Identification of subgroup effect with an individual participant data meta-analysis of randomised controlled trials of three different types of therapist-delivered care in low back pain.

IPD Low back pain Physical interventions Psychological interventions Stratification Subgroups Therapist delivered interventions

Journal

BMC musculoskeletal disorders
ISSN: 1471-2474
Titre abrégé: BMC Musculoskelet Disord
Pays: England
ID NLM: 100968565

Informations de publication

Date de publication:
16 Feb 2021
Historique:
received: 24 02 2020
accepted: 28 01 2021
entrez: 17 2 2021
pubmed: 18 2 2021
medline: 15 5 2021
Statut: epublish

Résumé

Proven treatments for low back pain, at best, only provide modest overall benefits. Matching people to treatments that are likely to be most effective for them may improve clinical outcomes and makes better use of health care resources. We conducted an individual participant data meta-analysis of randomised controlled trials of three types of therapist delivered interventions for low back pain (active physical, passive physical and psychological treatments). We applied two statistical methods (recursive partitioning and adaptive risk group refinement) to identify potential subgroups who might gain greater benefits from different treatments from our individual participant data meta-analysis. We pooled data from 19 randomised controlled trials, totalling 9328 participants. There were 5349 (57%) females with similar ratios of females in control and intervention arms. The average age was 49 years (standard deviation, SD, 14). Participants with greater psychological distress and physical disability gained most benefit in improving on the mental component scale (MCS) of SF-12/36 from passive physical treatment than non-active usual care (treatment effects, 4.3; 95% confidence interval, CI, 3.39 to 5.15). Recursive partitioning method found that participants with worse disability at baseline gained most benefit in improving the disability (Roland Morris Disability Questionnaire) outcome from psychological treatment than non-active usual care (treatment effects, 1.7; 95% CI, 1.1 to 2.31). Adaptive risk group refinement did not find any subgroup that would gain much treatment effect between psychological and non-active usual care. Neither statistical method identified any subgroups who would gain an additional benefit from active physical treatment compared to non-active usual care. Our methodological approaches worked well and may have applicability in other clinical areas. Passive physical treatments were most likely to help people who were younger with higher levels of disability and low levels of psychological distress. Psychological treatments were more likely to help those with severe disability. Despite this, the clinical importance of identifying these subgroups is limited. The sizes of sub-groups more likely to benefit and the additional effect sizes observed are small. Our analyses provide no evidence to support the use of sub-grouping for people with low back pain.

Sections du résumé

BACKGROUND BACKGROUND
Proven treatments for low back pain, at best, only provide modest overall benefits. Matching people to treatments that are likely to be most effective for them may improve clinical outcomes and makes better use of health care resources.
METHODS METHODS
We conducted an individual participant data meta-analysis of randomised controlled trials of three types of therapist delivered interventions for low back pain (active physical, passive physical and psychological treatments). We applied two statistical methods (recursive partitioning and adaptive risk group refinement) to identify potential subgroups who might gain greater benefits from different treatments from our individual participant data meta-analysis.
RESULTS RESULTS
We pooled data from 19 randomised controlled trials, totalling 9328 participants. There were 5349 (57%) females with similar ratios of females in control and intervention arms. The average age was 49 years (standard deviation, SD, 14). Participants with greater psychological distress and physical disability gained most benefit in improving on the mental component scale (MCS) of SF-12/36 from passive physical treatment than non-active usual care (treatment effects, 4.3; 95% confidence interval, CI, 3.39 to 5.15). Recursive partitioning method found that participants with worse disability at baseline gained most benefit in improving the disability (Roland Morris Disability Questionnaire) outcome from psychological treatment than non-active usual care (treatment effects, 1.7; 95% CI, 1.1 to 2.31). Adaptive risk group refinement did not find any subgroup that would gain much treatment effect between psychological and non-active usual care. Neither statistical method identified any subgroups who would gain an additional benefit from active physical treatment compared to non-active usual care.
CONCLUSIONS CONCLUSIONS
Our methodological approaches worked well and may have applicability in other clinical areas. Passive physical treatments were most likely to help people who were younger with higher levels of disability and low levels of psychological distress. Psychological treatments were more likely to help those with severe disability. Despite this, the clinical importance of identifying these subgroups is limited. The sizes of sub-groups more likely to benefit and the additional effect sizes observed are small. Our analyses provide no evidence to support the use of sub-grouping for people with low back pain.

Identifiants

pubmed: 33593341
doi: 10.1186/s12891-021-04028-8
pii: 10.1186/s12891-021-04028-8
pmc: PMC7885433
doi:

Types de publication

Journal Article Meta-Analysis

Langues

eng

Sous-ensembles de citation

IM

Pagination

191

Subventions

Organisme : Department of Health
ID : RP-PG-0608-10076
Pays : United Kingdom
Organisme : Programme Grants for Applied Research
ID : RP-PG-0608-10076

Investigateurs

Christer Carlsson (C)
Francesca Cecchi (F)
Ninna Dufour (N)
Heinz Endres (H)
Mark Hancock (M)
Elaine Hay (E)
Von Korff (V)
Sarah Lamb (S)
Luciana Macedo (L)
Hugh MacPherson (H)
Chris Maher (C)
Suzanne McDonough (S)
Rob Smeets (R)
David Torgerson (D)
Claudia Witt (C)

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Auteurs

Siew Wan Hee (SW)

Statistics and Epidemiology Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.

Dipesh Mistry (D)

Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK. D.Mistry@warwick.ac.uk.

Tim Friede (T)

Department of Medical Statistics, University Medical Center Göttingen, 37073, Göttingen, Germany.

Sarah E Lamb (SE)

University of Exeter Medical School, St Lukes Campus, Heavitree Road, Exeter, EX1 2LU, UK.

Nigel Stallard (N)

Statistics and Epidemiology Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.

Martin Underwood (M)

Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
University Hospitals of Coventry and Warwickshire, Coventry, UK.

Shilpa Patel (S)

Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.

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Classifications MeSH