Effectiveness of Implementing Shared Decision-Making on Quality of Care Among Patients With Lumbar Degenerative Diseases.: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Effectiveness of Implementing Shared Decision-Making on Quality of Care Among Patients With Lumbar Degenerative Diseases. (NCT03679494), a randomized na study. The primary outcome is The Change of Patients' Control Preference.

2. Clinical Question

The trial addresses shared decision-making for lumbar degenerative disease, where the registry summary describes complex treatment options and a lack of consensus on evidence-based practice guidelines. Its stated aim was to assess the effect of implementing shared decision-making on quality of care. The record also lists secondary outcomes concerning decision-process quality, satisfaction with the decision, decisional conflict, lower-back-pain disability, and quality of healthy living.

The clinical question is therefore broader than whether the tool changed patients’ preferred decision-making role. The listed outcomes cover decision experience and health-related measures, but their presence in the registry does not establish that results were collected or reported. Without outcome estimates and the sponsor’s analysis, the registry record cannot establish the direction, magnitude, or precision of any effect.

3. Trial Design

The registry identifies this as a randomized, double-masked, parallel-design trial with cluster randomization. The listed groups are the SDM intervention group and the usual care group. The intervention group used a shared decision-making support tool; the usual care group continued usual care without the intervention. The registry lists enrollment of 130 and marks the study as completed.

The record gives a start date of 2018-09-26 and a primary completion date of 2019-12-29. It lists the phase as NA and Taipei Medical University as the sponsor. These administrative details describe the registered study, but do not supply the results of its primary or secondary outcomes.

Cluster randomization means assignment was organized by groups rather than by treating every participant as an independently randomized unit. The registry record does not report the cluster unit, the number of clusters, how clusters were allocated, or whether the analysis accounted for within-cluster similarity. Those details matter because participants in the same cluster may have more similar outcomes than participants in different clusters. A simple individual-level re-analysis would therefore be misleading; the record specifically indicates that only the sponsor’s own analysis should be shown.

The registry gives a timeframe for the primary outcome of before intervention to up to 12 weeks after intervention. It does not report more detailed timing or procedures for other outcome assessments. Although the masking field is marked double, who was masked and how masking was implemented are not reported in the registry record. The allocation method beyond the randomized designation, the analysis population, and the sponsor’s statistical methods are also not reported in the registry record.

The study summary describes a project to develop a multimedia interactive communication system and conduct a cluster randomized controlled trial to validate an integrated shared decision-making model. The short registry summary ends before giving a complete methods account. As a result, it is not possible to infer additional design procedures from that summary.

4. Participant Flow and Accounting

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

Overall Study

Analysis population for the primary outcome, as defined in the registry: Patients lost after 12 weeks of intervention.

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 The Change of Patients' Control Preference, measured over Before intervention, up to 12 weeks after intervention.

The primary endpoint is titled “The Change of Patients’ Control Preference.” It is described as measuring whether a patient’s preferred role in making decisions with a medical provider changed between assessment before intervention and assessment after intervention, up to 12 weeks after intervention. The measure is reported as a count of participants.

The registry describes use of the Control Preference Scale. It consists of five cards, each showing a different character in medical decision-making in a cartoon pattern. Patients make a series of comparisons to rank their preference. Conceptually, this approach represents a patient’s preferred role in a decision, rather than directly measuring the quality of a treatment choice or a health outcome.

Change in preference can be relevant to a shared decision-making intervention because the intervention is intended to support communication about treatment options and patients’ preferences and values. A change in a preferred role, however, is not by itself evidence that communication improved, that a decision was better informed, or that care quality changed. Those are distinct questions, and the registry lists other decision-related outcomes separately.

The endpoint description names its population as “Patients lost after 12 weeks of intervention.” That wording is unclear: it does not explain whether the measure concerns participants lost to follow-up, another subset, or a different meaning of “lost.” The registry record does not report a clarification, the actual count of participants whose preference changed, or an effect estimate for this endpoint.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

Randomization is a method for assigning interventions by chance, intended to reduce systematic differences between comparison groups. This trial is marked randomized, but it is also identified as cluster randomized. In cluster randomization, groups rather than individual participants are assigned. The cluster unit and allocation details are not reported in the registry record, so the structure of the randomization cannot be described further.

Why does cluster randomization affect analysis?

Participants in the same cluster may share clinicians, care processes, or other contextual features. Their outcomes can therefore be correlated. A statistical analysis that treats all participants as independent may give uncertainty estimates that are too small. Appropriate methods account for the clustering, often by modeling cluster structure or adjusting uncertainty estimates for it. The amount of within-cluster correlation and the precise method used for this trial are not reported in the registry record. The record explicitly cautions that a simple individual-level re-analysis would be misleading and says that only the sponsor’s own analysis is shown.

Because the sponsor’s analysis is not reported in the registry record, there is no basis for reconstructing its estimate or uncertainty from the enrollment figure or adverse-event counts. The allocation unit, outcome distribution, cluster structure, and analysis details all matter. Treating the enrolled participants as independent observations would not recover the sponsor’s analysis.

What does the primary measure represent?

The primary endpoint is the change in patients’ control preference, measured with the Control Preference Scale. The record describes the scale as five cards that depict different roles in medical decision-making and says that a series of comparisons is used to rank a patient’s preference. The endpoint is listed as a count of participants. A count alone does not state the comparison between groups, the uncertainty around that comparison, or the rule for deciding that a preference changed. Those analysis details are not reported in the registry record.

For a binary outcome, statistical analysis commonly compares how many participants meet a defined event criterion in each group. Interpretation depends on a clearly specified event, the relevant denominators, and the design-aware method used to compare groups. Here, the “change” classification and the sponsor’s primary analysis are not reported, so no particular binary effect estimate should be inferred.

How should uncertainty be understood?

In general, a confidence interval describes a range of effect estimates compatible with the data and the assumptions of the statistical method. Narrower intervals indicate greater precision, while wider intervals indicate more uncertainty. In a cluster-randomized study, the method must account for clustering for the interval to reflect the design appropriately. No confidence interval or effect estimate for the primary outcome is reported in the registry record, so the precision of the result cannot be evaluated.

A p-value, when reported, summarizes how compatible data are with a specified statistical model and null hypothesis; it does not measure the size or clinical importance of an effect. No p-value for the primary endpoint is reported in the registry record. It would be inappropriate to infer statistical significance from study completion, the enrollment figure, or the adverse-event counts.

What do the planning assumptions mean?

The registry describes sample-size planning illustrations based on the observed effect, with a two-sided significance level of 0.05, equal allocation between the arms, and a normal approximation. The illustration is framed around detecting a stated fraction of the observed effect. Such calculations are planning illustrations, not observed power and not evidence that the trial achieved a particular degree of precision. The registry also distinguishes participant-per-arm displays from total-event displays for time-to-event outcomes. The primary outcome in this record is a participant count, and no numerical planning illustration is reported in the registry information available for interpretation.

In general, over-dispersion occurs when variability is greater than a statistical model expects. If present and unaccounted for, it can make standard errors and confidence intervals unreliable. This concern is separate from cluster correlation, although both involve departures from a simple independent-observation model. The registry record does not report an over-dispersion assessment or the sponsor’s model choices.

What can be concluded from a count?

A count reports the number of participants meeting a definition, but it does not by itself show the probability of that outcome, the difference between groups, or the precision of a comparison. Group denominators and analysis populations also matter. Although adverse-event fields include counts and participants at risk, those figures are not substitutes for the unreported primary-outcome analysis. No calculations from the registry’s counts can reconstruct an adjusted cluster-randomized result.

10. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary outcome and multiple secondary outcomes. When a study evaluates several outcomes or several comparisons, the chance of at least one apparently unusual result can increase if each is assessed without accounting for the multiplicity. Statistical plans may identify a primary comparison, specify an outcome hierarchy, or adjust inference across tests. The registry record does not report a multiplicity strategy, the sponsor’s statistical analysis plan, or whether secondary outcomes were treated as confirmatory or exploratory.

The primary endpoint description gives a timeframe from before intervention to up to 12 weeks after intervention, but the listed population is “Patients lost after 12 weeks of intervention.” The phrase is ambiguous and does not explain who contributed data to the primary analysis. The registry record does not report the number of missing primary assessments, reasons for missingness, or how missing data were handled. It also does not describe an analysis population such as all randomized participants or participants with complete follow-up.

In general, missing outcome data can affect interpretation if the likelihood of missingness is related to participants’ outcomes or group assignment. Different assumptions about missing observations can produce different estimates. Analyses may address this with prespecified methods and sensitivity analyses, but none are reported in the registry record.

Interim analyses, stopping rules, and a protocol-based statistical plan are not reported in the registry record. Their absence from the registry information does not establish whether such procedures were used; it means their role cannot be assessed from this record. Likewise, outcome changes, deviations from the registered analysis, and the handling of multiple measures of the Control Preference Scale are not reported. These gaps limit assessment of selective reporting and of how closely any analysis followed the planned approach.

11. 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.

The adverse-event fields list the SDM intervention group with 67 participants at risk, zero serious events, zero other events, and zero deaths. The usual care group is listed with 63 participants at risk, zero serious events, zero other events, and zero deaths. The registry gives no serious adverse-event terms.

These entries are counts as recorded; they are not estimates of comparative risk and do not establish that the groups had identical safety profiles. A zero count means no event was recorded in that field for the listed group. It does not prove that an event is impossible or that the study can exclude uncommon events. General statistical interpretation of a zero count depends on the amount and completeness of follow-up, event definitions, and ascertainment procedures.

The registry record does not report how adverse events were collected, the period covered by these fields, or whether the counts include all events or only events meeting particular criteria. It also does not provide a comparative analysis or uncertainty interval. The counts should therefore be read as descriptive registry entries, not as a definitive test of safety differences.

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

The registry record establishes that a completed, cluster-randomized trial compared a shared decision-making support tool with usual care for lumbar degenerative disease. It identifies a primary outcome focused on change in patients’ preferred role in decisions with a medical provider, assessed before intervention and up to 12 weeks after intervention. It also lists secondary measures related to decision quality and patient experience.

The record does not report the primary outcome result, secondary outcome estimates, confidence intervals, p-values, or the sponsor’s analysis. It therefore does not show whether the groups differed, how large any difference was, or how precisely it was estimated. The study’s completion status and its registered aims cannot substitute for outcome findings. Nor do the zero adverse-event counts answer the effectiveness question.

Even if an outcome estimate were available, a change in decision preference would need to be interpreted according to its definition and clinical context. A preference change is not automatically evidence of better communication, improved care quality, or a health benefit. The registry’s other listed outcomes concern distinct concepts and would require their own results and interpretation.

In general, statistical evidence is most informative when an effect estimate is presented with uncertainty and when the analysis matches the design. For this trial, cluster-aware analysis is important; the registry specifically cautions against an individual-level re-analysis. Without the sponsor’s analysis and a description of its methods, the posted information cannot support a quantitative estimate of intervention effect.

The defensible interpretation is limited to what the registry records: the study design, intervention and comparator descriptions, endpoint definition, listed outcome domains, and adverse-event counts. Any claim that the support tool improved or failed to improve an outcome would go beyond the results reported in the registry record.

13. Why This Trial Matters Statistically

This trial is statistically notable for its cluster-randomized design. Cluster assignment changes the relationship between the randomization unit and the participant-level outcome. Analyses need to reflect that structure because people within a cluster may have related responses. The registry’s warning against a simple individual-level re-analysis highlights why design-aware inference is not an optional technical detail.

The primary outcome also raises a measurement and interpretation question. The Control Preference Scale is described as ranking patients’ preferred decision-making roles through comparisons among cards, while the registered endpoint is a count of participants whose preference changed. The registry record does not explain the rule used to define a change or how the scale responses entered the analysis. Clear operational definitions are essential for interpreting a count and reproducing an analysis.

The trial’s stated aim concerns quality of care, but its primary measure focuses on a patient’s preferred role in decision-making. That measure is relevant to the intervention’s rationale, yet it represents a particular aspect of care rather than the entire quality-of-care construct. The listed secondary outcomes broaden the evaluation to decision processes, satisfaction, conflict, disability, and healthy living, but their results are not reported in the registry record.

Finally, the distinction between a registered study and a reported analysis matters. The record identifies a completed trial and enrollment of 130, but does not give outcome estimates or the sponsor’s analysis. Without these, readers cannot assess effect magnitude, precision, or statistical evidence. The case illustrates why design information, endpoint definitions, analysis methods, and transparent results are all needed to evaluate clinical trial evidence.

14. 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 most consequential limitation of the registry record for interpretation is the absence of reported outcome estimates and the sponsor’s analysis. The primary endpoint is named, but the result and its precision are not reported. The registry also lists secondary outcomes without reporting their results. This prevents assessment of intervention effects and of consistency across outcomes.

The primary outcome population wording, “Patients lost after 12 weeks of intervention,” is unclear. The record does not clarify whether this refers to loss to follow-up, a particular subset, or another definition. The rule for classifying a change in control preference is also not reported. These ambiguities impede understanding of who contributed to the endpoint and what the reported count would represent.

Important design and analysis details are not reported in the registry record, including the cluster unit, number of clusters, allocation procedures, masking implementation, analysis population, handling of missing outcomes, and cluster-adjusted statistical methods. The registry specifically states that a simple individual-level re-analysis would be misleading, but without the sponsor’s analysis no design-aware effect estimate can be evaluated.

The brief study summary is incomplete, and the registry does not explain what usual care entailed beyond continued usual care without the intervention. It also does not report the collection procedures or observation period for adverse events. These limitations concern the available record and should not be taken as proof that the study itself lacked procedures or analyses; those details are simply not reported in the registry record.

15. Related Tutorials

16. Related Statistical Calculators

17. Sources

• ClinicalTrials.gov: Effectiveness of Implementing Shared Decision-Making on Quality of Care Among Patients With Lumbar Degenerative Diseases. — NCT03679494.
• Registry record last updated 2020-02-19; status last verified 2019-12.
• Results first posted 2020-02-19.
• 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.

18. Record Summary

The record describes a completed randomized, double-masked, parallel-design, cluster-randomized trial sponsored by Taipei Medical University. Enrollment is listed as 130, with a start date of 2018-09-26 and primary completion date of 2019-12-29. The groups were a shared decision-making support tool intervention and usual care.

The primary outcome is change in patients’ control preference, assessed with the Control Preference Scale before intervention and up to 12 weeks afterward. The registry lists secondary outcomes on decision-process quality, decision satisfaction, decisional conflict, lower-back-pain disability, and quality of healthy living. It records zero serious events, zero other events, and zero deaths in each group.

Outcome estimates, confidence intervals, p-values, and the sponsor’s analysis are not reported in the registry record. The record cautions that an individual-level re-analysis would be misleading because of the cluster-randomized design. Its entries support a description of the study, but not a quantitative conclusion about intervention effects.