Menopausal Sleep Fragmentation and Body Fat Gain: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Menopausal Sleep Fragmentation and Body Fat Gain (NCT03047330), a na phase 4 study. The primary outcome is Normalized Serum Leptin Levels.

2. Clinical Question

The study focuses on a metabolic biomarker rather than a direct measure of body fat gain. Its primary outcome is normalized serum leptin, measured in morning blood samples after an overnight fast. The protocol compares the mean of samples collected before sleep fragmentation with the mean of samples collected after fragmentation. The registry describes the sleep sequence during a high-estradiol condition and says that a subset repeated it in an experimentally induced low-estradiol state.

This is not a randomized comparison between separate intervention and control groups. The registry identifies a single study arm, no allocation, and no masking. The unfragmented-sleep observations form a within-participant reference condition in the described sequence; they are not a separately randomized control group. Because the registry does not report results, it is not possible to establish from this record whether leptin rose or fell, how large any difference was, or how precisely it was estimated.

3. Trial Design

The registry describes a completed, phase 4, single-group study with enrollment of 41. The listed condition is menopause, and the study was sponsored by Brigham and Women's Hospital. The record lists a start date of 2017-07-15 and a primary completion date of 2022-08-01. Allocation is recorded as not applicable, and masking is recorded as none.

Participants completed 2 nights of unfragmented sleep followed by 3 nights of experimentally fragmented sleep during a high-estradiol phase of their menstrual cycle. This sequence is called Sleep Block 1 in the arm description. A subset repeated the procedures in an experimentally induced low-estradiol state, called Sleep Block 2. The registry lists estradiol withdrawal and fragmented sleep as interventions. It does not report how many participants completed the repeated procedures, the detailed sleep-fragmentation protocol, or the estradiol-withdrawal procedure.

The sequence described is not a randomized, parallel-group comparison. The record does not describe a separate group assigned to continue unfragmented sleep, and it does not say that the order of conditions was randomized. Therefore, differences between the before- and after-fragmentation observations should be understood in light of their order and timing, rather than as a randomized treatment contrast. The low-estradiol observations also apply only to a subset, whose size is not reported in the registry record.

The record marks the study as completed, but completion status alone does not establish that every enrolled participant contributed data to every analysis. The number analyzed for the primary outcome and the handling of participants who did not complete all procedures are not reported in the registry record. Nor are the study's detailed eligibility criteria, recruitment approach, or planned statistical analysis reported. These omissions limit how fully the actual analysis can be reconstructed from the 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.

Sleep Block 1

Reasons for not completing this period:

Sleep Block 2

Analysis population for the primary outcome, as defined in the registry: To compare between study conditions, leptin levels \[12-hr overnight fasted AM samples\] were averaged across 2 samples \[on study days 2-3\] before 2 nights of sleep fragmentation and across 3 samples \[on study days 4-6\] after 3 nights of sleep fragmentation. Leptin levels were averaged across 5 samples \[on study days 2-6\] \~1 week before leuprolide administration (pre-estradiol withdrawal) and across 5 samples \[on study days 2-6\] \~4 weeks after leuprolide administration (post-estradiol withdrawal)

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 Normalized Serum Leptin Levels, measured over pre/post sleep fragmentation (3 days); pre/post estradiol withdrawal (~5 weeks).

The primary endpoint is normalized serum leptin levels, reported as a mean with standard error and expressed as a percentage of mean baseline leptin. Morning blood samples were collected after a 12-hr overnight fast. The record says leptin was assayed on study days 2-6 under estrogenized and estradiol-withdrawal conditions, for a total of 10 samples. For each participant, values were normalized relative to that participant's mean baseline leptin, defined using the unfragmented estrogenized condition on study days 2-3.

For the comparison around sleep fragmentation, the registry describes averaging samples from study days 2-3 before 2 nights of fragmentation and samples from study days 4-6 after 3 nights of fragmentation. Leptin is used as a metabolic biomarker in the study's stated hypothesis about body fat gain. It is not itself a measurement of body fat gain, and the primary endpoint does not establish a clinical change in body composition.

General: Normalization expresses measurements in relation to a reference value, which can make within-participant changes easier to describe. A mean gives a central summary across observations; a standard error describes the precision of an estimated mean, not the spread of individual participants' values. No numerical means or standard errors for the primary endpoint are reported in the registry record. The endpoint description also ends with an incomplete statement about values being “averaged across 5,” so the final averaging detail is not fully reported.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

What does a mean describe?

General: A mean is a summary of the central level of a continuous measurement. In this study, the primary outcome is described as a mean of normalized serum leptin levels. A mean alone does not show how much individual participants varied, whether the observations were concentrated near the mean, or whether a small number of unusual measurements influenced it. Those questions require additional information about the distribution and the analysis.

The registry says that measurements were normalized relative to each participant's mean baseline leptin. Baseline was the unfragmented estrogenized condition, using the average from study days 2-3. The reported unit is percentage of mean baseline leptin. This gives a participant-specific reference for interpreting subsequent measurements. The record does not give numerical normalized values, so the scale's actual observed levels cannot be interpreted from the registry results.

What does the standard error communicate?

General: The standard error describes the precision of an estimated mean. It is not the same as the standard deviation, which describes variability among observations. A smaller standard error, in a given setting, indicates a more precisely estimated mean; it does not mean that participants' measurements themselves were less variable. The registry identifies standard error as the dispersion summary for the primary outcome, but does not report any numerical standard errors.

A standard error is often used with an estimate to construct a confidence interval. General: A confidence interval gives a range of values compatible with the data and the assumptions of the analysis at a stated confidence level. Its width reflects uncertainty, and it can help readers judge how precise an estimated effect is. The registry record does not report a confidence interval or the confidence level for the primary comparison, so the precision of the estimated response cannot be assessed.

How should a before-and-after comparison be understood?

The described comparison averages samples collected before sleep fragmentation and samples collected after fragmentation. General: When measurements come from the same participant at different times, the observations are commonly correlated. An analysis that treats them as independent can misrepresent uncertainty; an appropriate repeated-measures method can account for their relationship. The registry does not identify the method used for the primary comparison, so it is not possible to determine from the record how within-participant correlation was handled.

The protocol also fixes the order: unfragmented sleep precedes fragmented sleep in the described block. General: In a fixed-sequence before-and-after study, a measured change may be associated with the intervention, but time or order can also be relevant. A randomized concurrent comparison can address some alternative explanations differently, but this record does not describe such a comparison. This is a general design consideration, not a claim that any particular bias occurred in this study.

The low-estradiol procedures were repeated by a subset after estradiol withdrawal. The registry does not state the subset size or report the analysis comparing estradiol conditions. General: Repeated observations can be useful because each participant can serve as a reference for their own measurements, but the analysis still needs to account for dependence among observations and any differences in which participants contribute to each condition. Those details are not reported in the registry record.

Why do confidence intervals and p-values matter?

General: An effect estimate describes the size and direction of a contrast, while a confidence interval describes its uncertainty. A p-value, when reported, summarizes how compatible the observed data are with a specified null model under the assumptions of the test. Neither measure, by itself, establishes clinical importance or causation. Interpretation depends on the study design, endpoint, analysis assumptions, and the precision of the estimate.

No primary-outcome estimate, confidence interval, or p-value is reported in the registry record. It is therefore not possible to judge whether an estimated contrast is close to no change, whether the data are compatible with a range of effects, or whether a stated test crossed a statistical threshold. The hypothesis in the study description should not be substituted for those missing results.

What do the planning assumptions mean?

The sample-size planning illustration is described as using a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. It is described as targeting a stated fraction of the observed effect. These are planning assumptions, not reported features of the study's single-group protocol. The registry does not report the trial's own sample-size justification or a statistical plan explaining why those assumptions were selected.

General: A planning calculation estimates how many participants might be needed under specified assumptions about an effect and analysis. If the assumed effect is smaller, a larger sample may be needed to detect it under otherwise comparable assumptions. Such calculations do not show what the completed study actually observed. Any sample sizes based on a fraction of an observed effect are planning illustrations, not observed power. They also depend on assumptions matching the intended design; an equal-allocation, two-arm approximation does not describe the single-group sequence reported for this study.

The registry does not provide the numerical observed effect needed to evaluate the planning illustration's basis, nor does it report its own design justification. No planning estimate should therefore be read as evidence that the study had a particular probability of detecting an effect. The distinction is between a prospective calculation under assumptions and an analysis of observed data; the latter cannot be assessed for the primary endpoint from the reported results.

10. Confidence Intervals for Each Arm

No two-arm comparison is shown for this trial, but each arm's mean can be given with a t-based 95% confidence interval computed from the posted mean, spread and number of participants.

R code and output

arm <- c("Sleep Fragmentation - Active", "Sleep Fragmentation - Control", "Estradiol Withdrawal - Active", "Estradiol Withdrawal - Control")
n   <- c(38, 38, 27, 38)
m   <- c(96.7, 94, 90, 101)
sp  <- c(2.5, 1.5, 3, 1.1)
sd  <- sp * sqrt(n)      # standard errors converted to SDs
for (i in seq_along(n)) {
  se <- sd[i] / sqrt(n[i]); h <- qt(0.975, n[i] - 1) * se
  cat(sprintf("CARM|%s|%d|%.3f|%.3f|%.3f|%.3f\n", arm[i], n[i], m[i], sd[i], m[i] - h, m[i] + h))
}

CARM  Sleep Fragmentation - Active  38  96.700  15.411  91.635  101.765
CARM  Sleep Fragmentation - Control  38  94.000  9.247  90.961  97.039
CARM  Estradiol Withdrawal - Active  27  90.000  15.588  83.833  96.167
CARM  Estradiol Withdrawal - Control  38  101.000  6.781  98.771  103.229

11. Multiplicity, Missing Data and Analysis Populations

The registry lists 2 outcome measures and 4 analyses. It names Normalized Serum Leptin Levels as the primary outcome and Normalized Satiety Scores as a secondary outcome. The record does not explain how the 4 analyses relate to the listed outcomes, which comparisons were prespecified, or whether any multiplicity adjustment was planned or used. A correction or testing hierarchy is not reported in the registry record.

General: When several outcomes or comparisons are tested, the chance of at least one apparently unusual result can increase if each test is interpreted independently. A prespecified hierarchy or adjustment can help readers understand how multiple tests were handled. This general issue does not establish that any particular analysis in this study was misleading; the missing information simply prevents assessment of the approach.

Missing-data methods are not reported in the registry record. The enrollment of 41 is stated, but the number contributing to each outcome or each condition is not. The registry does not describe exclusions, incomplete sleep blocks, unavailable blood samples, imputation, or sensitivity analyses. It is therefore not possible to establish whether all enrolled participants were included in each analysis or how missing observations affected estimates.

The record also does not define the analysis population for the primary outcome or state whether participants were analyzed according to a particular prespecified rule. Completion status does not, by itself, establish the number with usable measurements. These gaps matter for interpreting both the analysis denominator and the generalizability of any estimate; the registry record does not report the information needed to resolve them.

12. 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.

Most frequent non-serious adverse event terms

The registry lists 41 participants at risk for serious adverse events and reports 0 participants affected. It also lists 41 at risk for other adverse events and reports 32 affected. Deaths are listed as 0. No top serious-event terms are listed in the record.

These figures describe the registry's adverse-event categories; they do not provide event-level detail, timing, severity beyond the category, or an assessment of whether an event was caused by an intervention. The record does not report the observation period used for these counts or explain how events were collected. The absence of listed serious-event terms should not be expanded into claims about specific events that were or were not assessed.

General: A participant count is not necessarily an event count: a participant may experience more than one event, and an event may be counted under the registry's reporting conventions. The figures should therefore be read as the reported numbers of participants affected in each category, not converted into an event rate or interpreted as evidence about causality. The 0 serious adverse events and 0 deaths are registry-reported counts for this study record; they do not establish that no risk exists in other settings.

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

The registry record documents a study that experimentally varied sleep continuity and included an estradiol-withdrawal condition for a subset. Its primary outcome was normalized serum leptin, a metabolic biomarker identified in the study hypothesis. The description provides a sampling schedule, a baseline definition, and a before-and-after comparison around sleep fragmentation. Those details clarify what the investigators intended to measure.

The record does not report numerical primary-outcome results. It is therefore not possible to state whether leptin increased, decreased, or remained similar after fragmentation, or to quantify the precision of any difference. The record also does not provide numerical results for the secondary outcome. The protocol and hypothesis are not substitutes for results, and the study's completion status does not reveal what the analyses found.

Even if numerical leptin results were available, a biomarker response would not by itself establish a change in body fat. The endpoint is not a direct body-composition measure. Likewise, because the described sequence is not a randomized comparison between separate groups, a before-and-after difference would need to be interpreted with attention to order, timing, and other conditions. The registry does not provide enough analysis detail to determine how these considerations were addressed.

The described low-estradiol procedures apply to a subset, whose size and outcome results are not reported. The record therefore cannot establish from its posted information whether responses differed across estradiol conditions. Overall, the registry supports a description of the research question and measurement design, but not a conclusion about the observed response or its magnitude.

14. Why This Trial Matters Statistically

Statistically, the study is notable for measuring a continuous biomarker repeatedly around a controlled sleep-fragmentation sequence. The samples were collected under specified fasting and morning conditions, and the primary outcome was normalized to an individual baseline. These features define the intended comparison and make clear that the research question concerns change within a participant, rather than a contrast between randomized parallel groups.

General: Repeated measurements can offer information about how outcomes vary within people, while also requiring methods that account for dependence among those measurements. Normalization can make a reference condition explicit, but interpretation still depends on the resulting estimates and their uncertainty. These are useful methodological considerations for clinical researchers reading studies that use repeated biomarker measurements.

The fixed sequence and the subset's repeat procedures also make design and analysis transparency important. Readers would need to know which participants contributed to each condition, how repeated observations were modeled, and how order or timing was considered. Those details are not reported in the registry record. The record also does not report numerical results, so the statistical importance lies in understanding what was measured and what information is needed for a defensible interpretation, rather than drawing a conclusion about an effect.

The record illustrates why a stated hypothesis, a completed study, and an endpoint description should be distinguished from posted statistical evidence. Without estimates and measures of precision, readers cannot judge the size or uncertainty of the primary comparison. Without details on missing data and multiplicity, readers cannot fully reconstruct the analysis. These limitations are especially relevant when a study combines repeated conditions and reports a biomarker as a proxy for a broader biological question.

15. 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 record identifies a single-group sequence with unfragmented sleep before fragmented sleep, rather than a randomized parallel-group comparison. Since the order is specified, the description does not show that order effects were controlled by randomization. General: In a fixed sequence, changes over time may be difficult to distinguish from changes associated with the experimental condition. This is a design consideration and does not establish that a particular alternative explanation affected the findings.

The low-estradiol procedures were repeated by a subset, but the subset's size and the criteria for inclusion in that repeat are not reported. Participant characteristics and detailed eligibility criteria are also not reported in the registry record. These omissions limit assessment of how broadly the study participants represent people to whom the research question might be relevant.

The registry does not report the primary outcome estimates, standard errors, confidence intervals, or p-values. It also does not report the analysis method, missing-data approach, analysis population, or multiplicity plan. Consequently, the statistical results cannot be independently interpreted from this record. The enrollment of 41 should not be treated as the number analyzed for every outcome or condition.

The endpoint description includes a final incomplete phrase about averaging values across 5, leaving that part of the aggregation procedure unclear. The record identifies leptin as a biomarker related to the hypothesis about body fat gain, but does not describe a direct body-fat endpoint among the listed outcomes. Safety reporting is limited to aggregate counts without event terms, timing, or causal attribution. These limitations concern the information available in the registry record; they do not establish that the unreported procedures or results were absent from other study materials.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Menopausal Sleep Fragmentation and Body Fat Gain — NCT03047330.
• Registry record last updated 2024-10-08; status last verified 2024-09.
• Results first posted 2024-10-08.
• 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.

19. Record Summary

NCT03047330, titled “Menopausal Sleep Fragmentation and Body Fat Gain,” is a completed phase 4, single-group study sponsored by Brigham and Women's Hospital. The registry lists enrollment of 41 and describes 2 nights of unfragmented sleep followed by 3 nights of fragmented sleep during a high-estradiol phase. A subset repeated the procedures after estradiol withdrawal.

The primary outcome was normalized serum leptin levels, measured in fasting morning blood samples and expressed as a percentage of mean baseline leptin. The record lists Normalized Satiety Scores as a secondary outcome. It reports 0 participants affected by serious adverse events, 32 affected by other adverse events, and 0 deaths.

Numerical outcome results, analysis methods, missing-data procedures, and multiplicity handling are not reported in the registry record. Consequently, the posted information describes the research question and protocol but does not establish the direction, magnitude, or precision of the primary outcome response.