Augmented Care at Worksite for Diabetes Prevention: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Augmented Care at Worksite for Diabetes Prevention (NCT03382873), a randomized na study. The primary outcome is Percent Weight Change.

2. Clinical Question

3. Trial Design

The registry record describes a randomized, single-masked, parallel-group trial sponsored by Ohio State University. Its title is “Augmented Care at Worksite for Diabetes Prevention,” and its stated purpose was to evaluate augmented worksite care through a lifestyle intervention for employees with prediabetes who were slower to respond to a standard diabetes prevention intervention. The record lists an enrollment of 208 and a completed status. The study start date was 2018-02-08, and primary completion was 2022-04-15.

The two behavioral interventions were Group Lifestyle Balance (GLB) and Group Lifestyle Balance Plus (GLB+). GLB was described as a standard lifestyle intervention to promote weight loss; GLB+ was described as an augmented lifestyle intervention for early slow responders. The record does not identify who was masked, so the meaning of single masking beyond that label is not reported in the registry record. The allocation sequence and procedures for assignment are not reported in the registry record.

The primary outcome was percent weight change measured at baseline and 4 months. The posted least-squares mean estimates were based on 80.0 participants in GLB and 111.0 in GLB+. The registry’s enrollment figure and these outcome analysis counts are different descriptions of the trial record; why they differ is not reported in the registry record. The record does not report the analysis population definition or explain missing outcome data.

The registry lists a phase of NA. The original sample-size justification, planned allocation ratio, and original design assumptions are not reported in the registry record. The planning illustrations associated with the posted results use separate, explicitly stated assumptions and should not be mistaken for the trial’s original design.

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.

Before assignment: Participants completed 4 weeks of the standard Group Lifestyle Balance (GLB) intervention. Those who achieved \>2.5% weight loss at week 5 remained in GLB at week 5. Those who achieved \<2.5% weight loss at week 5 were stratified to the GLB Plus (GLB+) intervention at week 5. Participants were paired based on percent weight change at month 5. Each member in the pair was randomized to group at month 5 for the study duration. Thus, group sample sizes are different at month 5 compared to month 4.

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.

Population: Excludes people who withdrew from the study following stratification to treatment group

6. Primary Endpoint

The registry lists the primary outcome as Percent Weight Change, measured over baseline and 4-months.

The primary endpoint is percent weight change from baseline to 4 months. Weight was measured in light clothing with shoes removed. The posted measure is a least-squares mean, a model-based group estimate, with standard error as its reported measure of dispersion. The estimates were -8.13 for GLB and -3.46 for GLB+. Negative change indicates that measured weight was lower at follow-up than at baseline; the contrast between groups is the statistical comparison of interest.

Weight change is a continuous outcome: it can take a range of values rather than being limited to categories such as event or no event. A continuous measure can retain information about the degree of change across participants, rather than reducing each observation to a binary outcome. Percent change expresses the change relative to the starting measurement, while the registry’s description specifies how the weight measurement was taken.

The endpoint captures a measured outcome over the stated follow-up period. It does not by itself establish how durable any change was, how participants experienced the interventions, or whether other health outcomes changed. Those questions require other outcomes and follow-up information; they are not answered by this primary endpoint alone. The registry record does not report additional details about the calculation of percent change or the model used to obtain the least-squares means.

7. Results Posted in the Registry

• Group Lifestyle Balance Plus (GLB+): mean -3.46 versus -8.13 on control; difference 4.67 (95% CI 3.55 to 5.79), standardized effect d = 1.25; z-test p = <0.001.

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.

Fasting Glucose

• 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:
  • Group Lifestyle Balance Plus (GLB+): mean -3.18 versus -7.01 on control; difference 3.83 (95% CI 0.90 to 6.76), standardized effect d = 0.37; z-test p = 0.010.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Group Lifestyle Balance Plus (GLB+) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary outcome results concern fasting glucose. The registry record lists that outcome repeatedly among the secondary outcomes, while the detailed result information includes one fasting glucose comparison. The overall secondary pattern is directionally consistent with the primary comparison in that the posted group estimates differ. The secondary result should be treated as supportive context rather than as a replacement for the primary weight-change analysis.

Secondary outcomes can help describe whether findings extend beyond the primary endpoint, but they are usually interpreted with less weight when several outcomes or analyses are examined and no multiplicity approach is reported. A nominally small unadjusted p-value does not account for the number of statistical questions considered. Without a reported adjustment or a prespecified hierarchy for secondary outcomes, the possibility of chance findings across multiple comparisons remains relevant.

The registry record reports six outcome measures and six analyses, but does not explain the full analysis plan, how the repeated fasting glucose entries relate to distinct measurement occasions, or whether additional secondary results were planned but not posted. The record also does not state whether the secondary outcome analyses were prespecified, adjusted for baseline values, or part of a confirmatory testing sequence. These omissions limit how strongly the secondary findings can be used to reinforce the primary result.

The appropriate reading is therefore cautious: the secondary findings add another measured comparison, but the record does not establish that they were protected against multiplicity or designed to provide independent confirmation. Their interpretation depends on the analysis plan and missing-data procedures, which are not reported in the registry record.

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

• Fasting Glucose
• Fasting Glucose
• Fasting Glucose

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous, and the registry reports least-squares means with standard errors. A least-squares mean is a model-based estimate of a group average. It can differ from a simple arithmetic average when the analysis model accounts for other factors or features of the data. The registry does not report the model specification, covariates, or other details needed to explain exactly how these least-squares means were obtained.

What does the mean difference represent?

The reported primary mean difference is 4.67, comparing GLB+ with GLB. It is calculated as a contrast between the posted group estimates, with the direction defined as GLB+ relative to GLB. The estimates themselves are -3.46 for GLB+ and -8.13 for GLB. The contrast expresses the separation between the groups on the percent-change scale; it is not a separate outcome measured directly in participants.

A mean difference is useful because it preserves the units of the outcome. Here, the outcome is percent weight change, so the contrast is interpreted on that scale. The registry’s reported superiority analysis is an ANOVA; the record does not provide its test statistic, degrees of freedom, model details, or a confidence interval for that analysis.

What does a standard error describe?

The standard error describes the estimated sampling variability of an estimate. It is not the spread of individual participant measurements. For the primary group estimates, the posted standard errors are 0.48 for GLB and 0.31 for GLB+. A smaller standard error generally indicates a more precise estimate under the analysis assumptions, but it does not alone show how much participants varied from one another. The registry does not report the standard deviation of individual primary outcome values.

How should the recalculation be understood?

The recalculated primary comparison is an unadjusted re-analysis based on the posted summary statistics. It gives a mean difference of 4.67, a 95% confidence interval from 3.55 to 5.79, and p < 0.001. It is a simple comparison and should not be treated as a replacement for the sponsor’s analysis. The registry’s reported ANOVA result is the sponsor’s own analysis, with p < 0.001.

The recalculation uses a pooled standard deviation estimated from the posted standard errors and analysis counts. That estimate is marked approximate. Such a reconstruction can help describe the scale and precision of a contrast, but it cannot reproduce an analysis model whose full specification is not reported. The recalculated standardized effect is also a derived summary, not a directly measured clinical outcome.

Why does the confidence interval matter?

A confidence interval describes uncertainty about an estimated contrast under the method’s assumptions. It is not a range containing a known proportion of individual participant outcomes, nor does it assign a probability to the true value after the data are observed. The primary interval runs from 3.55 to 5.79. Because the interval does not include zero, it is consistent with a difference in the direction of the reported contrast at the corresponding confidence level. Its limits also show that the exact size of the contrast remains uncertain.

The interval is paired with an unadjusted comparison. If other outcomes or analyses are also examined, the chance of at least one apparently strong finding can increase unless the analysis accounts for multiple comparisons. The registry record does not report whether or how multiplicity was addressed. This does not change the arithmetic of the displayed contrast, but it affects how the body of results should be interpreted.

What does ANOVA contribute?

ANOVA is a method for comparing means across groups. In this record, it is the named method for the sponsor-reported comparison of the two arms. A p-value assesses how compatible the observed data are with a specified null model, under the assumptions of the test. It is not the probability that the null hypothesis is true, and it does not quantify the size or practical importance of a difference.

The registry reports p < 0.001 for the primary ANOVA analysis. The separate recalculated p-value is p < 0.001. The two values are not identical, but both are reported their respective thresholds and both indicate evidence against a no-difference null under their own analyses. The record does not report enough methodological detail to reconcile their exact values. Because the discrepancy field is false, the sponsor’s result is not flagged as contradicted by the recalculation; the sponsor’s analysis remains the primary reported analysis.

Why can over-dispersion matter?

Over-dispersion is a general issue that can arise when observed variability is greater than a model assumes. When that happens, standard errors can be too small if the model does not account for the extra variability, making intervals narrower and tests more likely to indicate a difference than warranted. This is a general statistical consideration, not a reported problem in this trial. The primary endpoint is continuous, and the registry record does not report evidence of over-dispersion, a diagnostic assessment, or a model adjustment addressing it.

In general, the choice of outcome model, the handling of missing observations, and the analysis population all affect interpretation. The registry gives the endpoint summary and identifies ANOVA for the reported analysis, but details such as assumptions checks, participant-level data handling, and model diagnostics are not reported in the registry record. Readers should therefore distinguish the available numerical result from methodological details that cannot be verified from this record.

11. Confidence Intervals and Precision

The primary contrast is 4.67, with a 95% confidence interval from 3.55 to 5.79. The interval describes a range of values compatible with the observed data under the analysis assumptions. It conveys more about precision than the point estimate alone: the point estimate identifies the central reported contrast, while the interval shows uncertainty around that estimate.

The interval does not include zero. In the context of a mean difference, zero represents no difference between the group means. The interval therefore supports a difference in the direction of the reported contrast under the method used. It does not mean that every participant experienced a change matching the group estimate, or that the true effect is known exactly. The estimate remains subject to sampling variation, model assumptions, and limitations in the registry’s reporting.

The group estimates also have reported standard errors: 0.48 for GLB and 0.31 for GLB+. Standard errors characterize the precision of estimated means, not the range of individual outcomes. The registry does not provide a primary-outcome standard deviation for individual participants, and the recalculated pooled standard deviation is marked approximate. These measures should not be treated as interchangeable.

The sponsor’s primary ANOVA result is reported as p < 0.001, while the unadjusted recalculation reports p < 0.001. A p-value and confidence interval answer related but different questions: the p-value summarizes evidence against a null model, whereas the interval gives a range of plausible contrast values under stated assumptions. Neither alone describes clinical importance. The record does not report an adjusted confidence interval or the full model specification.

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 illustrations give participants per arm, not total events. Their 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 set to the stated fraction of the observed effect. The original trial’s sample-size assumptions and design justification are not reported in the registry record.

The 80% power column gives the planned number of participants per arm for an illustration designed to have an 80% chance of detecting the specified effect, if that effect is real and the planning assumptions hold. The 90% power column gives the corresponding per-arm number for a higher chance of detecting that effect under the same assumptions. These are planning quantities, not counts of participants observed in the trial.

For the primary outcome, planning for 100% of the observed effect gives 11 participants per arm for 80% power and 14 participants per arm for 90% power. Planning for 75% of the observed effect gives 18 participants per arm for 80% power and 24 participants per arm for 90% power. Planning for 50% of the observed effect gives 41 participants per arm for 80% power and 54 participants per arm for 90% power. Smaller target effects require more participants because a more modest difference is harder to distinguish from random variation.

The associated curve represents the same general planning relationship across effect sizes: as the target effect becomes smaller, the participant requirement rises. These figures are illustrations based on the observed effect, not the trial’s original design assumptions. They are not observed power; observed power is not an appropriate way to reinterpret the completed trial’s findings.

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 = 4.670; pooled standard deviation σ = 3.730.
2. n = 2 × (1.96 + 0.842)² × 3.730² / 4.670² = 10.0, rounded up to 11 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 <- 4.67                      # observed difference in means
sigma <- 3.72997936076676                     # 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(3382873)
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 = 11 (80% power), 14 (90% power)
 75% of observed effect: n per arm = 18 (80% power), 24 (90% power)
 50% of observed effect: n per arm = 41 (80% power), 54 (90% power)
power.t.test: 11.1 
Simulated power at n = 11 per arm: 0.796 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The sponsor-reported primary analysis is an ANOVA superiority analysis with p < 0.001. The unadjusted recalculation from the posted summary statistics gives a mean difference of 4.67 for GLB+ relative to GLB, a 95% confidence interval from 3.55 to 5.79, and p < 0.001. The registry does not flag a discrepancy between the sponsor analysis and the recalculation.

The recalculation is a simple reconstruction from group summary estimates and analysis counts. It is not a reproduction of the sponsor’s full model, whose details are not reported in the registry record. The two p-values are reported at different thresholds, and the registry does not provide the test statistic or other information needed to explain their exact numerical difference. Both are reported their stated thresholds and point to evidence of a group difference in the primary outcome.

The sponsor’s own analysis is the primary reported result. The recalculated contrast and interval can help readers understand the estimated size and precision of the comparison, but should be read as an unadjusted supplementary analysis rather than a substitute for the sponsor’s ANOVA.

15. Multiplicity, Missing Data and Analysis Populations

The registry record does not report whether statistical testing was adjusted for multiplicity. This matters because the record lists multiple outcomes and analyses. When researchers test several hypotheses, the chance of obtaining at least one result that appears statistically notable can increase. Methods such as a prespecified testing sequence or a formal adjustment can address that issue, but no such method is described in the registry record. The unadjusted secondary p-value should therefore not be read as if it were necessarily protected against multiple testing.

Missing-data procedures are also not reported in the registry record. The primary endpoint is described as measured at baseline and 4 months, but the record does not explain how participants without a follow-up measurement were handled, whether any values were imputed, or whether the analysis required complete observations. Without these details, it is not possible to assess whether the posted estimates could be affected by missingness or to determine how the outcome analysis population was formed.

The record gives arm-specific analysis counts of 80.0 for GLB and 111.0 for GLB+, alongside an enrollment figure of 208. It does not explain the relationship between these figures. The randomized population, participants who began an intervention, and those included in an outcome analysis can differ; which population the reported ANOVA represents is not reported in the registry record. No assumption about that population should be made from the outcome counts alone.

The registry record also does not report protocol deviations, exclusions, the handling of participants who changed groups or discontinued intervention, or whether analyses followed an intention-to-treat principle. These are distinct methodological questions. Their absence does not invalidate the posted numerical result, but it leaves uncertainty about the robustness and scope of the comparison.

16. 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: colon abscess (1.2%), hand surgery (1.2%), hamstring tear (0.8%), hip replacement (0.8%), arrythmia (0.8%).

The registry reports serious adverse event figures for both groups: 2 affected in GLB and 3 affected in GLB+. It reports other adverse event figures of 4 in GLB and 13 in GLB+. The corresponding at-risk counts are 86 for GLB and 122 for GLB+. The registry also records 0 deaths in GLB and 1 in GLB+. These safety counts are distinct from the arm counts reported for the primary outcome analysis, and the record does not explain the difference between them.

The record lists serious-event terms including Colon abscess and Hand surgery at 1.2% each, and Hamstring tear, Hip replacement, and Arrythmia at 0.8% each. These are registry-listed terms and percentages; they do not establish that an event was caused by an intervention. The record does not provide enough information in the posted summary to assess event timing, duration, relatedness, or the clinical circumstances of the events.

Safety counts should be interpreted descriptively. Differences in counts alone do not establish a comparative safety effect, especially when the registry record does not explain the analysis population or provide a formal safety comparison.

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

The posted primary result describes a difference in estimated percent weight change between GLB and GLB+ at the stated follow-up. The GLB estimate was -8.13, the GLB+ estimate was -3.46, and the mean difference for GLB+ relative to GLB was 4.67. The 95% confidence interval from 3.55 to 5.79 expresses uncertainty around that contrast under the recalculation’s assumptions. The sponsor’s reported ANOVA result is p < 0.001.

These results do not show what would happen for every participant. Group averages summarize outcomes across participants, and individual responses can differ. Nor do the results establish the reason for the difference, its persistence beyond the recorded follow-up, or its practical importance for any particular person. The registry record does not report later primary weight measurements.

The result should not be generalized automatically beyond employees with prediabetes described as slower to respond to the standard intervention. The record does not provide detailed eligibility criteria, participant characteristics, or the operational definition of early slow response. Those omissions make it difficult to judge how closely the studied group resembles other populations.

The secondary fasting glucose comparison provides another outcome measurement but is subject to the limitations of unadjusted secondary analyses and incomplete reporting of multiplicity. The adverse-event figures are descriptive and do not establish causation or comparative safety. Taken together, the record supports a statistical description of the reported outcomes, not medical advice or a definitive conclusion about outcomes outside the study’s measured period and population.

18. Why This Trial Matters Statistically

This trial illustrates the distinction between a primary outcome and secondary outcomes. The primary endpoint is clearly identified as percent weight change from baseline to 4 months, and the registry reports a sponsor ANOVA result as well as summary estimates that permit an unadjusted comparison. Keeping the primary question distinct from secondary questions helps readers avoid treating every measured outcome as equally confirmatory.

It also shows why effect estimates and uncertainty deserve attention alongside p-values. The primary contrast is reported with a confidence interval, making the estimated magnitude and its uncertainty more interpretable than a significance threshold alone. The recalculation is useful as a transparent summary of posted statistics, while the sponsor’s analysis remains the reported analysis because its full model cannot be reconstructed from the registry entry.

The record also highlights a practical reporting issue: enrollment, outcome-analysis counts, and safety at-risk counts are not the same figures, and the registry does not explain their relationship. Clear reporting of analysis populations and missing-data handling is important for judging which participants contribute to each result. Likewise, reporting how multiplicity was handled would help readers assess secondary findings.

Finally, planning illustrations based on observed effects should not be confused with original design planning or post hoc evidence that a study had adequate power. Their value is educational: they show how required sample sizes change with the effect targeted and the desired power, under explicit assumptions. They do not settle the trial’s design adequacy or replace a prespecified sample-size justification.

19. Limitations

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

Several limitations specific to this record constrain interpretation. The registry provides a brief description of the target group but does not define how a participant was classified as a slower responder or give further eligibility criteria. Participant characteristics are not reported in the record, limiting assessment of how well the findings may apply to other groups of employees or people with prediabetes.

The primary endpoint is recorded at baseline and 4 months, with no later primary outcome time point reported. The record does not describe the least-squares mean model, its covariates, or diagnostic checks. It also does not report the analysis population, missing-data procedures, or reasons the primary outcome analysis counts differ from the enrollment figure. These gaps make it difficult to assess potential bias from incomplete follow-up or to reproduce the sponsor’s analysis.

The registry identifies a sponsor ANOVA result but does not provide its test statistic, degrees of freedom, confidence interval, or full model details. The recalculated standard deviation is marked approximate, so standardized summaries derived from it should not be treated as exact. The record does not report how multiplicity was handled despite multiple outcomes and analyses being listed.

Finally, the safety summary gives event counts and selected terms but does not report event timing, relatedness, or a formal comparison. The record does not explain whether the at-risk counts correspond to the same analysis populations as the primary outcome. These limitations concern what can be determined from the registry record; they do not establish that unreported procedures were or were not performed.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Augmented Care at Worksite for Diabetes Prevention — NCT03382873.
• Registry record last updated 2024-04-11; status last verified 2024-03.
• Results first posted 2024-04-11.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

The registry record describes a completed randomized, single-masked, parallel-group behavioral trial sponsored by Ohio State University. It compared standard Group Lifestyle Balance (GLB) with augmented Group Lifestyle Balance Plus (GLB+) for employees with prediabetes who were slower to respond to a standard diabetes prevention intervention.

The primary outcome was percent weight change from baseline to 4 months. The posted estimates were -8.13 for GLB and -3.46 for GLB+, with an unadjusted mean difference of 4.67 and a 95% confidence interval from 3.55 to 5.79. The registry reports an ANOVA superiority result with p < 0.001. Fasting glucose is the detailed secondary outcome result, while multiplicity and missing-data procedures are not reported in the registry record.

The record does not explain the differing enrollment, outcome-analysis, and safety at-risk counts. Its posted findings should therefore be read as descriptions of the reported analyses, with the population and methodological details limited by the information recorded.