Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity (NCT01814072), a randomized phase 2, phase 3 study. The primary outcome is Weight Change (kg) From Baseline to Month 6, Time by Factor Interaction.

2. Clinical Question

3. Trial Design

Opt-IN was a randomized, factorial study of remotely delivered intensive lifestyle treatment for obesity. The registry lists the trial as Phase 2 and Phase 3, reports enrollment of 562, and gives Northwestern University as the sponsor. Its status is completed. The recorded start is 2013-09, and primary completion is 2017-09-20. Masking is listed as none.

The design tested combinations of lifestyle-treatment components rather than only comparing two fixed treatment packages. Across the 32 conditions, participants received a lifestyle core and either 12 or 24 telephone coaching sessions. Other factors were reports to a primary care physician, regular text messages, recommendations to use meal replacements, and buddy training via webinars. The combination of these components differed across conditions.

The study’s stated objective was to use the Multiphase Optimization Strategy, or MOST, to identify components and component levels that contribute to a program’s weight-loss effect, with the longer-term aim of assembling a scalable treatment package. That objective gives the factorial structure its purpose: estimates can address individual components while other features vary across combinations. The registry record does not report the allocation ratio, the randomization procedure, or a more detailed account of how the factorial factors were coded in the analysis.

The primary outcome was weight change from baseline to month 6, analyzed using a linear mixed models analysis of variance. The registry says the model accounted for missing data and used all randomized participants in an intention-to-treat analysis; it separately defines the analysis population as randomized participants with at least one weight value. Details of the analysis plan beyond the information described in the registry record are not reported.

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 who signed an informed consent were invited to attend a baseline assessment where they were screened further for entry and exclusion criteria. Baseline data was gathered. Participants who continued to be eligible were scheduled for the randomization session where they learned their study assignment.

Only the first 6 of 32 groups are shown.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: ITT population: all randomized participants with at least one weight value. The decision to analyze all randomized participants together for aim 1, rather than separately by arm, was pre-specified in the the Protocol/Statistical Analysis Plan for the purposes of assessing the main effect of time.

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: Baseline population included all randomized participants (562).

Only the first 6 of 33 groups are shown.

6. Primary Endpoint

The registry lists the primary outcome as Weight Change (kg) From Baseline to Month 6, Time by Factor Interaction, measured over From Baseline to Month 6.

The primary endpoint is estimated mean weight change, in kilograms, from baseline to month 6. The registry describes it as a time-by-factor interaction and reports component estimates from a linear mixed models analysis of variance. These estimates are presented as additional effects beyond the weight change calculated in another outcome-measure analysis, rather than as the total change attributable to the lifestyle program. The other outcome measure’s details are not reported in the registry record.

Weight is a continuous measure, so the reported summary is a mean change. Mean change provides a group-level estimate of how weight differs over the stated measurement period; it does not describe every participant’s change or the spread of individual responses. The registry reports 95% confidence intervals around the estimates, which communicate their statistical precision.

Month 6 is the stated endpoint time for this measure. The model was used to account for missing data, but the registry record does not specify the timing or number of weight measurements included in the model, the precise missing-data assumptions, or whether additional endpoint definitions were used. The endpoint therefore addresses estimated component-associated change over the defined period, not long-term weight change or other health outcomes.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

What does the factorial analysis estimate?

A factorial design tests combinations of intervention components. Here, all conditions included a lifestyle core and one of two telephone-coaching schedules, while other features varied: physician reports, text messages, meal-replacement recommendations, and buddy training. Rather than treating every condition as an unrelated treatment package, an analysis can estimate the contribution associated with a component across different combinations. That is the purpose described for this study.

The registry calls the primary endpoint a time-by-factor interaction. In general, an interaction asks whether change over time differs according to a factor. For this outcome, the model estimates component-associated differences in weight change between baseline and month 6. The registry further says these estimates are additional effects beyond the change calculated in another outcome-measure analysis; the details of that analysis are not reported in the registry record.

Why use a mixed model?

The registry reports a linear mixed models analysis of variance and says it was used to account for missing data. In general, mixed models can use repeated measurements while representing the correlation between observations from the same participant. They can also make use of available measurements when some observations are missing, rather than requiring a complete record at every measurement time. The registry does not describe the exact model specification, covariance structure, or assumptions about why measurements might be missing, so those details should not be inferred.

The record describes an intention-to-treat analysis using all randomized participants in the model and also defines the analysis population as all randomized participants with at least one weight value. These descriptions indicate that the analysis was based on randomized assignment rather than only on treatment received, while the population definition acknowledges the requirement for at least one weight measurement. The registry does not state how participants without any weight value were handled or provide the amount of missing weight data.

What do estimates and confidence intervals convey?

The model reports estimated mean changes and 95% confidence intervals. In general, a confidence interval combines the estimated effect with its uncertainty: narrower intervals indicate greater precision under the model, while wider intervals leave more possible values compatible with the data. An interval that includes zero does not establish that the effect is exactly zero. It indicates that the data and method do not clearly separate the estimate from no effect at the stated confidence level.

Four component intervals span zero, and the buddy-training interval lies below zero. The point estimates and intervals refer to the component contrasts described in the registry, not necessarily to raw changes within individual conditions. Without arm-level outcome values, they should not be read as average weight loss within a particular treatment condition. The registry’s summaries do not provide enough information to reconstruct the full model or assess all of its assumptions.

How should p-values be read?

Each component has a reported p-value from a mixed models analysis. In general, a p-value measures how unusual data at least as incompatible with a null hypothesis would be if that hypothesis and the analysis assumptions were correct. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of an effect. The reported values are 0.567 for coaching calls, 0.917 for physician reports, 0.699 for text messages, 0.619 for meal-replacement recommendations, and 0.046 for buddy training.

The registry record does not report the full statistical analysis plan, the planned significance threshold for these tests, or whether adjustment for multiple comparisons was used. The sample-size assumptions listed for planning illustrations are not a substitute for the trial’s analysis plan. They describe a two-sided significance level of 0.05, equal allocation between two arms, a normal approximation, and effects defined as stated fractions of an observed effect. Such illustrations are planning exercises, not observed power, and do not alter the interpretation of the trial’s reported estimates.

What cannot be checked from the registry summary?

The record does not report model coefficients beyond the component summaries, model diagnostics, degrees of freedom, or a detailed explanation of factor coding. It lists nine outcome measures and seven analyses, but does not explain the relationship among all of them or identify a multiplicity strategy. Consequently, readers can describe the posted estimates, intervals, and p-values, but cannot independently verify the complete analysis from the registry summary alone.

10. Analyses Reported by the Sponsor

The registry reports the sponsor’s mixed-model estimates, 95% confidence intervals, and p-values for the five components. The posted component summaries and the reported analyses agree in their estimates and interval bounds, apart from differences in how leading zeros are displayed. No discrepancy is flagged. The sponsor’s analysis therefore remains the relevant reported analysis; there is no recorded conflicting recalculation that would call for a different interpretation.

The component estimates are additional effects beyond the change reported in another outcome-measure analysis, according to the registry description. A simple recalculation from summary statistics cannot reproduce the fitted mixed model, its use of repeated measurements, or its handling of missing data. It should not be treated as a replacement for the sponsor’s model-based result. No recalculated two-arm comparison is estimable from the posted continuous-endpoint information, as the registry notes that values are missing or not estimable for that type of comparison.

The sponsor’s reported p-values and confidence intervals support the same broad reading: four component intervals include zero, and the buddy-training interval does not. That reading is limited to the reported component tests. The registry record does not provide enough detail to audit the full model, establish the multiplicity procedure, or determine clinical importance from these results alone.

11. Multiplicity, Missing Data and Analysis Populations

The primary endpoint is reported through five component estimates, each with a confidence interval and p-value. The registry also lists nine outcome measures and seven analyses. Because multiple outcomes and component tests can create multiple opportunities for a nominally unusual result, the analysis plan should specify how those comparisons are interpreted and whether any adjustment is used. The registry record does not report a multiplicity adjustment, a testing hierarchy, or a designated family of primary comparisons. It is therefore not possible to tell from the record whether the p-value of 0.046 was adjusted or intended to be interpreted as one test among a larger set.

The record states that the linear mixed models analysis accounted for missing data and that all randomized participants were used in the intention-to-treat model. It also defines the analysis population as all randomized participants with at least one weight value. These statements do not give the number of participants with missing measurements, the pattern of missingness, or the assumptions used to handle missing observations. The model’s exact missing-data method and any sensitivity analyses are not reported in the registry record.

In general, an intention-to-treat approach preserves the comparison according to randomized assignment, rather than selecting participants according to what treatment they received. Mixed models can use available outcome measurements, but their validity depends on the model and missing-data assumptions. Those general points do not establish which assumptions were made in this trial. The registry does not report whether participants with no weight values were excluded from the model, imputed, or handled in another way.

The trial was randomized and unmasked. The record does not provide a detailed statistical analysis plan, interim-analysis information, prespecified decision rules, or the handling of every outcome and comparison. The planning assumptions listed for sample-size illustrations do not answer these questions and should not be taken as evidence of the trial’s own sample-size justification or analysis strategy.

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 serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: brain bleed (5.6%), severe head injury (5.6%).

The registry lists serious adverse events by condition. One serious event is recorded in Condition 5 and one in Condition 16; the other listed conditions have none. No deaths are recorded in any condition. The number at risk varies across conditions, so the event counts should be read with their corresponding condition-level denominators rather than treated as directly comparable rates.

The serious-event terms listed are “Brain bleed” and “severe head injury,” each reported as 5.6%. The record does not explain the relationship between those terms and the condition-level serious-event counts, whether they refer to the same or different participants, or the event attribution. The percentages should therefore be described as registry listings, not as evidence that an event was caused by an intervention component.

The non-serious adverse-event counts also vary by condition. These figures are descriptive registry reports; they do not establish comparative safety across the factorial combinations. The record does not provide further details on event definitions, severity grading, relatedness assessment, or exposure time. Small condition-level counts and differing numbers at risk limit the conclusions that can be drawn from the listings.

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

The results describe model-based estimates of component-associated weight change from baseline to month 6 in a factorial lifestyle-treatment study. Four estimates have confidence intervals that include zero, while the interval for buddy training is below zero and its reported p-value is 0.046. These are the reported data patterns; they do not by themselves establish which full treatment package should be used or the importance of any component in practice.

The estimates do not represent total weight change in each condition. They are described as additional effects beyond an analysis of another outcome measure, and they address component effects across combinations rather than a single package-versus-no-treatment contrast. The registry does not report a no-treatment condition or all details needed to interpret the model’s factor coding. The sign of an estimate should therefore be read in the context of that model, not translated automatically into a claim about benefit.

Confidence intervals express uncertainty about the component estimates. Intervals spanning zero leave effects in either direction compatible with the analysis, and an interval excluding zero does not show that an effect is large or important to participants. Likewise, a p-value does not quantify clinical importance or the probability that the effect is real.

The registry’s stated purpose was to identify useful intervention components while considering resource use and participant burden. The reported primary outcome alone does not establish scalability, resource consumption, participant burden, or long-term outcomes. Those aims would require evidence beyond the month-6 weight-change estimates described in the record. The findings should be understood as a set of component-level statistical results, with uncertainty and reporting gaps, rather than a definitive evaluation of every possible treatment package.

14. Why This Trial Matters Statistically

This trial’s factorial structure is statistically important because it shifts the question from “Which of two complete packages is better?” to “What contribution is associated with each component across different combinations?” That distinction can help researchers separate the effects of coaching intensity, physician reports, texts, meal-replacement recommendations, and buddy training rather than treating a multicomponent program as indivisible.

The approach aligns with the registry’s stated use of the Multiphase Optimization Strategy: estimate component contributions and use those results to guide a treatment package intended to balance outcomes with resources and participant burden. The design therefore treats intervention development as a sequence of decisions about components, not only as a test of a finished program.

The findings also illustrate why factorial results require careful interpretation. The estimates are incremental component effects, not observed outcomes for each complete package. The intervals for four components include zero, while the buddy-training interval lies below zero; the registry does not report a multiplicity strategy or detailed analysis plan. Those features make uncertainty and context central to reading the results.

More broadly, the record shows the value of reporting how outcomes are modeled and which population is included. It identifies a mixed-model intention-to-treat analysis and a population requiring at least one weight value, yet leaves details of missing-data assumptions and the full model unavailable. For biostatisticians and clinical researchers, that contrast underscores the difference between a clearly framed estimand and a fully auditable analysis. The component-focused question is statistically informative, while the registry summary alone cannot answer every design or analysis 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 registry summary does not provide the full statistical analysis plan, factor coding, model specification, diagnostic information, or complete outcome data. It reports estimates and intervals, but the model cannot be independently reconstructed from the description. The outcome is described as an additional effect beyond another outcome-measure analysis, whose details are not reported in the registry record.

Multiplicity is an important unresolved issue because the record lists multiple outcomes and analyses and reports five component p-values, but does not describe an adjustment or testing hierarchy. The p-value of 0.046 should consequently be interpreted cautiously and in light of the unreported plan. It does not establish clinical importance.

Missing-data reporting is limited. The registry states that mixed models accounted for missing data and describes an intention-to-treat analysis, but does not report the amount or pattern of missingness, assumptions, or sensitivity analyses. Its analysis-population definition requires at least one weight value, so the handling of randomized participants without any weight value is unclear.

The registry also does not report demographic characteristics, detailed eligibility criteria, allocation ratio, or the randomization procedure. These omissions limit assessment of participant representativeness and the practical context of the results. The unmasked design is stated, but the consequences of masking status for outcome measurement or other processes are not described.

Finally, the primary endpoint concerns estimated weight change through month 6. The record does not establish longer-term effects, outcomes beyond those reported, or whether a component combination meets the program’s stated goals for scalability and participant burden. The safety listings are sparse and do not describe attribution. These registry-level limitations constrain conclusions to the posted component estimates and their reported uncertainty.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity — NCT01814072.
• Registry record last updated 2023-02-01; status last verified 2023-01.
• Results first posted 2021-01-05.
• 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

NCT01814072, Opt-IN: Optimization of Remotely Delivered Intensive Lifestyle Treatment for Obesity, is a completed randomized factorial study sponsored by Northwestern University. The registry lists Phase 2 and Phase 3, enrollment of 562, no masking, a start date of 2013-09, and primary completion on 2017-09-20.

The 32 conditions combined a lifestyle core, either 12 or 24 telephone coaching sessions, and varying combinations of physician reports, text messages, meal-replacement recommendations, and buddy training. The primary endpoint was modeled mean weight change from baseline to month 6, described as a time-by-factor interaction and analyzed using mixed models.

Four component confidence intervals span zero; the buddy-training interval lies below zero, with a reported p-value of 0.046. The registry does not report a multiplicity procedure, full analysis plan, or detailed missing-data assumptions. These findings are component-level estimates, not total effects for a complete treatment package.