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Fixed-effect or random-effect models? A methodological reappraisal of subgroup analyses in mesenchymal stem cell
1Clinical Laboratory Department, The Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, No. 33 Beiling Street, Shenyang, 110032, Liaoning, China. wuma19811982@163.com.
Abstract:
We commend Cao et al. for their systematic review demonstrating the efficacy of intra-articular mesenchymal stem cell (MSC) therapy in alleviating pain and improving function in patients with non-surgical knee osteoarthritis (OA). However, we reanalyzed their subgroup analyses to evaluate the methodological implications of statistical model selection (fixed-effect vs. random-effect models) on result reliability. In dose-stratified analyses, Cao et al. applied fixed-effect models to low-dose (I2 = 0%) and high-dose (I2 = 80%) MSC subgroups. Upon reanalysis using random-effect models, the high-dose group showed no statistically significant differences in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total scores compared to the control group at 6 months [MD = 8.75; 95% CI (-2.10, 19.61); P = 0.11] or 12 months [MD = 12.68; 95% CI (-4.96, 30.32); P = 0.16], contrasting with Cao et al.'s original findings. The low-dose subgroup, with no heterogeneity, yielded identical results across both models. Similarly, in cell-source stratification (adipose-derived MSCs [ADMSCs] vs. bone marrow-derived MSCs [BM-MSCs]), reanalysis of ADMSCs using random-effect models demonstrated significant 6-month WOMAC improvement [MD = 9.32; 95% CI (3.73, 14.92); P = 0.001] but non-significant 12-month differences [MD = 12.90; 95% CI (-1.76, 27.55); P = 0.08], diverging from Cao et al.'s conclusions. BM-MSCs results remained consistent due to negligible heterogeneity (I2 = 0%). These findings underscore that fixed-effect models artificially narrow confidence intervals in heterogeneous populations, overestimating clinical significance. Our results align with Cochrane guidelines, emphasizing that random-effect models better accommodate inter-study diversity, yielding conservative and clinically generalizable estimates. This critique reinforces the necessity of transparent statistical model selection in meta-analyses, particularly when subgroup heterogeneity may influence therapeutic interpretations.
