How to Use QUADAS-2 to Assess Bias in Diagnostic Accuracy Studies
Introduction Diagnostic accuracy studies are essential for understanding whether a test can correctly distinguish between those with and...
STARD 2015: How to Report Diagnostic Accuracy Studies with Clarity and Rigor
Introduction Diagnostic tests form the cornerstone of clinical reasoning. But understanding whether a test is truly accurate requires...
Detecting Bias in Diagnostic Accuracy Studies: Types, Examples, and How to Avoid Them
Introduction In diagnostic research, bias can silently distort results and mislead conclusions—even in the absence of statistical errors....
How to Design Diagnostic Accuracy Studies: Object, Method, and Analysis Explained
Introduction Diagnostic tests serve as crucial decision points in clinical medicine. Whether confirming a suspected disease or ruling one...
Diagnostic Indices: Sensitivity, Specificity, Predictive Values, and Beyond
Introduction Every clinical diagnosis involves uncertainty. Diagnostic tests are tools that help us reduce that uncertainty by providing...
Phases and Types of Diagnostic Research: From Accuracy to Outcome Impact
Introduction Diagnostic research is not merely about test accuracy—it’s about how diagnostic tools operate across different clinical...
Clinical Diagnosis: Bayesian Reasoning, Thresholds, and Diagnostic Value
Introduction Clinical diagnosis sits at the very heart of medicine. It is the intellectual, evidence-informed process by which a...
Patient-Reported Outcomes (PROs) and Minimal Clinically Important Difference (MCID): Measuring What Truly Matters in Clinical Care
🎯 Why It Matters Imagine you’re treating a patient with chronic back pain. You prescribe a new therapy, and afterward their pain score...
Clinimetrics and the DEPTh Model: Choosing the Right Clinical Metrics for Research
📌 Why We Measure: The Role of Clinimetrics Clinical research isn’t just about collecting data—it’s about asking, “How do we know what...
Guide to Common Measures (Data type, Clinimetrics) in Clinical Research Using the DEPTh Model
🔍 Why Clinical Metrics Matter (Beyond “Significance”) Imagine you’re comparing two treatments in a heart failure unit. Or evaluating a...
Mastering Confounding in Causal (Explanatory) Research: Design, DAGs & Control Strategies
1. 🔍 What’s the Real Question Here? Before you even say “confounder,” ask this : Is this a causal (explanatory) question or a predictive...
Clarifying Adjustment in DAGs: Controlling Bias Paths Without Creating New Effects
เข้าใจการปรับตัวแปรใน DAG อย่างถูกต้อง: ควบคุมอคติ ไม่ใช่ลบตัวแปร 🧠 บริบทของความเข้าใจที่คุณมี การเข้าใจว่า: การปรับตัวแปรเหมือน...
DAGs vs. Causal Diagrams: What to Use for Clinical Causal Inference
🎯 TL;DR Summary Table Feature DAG (Directed Acyclic Graph) Causal Diagram (General) ✅ Arrows only Yes – all edges are directional...
Causal vs Non-Causal Paths in DAGs — How to Detect Bias and Adjust Causal Paths in Clinical Research Using DAGs
🎯 Objectives By the end, you’ll be able to: ✅ Distinguish causal from non-causal paths ✅ Diagnose if a path is open or closed ✅ Know...
Causal Inference with DAGs in Pediatric Clinical Research
🎯 Learning Objectives By the end of this guide, you’ll be able to: ✅ Classify variables into confounders, mediators, colliders, and...
How to Think Causally in Clinical Research — The Counterfactual + DAG Blueprint
“Causation isn’t just about what happened—it’s about what would have happened instead.” 🎯 The Clinical Dilemma Your patient, Mr. G, age...
Deep Dive: DDO vs. PICO (Aligned with DEPTh + Design Triad)
🧠 Think "DEPTh" First—Not Framework Before choosing DDO or PICO, classify the clinical challenge : Type Description Examples Diagnosis...
The Core of Clinical Research Design in 3 Moves: Object, Method, Analysis
1️⃣ Object Design: What’s the Clinical Problem? Use the DEPTh Model to define your study’s core question: DEPTh Type Clinical Aim...
Causal vs Predictive Analysis: How DEPTh Typing Shapes Clinical Research Design
The analysis design stage forces a fundamental decision: 🧠 Are we explaining a cause , or predicting an event ? This logic determines...
Clinical Research Variables and the Occurrence Equation
🔍 Why Start with Variables? Every clinical research question—whether it’s about diagnosis, treatment, or prognosis—can be boiled down to...
A Clinician‑Friendly Guide to Mastering EKG Interpretation
1. What an ECG Really Records An electrocardiogram is a time‑stamped graph of the heart’s changing electrical vector as impulses leave...
Heart Failure Treatment Protocol: Thailand’s 2025 Step-by-Step Guide
1 | Classify the Patient Echo first – confirm EF. Assign phenotype • HFrEF ≤ 40 % • HFmrEF 41–49 % • HFpEF ≥ 50 % • HFimpEF (normalised...
Sample Size for Hypothesis Testing: Understanding the BRAVES Method
Introduction Sample size calculation is one of the most misunderstood aspects of medical research because there is no single universal rule . The correct approach depends entirely on what the study is trying to achieve . Designing a study is not merely about enrolling participants and running analyses. It is about anticipating the interplay between clinical importance, statistical rigor, ethical responsibility, and resource constraints . Sample size sits at the center of this
Using Statulator: Sample Size for Estimating a Single Proportion
🎯 When to Use This Calculator This calculator is built for descriptive study designs , where your goal is to estimate a single...