Dr. Imran Sarmad
PhD Statistical Consultant for Clinical, RWE, HTA & Decision-Critical Research
I help researchers, clinical teams, and organizations turn complex data into statistically defensible results that can withstand peer review, stakeholder scrutiny, and real-world decision pressure.Causal Inference | RWE | HEOR/HTA | Bayesian Evidence Synthesis
Clinical, Survival & Longitudinal Modeling
SEM/LPA/LCA | Econometrics
R | Mplus | Python | Stata | SPSS | AMOS
STATISTICAL CONSULTING
Problems I Help Solve
Research becomes difficult when the statistical method, assumptions, uncertainty, output, or interpretation are unclear. I help researchers, clinical teams, doctoral candidates, academic authors, and organizations identify an appropriate analytical path and turn complex results into clear, defensible conclusions that can withstand peer review and decision scrutiny.
Unclear Statistical Method or Model Choice
I review the research question, variables, dataset structure, and study design to recommend a suitable statistical approach for thesis, dissertation, manuscript, clinical, health, or applied research.
Difficult Output Interpretation
I translate R, Mplus, Python, Stata, SPSS, AMOS, or other statistical output into clear research-facing interpretation with attention to assumptions, model results, uncertainty, and defensible reporting.
Supervisor, Reviewer, or Stakeholder Comments
I help identify the statistical concern, strengthen the explanation, and suggest defensible revisions, robustness checks, or additional analyses where needed.
METHODS & EXPERTISE
Core Statistical Consulting Areas
Advanced quantitative support for research projects where model choice, assumptions, uncertainty, and interpretation directly affect whether conclusions can be trusted.
Clinical, Survival & Longitudinal Modeling
Clinical trial interpretation, Kaplan-Meier estimation, Cox models, competing-risk analysis, longitudinal mixed-effects modeling, endpoint interpretation, subgroup analysis, model diagnostics, and statistically defensible clinical reporting.
HEOR/HTA & Decision Modeling
Cost-effectiveness modeling, expected costs and QALYs, incremental analysis, ICER interpretation, net monetary benefit, probabilistic sensitivity analysis, scenario analysis, and decision-relevant reporting.
Causal Inference & Real-World Evidence
Observational study design, confounding, covariate adjustment, treatment or exposure comparisons, IPTW, doubly robust AIPW estimation, overlap and covariate-balance diagnostics, sensitivity analysis, and cautious causal interpretation.
Applied Econometrics & Policy Evaluation
Panel data models, difference-in-differences, policy impact evaluation, synthetic control-style interpretation, heterogeneous effects, and decision-relevant causal evidence.
SEM, LPA/LCA & Psychometric Modeling
Measurement models, CFA/SEM, mediation, latent profile/class analysis, profile enumeration, entropy, covariates, distal outcomes, and R3STEP/BCH-style interpretation.
Bayesian Evidence Synthesis
Hierarchical evidence synthesis, fixed-effect and random-effects benchmarks, between-study heterogeneity, predictive checks, convergence diagnostics, prior-sensitivity analysis, and comparative-treatment-effect interpretation.
HOW I CAN HELP
Focused Consulting Options
Start with a clear, manageable review before moving into full analysis, interpretation, manuscript support, or reviewer-response work.
Focused Review
Focused Statistical Review
Share your research question, variables, dataset structure, current output, or supervisor/reviewer comment. I will review the issue and suggest the clearest next statistical step before full analysis.
ADVANCED SUPPORT
Advanced Analysis & Reporting Support
Support for clearly scoped research projects requiring advanced statistical analysis, interpretation, and reporting. This may include clinical and RWE studies, HEOR/HTA decision modeling, causal inference, survival or longitudinal analysis, Bayesian evidence synthesis, SEM/CFA, LPA/LCA, econometrics, manuscript wording, and reviewer-ready explanation.
SELECTED APPLIED PROJECTS
Selected Quantitative Modeling Projects
Selected reproducible public workflows demonstrating advanced statistical modeling, uncertainty analysis, diagnostics, and defensible interpretation across clinical research, RWE, HEOR/HTA, and comparative-effectiveness evidence.
BAYESIAN EVIDENCE SYNTHESIS
Bayesian Evidence Synthesis for Comparative Treatment Effectiveness
A hierarchical evidence-synthesis workflow across 22 beta-blocker trials, including conventional benchmarks, heterogeneity assessment, predictive checks, convergence diagnostics, future comparable-trial prediction, and prior-sensitivity analysis.
HEOR/HTA DECISION MODELING
Probabilistic Cost-Effectiveness Analysis for Health Technology Assessment
A transparent two-strategy decision-modeling workflow with expected costs and QALYs, incremental analysis, ICER interpretation, net monetary benefit, Monte Carlo probabilistic sensitivity analysis, scenario analysis, and careful communication of decision uncertainty.
LONGITUDINAL CLINICAL MODELING
Longitudinal Bilirubin Trajectories Using Linear Mixed-Effects Models
A repeated-measures clinical workflow using data-integrity checks, patient-specific random intercepts and slopes, quadratic time modeling, adjusted trajectory prediction, residual diagnostics, and focused sensitivity analyses.
CLINICAL RWE CAUSAL INFERENCE
Clinical RWE Causal Inference: IPTW, AIPW & Sensitivity Analysis
An observational clinical workflow estimating the effect of early right heart catheterization on 30-day mortality, with IPTW, doubly robust AIPW, overlap and covariate-balance diagnostics, bootstrap uncertainty analysis, and sensitivity checks.
CLINICAL SURVIVAL ANALYSIS
Survival and Competing-Risk Analysis for Clinical Research
A reproducible time-to-event workflow using Kaplan-Meier estimation, Cox modeling, proportional-hazards diagnostics, standardized survival curves, Aalen-Johansen cumulative incidence, cause-specific hazards, and competing-risk sensitivity analysis.
CLINICAL/RWE PREDICTIVE MODELING
Predicting 30-Day Hospital Readmission Among Patients With Diabetes
An interpretable patient-level risk-modeling workflow with leakage prevention, protected test evaluation, benchmark comparison, probability calibration, feature-importance analysis, and clinically cautious interpretation.
Ready to Clarify Your Analysis?
Share your research question, study design, dataset structure, current output, statistical issue, and deadline.I will review whether the project is a good fit and suggest a clear next step, such as a focused review, clearly scoped analysis, interpretation support, or publication-facing reporting.Professional integrity: Statistical consulting is provided to support transparent, ethical, and defensible research.