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

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

Detecting Global Treatment Effects Across ROI

Talk from the 23 & 24 January 2012 "GlaxoSmithKline - Neurophysics Workshop on Pharmacological MRI", an activity hosted at Warwick University and coordinated with the Neurophysics Marie Curie Initial Training Network of which GSK is a participant

Can We Convert Between Outcome Measures of Disability for Chronic Low Back Pain?

Study Design. Retrospective database analysis. Objective. A range of patient-reported outcomes were used to measure disability due to low back pain. There is not a single back pain disability measurement commonly used in all randomized controlled trials. We report here our assessment as to whether different disability measures are sufficiently comparable to allow data pooling across trials. Summary of Background Data. We used individual patient data from a repository of data from back pain trials of therapistdelivered interventions. Methods. We used data from 11 trials (n = 6089 patients) that had at least 2 of the following 7 measurements: Roland-Morris Disability Questionnaire, Chronic Pain Grade disability score, Physical Component Summary of the 12- or 36-Item Short Form Health Survey, Patient Specific Functional Scale, Pain Disability Index, Oswestry Disability Index, and Hannover Functional Ability Questionnaire. Within each trial, the change score between baseline and short-term follow-up was computed for each outcome and this was used to calculate the correlation between the change scores and the Cohen’s κ for the 3-level outcome of change score of less than, equal to, and more than zero. It was considered feasible to pool 2 measures if they were at least moderately correlated (correlation >0.5) and have at least moderately similar responsiveness (κ >0.4). Results. Although all pairs of measures were found to be positively correlated, most correlations were less than 0.5, with only 1 pair of outcomes in 1 trial having a correlation of more than 0.6. All κ statistics were less than 0.4 so that in no cases were the criteria for acceptability of pooling measures satisfied. Conclusion. The lack of agreement between different outcome measures means that pooling of data on these different disability measurements in a meta-analysis is not recommended

Additional file 1: of Systematic reviews in paediatric multiple sclerosis and Creutzfeldt-Jakob disease exemplify shortcomings in methods used to evaluate therapies in rare conditions

Supplementary web material. Supplementary material related to this article is available online. (PDF 93 kb

Optimizing Trial Designs for Targeted Therapies

<div><p>An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor’s as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.</p></div

Weak biomarker prior and a large market with no biomarker costs (Case 1).

<p>Optimized expected utilities and sample sizes for the enrichment, classical and stratified design as functions of the prevalence for <i>λ</i>_<i>S</i> ∈ [0.05, 0.95]. For the stratified design, optimized levels <i>α</i>_<i>S</i> and <i>α</i>_<i>F</i> for the multiple testing procedure are given. The last row shows the overall probability (averaged over the prior) that a significant treatment effect in <i>H</i>_<i>S</i> or <i>H</i>_<i>F</i> can be shown (and, for the stratified design, that the thresholds <i>τ</i>_<i>S</i> and <i>τ</i>_<i>S</i>′ are crossed). The priors are defined as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163726#pone.0163726.t001" target="_blank">Table 1</a> with <i>δ</i> = 0.3.</p

Strong biomarker prior and a small market with no biomarker costs (Case 2).

<p>See the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163726#pone.0163726.g001" target="_blank">Fig 1</a>.</p

Optimal designs for different combinations of the prevalence <i>λ</i>_<i>S</i> ∈ [0.05, 0.95] and effect size parameter <i>δ</i> ∈ [0, 1].

<p>Optimized designs for the sponsor and the public health authority are shown for both the weak and the strong biomarker prior (as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163726#pone.0163726.t001" target="_blank">Table 1</a>) under the three different cost structures defined by Cases 1, 2 and 3. The colour in a specific point indicates the type of the optimal design. Grey areas correspond to regions where all optimized designs have negative utilities, implying that the optimal choice is to perform no trial.</p

Weak biomarker prior and a small market with no biomarker costs (Case 2).

<p>See the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163726#pone.0163726.g001" target="_blank">Fig 1</a>.</p

Additional file 1 of Value of information methods to design a clinical trial in a small population to optimise a health economic utility function

Derivation of expected utility function. Detailed mathematical derivation of results cited in main paper. (PDF 102 kb