Network meta-analysis

Weight loss of 10 percent or more

Overweight or obesity without diabetes (adults)

weeks 40 to 104 (target week 68)Treatment policyEach dose separatelyOdds ratio

Randomized trials

Treatments

2,082

Participants

1,054

Events

In plain language

Across 2 trials with 2,082 participants, 4 of 5 treatments had a clearly better result than placebo on weight loss of 10 percent or more, and for 1 the evidence could not tell it apart from placebo. The most favorable estimate was for Semaglutide 2.4 mg subcutaneous once weekly: odds ratio 15.76 (95% credible interval 3.51 to 37.51).

Higher values favor the treatment. "Clearly" means the 95% credible interval excludes no difference. These are averages across trials; they do not say how any one person will respond. Not medical advice.

Network of trials

Each circle is a treatment; each line joins treatments that were randomized against each other in at least one trial.

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.

Effects compared with Placebo

Odds ratio. Higher values favor the treatment.

Versus Placebo, log scale. Blue: Bayesian random effects posterior median with 95% credible interval (thick) and 95% prediction interval (dashed). Amber diamond: frequentist estimate with 95% confidence interval. Hover over or tab to a row for its values.

Effects of each treatment versus Placebo
Treatment	Bayesian OR (95% CrI)	95% prediction interval	Frequentist OR (95% CI)	Verdict
Semaglutide 2.4 mg subcutaneous once weekly SEMA_SC_2p4MG_QW	15.76 (3.51 to 37.51)	2.22 to 61.02	16.50 (12.47 to 21.81)	Better
Survodutide 4.8 mg subcutaneous once weekly SURV_SC_4p8MG_QW	12.58 (2.49 to 38.93)	1.70 to 52.33	17.60 (6.48 to 47.83)	Better
Survodutide 3.6 mg subcutaneous once weekly SURV_SC_3p6MG_QW	10.74 (2.17 to 34.10)	1.46 to 44.17	15.24 (5.61 to 41.41)	Better
Survodutide 2.4 mg subcutaneous once weekly SURV_SC_2p4MG_QW	10.73 (2.23 to 34.74)	1.40 to 47.56	15.20 (5.55 to 41.60)	Better
Survodutide 0.6 mg subcutaneous once weekly SURV_SC_0p6MG_QW	2.94 (0.66 to 9.64)	0.42 to 13.12	4.11 (1.49 to 11.31)	Unclear

Ranking

How likely each treatment is to be among the best. Rankings ignore the size of differences and their certainty, so read them with the effects above.

League table

Every treatment against every other: odds ratio of the row treatment versus the column treatment, posterior median and 95% credible interval. Green: row clearly better; red: row clearly worse.

Every treatment in a row compared with every treatment in a column
Row versus column	PLACEBO	SEMA_SC_2p4MG_QW	SURV_SC_4p8MG_QW	SURV_SC_3p6MG_QW	SURV_SC_2p4MG_QW	SURV_SC_0p6MG_QW
PLACEBO	PLACEBO	0.06 0.03 to 0.28	0.08 0.03 to 0.40	0.09 0.03 to 0.46	0.09 0.03 to 0.45	0.34 0.10 to 1.52
SEMA_SC_2p4MG_QW	15.76 3.51 to 37.51	SEMA_SC_2p4MG_QW	1.22 0.25 to 6.64	1.44 0.29 to 7.94	1.41 0.28 to 8.02	5.21 1.01 to 27.72
SURV_SC_4p8MG_QW	12.58 2.49 to 38.93	0.82 0.15 to 4.06	SURV_SC_4p8MG_QW	1.17 0.34 to 4.22	1.16 0.32 to 3.92	4.26 1.23 to 13.77
SURV_SC_3p6MG_QW	10.74 2.17 to 34.10	0.69 0.13 to 3.41	0.86 0.24 to 2.98	SURV_SC_3p6MG_QW	1.00 0.28 to 3.45	3.68 1.02 to 11.78
SURV_SC_2p4MG_QW	10.73 2.23 to 34.74	0.71 0.12 to 3.52	0.86 0.26 to 3.08	1.00 0.29 to 3.63	SURV_SC_2p4MG_QW	3.70 1.05 to 12.21
SURV_SC_0p6MG_QW	2.94 0.66 to 9.64	0.19 0.04 to 0.99	0.23 0.07 to 0.81	0.27 0.08 to 0.98	0.27 0.08 to 0.95	SURV_SC_0p6MG_QW

Heterogeneity, fit and convergence

Bayesian random effects (primary)

Heterogeneity (tau): 0.295 (95% CrI 0.062 to 1.391), log scale
Residual deviance: 6.9 for 7 data points
DIC: 13.5 (pD 6.5)
Largest R-hat: 1.001
Smallest ESS: bulk 2,423, tail 2,245
Divergent transitions: 0
Convergence: All criteria met
Priors: PR02 N(0, 5^2); PR05 N(0, 2.5^2); PR09 log normal(-2.53, 1.58^2) on tau^2 (General physical health indicators, pharm vs pharm)

Frequentist check (netmeta)

Method: inverse variance, REML, OR
Tau: 0.000
Q (total): 0.00 on 0 df
Q (between designs): 0.00

Inconsistency

Do trials comparing two treatments directly agree with the indirect evidence through other treatments?

No comparison here has both direct and indirect evidence, so inconsistency cannot be checked by node splitting; the between designs Q above is the global check.

Small study effects

A comparison adjusted funnel plot: each point is one trial comparison, centered on its own network estimate. Asymmetry can signal that small trials with unfavorable results are missing.

Fewer than ten trial comparisons, too few to judge funnel plot asymmetry.

Baseline characteristics of the trials

The baseline value of each effect modifier or prognostic factor that applies here, per trial: mean or percent over arms, weighted by the number randomized. A note under a value names a stand in (a median for a mean, 100 minus the male share, Asian subgroup rows, an all Asian site list); "not reported" means no source of the trial prints it.

Baseline effect modifiers and prognostic factors per trial
Trial	Baseline BMI or body weight (kg/m2) effect modifier, prognostic factor; 1 of 2	Type 2 diabetes share in weight trials (percent of participants) effect modifier; 2 of 2	Sex (percent female) effect modifier, prognostic factor; 2 of 2	Treatment duration (weeks) effect modifier; 2 of 2	Lifestyle co-intervention intensity (none standard intensive) effect modifier, prognostic factor; 2 of 2
STEP 1 S000144	37.87	0 population codes	74.07	68	standard
NCT04667377 S002375	body weight 105.7 kg	0 population codes	67.7	46	standard

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.

FlagBaseline, Baseline BMI or body weight: not reported by 1 of 2 trials
FlagOutcome assessment, definition: confounded with design: not stated 1, weight loss of 5 percent or more at week 46 1
FlagOutcome assessment, proportion method: confounded with design: not stated 1, not_stated 1
FlagOutcome assessment, intercurrent events (rescue): confounded with design: not stated 1, treatment_policy 1
NoteOutcome assessment, time point: selected time points span 46 to 68 weeks
NoteData handling, input conversions: count_from_percent 5

Comparability items per trial
Trial	time weeks	analysis set	definition	instrument	adjudicated	proportion method	intercurrent events (discontinuation)	intercurrent events (rescue)	blinding	funding	start year	Eligibility
STEP 1	68	full_analysis	not stated	not stated	not stated	not stated	treatment_policy	treatment_policy	double_blind	industry	2018	adults; BMI 30 or more, or 27 or more with a weight related condition; no diabetes; HbA1c below 6.5 percent
NCT04667377	46	full_analysis	weight loss of 5 percent or more at week 46	not stated	not stated	not_stated	treatment_policy	not stated	double_blind	industry	2021	age 18 to under 75; BMI 27 or more; HbA1c below 6.5 percent; weight 70 kg women or 80 kg men or more

Trial data

What each trial contributes. Arm rows give the arm value with its standard error, or events over participants; contrast rows give the odds ratio on the analysis scale (log) against the trial's first row. Flags mark converted, combined or approximated inputs.

Trial data entering this analysis
Trial	Arm	Treatment	Week	Data	Events / N	Flags	Source
STEP 1	A1	SEMA_SC_2p4MG_QW	68	arm, responders	838 / 1,212		R0023399:p7
STEP 1	A2	PLACEBO	68	arm, responders	69 / 577		R0023399:p7
NCT04667377	A1	SURV_SC_0p6MG_QW	46	arm, responders	19 / 56	count_from_percent	registry:outcomes
NCT04667377	A2	SURV_SC_2p4MG_QW	46	arm, responders	38 / 58	count_from_percent	registry:outcomes
NCT04667377	A3	SURV_SC_3p6MG_QW	46	arm, responders	40 / 61	count_from_percent	registry:outcomes
NCT04667377	A4	SURV_SC_4p8MG_QW	46	arm, responders	44 / 64	count_from_percent	registry:outcomes
NCT04667377	A5	PLACEBO	46	arm, responders	6 / 54	count_from_percent	registry:outcomes

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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