scholarly journals Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

2016 ◽  
Vol 4 (10) ◽  
pp. 1-278 ◽  
Author(s):  
Shilpa Patel ◽  
Siew Wan Hee ◽  
Dipesh Mistry ◽  
Jake Jordan ◽  
Sally Brown ◽  
...  
Keyword(s):  
Back Pain ◽  
Cost Effectiveness ◽  
Recursive Partitioning ◽  
Research Programme ◽  
Clinical Effectiveness ◽  
Individual Participant Data ◽  
Subgroup Identification ◽  
Individual Participant ◽  
Participant Characteristics ◽  
Treatment Moderators

BackgroundThere is good evidence that therapist-delivered interventions have modest beneficial effects for people with low back pain (LBP). Identification of subgroups of people with LBP who may benefit from these different treatment approaches is an important research priority.Aim and objectivesTo improve the clinical effectiveness and cost-effectiveness of LBP treatment by providing patients, their clinical advisors and health-service purchasers with better information about which participants are most likely to benefit from which treatment choices. Our objectives were to synthesise what is already known about the validity, reliability and predictive value of possible treatment moderators (patient factors that predict response to treatment) for therapist-delivered interventions; develop a repository of individual participant data from randomised controlled trials (RCTs) testing therapist-delivered interventions for LBP; determine which participant characteristics, if any, predict clinical response to different treatments for LBP; and determine which participant characteristics, if any, predict the most cost-effective treatments for LBP. Achieving these objectives required substantial methodological work, including the development and evaluation of some novel statistical approaches. This programme of work was not designed to analyse the main effect of interventions and no such interpretations should be made.MethodsFirst, we reviewed the literature on treatment moderators and subgroups. We initially invited investigators of trials of therapist-delivered interventions for LBP with > 179 participants to share their data with us; some further smaller trials that were offered to us were also included. Using these trials we developed a repository of individual participant data of therapist-delivered interventions for LBP. Using this data set we sought to identify which participant characteristics, if any, predict response to different treatments (moderators) for clinical effectiveness and cost-effectiveness outcomes. We undertook an analysis of covariance to identify potential moderators to apply in our main analyses. Subsequently, we developed and applied three methods of subgroup identification: recursive partitioning (interaction trees and subgroup identification based on a differential effect search); adaptive risk group refinement; and an individual participant data indirect network meta-analysis (NWMA) to identify subgroups defined by multiple parameters.ResultsWe included data from 19 RCTs with 9328 participants (mean age 49 years, 57% females). Our prespecified analyses using recursive partitioning and adaptive risk group refinement performed well and allowed us to identify some subgroups. The differences in the effect size in the different subgroups were typically small and unlikely to be clinically meaningful. Increasing baseline severity on the outcome of interest was the strongest driver of subgroup identification that we identified. Additionally, we explored the application of Bayesian indirect NWMA. This method produced varying probabilities that a particular treatment choice would be most likely to be effective for a specific patient profile.ConclusionsThese data lack clinical effectiveness or cost-effectiveness justification for the use of baseline characteristics in the development of subgroups for back pain. The methodological developments from this work have the potential to be applied in other clinical areas. The pooled repository database will serve as a valuable resource to the LBP research community.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. This project benefited from facilities funded through Birmingham Science City Translational Medicine Clinical Research and Infrastructure Trials Platform, with support from Advantage West Midlands (AWM) and the Wolfson Foundation.

scholarly journals Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning

2021 ◽  
Author(s):  
Cynthia Huber ◽  
Norbert Benda ◽  
Tim Friede
Keyword(s):  
Fixed Effects ◽  
Meta Analysis ◽  
Recursive Partitioning ◽  
Individual Participant Data ◽  
Controlled Clinical Trials ◽  
Randomized Controlled Clinical Trials ◽  
Randomized Controlled ◽  
Model Based ◽  
Subgroup Identification ◽  
Individual Participant

AbstractModel-based recursive partitioning (MOB) can be used to identify subgroups with differing treatment effects. The detection rate of treatment-by-covariate interactions and the accuracy of identified subgroups using MOB depend strongly on the sample size. Using data from multiple randomized controlled clinical trials can overcome the problem of too small samples. However, naively pooling data from multiple trials may result in the identification of spurious subgroups as differences in study design, subject selection and other sources of between-trial heterogeneity are ignored. In order to account for between-trial heterogeneity in individual participant data (IPD) meta-analysis random-effect models are frequently used. Commonly, heterogeneity in the treatment effect is modelled using random effects whereas heterogeneity in the baseline risks is modelled by either fixed effects or random effects. In this article, we propose metaMOB, a procedure using the generalized mixed-effects model tree (GLMM tree) algorithm for subgroup identification in IPD meta-analysis. Although the application of metaMOB is potentially wider, e.g. randomized experiments with participants in social sciences or preclinical experiments in life sciences, we focus on randomized controlled clinical trials. In a simulation study, metaMOB outperformed GLMM trees assuming a random intercept only and model-based recursive partitioning (MOB), whose algorithm is the basis for GLMM trees, with respect to the false discovery rates, accuracy of identified subgroups and accuracy of estimated treatment effect. The most robust and therefore most promising method is metaMOB with fixed effects for modelling the between-trial heterogeneity in the baseline risks.

scholarly journals Diet and physical activity in pregnancy to prevent gestational diabetes: a protocol for an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e048119
Author(s):  
Dyuti Coomar ◽  
Jonathan M Hazlehurst ◽  
Frances Austin ◽  
Charlie Foster ◽  
Graham A Hitman ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cost Effectiveness ◽  
Gestational Diabetes ◽  
Systematic Reviews ◽  
Meta Analysis ◽  
Individual Participant Data ◽  
Randomised Trials ◽  
Diet And Physical Activity ◽  
Individual Participant ◽  
In Pregnancy

IntroductionMothers with gestational diabetes mellitus (GDM) are at increased risk of pregnancy-related complications and developing type 2 diabetes after delivery. Diet and physical activity-based interventions may prevent GDM, but variations in populations, interventions and outcomes in primary trials have limited the translation of available evidence into practice. We plan to undertake an individual participant data (IPD) meta-analysis of randomised trials to assess the differential effects and cost-effectiveness of diet and physical activity-based interventions in preventing GDM and its complications.MethodsThe International Weight Management in Pregnancy Collaborative Network database is a living repository of IPD from randomised trials on diet and physical activity in pregnancy identified through a systematic literature search. We shall update our existing search on MEDLINE, Embase, BIOSIS, LILACS, Pascal, Science Citation Index, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Database of Abstracts of Reviews of Effects and Health Technology Assessment Database without language restriction to identify relevant trials until March 2021. Primary researchers will be invited to join the Network and share their IPD. Trials including women with GDM at baseline will be excluded. We shall perform a one and two stage random-effect meta-analysis for each intervention type (all interventions, diet-based, physical activity-based and mixed approach) to obtain summary intervention effects on GDM with 95% CIs and summary treatment–covariate interactions. Heterogeneity will be summarised using I2 and tau2 statistics with 95% prediction intervals. Publication and availability bias will be assessed by examining small study effects. Study quality of included trials will be assessed by the Cochrane Risk of Bias tool, and the Grading of Recommendations, Assessment, Development and Evaluations approach will be used to grade the evidence in the results. A model-based economic analysis will be carried out to assess the cost-effectiveness of interventions to prevent GDM and its complications compared with usual care.Ethics and disseminationEthics approval is not required. The study is registered on the International Prospective Register of Systematic Reviews (CRD42020212884). Results will be submitted for publication in peer-reviewed journals.

scholarly journals Systematic review and network meta-analysis with individual participant data on cord management at preterm birth (iCOMP): study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. e034595
Author(s):  
Anna Lene Seidler ◽  
Lelia Duley ◽  
Anup C Katheria ◽  
Catalina De Paco Matallana ◽  
Eugene Dempsey ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Primary Outcome ◽  
Meta Analysis ◽  
Management Strategies ◽  
Preterm Births ◽  
Individual Participant Data ◽  
Mortality And Morbidity ◽  
Cord Clamping ◽  
Individual Participant ◽  
Participant Characteristics

IntroductionTiming of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups. Previous and current trials compare various policies, including time-based or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enable exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons.Objectives(1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis. (2) To evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA.Methods and analysisSystematic searches of Medline, Embase, clinical trial registries, and other sources for all ongoing and completed randomised controlled trials comparing cord management strategies at preterm birth (before 37 weeks’ gestation) have been completed up to 13 February 2019, but will be updated regularly to include additional trials. IPD will be sought for all trials; aggregate summary data will be included where IPD are unavailable. First, deferred clamping and cord milking will be compared with immediate clamping in pairwise IPD meta-analyses. The primary outcome will be death prior to hospital discharge. Effect differences will be explored for prespecified participant subgroups. Second, all identified cord management strategies will be compared and ranked in an IPD NMA for the primary outcome and the key secondary outcomes. Treatment effect differences by participant characteristics will be identified. Inconsistency and heterogeneity will be explored.Ethics and disseminationEthics approval for this project has been granted by the University of Sydney Human Research Ethics Committee (2018/886). Results will be relevant to clinicians, guideline developers and policy-makers, and will be disseminated via publications, presentations and media releases.Registration numberAustralian New Zealand Clinical Trials Registry (ANZCTR) (ACTRN12619001305112) and International Prospective Register of Systematic Reviews (PROSPERO, CRD42019136640).

scholarly journals Cost effectiveness of guided Internet-based interventions for depression in comparison with control conditions: An individual-participant data meta-analysis

2018 ◽  
Vol 35 (3) ◽  
pp. 209-219 ◽  
Author(s):  
Spyros Kolovos ◽  
Johanna M. van Dongen ◽  
Heleen Riper ◽  
Claudia Buntrock ◽  
Pim Cuijpers ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Meta Analysis ◽  
Individual Participant Data ◽  
Individual Participant

scholarly journals Systematic review and network meta-analysis with individual participant data on Cord Management at Preterm Birth (iCOMP): study protocol

2019 ◽  
Author(s):  
Anna Lene Seidler ◽  
Lelia Duley ◽  
Anup Katheria ◽  
Catalina De Paco Matallana ◽  
Eugene Dempsey ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Preterm Infants ◽  
Primary Outcome ◽  
Meta Analysis ◽  
Management Strategies ◽  
Individual Participant Data ◽  
Cord Clamping ◽  
Individual Participant ◽  
Participant Characteristics ◽  
Meta Analyses

ABSTRACTIntroductionTiming of cord clamping and other cord management strategies may improve outcomes at preterm birth. However, it is unclear whether benefits apply to all preterm subgroups such as those who usually receive immediate neonatal care. Previous and current trials compare various policies, including immediate cord clamping, time- or physiology-based deferred cord clamping, and cord milking. Individual participant data (IPD) enables exploration of different strategies within subgroups. Network meta-analysis (NMA) enables comparison and ranking of all available interventions using a combination of direct and indirect comparisons.Objectives1) To evaluate the effectiveness of cord management strategies for preterm infants on neonatal mortality and morbidity overall and for different participant characteristics using IPD meta-analysis; and 2) to evaluate and rank the effect of different cord management strategies for preterm births on mortality and other key outcomes using NMA.Methods and analysisWe will conduct a systematic search of Medline, Embase, clinical trial registries, and other sources for all planned, ongoing and completed randomised controlled trials comparing alternative cord management strategies at preterm birth (before 37 weeks’ gestation). IPD will be sought for all trials. First, deferred clamping and cord milking will be compared with immediate clamping in pairwise IPD meta-analyses. The primary outcome will be death prior to hospital discharge. Effect differences will be explored for pre-specified subgroups of participants. Second, all identified cord management strategies will be compared and ranked in an IPD NMA for the primary outcome and the key secondary outcomes intraventricular haemorrhage (any grade) and infant blood transfusions (any). Treatment effect differences by participant characteristics will be identified. Inconsistency and heterogeneity will be explored.Ethics and disseminationApproved by University of Sydney Human Research Ethics Committee (2018/886). Results will be relevant to clinicians, guideline-developers and policy-makers, and will be disseminated via publications, presentations, and media releases.RegistrationAustralian New Zealand Clinical Trials Registry: ACTRN12619001305112.STRENGTH AND LIMITATIONS OF THIS STUDYThis will be the most comprehensive review to date of interventions for umbilical cord management in preterm infants and the findings will be highly relevant to clinicians and guideline developersThe use of individual participant data will allow assessment of the best treatment option for key subgroups of participantsNetwork meta-analysis will enable the comparison and ranking of all available treatment options using direct and indirect evidenceFor some of the trials it will not be possible to obtain individual participant data, so published aggregate results will be used insteadRisk of bias in the primary trials will be assessed using Cochrane criteria, and certainty of evidence for the meta-analyses will be appraised using the GRADE approach for the pairwise comparisons, and the CINeMA approach for the network meta-analysis

scholarly journals 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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siew Wan Hee ◽  
◽  
Dipesh Mistry ◽  
Tim Friede ◽  
Sarah E. Lamb ◽  
...  
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Usual Care ◽  
Randomised Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Individual Participant Data ◽  
Low Back ◽  
Individual Participant ◽  
Randomised Controlled

Abstract 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 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 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 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.

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