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

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

Domains of measurement in non-specific low back pain trials.

<p>The figure shows the domains of measurement in non-specific low back pain trials published between 1980 and 2012.</p

The most common back-specific PROMs: Frequency of use.

<p>The most common back-specific PROMs: Frequency of use.</p

The five most common back-specific patient reported outcome measures.

<p>The figure shows the use of the five most common back-specific patient reported outcome measures as primary and secondary outcome measures.</p

Flow chart showing search results.

<p>The figure shows the number of initial hits, duplicates, exclusions based on titles and abstracts screening, and assessments at full text level evaluation.</p

Reporting methods and statistical analysis: Prevalence of use.

<p>Reporting methods and statistical analysis: Prevalence of use.</p

The five most common back-specific patient-reported outcome measures: Relative use by year.

<p>The figure shows relative frequency of use for the most common back-specific patient-reported outcome measures, by publication year.</p

Number of published non-specific low back pain trials by publication year between 1980 and 2012.

<p>The figure shows the increase in the number of published non-specific low back pain trials by year of publication and change in publication rate over time. A Lowess smoother is fitted to these data.</p