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Shuyi Shen

Publications and source records attributed to Shuyi Shen.

2 recordsLinked to original sources

Does treatment with duloxetine for neuropathic pain impact glycemic control?

OBJECTIVE: We examined changes in metabolic parameters in clinical trials of duloxetine for diabetic peripheral neuropathic pain (DPNP). RESEARCH DESIGN AND METHODS: Data were pooled from three similarly designed clinical trials. Adults with diabetes and DPNP (n = 1,024) were randomized to 60 mg duloxetine q.d., 60 mg b.i.d., or placebo for 12 weeks. Subjects (n = 867) were re-randomized to 60 mg duloxetine b.i.d. or routine care for an additional 52 weeks. Mean changes in plasma glucose, lipids, and weight were evaluated. Regression and subgroup analyses were used to identify relationships between metabolic measures and demographic, clinical, and electrophysiological parameters. RESULTS: Duloxetine treatment resulted in modest increases in fasting plasma glucose in short- and long-term studies (0.50 and 0.67 mmol/l, respectively). A1C did not increase in placebo-controlled studies; however, a greater increase was seen relative to routine care in long-term studies (0.52 vs. 0.19%). Short-term duloxetine treatment resulted in mean weight loss (-1.03 kg; P < 0.001 vs. placebo), whereas slight, nonsignificant weight gain was seen in both duloxetine and routine care groups with longer treatment. Between-group differences were seen for some lipid parameters, but these changes were generally small. Metabolic changes did not appear to impact improvement in pain severity seen with duloxetine, and nerve conduction was also not significantly impacted by treatment. CONCLUSIONS: Duloxetine treatment was associated with modest changes in glycemia in patients with DPNP. Other metabolic changes were limited and of uncertain significance. These changes did not impact the significant improvement in pain observed with duloxetine treatment.

Adult↗

A local influence sensitivity analysis for incomplete longitudinal depression data.

In the analyses of incomplete longitudinal clinical trial data, there has been a shift, away from simple ad hoc methods that are valid only if the data are missing completely at random (MCAR), to more principled (likelihood-based or Bayesian) ignorable analyses, which are valid under the less restrictive missing at random (MAR) assumption. The availability of the necessary standard statistical software allows for such analyses in practice. Although the possibility of data missing not at random (MNAR) cannot be ruled out, it is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials. Therefore, rather than either forgetting about or blindly shifting to an MNAR framework, the optimal place for MNAR analyses is within a sensitivity analysis context. Such analyses can be used, for example, to assess how sensitive results from an ignorable analysis are to possible departures from MAR and how much results are affected by influential observations. In this article, we apply the local influence sensitivity tool (Verbeke et al., 2001) to a longitudinal depression trial, thereby applying it to continuous outcomes from clinical trials.

Antidepressive Agents↗