PubMed · 15574038
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Diazepam.. https://pubmed.ncbi.nlm.nih.gov/15574038/
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CONTEXT: Up to 25% of individuals with diabetes develop painful diabetic neuropathy, suffering spontaneous pain, allodynia, hyperalgesia, and other unpleasant symptoms. Decreased physical activity, increased fatigue, and mood and sleep problems may result. EVIDENCE ACQUISITION: A MEDLINE search was conducted, limiting searching to double-blind, randomized, controlled trials (1978 to present) of antiepileptic drugs (carbamazepine, gabapentin, pregabalin, topiramate, and lamotrigine) used in the treatment of chronic neuropathic pain. EVIDENCE SYNTHESIS: The most important aspect of treatment is targeted at modification of the underlying disease. However, approaches to symptomatic pain control are essential and include multiple drug classes. Tricyclic antidepressants, including imipramine, nortriptyline, and amitriptyline, have been the mainstays of treatment, but anticholinergic effects, such as dry mouth, blurring of vision, constipation, orthostatic hypotension, and cardiac arrhythmias, as well as other adverse effects, often limit their use. Other treatments include capsaicin, clonidine, acupuncture, and electrical stimulation, suggesting that there is no single effective treatment. First-generation antiepileptic drugs have been shown to be effective in neuropathic pain. The evidence supporting the use of a new generation of antiepileptic drugs in painful diabetic neuropathy is reviewed.
Differences across studies in terms of design features and methodology, clinical procedures, and patient characteristics, are factors that can contribute to variability in the treatment effect between studies in a meta-analysis (statistical heterogeneity). Regression modelling can be used to examine relationships between treatment effect and covariates with the aim of explaining the variability in terms of clinical, methodological, or other factors. Such an investigation can be undertaken using aggregate data or individual patient data. An aggregate data approach can be problematic as sufficient data are rarely available and translating aggregate effects to individual patients can often be misleading. An individual patient data approach, although usually more resource demanding, allows a more thorough investigation of potential sources of heterogeneity and enables a fuller analysis of time to event outcomes in meta-analysis. Hierarchical Cox regression models are used to identify and explore the evidence for heterogeneity in meta-analysis and examine the relationship between covariates and censored failure time data in this context. Alternative formulations of the model are possible and illustrated using individual patient data from a meta-analysis of five randomized controlled trials which compare two drugs for the treatment of epilepsy. The models are further applied to simulated data examples in which the degree of heterogeneity and magnitude of treatment effect are varied. The behaviour of each model in each situation is explored and compared.
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