for diabetes. The primary outcome was hemoglobin A1c at 6 months, reported as a mean with standard deviation. The registry also lists health-related quality of life assessed by SF-36V at 13 months and health-care costs to the VHA as secondary outcomes. The detailed results for those secondary outcomes are not reported in the registry record.

The endpoint summaries include 102 participants in the pharmacist-led group-visit arm and 122 in usual care. Those are the reported group counts for the primary outcome, not an accounting of all participants’ study experience. The record notes that some patients dropped out or died by the 6 month visit, but does not give the reasons or a fuller participant-flow description.

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

Analysis population for the primary outcome, as defined in the registry: At the 6 month visit, some patients dropped out of the study or died.

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 Hemoglobin A1c, measured over 6 months.

The primary endpoint was hemoglobin A1c at 6 months, measured as percent hemoglobin A1c. It is a continuous outcome, and the registry reports a mean and standard deviation for each arm. A continuous measure retains information about the range of observed values rather than reducing each participant’s result to a category. Comparing group means therefore describes the average level in each arm at the specified visit.

Hemoglobin A1c is used in diabetes management as a measure of glycemic status. In general, a continuous endpoint can be useful when a study question concerns whether the average measured value differs between groups. The group means alone do not show how individual results varied; the standard deviations give information about spread within each group. Neither the mean nor the standard deviation describes every participant’s outcome.

The registry gives the endpoint timeframe as 6 months and notes that some patients dropped out or died by that visit. It does not report a detailed rule for handling missing measurements, nor does it describe whether the endpoint was analyzed according to assigned group or another analysis population. The reported values should therefore be interpreted as the results recorded for the endpoint, with the limits of the available population information in mind.

7. Results Posted in the Registry

• Pharmacist-led Group Medical Visits: mean 7.80 versus 8.10 on control; difference -0.30 (95% CI -0.64 to 0.04), standardized effect d = -0.23; z-test p = 0.084.

8. Statistical Interpretation

9. Statistical Methods Explained

The primary outcome is a continuous measure, so the registry reports the mean and standard deviation for each arm. The mean describes the arithmetic average of the recorded values. The standard deviation describes how spread out values are around that average. In general, these two summaries answer different questions: the mean locates the group average, while the standard deviation conveys variation among individuals. A difference between group means does not imply that every participant had a result near either mean.

What does the mean difference represent?

The reported comparison is a mean difference, with the pharmacist-led group-visit arm compared with usual care. Its estimate is -0.3. In general, a mean difference expresses the separation between two group averages on the outcome’s original scale. The negative direction is consistent with the posted means of 7.8 percent in the group-visit arm and 8.1 percent in usual care. The estimate is not an individual-level change and should not be interpreted as showing that each participant’s hemoglobin A1c changed by that amount.

The registry reports a pooled standard deviation of 1.313 and a standardized effect size of -0.229. A standardized effect expresses a mean difference relative to a measure of outcome variability, making it unitless. It can help describe the magnitude of a contrast relative to spread, but it does not replace the original-scale estimate or establish clinical importance. These standardized statistics are based on the posted summary values and do not describe adjusted results.

What does the confidence interval add?

A confidence interval gives a range of values compatible with the data and the statistical procedure used to construct it. In general, a narrower interval indicates greater precision, while a wider interval indicates more uncertainty about the size of the difference. The interval reported for this comparison extends from -0.640 to 0.041 and includes zero. Thus, the posted result does not pin down a single direction or magnitude with high precision. The registry does not report a confidence level or sufficient detail to independently assess the interval’s construction, so the reported interval is the appropriate one to describe.

A confidence interval is not a range containing a specified proportion of individual participant outcomes. Nor does it mean that every value inside it is equally likely. It concerns uncertainty in the estimated group contrast under the statistical method used. Because the interval crosses zero, it includes a value representing no mean difference, as well as nonzero values on both sides.

How should the p-value be read?

The registry reports an unadjusted p-value of 0.084. In general, a p-value measures how unusual data at least as discrepant as those observed would be under a null model and its assumptions. It is not the probability that the observed difference arose by chance, and it is not a direct measure of effect size or importance. “Unadjusted” indicates that the posted value is not adjusted for other factors in the reported comparison. The registry record does not describe a prespecified significance threshold for the primary endpoint or provide the detailed statistical analysis plan.

The p-value and confidence interval are related ways of conveying statistical uncertainty, but neither summarizes all relevant features of a study. The estimate, interval, outcome variability, missing data, and analysis choices all matter. For this record, the comparison is based on the posted means and group counts; a separate adjusted estimate is not reported in the registry record.

What does randomization contribute?

In general, random allocation is intended to make assigned groups comparable on average with respect to factors known and unknown at baseline. That intention does not guarantee that every realized group is balanced, particularly in any one study. It also does not resolve missing-outcome concerns or prevent differences in care after assignment. Because masking was listed as none, participants and study personnel were not masked according to the registry entry. The record does not describe how outcome assessment or other procedures addressed the open-label design.

The reported analysis counts differ by arm, and the primary endpoint description notes that some patients dropped out or died before the 6 month visit. The registry does not report a detailed missing-data method, reasons for missingness, or the specific analysis population. In general, these details matter because a comparison of observed outcomes can be affected if participants with missing measurements differ from those whose measurements are recorded. The available summaries do not permit an assessment of the direction or size of any such effect.

What can be learned from the effect-size planning values?

The planning illustrations use fractions of the observed effect and assumptions stated with the registry material. In general, power is the probability that a specified study design will detect an effect of a chosen size under a model and assumptions. A planning calculation asks how many participants might be needed for a future study under those assumptions; it does not measure what this completed study achieved. The effect-size illustrations are separate from the primary result and should not be treated as evidence about the trial’s original design, because the trial’s own design assumptions are not reported in the registry record.

All statistical methods described in general terms above are explanations of concepts, not claims about unreported procedures in this study. The registry record does not report adjustment methods, handling of multiple outcomes, interim analyses, or a fuller statistical plan.

10. Confidence Intervals and Precision

The mean difference estimate is -0.3, and the registry reports a confidence interval extending from -0.640 to 0.041. The interval includes zero, so the result does not identify a single direction of difference with certainty. It is compatible with a negative mean difference and also includes a small positive value. This range communicates more than the point estimate alone: it shows that the estimate is imprecise enough to leave several possible underlying contrasts consistent with the data and the reported method.

In general, precision concerns how narrowly a study estimates a quantity. A confidence interval is narrower when the data and method provide more information about the estimate, and wider when uncertainty remains greater. The reported group standard deviations, 1.2 and 1.4, describe variation in participant measurements, while the confidence interval describes uncertainty about the group comparison. These are related but distinct kinds of information.

The unadjusted p-value is 0.084. It should be read together with the estimate and interval rather than as a stand-alone verdict. The record does not report the confidence level, a prespecified threshold for the primary test, or a detailed analysis plan. The endpoint description also notes that some patients dropped out or died by the 6 month visit, and the registry does not report how missing outcomes were handled. Those limitations affect how confidently the precision of the comparison can be interpreted.

11. 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 fractions of the observed effect, not counts from the completed trial. They are participants per arm, not total events. The calculations assume a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation; each scenario uses the stated fraction of the observed effect as the effect to detect. The trial’s own original sample-size assumptions are not reported in the registry record.

For planning for 100% of the observed effect, the illustration gives 301 participants per arm for 80% power and 403 participants per arm for 90% power. For planning for 75% of the observed effect, it gives 535 participants per arm for 80% power and 716 participants per arm for 90% power. For planning for 50% of the observed effect, it gives 1,203 participants per arm for 80% power and 1,610 participants per arm for 90% power.

In plain terms, the 80% power column describes a design intended to have an 80% chance of detecting the chosen effect under the listed assumptions if that effect is real. The 90% power column describes a design intended to have a 90% chance under the same type of assumptions. Smaller effects need more participants to be detected with the same target power, which is why the illustrations increase as the assumed fraction of the observed effect decreases. The accompanying curve should be read as a visual representation of this planning relationship. These calculations are not observed power.

12. 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 = -0.300; pooled standard deviation σ = 1.313.
2. n = 2 × (1.96 + 0.842)² × 1.313² / -0.300² = 300.6, rounded up to 301 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 <- -0.3                      # observed difference in means
sigma <- 1.31279222401088                     # 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(554671)
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 = 301 (80% power), 403 (90% power)
 75% of observed effect: n per arm = 535 (80% power), 716 (90% power)
 50% of observed effect: n per arm = 1,203 (80% power), 1,610 (90% power)
power.t.test: 301.6 
Simulated power at n = 301 per arm: 0.799 (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.

13. Multiplicity, Missing Data and Analysis Populations

The registry lists the primary outcome as hemoglobin A1c at 6 months and lists two secondary outcomes: change from baseline in health-related quality of life assessed by SF-36V at 13 months, and health-care costs to the VHA. The detailed results for those secondary outcomes are not reported in the registry record. The record lists four outcome measures and zero analyses, but does not explain how outcomes were prioritized or analyzed.

Multiplicity arises when several outcomes, time points, groups, or statistical tests are examined. In general, the more tests conducted, the greater the chance that at least one result will appear noteworthy by chance if no adjustment or prespecified hierarchy is used. The registry record does not report a multiplicity adjustment, a testing hierarchy, or a full statistical analysis plan. It is therefore not possible to determine from the record whether or how the primary and secondary outcomes were considered together.

The endpoint description states that some patients dropped out or died by the 6 month visit. The endpoint counts are 102 for the pharmacist-led group-visit arm and 122 for usual care, while the record lists planned enrollment of 250. The record does not provide participant flow, reasons for missing measurements, or a method for handling missing data. It also does not state whether the primary comparison used all randomized participants, only participants with observed outcomes, or another analysis population. These details are important because missing outcomes can affect the interpretation of a group comparison.

In general, an intention-to-treat analysis compares participants according to their randomized assignment, while other analysis populations may use different inclusion rules. The registry record does not identify the approach used for the posted endpoint summaries, so no such label should be assumed. The record also does not report interim analyses or the statistical plan used to reach the posted result.

14. 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

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hospitalization (22.2%), er visits (20.5%), death (3.0%).

The registry reports serious adverse events for 117 participants at risk in the pharmacist-led group-visit arm, with 38 affected, and 133 participants at risk in usual care, with 44 affected. For other adverse events, the reported at-risk counts are 117 and 133, with 5 and 7 affected, respectively. These are counts as recorded, not estimates of comparative risk. The record does not specify event definitions, observation windows, or whether the counts represent participants with at least one event or events themselves.

In general, an at-risk denominator indicates the group size used for the corresponding safety summary. It should not be confused with the endpoint counts for hemoglobin A1c, which are reported separately. Counts alone do not establish whether groups differ in safety, and differences in observation or reporting can matter when interpreting adverse-event summaries. The registry does not report a comparative statistical analysis of these events.

The listed frequent serious-event terms are hospitalization at 22.2%, ER visits at 20.5%, and Death at 3.0%. The registry record does not report a death count in the safety summary, so the term listing should not be read as a count of deaths. These event summaries do not establish whether an event was caused by the intervention.

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

The registry reports a randomized comparison of pharmacist-led group medical visits with usual care and a primary outcome of mean hemoglobin A1c at 6 months. The posted means were 7.8 percent and 8.1 percent, respectively, with a reported mean difference of -0.3. The confidence interval includes zero, and the unadjusted p-value is 0.084. Taken together, these results describe the observed group summaries while leaving uncertainty about the size and direction of the underlying difference.

In general, an estimate is not the same as a definitive statement about what would happen in every setting or for every participant. The comparison concerns the participants and outcome data recorded for this study. The registry does not provide detailed eligibility criteria, a full participant-flow account, or enough information about missing-data methods to determine how those issues may affect the estimate. It also does not report an adjusted analysis or a complete statistical plan.

The study evaluated a package of group visits, medication titration, monitoring, group support, and self-efficacy activities. The posted comparison cannot isolate the contribution of any one component. Because the study was open label, participants and personnel were not masked according to the registry record; the record does not describe procedures that might address effects of that design on care or measurement.

The listed secondary outcomes include health-related quality of life and health-care costs, but their detailed results are not reported in the registry record. The safety summaries report event counts but do not establish that events were caused by the intervention. Accordingly, the posted primary result should be understood as a specific, uncertain comparison of mean hemoglobin A1c at the recorded follow-up, not as a complete assessment of every outcome or a general guarantee about outcomes in other populations.

16. Why This Trial Matters Statistically

This trial is statistically informative because it pairs a randomized, parallel-group design with a continuous primary outcome and reports group means, standard deviations, a mean difference, a confidence interval, and an unadjusted p-value. Reading these quantities together illustrates why an estimated direction alone is not enough: the interval communicates uncertainty around the comparison, while the spread values show that participants’ measurements varied within each arm.

The result also illustrates the distinction between statistical estimation and clinical interpretation. The reported mean difference is -0.3, but the confidence interval extends across zero. The registry record does not report a prespecified interpretation threshold or detailed analysis plan. A reader therefore has to separate what is directly recorded—the group summaries and comparison—from any stronger claim about the meaning of the result.

The record highlights practical analysis questions that matter in randomized trials: who contributed an endpoint measurement, how missing data were handled, whether secondary outcomes were adjusted for multiplicity, and whether an adjusted analysis was planned. Some patients dropped out or died by the primary visit, but the available description does not explain how those cases entered the analysis. These are not minor details; they affect what population the posted means represent and how broadly the comparison can be interpreted.

Finally, the planning illustrations show how assumed effect size and target power relate to participant requirements under stated assumptions. They are planning scenarios based on the observed effect, not evidence about the original design or a retrospective measure of trial success. Keeping that distinction clear helps prevent sample-size illustrations from being mistaken for trial results.

17. 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 description leaves several trial-specific limitations unresolved. Although the study is described as multisite and randomized, the record does not report detailed eligibility criteria, site characteristics, baseline participant characteristics, the randomization procedure, or allocation concealment. Without those details, the extent of representativeness and the implementation of allocation cannot be assessed from the registry record alone.

The study was open label. The registry lists masking as none and does not describe whether outcome measurement or other study procedures were protected from possible effects of knowing the assigned group. The intervention was also a package of medication titration and behavioral and group components, so the posted comparison does not identify which component, if any, accounts for the observed group contrast.

Some patients dropped out or died by the 6 month visit, but the record does not give the number of missing outcomes, reasons for missingness, or the method used to handle missing data. The primary-outcome group counts are reported separately from the planned enrollment, and the record does not provide a detailed flow of participants from assignment through measurement. The analysis population and any covariate adjustment are not reported.

The record does not report the original sample-size justification, the trial’s statistical analysis plan, interim analyses, or procedures for addressing multiple outcomes. Detailed secondary-outcome results are also not reported. Finally, the safety summary does not specify event definitions or comparative analyses. These gaps limit assessment of the endpoint comparison, secondary outcomes, and safety findings.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Pharmacist-led Group Medical Visits to Help With Diabetes Management — NCT00554671.
• Registry record last updated 2018-07-11; status last verified 2018-07.
• Results first posted 2014-12-31.
• 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.

21. Record Summary

NCT00554671, titled “Pharmacist-led Group Medical Visits to Help With Diabetes Management,” is listed as a completed, multisite, randomized, open-label, parallel-group study. The sponsor is the VA Office of Research and Development, planned enrollment was 250, and the record lists a start date of 2008-05 and primary completion in 2011-12.

The study compared pharmacist-led group medical visits, including algorithm-driven medication titration and behavioral components, with usual care for diabetes. The primary endpoint was mean hemoglobin A1c at 6 months. The posted means were 7.8 percent and 8.1 percent, with a mean difference of -0.3, a confidence interval that includes zero, and an unadjusted p-value of 0.084.

The registry also lists health-related quality of life and health-care costs as secondary outcomes. Their detailed results, the original sample-size assumptions, and a fuller statistical analysis plan are not reported in the registry record.