RISPERIDONE vs PERHENAZINE ‒ long‐term data: Leaving the study early
- Population
- people with both severe mental illness and substance misuse
- Intervention
- RISPERIDONE
- Comparison
- PERHENAZINE ‒ long‐term data
- Outcome
- Leaving the study early
RISPERIDONE showed no clear difference in leaving the study early: any reason compared with PERHENAZINE ‒ long‐term data (low certainty).
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
RISPERIDONE vs PERHENAZINE ‒ long‐term data Leaving the study early: any reason · people with severe mental illness and co‐occurring substance misuse RISPERIDONE showed no clear difference in leaving the study early: any reason compared with PERHENAZINE ‒ long‐term data (low certainty). Risperidone versus other antipsychotics for people with severe mental illness and co‐occurring substance… (2018) · Cochrane Headline outcomes only | RR 1.05 [0.92, 1.2] | 1 study · 281 participants | Low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Risperidone versus other antipsychotics for people with severe mental illness and co‐occurring substance misuse 2018 · Cochrane Headline outcomes only | Not reported | Not extracted | Not assessed |
Related questions
The same intervention and outcome, asked in another population or against another comparator.
- RISPERIDONE vs OLANZAPINE · people with both severe mental illness and substance misuse · 1 review
- RISPERIDONE vs CLOZAPINE ‐ all data short term · people with both severe mental illness and substance misuse · 1 review
- RISPERIDONE vs ZIPRASIDONE ‒ long‐term data (> 12 months) · people with both severe mental illness and substance misuse · 1 review
- RISPERIDONE vs QUETIAPINE · people with both severe mental illness and substance misuse · 1 review
- RISPERIDONE vs placebo · patients with schizophrenia · 0 reviews
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.