Cost-effectiveness of antidepressants versus active monitoring for mild-to-moderate major depressive disorder: a multisite non-randomized-controlled trial in primary care (INFAP study)

Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and Analysis

Background: Big data and real-world data (RWD) have been increasingly used to measure the effectiveness and costs in cost-effectiveness analysis (CEA). However, the characteristics and methodologies of CEA based on big data and RWD remain unknown. The objectives of this study were to review the characteristics and methodologies of the CEA studies based on big data and RWD and to compare the characteristics and methodologies between the CEA studies with or without decision-analytic models. Methods: The literature search was conducted in Medline (Pubmed), Embase, Web of Science, and Cochrane Library (as of June 2020). Full CEA studies with an incremental analysis that used big data and RWD for both effectiveness and costs written in English were included. There were no restrictions regarding publication date. Results: 70 studies on CEA using RWD (37 with decision-analytic models and 33 without) were included. The majority of the studies were published between 2011 and 2020, and the number of CEA based on RWD has been increasing over the years. Few CEA studies used big data. Pharmacological interventions were the most frequently studied intervention, and they were more frequently evaluated by the studies without decision-analytic models, while those with the model focused on treatment regimen. Compared to CEA studies using decision-analytic models, both effectiveness and costs of those using the model were more likely to be obtained from literature review. All the studies using decision-analytic models included sensitivity analyses, while four studies no using the model neither used sensitivity analysis nor controlled for confounders. Conclusion: The review shows that RWD has been increasingly applied in conducting the cost-effectiveness analysis. However, few CEA studies are based on big data. In future CEA studies using big data and RWD, it is encouraged to control confounders and to discount in long-term research when decision-analytic models are not used.

Effect of sertraline on depression severity and prolactin levels in women with polycystic ovary syndrome: a placebo-controlled randomized trial

There is a paucity of data regarding the safety and efficacy of antidepressant therapy in women with polycystic ovary syndrome and depression. The effect of antidepressant medications on circulating prolactin levels is of concern in this patient population. We aimed to evaluate the effect of sertraline on depression severity and serum prolactin levels in women with polycystic ovary syndrome and mild-to-moderate depression. In a parallel-design, two-center, randomized controlled trial, we stratified participants according to their baseline prolactin level into normal (<25 ng/mL) and high (≥25 ng/mL) prolactin groups. Each group was randomized to receive 50 mg daily sertraline (up-titrated after 25 mg daily for 1 week) or placebo. The enrolling physicians, outcome assessors, and study subjects were all blind to the treatment. Depression severity was assessed by the Hamilton depression rating scale at baseline, the third, and the sixth weeks. The primary efficacy outcome was a change in depression severity. Prolactin levels were checked at baseline and after 6 weeks, and the safety outcome was the alteration in prolactin levels. Overall, 513 women were screened for eligibility in two outpatient clinics. Ultimately, 74 (38 normal prolactin and 36 high prolactin level) individuals were randomized. After 6 weeks of follow-up, depression severity was significantly reduced among patients who received sertraline regardless of the baseline prolactin levels (all between subjects P < 0.001). Furthermore, there was no difference in prolactin levels between the sertraline and placebo arms in normal (P = 0.80) or high prolactin (P = 0.21) groups. Sertraline is a well-tolerated and effective choice for treating depression in women with polycystic ovary syndrome. Future studies with longer follow-up periods are required to draw more robust conclusions.