Weight Loss Intervention for the Reduction of Cancer Risk and Health Disparities in Rural Ohio: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Weight Loss Intervention for the Reduction of Cancer Risk and Health Disparities in Rural Ohio (NCT05040152), a randomized na study. The primary outcome is Feasibility of a 15-week Telephone-based Weight Loss Intervention.

2. Clinical Question

3. Trial Design

The registry lists a randomized, single-masked, parallel-group study sponsored by the Ohio State University Comprehensive Cancer Center. The study is marked as completed, with enrollment of 40 participants. The listed phase is NA. The registry dates are 2021-11-06 for study start and 2022-05-17 for primary completion.

Participants were assigned to one of two groups. Arm I received a telephone-based intervention for 15 weeks, combining tailored dietary recommendations, home-based aerobic and resistance exercise, and weekly counseling calls. Arm II received educational brochures on physical activity and dietary guidelines. The registry describes several behavioral and other intervention components, but it does not report the randomization procedure, allocation concealment, or which party was masked.

The posted primary results include 27 participants in Arm I and 13 in Arm II. The primary endpoint counts study completers: 22 in Arm I and 11 in Arm II. The secondary body-weight and body-fat results list 22 and 11 participants, respectively. The record does not explain why the posted group sizes differ from the enrollment counts or give reasons for any participants not included in the secondary outcome measurements.

The design addresses whether a telephone-based program can be delivered and completed in a rural setting, while also recording short-term weight and body-fat changes. The registry record does not report the original sample-size justification, planned statistical analysis, or procedures for handling missing measurements. Although the study is randomized, the posted results alone do not establish the effects of the intervention in a broader population.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

Reasons for not completing this period:

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Feasibility of a 15-week Telephone-based Weight Loss Intervention, measured over Up to 15 weeks.

The primary endpoint is feasibility, defined in the registry as the number of enrolled participants who complete the study, with assessment up to 15 weeks. The posted counts are 22 completers among 27 participants in Arm I and 11 among 13 participants in Arm II. This endpoint focuses on whether participants remain in the study through its completion point. It is not a direct measure of weight loss, cancer risk, or cancer incidence.

Feasibility is relevant to an intervention delivered over repeated telephone contacts and involving dietary and exercise recommendations. Completion can indicate whether the study procedures and follow-up were carried through by participants, but it does not, by itself, establish why participants completed or did not complete the study. The record does not report reasons for non-completion, recruitment yield, adherence to intervention components, or participant acceptability measures.

The registry record reports a comparison of completion between the randomized groups. Such a comparison describes the observed difference in completion in this study, but the small group counts and uncertainty around the estimate matter when interpreting it. Completion is a practical endpoint for evaluating feasibility; it should not be treated as evidence that the intervention reduced cancer risk. The record reports body weight and body fat mass as secondary outcomes, but neither is the primary feasibility endpoint.

7. Results Posted in the Registry

• Arm II (Education Brochures): 84.6% (11/13) versus 81.5% (22/27) on control; risk difference 3.1 percentage points (95% CI -21.3 to 27.6), risk ratio 1.04 (0.77–1.39); unadjusted two-proportion z-test p = 0.807.

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.

Changes in Body Weight (KG)

• 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:
  • Arm II (Education Brochures): mean -2.09 versus -4.93 on control; difference 2.84 (95% CI -0.96 to 6.64), standardized effect d = 0.54; z-test p = 0.143.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Arm II (Education Brochures) and the control. The interval gives the range of effects that remain compatible with the data.

Changes in Body Fat Mass

• 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:
  • Arm II (Education Brochures): mean 0.11 versus 0.23 on control; difference -0.12 (95% CI -8.35 to 8.11), standardized effect d = -0.01; z-test p = 0.977.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Arm II (Education Brochures) and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes extend the primary feasibility result by describing changes in body weight and body fat mass through up to 15 weeks. Both groups have posted measurements, and the comparisons are expressed as mean differences. The intervals around these estimates are broad and include zero, so the results do not establish a clear difference between groups for either measure. The secondary results add information about short-term body-composition measurements, but they do not convert the feasibility endpoint into a cancer outcome.

The primary endpoint counts study completers, whereas the secondary endpoints concern measured changes among participants with listed values. These outcomes answer different questions and can have different numbers of available observations. The registry record does not give reasons for the smaller measurement counts relative to enrollment, so it is not possible to determine from the record whether those missing measurements reflect non-completion, unavailable assessments, or another reason.

The posted secondary comparisons are unadjusted. When several outcomes are examined, the chance of seeing an apparently notable result by chance can increase across the set of tests. Without a reported multiplicity strategy, the secondary p-values should be treated as exploratory rather than as confirmatory evidence. This caution is especially relevant when multiple outcomes, time points, or analytic choices may be considered. The record lists 12 outcome measures but posts two secondary outcomes in this results set; it does not report how the outcomes relate to any planned testing hierarchy.

Overall, the secondary pattern is best read as descriptive and uncertain. It can inform the design of future studies and identify measurements worth assessing further, but the registry record does not establish a treatment effect on body composition or cancer risk. The study's primary feasibility question remains distinct from these secondary measures.

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

• Changes in Body Weight (KG)
• Changes in Body Fat Mass

9. Statistical Interpretation

10. Statistical Methods Explained

What does the primary comparison estimate?

For a binary outcome such as completion, a risk difference expresses the difference between the two groups in the proportion experiencing the event. Here, the event is study completion. A risk difference of zero corresponds to equal observed completion proportions; a positive or negative value indicates which group had the higher observed proportion according to the comparison direction. The posted estimate is 0.031 for Arm II relative to Arm I. This is an unadjusted summary-based comparison, not an adjusted estimate from a reported model.

A confidence interval represents a range of values compatible with the observed data under the assumptions of the method. It communicates uncertainty around an estimate, rather than giving a range in which the true value is guaranteed to fall. For the primary comparison, the interval extends from -0.213 to 0.276 and includes zero. The data are therefore compatible with differences in either direction as well as little or no difference. The width is important: the point estimate alone does not convey how uncertain the comparison is.

How should a p-value be read?

A p-value describes how unusual data at least as discrepant as those observed would be under a specified null model and its assumptions. It is not the probability that the null hypothesis is true, nor does it measure the size or practical importance of an effect. The primary comparison has an unadjusted p-value of 0.807. That value does not provide evidence of a clear group difference under the test used, but it does not prove the groups are equivalent. Establishing equivalence would require an appropriate design and prespecified equivalence margins, which are not reported in the registry record.

More generally, statistical significance and estimation answer different questions. A p-value relates the data to a null hypothesis, while an estimate and its confidence interval describe the range and direction of effects compatible with the data. In a small study, limited information can leave estimates imprecise even when the observed point estimate appears modest. The number of participants and the width of the intervals should be considered alongside the p-values.

What do the secondary mean comparisons represent?

For body weight and body fat mass, the posted group values are means of change, and the comparisons are differences in means. The body-weight values are reported in kg, and the body-fat values in lb. A mean difference compares the average change in one group with the average change in the other. The sign depends on the order of the groups in the comparison; it does not, by itself, identify a clinical benefit.

The record labels the within-group dispersion as standard error. A standard error describes the uncertainty in an estimated mean, rather than the spread of individual participants' measurements. The comparison results also include pooled standard deviations and standardized effect estimates. A standardized effect expresses a mean difference relative to a measure of variability, which can help describe magnitude on a scale independent of the original measurement units. These summaries do not replace the original-unit estimates, and interpretation remains dependent on the study context.

General statistical point: a confidence interval for a mean difference that includes zero is compatible with no average difference, as well as with differences in either direction that remain within the interval. The body-weight interval ranges from -0.956 to 6.636 kg, and the body-fat interval ranges from -8.351 to 8.111 lb. Their breadth indicates uncertainty about the magnitude and direction of the group contrasts.

Why can small samples produce uncertain estimates?

In general, estimates based on fewer observations tend to be more sensitive to the individual outcomes observed and may have wider confidence intervals. This does not make an estimate invalid, but it can limit how specifically the data identify the underlying effect. The posted primary group counts are 27 and 13, and the secondary measurements list 22 and 11 participants. Those counts are important context when reading the confidence intervals and when considering how well the results might generalize.

For binary outcomes, the observed counts determine the estimated completion proportions and inform the uncertainty around their comparison. For continuous outcomes, variability in measurements and the number of observations contribute to the precision of the mean difference. A lack of a small p-value in a limited study is not the same as evidence that an effect is absent. Conversely, a point estimate does not establish an effect without considering its uncertainty and the design.

What do the planning calculations mean?

The registry includes planning illustrations based on the observed effects, with assumed two-sided significance level 0.05, equal allocation between groups, and a normal approximation. The calculations consider detecting the full observed effect and smaller fractions of it. They express the number of participants per arm needed for a specified power under those assumptions. Smaller effects require more participants because a subtler difference is harder to distinguish from sampling variation. The original design assumptions and sample-size justification are not reported in the registry record.

General statistical point: power is the probability that a specified design would detect an effect of a particular size under the assumptions used, if that effect were the true effect. Planning calculations depend on the chosen effect, variability or event rates, analysis, and significance level. They are not a substitute for careful design choices, and changing assumptions can change the resulting sample-size illustration. The observed comparisons and the planning calculations answer different questions: one describes the posted study results, while the other illustrates what a hypothetical future design might require under stated assumptions.

11. Confidence Intervals and Precision

The primary risk-difference estimate is 0.031, with a 95% confidence interval from -0.213 to 0.276. The interval includes zero and extends in both directions, so the posted data do not precisely determine whether completion was higher in the telephone-based intervention group or in the brochure group. The estimate should not be read alone: the interval communicates substantial uncertainty around it.

For body weight, the mean difference is 2.84 kg, and its 95% confidence interval runs from -0.956 to 6.636. For body fat mass, the mean difference is -0.12 lb, with a 95% confidence interval from -8.351 to 8.111. Each interval includes zero and includes values on either side of it. The data therefore leave uncertainty about the direction and size of each group contrast. The unit-specific intervals also show why the point estimates alone are incomplete summaries.

In general, a confidence interval reflects sampling uncertainty under the analysis assumptions. It is not a guarantee about the true value, and it does not account automatically for every possible source of bias or missing information. The record does not report adjusted analyses or additional sensitivity analyses that could clarify how robust these estimates are to alternative assumptions.

The group counts are modest, particularly for the brochure arm, and fewer participants are listed for secondary measurements than were enrolled in that arm. The record does not report reasons for this difference. These factors are relevant to precision, but the available information does not support reconstructing unreported data or analyses. The intervals are the most direct posted indication of uncertainty in the comparisons.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are participants per arm, not total events. The registry's calculation assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is specified as a fraction of the observed effect. The original trial's design assumptions and sample-size justification are not reported in the registry record.

For the primary completion comparison, planning for 100% of the observed effect gives 2,249 participants per arm for 80% power and 3,011 participants per arm for 90% power. Planning for 75% of the observed effect gives 4,073 participants per arm for 80% power and 5,452 participants per arm for 90% power. Planning for 50% of the observed effect gives 9,327 participants per arm for 80% power and 12,486 participants per arm for 90% power.

In plain language, the 80% power column describes the number of participants per arm estimated to give an 80% chance of detecting the specified effect if that effect were real and the assumptions held. The 90% power column uses a higher chance of detection, so its planned group size is larger under the same effect assumption. The calculations show that smaller effects need more participants to distinguish them from random variation.

These are planning illustrations based on the observed effect, not observed power. They do not describe the number enrolled in the completed trial and should not be used to infer the probability that the trial's result is correct. Because the estimates are based on the observed effects in a small study, they are particularly sensitive to uncertainty in those estimates.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.815; treatment proportion p₁ = 0.846; average p̄ = 0.830.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5306 = 1.0400.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5302 = 0.4462.
4. n = (1.0400 + 0.4462)² / (0.0313)² = 2,249.0, rounded up to 2,249 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.814814814814815                          # control proportion
p1 <- 0.846153846153846                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(5040152)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 2,249 (80% power), 3,011 (90% power)
 75% of observed effect: n per arm = 4,073 (80% power), 5,452 (90% power)
 50% of observed effect: n per arm = 9,327 (80% power), 12,486 (90% power)
power.prop.test: 2249 
Simulated power at n = 2,249 per arm: 0.795 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry record does not report a multiplicity adjustment, a prespecified hierarchy for testing outcomes, or a plan for interpreting secondary comparisons. The record lists 12 outcome measures and posts two secondary outcomes. When many outcomes or analyses are considered, some results may appear unusual by chance even if there is no underlying effect. General statistical practice therefore distinguishes confirmatory analyses specified in advance from exploratory analyses used to generate hypotheses. In the absence of a reported strategy, the unadjusted secondary results warrant cautious interpretation.

The registry lists no reported analyses. It does not describe a primary statistical model, covariate adjustment, missing-data method, or analysis population such as intention-to-treat or per-protocol. The posted comparisons are unadjusted summaries, and the record does not indicate whether other analyses were planned or conducted. Details of interim analyses are not reported in the registry record.

For the primary endpoint, the enrolled group counts and completer counts are posted, but the reasons for non-completion are not. The secondary measurement counts are also lower than the enrollment counts. The record does not state how participants without measurements were handled, whether any values were imputed, or whether missingness was related to outcomes or group assignment. These omissions limit assessment of potential bias in the body-composition comparisons.

In general, missing data can affect both the precision and interpretation of a result, particularly if the probability of a measurement being missing is related to the outcome. Without information on reasons and handling, the direction and size of any such effect cannot be assessed. The registry record also does not report detailed participant characteristics or the randomization and masking procedures, which limits evaluation of balance and possible sources of bias. These reporting gaps do not change the posted estimates, but they constrain how fully the analyses can be assessed.

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 5% reporting threshold.

The registry reports zero serious adverse events among 27 participants at risk in Arm I and among 13 participants at risk in Arm II. It also reports zero other adverse events in both groups and zero deaths in both groups. These figures describe the events recorded in the registry for the participants listed as at risk.

Zero reported events does not show that no adverse events of any kind occurred outside what was recorded, and it does not establish that an intervention is free of risk. The record does not give event definitions, collection procedures, follow-up details beyond the listed study period, or descriptions of non-serious events. No serious-event terms are listed. With the group sizes reported, the figures are a limited safety description rather than a precise estimate of event risk.

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

The results show that study completion was recorded for 22 participants in Arm I and 11 in Arm II. The unadjusted comparison is close to zero in its point estimate, but its confidence interval includes differences favoring either group. The completion data therefore describe the experience of these enrolled participants without establishing a precise difference between the intervention and brochure groups.

Body weight and body fat mass were posted as secondary outcomes through up to 15 weeks. Their mean-difference estimates are uncertain, with confidence intervals that include zero. These findings do not establish a clear group difference in either outcome. They also do not answer whether the program changes cancer incidence or cancer risk. The registry's stated motivation links obesity and cancer risk, but the posted primary endpoint is study completion, not a cancer endpoint.

The study evaluates a multicomponent program in rural Ohio, including telephone counseling, dietary recommendations, and home exercise. Its randomized parallel-group design allows a comparison between the assigned groups, but the available record does not provide enough detail to assess every aspect of conduct or analysis. The trial's enrollment and posted outcome counts are limited, and the confidence intervals show considerable uncertainty.

These findings should not be interpreted as proof that the intervention is effective or ineffective. They describe the outcomes reported for this study and the uncertainty around the unadjusted comparisons. They may help identify feasibility questions and inform future research, but the registry record does not report a definitive cancer-risk result, an adjusted analysis, or evidence establishing the result in other populations or settings.

17. Why This Trial Matters Statistically

This study is statistically notable because its primary question is feasibility rather than a clinical endpoint such as cancer incidence. Feasibility outcomes address whether a research program can be completed by participants and are often important when an intervention requires repeated contact and several behavior-change components. Keeping that endpoint distinct from the study's cancer-risk motivation prevents a completion count from being mistaken for evidence about cancer outcomes.

The randomized comparison and posted confidence interval also illustrate why estimates should be read with their precision. The primary point estimate is close to zero, but the interval permits a range of differences in both directions. The secondary mean comparisons are similarly uncertain. A non-small p-value is not proof of equivalence, and an observed change within a group does not by itself show that the intervention caused a difference relative to the comparison group.

The registry's planning illustrations highlight how strongly sample-size requirements depend on the target effect. Smaller effects require larger groups under the stated planning assumptions. Because these illustrations use observed effects and the registry does not report the original design justification, they are not evidence about what the trial was designed to detect.

Finally, the record makes important limits visible: no reported analyses, no stated missing-data approach, and no reported multiplicity plan. Those omissions matter when judging the strength of secondary findings and the completeness of statistical reporting. The study can be useful as a feasibility-focused contribution while leaving the efficacy and cancer-risk questions unresolved.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The main limitation for interpreting the results is the limited enrollment and uneven group sizes: the posted primary counts are 27 in Arm I and 13 in Arm II. The confidence intervals are broad, which limits how specifically the group differences can be estimated. The registry record does not report the recruitment process or detailed participant characteristics, so it is difficult to judge how representative the enrolled participants were of people with obesity in rural Ohio.

The intervention is multicomponent, combining dietary recommendations, home-based aerobic and resistance exercise, and telephone counseling. The study therefore cannot identify the separate contribution of any one component from the posted group comparison. The record does not report adherence, intervention exposure, acceptability, or fidelity measures that would help explain participants' experiences with the program.

There are also reporting limitations. The registry does not report the randomization method, masking details, original sample-size justification, statistical analysis plan, missing-data procedures, or analysis populations. Reasons for non-completion and for the secondary outcome measurement counts are not reported. These gaps make it difficult to assess possible selection effects or determine how missing measurements may have influenced the estimates.

The results concern feasibility and short-term body-composition outcomes. Cancer incidence and other direct cancer outcomes are not posted, and the registry record does not establish long-term effects. The results should therefore be understood as specific to the reported study outcomes and follow-up, rather than as a direct test of cancer prevention.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Weight Loss Intervention for the Reduction of Cancer Risk and Health Disparities in Rural Ohio — NCT05040152.
• Registry record last updated 2025-06-17; status last verified 2025-06.
• Results first posted 2025-06-17.
• 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

NCT05040152 is a completed randomized, single-masked, parallel-group study sponsored by the Ohio State University Comprehensive Cancer Center. It enrolled 40 participants and compared a 15-week telephone-based weight-loss intervention with education brochures. The primary endpoint was the number of enrolled participants who completed the study. The posted results report 22 completers among 27 participants in Arm I and 11 among 13 in Arm II.

The primary unadjusted comparison is a risk difference of 0.031, with a 95% confidence interval from -0.213 to 0.276 and p = 0.807. Secondary outcomes were changes in body weight and body fat mass; their unadjusted comparisons are imprecise. The registry reports zero serious adverse events and zero deaths in both groups. It does not report cancer outcomes, a statistical analysis plan, or missing-data procedures.