METHODS: We performed systematic searches using electronic databases including PubMed and EMBASE until December 2012. Key words included "metformin" AND ("ovarian cancer" OR "ovary tumor"). All human studies assessing the effects of metformin on ovarian cancer were eligible for inclusion. All articles were reviewed independently by 2 authors with a standardized approach for the purpose of study, study design, patient characteristics, exposure, and outcomes. The data were pooled using a random-effects model.
RESULTS: Of 190 studies retrieved, only 3 observational studies and 1 report of 2 randomized controlled trials were included. Among those studies, 2 reported the effects of metformin on survival outcomes of ovarian cancer, whereas the other 2 reported the effects of metformin on ovarian cancer prevention. The findings of studies reporting the effects on survival outcomes indicated that metformin may prolong overall, disease-specific, and progression-free survival in ovarian cancer patients. The results of studies reporting the effects of metformin on ovarian cancer prevention were meta-analyzed. It indicated that metformin tended to decrease occurrence of ovarian cancer among diabetic patients with the pooled odds ratio of 0.57 (95% confidence interval, 0.16-1.99).
CONCLUSIONS: Our findings showed the potential therapeutic effects of metformin on survival outcomes of ovarian cancer and ovarian cancer prevention. However, most of the evidence was observational studies. There is a call for further well-conducted controlled clinical trials to confirm the effects of metformin on ovarian cancer survival and ovarian cancer prevention.
METHODS: A retrospective database analysis at a university-affiliated hospital in Thailand was used. Diabetic patients receiving glucose-lowering medications from July 2008 to June 2011 were included. Patients were categorized into those exposed and not exposed to thiazolidinediones (TZDs). PSs were estimated by using conventional PS and CTS-PS. In the CTS-PS, PS was separately estimated for three specific calendar time periods. Patients were matched 1:1 using caliper matching. The outcomes were cardiovascular and all-cause hospitalizations. The TZD and non-TZD groups were compared with Cox proportional hazard models.
RESULTS: A total of 2165 patients were included. The average conventional PS was 0.198 (95% confidence interval [CI] 0.195-0.202), while the average PS in the CTS-PS approach was 0.212 (0.206-0.218), 0.180 (0.173-0.188), and 0.205 (0.197-0.213) for July 2008 to June 2009, July 2009 to June 2010, and July 2010 to June 2011, respectively. The average difference in PS was 0.012 (P < 0.001), -0.009 (P ≤ 0.002), and 0.000 (P = 0.950) in the three calendar time periods. The adjusted hazard ratios of the conventional PS-matched cohort were 0.97 (95% CI 0.39-2.45) and 0.97 (95% CI 0.78-1.20) for CVD-related and all-cause hospitalizations, while the adjusted hazard ratios of the CTS-PS-matched cohort were 1.11 (95% CI 0.43-2.88) and 1.12 (95% CI 0.91-1.39), respectively.
CONCLUSION: CTS-PS is different from PS estimated by using the conventional approach. CTS-PS should be considered when a pattern of medication use has changed over the study period.
METHODS: A validated computer simulation model (the IMS CORE Diabetes Model) was used to estimate the long-term projection of costs and clinical outcomes. The model was populated with published characteristics of Thai patients with type 2 diabetes. Baseline risk factors were obtained from Thai cohort studies, while relative risk reduction was derived from a meta-analysis study conducted by the Canadian Agency for Drugs and Technology in Health. Only direct costs were taken into account. Costs of diabetes management and complications were obtained from hospital databases in Thailand. Both costs and outcomes were discounted at 3 % per annum and presented in US dollars in terms of 2014 dollar value. Incremental cost-effectiveness ratio (ICER) was calculated. One-way and probabilistic sensitivity analyses were also performed.
RESULTS: IGlar is associated with a slight gain in quality-adjusted life years (0.488 QALYs), an additional life expectancy (0.677 life years), and an incremental cost of THB119,543 (US$3522.19) compared with NPH insulin. The ICERs were THB244,915/QALY (US$7216.12/QALY) and THB176,525/life-year gained (LYG) (US$5201.09/LYG). The ICER was sensitive to discount rates and IGlar cost. At the acceptable willingness to pay of THB160,000/QALY (US$4714.20/QALY), the probability that IGlar was cost effective was less than 20 %.
CONCLUSIONS: Compared to treatment with NPH insulin, treatment with IGlar in type 2 diabetes patients who had uncontrolled blood glucose with oral anti-diabetic drugs did not represent good value for money at the acceptable threshold in Thailand.
DATA SOURCES: We conducted a systematic review of PubMed, EMBASE, Tufts CEA registry, Cochrane CENTRAL, and the UK National Health Services Economic Evaluation Database from 2009 to 2014.
STUDY SELECTION: All cost-effectiveness studies evaluating asthma medication(s) were included. Clinical evidence type, "E," was classified as efficacy-based if the evidence was from an explanatory randomized controlled trial(s) or meta-analysis, while evidence from pragmatic trial(s) or observational study(s) was classified as effectiveness-based. We defined three times the World Health Organization cost-effectiveness willingness-to-pay (WTP) threshold or less as a favorable cost-effectiveness finding. Logistic regression tested the likelihood of favorable versus unfavorable cost-effectiveness findings against the type of "E."
RESULTS AND CONCLUSIONS: 25 cost-effectiveness studies were included. Ten (40.0%) studies were effectiveness-based, yet 15 (60.0%) studies were efficacy-based. Of 17 studies using endpoints that could be compared to WTP threshold, 7 out of 8 (87.5%) effectiveness-based studies yielded favorable cost-effectiveness results, whereas 4 out of 9 (44.4%) efficacy-based studies yielded favorable cost-effectiveness results. The adjusted odds ratio was 15.12 (95% confidence interval; 0.59 to 388.75) for effectiveness-based versus efficacy-based achieving favorable cost-effectiveness findings. More asthma cost-effectiveness studies used efficacy-based evidence. Studies using effectiveness-based evidence trended toward being more likely to disseminate favorable cost-effective findings than those using efficacy. Health policy decision makers should pay attention to the type of clinical evidence used in cost-effectiveness studies for accurate interpretation and application.
METHODS: A validated IMS CORE Diabetes Model was used to estimate the long-term costs and outcomes. The efficacy parameters were identified and synthesized using a systematic review and meta-analysis. Baseline characteristics and cost parameters were obtained from published studies and hospital databases in Thailand. Costs were expressed in 2014 US Dollars. Outcomes were presented as an incremental cost-effectiveness ratio (ICER). One-way and probabilistic sensitivity analyses were performed to estimate parameter uncertainty.
RESULTS: From a societal perspective, treatment with DPP-4 inhibitors yielded more quality-adjusted life years (QALYs) (0.024) at a higher cost (>66,000 Thai baht (THB) or >1,829.27 USD) per person than SFU, resulting in the ICER of >2.7 million THB/QALY (>74,833.70 USD/QALY). The cost-effectiveness results were mainly driven by differences in HbA1c reduction, hypoglycemic events, and drug acquisition cost of DPP-4 inhibitors. At the ceiling ratio of 160,000 THB/QALY (4,434.59 USD/QALY), the probability that DPP-4 inhibitors are cost-effective compared to SFU was less than 10%.
CONCLUSIONS: Compared to SFU, DPP-4 inhibitor monotherapy is not a cost-effective treatment for people with T2DM and CKD in Thailand.