Evidence-Based Health Intelligence for India

Background: Translating epidemiological theory into rigorous field research requires precise sample size planning, yet tools capable of handling advanced trial designs and cluster adjustments are rarely accessible in a single, lightweight platform. Objective: To design and deploy a comprehensive, interactive tool for sample size estimation that caters to both observational epidemiology and interventional clinical trials. Methods: We developed a responsive, single-file HTML application encompassing a broad array of study designs. The computational logic supports both standard observational frameworks (cross-sectional utilizing odds ratios, case-control, cohort) and advanced experimental designs (superiority, non-inferiority, equivalence). Furthermore, the tool incorporates specialized parameters critical for community health research, including Design Effect (DEFF) multipliers for cluster randomized and field trials. Results: The calculator operates entirely client-side, eliminating data privacy concerns and internet dependency. The dynamic user interface prevents parameter collision and offers real-time recalculations, allowing investigators to visually assess the mathematical trade-offs between statistical power, confidence intervals, and required participant enrollment. Conclusion: EpiSize provides a seamless, highly accessible solution for complex biostatistical planning. It empowers field epidemiologists and medical researchers to design robust, adequately powered studies, ultimately elevating the quality of evidence generated in public health and clinical medicine.