LivingGLP1
Descriptive only

Intestinal obstruction or ileus

Type 2 diabetes (adults)

longest follow-upRecorded harmsDoses of a drug pooledHazard ratio (from event counts)
1
Randomized trials
2
Treatments
912
Participants
0
Events

In plain language

No network meta-analysis was fitted here. The protocol requires at least two trials forming a connected network; this outcome has 1 trial. The trial data below are shown as reported.

Network of trials

Each circle is a treatment; each line joins treatments that were randomized against each other in at least one trial.
EXENER_SCLIRA_SC
Node area shows participants; line width shows trials comparing two treatments directly. Blue: GLP-1 receptor agonist containing treatments; grey: comparators. Hover over or tab to a node or line for details.

Comparability of the trials

Checked across designs (the set of treatments each trial compares) for eligibility, outcome definition and assessment, and the baseline factors above. Flags point to possible intransitivity; notes describe variation that is not tied to design.

No comparability concerns were found.

Comparability items per trial
Trialtime weeksanalysis setdefinitioninstrumentadjudicatedproportion methodintercurrent events (discontinuation)intercurrent events (rescue)blindingfundingstart yearEligibility
NCT0102988626modified_ittnot statednot statednot statednot statednot statednot statedopen_labelindustry2010adults 18 or older with type 2 diabetes on oral agents, HbA1c 7.1 to 11.0 percent, BMI 45 or less

Trial data

What each trial contributes. Arm rows give the arm value with its standard error, or events over participants; contrast rows give the hazard ratio (from event counts) on the analysis scale (log) against the trial's first row. Flags mark converted, combined or approximated inputs.
Trial data entering this analysis
TrialArmTreatmentWeekDataValueSEEvents / NFlagsSource
NCT01029886A1EXENER_SC26arm, events0 / 461R0009388:Supplement Table S4
NCT01029886A2LIRA_SC26arm, events0 / 451R0009388:Supplement Table S4

Rerun this analysis yourself

Remove treatments or trials and refit the frequentist and Bayesian models in your browser, with the same methods as the published analysis.

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