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

Supplemental material for Group sequential designs with robust semiparametric recurrent event models

<p>Supplemental material for Group sequential designs with robust semiparametric recurrent event models by Tobias Mütze, Ekkehard Glimm, Heinz Schmidli and Tim Friede in Statistical Methods in Medical Research</p

The fractions of patients with progressing EDSS status over the years.

<p>The chances of progression also depend on the study duration (you can see that shorter studies have smaller fractions) but even after accounting for the duration, the decreasing trend remains statistically significant (p<0.0001). The red line shows the estimated regression line for a trial duration of 1 year.</p

The fractions of patients with progressing EDSS in the first and second year of study, for the 13 studies of at least 2 years duration, and where the data was provided.

<p>Connecting lines indicate the rates for the two subsequent years, line widths are proportional to study sizes (numbers of patients <i>N</i>). The weighted average (weighted by the inverse variances on the log odds scale) decreases from 16.9% to 13.1% from first to second year. [16.0% to 11.2% for the 8 most recent post-2000 studies].</p

Results of univariable and multivariable regression aiming at explaining the probability of EDSS progression.

<p>¹ heterogeneity is the same for all variables in the multivariable model.</p><p>Regression coefficients relate to the logarithmic odds of progression. τ² denotes the unexplained between-study heterogeneity, and the reduction percentages relate to the model including only follow-up duration as a predictor (which is also included in all univariable models). The multivariable model was selected based on the <i>Bayesian information criterion</i> (BIC).</p

Rate of first appropriate ICD shock by T wave loop circularity.

<p>Cumulative incidence functions for the probability of appropriate ICD shock according to 25% of patients with the least compact loops (highest T wave loop circularity, red dotted line) vs. 75% of patients with more compact loops (blue line).</p

T wave morphology descriptors and QRS duration.

<p>T wave morphology descriptors and QRS duration.</p

Baseline characteristics of the 605 patients.

<p>Baseline characteristics of the 605 patients.</p

Fine and Gray regression for appropriate shock.

<p>Fine and Gray regression for appropriate shock.</p

Rate of first appropriate ICD shock by QRS duration.

<p>Cumulative incidence functions for the probability of appropriate ICD shock according to QRS duration above (red dotted line) and below (blue line) the median.</p