Types of Econometrics/Statistical Models & Analytics

• Cross-Sectional, Time Series, and TSCS/Panel/Longitudinal, ECA, SCA designs

• Explanatory, Predictive, and Causal Analysis

• Linear Probability Models

• LDV: Logit, Probit, Tobit, Ordered Logit, Conditional Logit, MNL/MNP

• Poisson, Negative Binomial, Zero-Inflated, Left-Truncated

• Generalized Linear Model (GLM) with appropriate distribution and link function

• Duration/Survival Analysis: Nonparametric, Semi-parametric (Cox PH), Parametric
  Oaxaca-Blinder Decomposition Analysis

• Quasi-Experimental: PSM w/IPTW, D-i-D, Interrupted Time Series (ITS)

• Time Series: ARIMA, SARIMA, Spatial AR

• Endogeneity Correction Model using IV: 2SRI, N-SLS, GMM
  Panel/Longitudinal Data Methods: FE, RE, ME, Hierarchical, Dynamic

• Special Challenges: Missingness & Imputation, Reporting of Linear vs. Nonlinear Models' effects, Censoring (Left, Right, Interval), Frailty, Competing Risks, Time-varying Covariates (TVC), Monotonicity/Non-Monotonicity

• Pre-/Post-Estimation Diagnostics: Nonnormality, Linearity, Homoscedasticity, Independence, Multicollinearity, Serial Correlation, Errors in Variables, Endogeneity, Identification, Orthogonality, Model Specification and Fit 

Real World Data (RWD) Evaluation 

We assist clients in identifying, profiling and evaluating fit-for-purpose Real World Data (RWD) via checks for relevance, accuracy, plausibility of data ranges, completeness and benchmarking to reference databases for reliability. These activities are intended to help meet recommended guidelines from regulatory and health technology assessment bodies (e.g., FDA RWE Framework, EMA RWE initiatives, NICE RWE guidance).

Health Economic Modelling Methods & Analytics

We provide expert consultation and guidance on appropriate model structure, schema, input parameters including plausibility of assumptions and expert opinions, output/outcome measures, presentation and interpretation of results, sensitivity analysis and critique to ensure that clients' health economic models meet the needs and standards of payers, insurers, HTA bodies (e.g., NICE, CADTH, PBAC, IQWiG, HAS, ICER, VA) and other stakeholders. 

Evidence Synthesis Methods & Analytics

We provide comprehensive expert consultation and guidance for clients across both qualitative and quantitative Evidence Synthesis deliverables. This involves working directly with you on projects ranging from focused literature reviews to complex statistical integration. On the quantitative side, this includes advanced methodologies such as Meta-Analysis (MA), Network Meta-Analysis (NMA), and Meta-Regression, ensuring robust data synthesis and interpretation for value demonstration.