Can discrete event simulation be of use in modelling major depression?

BACKGROUND: Depression is among the major contributors to worldwide disease burden and adequate modelling requires a framework designed to depict real world disease progression as well as its economic implications as closely as possible. OBJECTIVES: In light of the specific characteristics associated with depression (multiple episodes at varying intervals, impact of disease history on course of illness, sociodemographic factors), our aim was to clarify to what extent "Discrete Event Simulation" (DES) models provide methodological benefits in depicting disease evolution. METHODS: We conducted a comprehensive review of published Markov models in depression and identified potential limits to their methodology. A model based on DES principles was developed to investigate the benefits and drawbacks of this simulation method compared with Markov modelling techniques. RESULTS: The major drawback to Markov models is that they may not be suitable to tracking patients' disease history properly, unless the analyst defines multiple health states, which may lead to intractable situations. They are also too rigid to take into consideration multiple patient-specific sociodemographic characteristics in a single model. To do so would also require defining multiple health states which would render the analysis entirely too complex. We show that DES resolve these weaknesses and that its flexibility allow patients with differing attributes to move from one event to another in sequential order while simultaneously taking into account important risk factors such as age, gender, disease history and patients attitude towards treatment, together with any disease-related events (adverse events, suicide attempt etc.). CONCLUSION: DES modelling appears to be an accurate, flexible and comprehensive means of depicting disease progression compared with conventional simulation methodologies. Its use in analysing recurrent and chronic diseases appears particularly useful compared with Markov processes

Which factors predict placebo response in anxiety disorders and major depression? An analysis of placebo-controlled studies of escitalopram

Background: The placebo response rate has increased in several psychiatric disorders and is a major issue in the design and interpretation of clinical trials. The current investigation attempted to identify potential predictors of placebo response through examination of the placebo-controlled clinical trial database for escitalopram in 3 anxiety disorders and in major depressive disorder (MDD). Method: Raw data from placebo-controlled studies (conducted from 2002 through the end of 2004) of escitalopram in patients meeting DSM-IV criteria for MDD and anxiety disorders (generalized anxiety disorder [GAD], social anxiety disorder [SAD], panic disorder) were used. Potential predictors examined were type of disorder, location of study, dosing regimen, number of treatment arms, gender of patients, and duration and severity of disorder. Results: Placebo response (defined as the percent decrease from baseline in the reference scale) was higher in GAD and MDD studies conducted in Europe (p < .0001 and p = .0006, respectively) and was not associated with gender or duration of episode. In GAD, the placebo response rate was higher in a European fixed-dose study, which also had more treatment arms. In SAD and in U.S. specialist-treated MDD, a higher placebo response rate was predicted by decreased baseline disorder severity. Conclusion: Additional work is needed before definitive recommendations can be made about whether standard exclusion criteria in clinical trials of antidepressants, such as mild severity of illness, maximize medication-to-placebo differences. This analysis in a range of anxiety disorders and MDD suggests that there may be instances in which the predictors of placebo response rate themselves vary across different conditions

Escitalopram and Duloxetine in Major Depressive Disorder: A Pharmacoeconomic Comparison Using UK Cost Data

Background: Selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs) are approved for the treatment of major depressive disorder (MDD). The allosteric SSRI escitalopram has been shown to be at least as clinically effective as the SNRIs venlafaxine and duloxetine in MDD, with a better tolerability profile. In addition, escitalopram has been shown to be cost saving compared with venlafaxine. Objective: To evaluate the cost effectiveness of escitalopram versus duloxetine in the treatment of MDD, and to identify key cost drivers. Methods: The pharmacoeconomic evaluation was conducted alongside a 24-week, double-blind, multinational randomized study (escitalopram 20 mg/day and duloxetine 60 mg/day) in outpatients with MDD, aged 18-65 years, with Montgomery-Asberg Depression Rating Scale (MADRS) score >=26 and Clinical Global Impression Severity (CGI-S) score >=4, and baseline duration of the current depressive episode of 12 weeks to 1 year. The analysis was conducted on the full analysis set (FAS), which included all patients with >=1 valid post-baseline health economic assessment. Effectiveness outcomes of the cost-effectiveness analyses (CEA) included the change in Sheehan Disability Scale (SDS) score (primary CEA), treatment response (MADRS score decrease >=50%) and remission (MADRS scoreCost-effectiveness, Depression, Duloxetine, Escitalopram, Randomised-controlled-trials

Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology

Background and objective: Automated healthcare databases (AHDB) are an important data source for real life drug and healthcare use. In the filed of depression, lack of detailed clinical data requires the use of binary proxies with important limitations. The study objective was to create a Depressive Health State Index (DHSI) as a continuous health state measure for depressed patients using available data in an AHDB. Methods: The study was based on historical cohort design using the UK Clinical Practice Research Datalink (CPRD). Depressive episodes (depression diagnosis with an antidepressant prescription) were used to create the DHSI through 6 successive steps: (1) Defining study design; (2) Identifying constituent parameters; (3) Assigning relative weights to the parameters; (4) Ranking based on the presence of parameters; (5) Standardizing the rank of the DHSI; (6) Developing a regression model to derive the DHSI in any other sample. Results: The DHSI ranged from 0 (worst) to 100 (best health state) comprising 29 parameters. The proportion of depressive episodes with a remission proxy increased with DHSI quartiles. Conclusion: A continuous outcome for depressed patients treated by antidepressants was created in an AHDB using several different variables and allowed more granularity than currently used proxies