Topic
Evaluating evidence
How to read a study and judge medical evidence on its merits, from a physician-scientist who reviews research.
This page collects every article by Dr. Damon Tojjar in this topic. For all topics see browse by topic, and for the source-anchored record see damontojjar.com/record.
Articles in this topic (168)
Study design and trials (26)
- Basket, Umbrella, and Platform Trials: What Master Protocols Actually Do
A master protocol is a single overarching study framework that runs several substudies at the same time, letting investigators evaluate one or more therapies across...
- Composite Endpoints: When Studies Combine Outcomes
A composite endpoint is a single trial outcome built by combining several individual events, so that a patient counts as having reached the endpoint if any one of...
- Data Monitoring Committees, Interim Looks, and When a Trial Stops Early
A data monitoring committee reviews a trial's unblinded interim data so it can be stopped for harm, futility, or clear benefit while the investigators stay blinded....
- Difference-in-Differences and Interrupted Time Series: Reading Quasi-Experiments
When a policy or program is switched on at a known moment and randomizing is impossible, researchers reach for quasi-experiments. Interrupted time series tracks one...
- Efficacy Versus Effectiveness, and Why a Careful Reader Checks Which One Is Being Claimed
Efficacy is whether a treatment works under ideal conditions, the kind a trial arranges on purpose. Effectiveness is whether it works in the conditions you actually...
- External Control Arms: When a Trial Has No Randomized Comparator, and Why That Is Hard
An external control arm compares people who received an investigational drug against patients drawn from outside the study, such as a historical cohort, a disease...
- How a Surrogate Endpoint Is Validated, and Why So Few Qualify
A surrogate endpoint is a measurement, often a biomarker like blood pressure or LDL cholesterol, used in place of the outcome that actually matters to patients,...
- How Adaptive and Platform Trial Designs Work, and What They Trade Off
An adaptive trial is a study allowed to change itself, but only in ways written down before the first participant enrolls. As data accumulate, prespecified rules...
- How Basket Trials Support Tumor-Agnostic Drug Approvals
A basket trial pools patients whose cancers arise in different organs but share one molecular alteration, then tests whether a drug aimed at that alteration works...
- How to Read a Cluster Randomized Trial: When Groups, Not People, Are Randomized
A cluster randomized trial assigns whole groups, such as clinics, schools, or villages, to a treatment rather than assigning each person separately. This is the...
- How to Read a Crossover Trial: Washout, Carryover, and Paired Analysis
A crossover trial gives each participant every treatment in a randomly assigned order, so each person acts as their own comparison and the study can detect...
- How to Read a Stepped-Wedge Trial: Rolling Out an Intervention in Waves
A stepped-wedge trial is a cluster trial in which every group eventually receives the intervention, but the order in which groups switch over is randomized and...
- How to Read an N-of-1 Trial: A Randomized Experiment in a Single Patient
An N-of-1 trial applies the machinery of a randomized trial to one person, cycling that patient through several pairs of treatment periods in a random, usually...
- How and Why a Trial Chooses Its Primary Endpoint
A trial chooses its primary endpoint by trading three things against each other: how directly the measure captures what patients care about, how feasibly it can be...
- Intention to Treat: Why Trials Count Everyone They Enrolled
Intention to treat means a trial analyzes people in the group they were randomly assigned to, even if they later stopped the treatment, switched, or dropped out. It...
- Per-Protocol Versus Intention-to-Treat: When Each Answers the Real Question
Intention-to-treat and per-protocol analyses answer two different questions, and the confusion between them explains a lot of arguments about trial results....
- Pragmatic Versus Explanatory Trials, and Why You Need Both to Read the Evidence
An explanatory trial asks whether a treatment can work when everything is arranged in its favor: ideal patients, expert sites, careful adherence, close monitoring....
- Prespecified or Post Hoc: Why the Timing of an Analysis Decides How Much to Trust It
An analysis written into the protocol before anyone saw the results is prespecified; one chosen after looking at the data is post hoc. The distinction matters...
- Cohort or Case-Control? How Two Observational Designs Answer Different Questions
A cohort study starts with people grouped by exposure and follows them forward to see who develops the outcome, so it can measure how often the outcome happens and...
- How to Read a RoB 2 Risk-of-Bias Assessment
RoB 2 is the current Cochrane tool for rating how much a randomized trial's design and conduct might have biased a particular result. It works through five domains,...
- Surrogate Endpoints Versus Outcomes in Diabetes Trials
A surrogate endpoint is a measurement that stands in for the thing you actually care about. In diabetes that thing is usually a complication you want to avoid: a...
- The Estimand Framework: Defining Exactly What a Trial Is Trying to Estimate
An estimand is a precise, written-down definition of exactly what treatment effect a clinical trial is trying to estimate, specified before anyone looks at the...
- Why No Single Study Settles a Question
A single study, even a good one, is one observation of the world, not the final word on it. The right way to treat a striking new result is as a contribution to a...
- Understanding Non-Inferiority Trials: A Reader's Guide
A non-inferiority trial asks a narrower question than most people assume from the headline. It does not try to show that a new treatment is better. It tries to show...
- What a Control Group Really Does, and Why It Decides What a Study Can Claim
A control group is the part of a study that tells you what would have happened anyway. Everything a trial claims about benefit rests on one question, asked plainly:...
- What External Validity Means, and Why a Solid Study Can Still Miss a Patient
External validity is the question of whether a study's result holds for people and settings the study did not include, and it is separate from whether the result is...
Statistics and measures of effect (30)
- Absence of Evidence Is Not Evidence of Absence: Reading a Nonsignificant Result
When a study reports no significant difference, that is a statement about the evidence, not about reality. It usually means the study was unable to detect an...
- Absolute Versus Relative Risk, and the One Question That Cuts Through the Hype
When a headline says a treatment "cuts your risk by half," the most useful question you can ask is: half of what? A relative number tells you the proportion by...
- Bayesian and Frequentist Results: Two Different Questions a Trial Can Answer
A frequentist analysis asks how often you would see data this extreme if the treatment truly did nothing, which is what a p value and confidence interval summarize....
- Checking the Proportional-Hazards Assumption: When One Hazard Ratio Can Mislead
The proportional-hazards assumption says the ratio of event rates between two groups stays constant over the whole follow-up, which is what lets a study summarize a...
- How to Read a Cancer Survival Statistic
A cancer survival statistic tells you what fraction of people diagnosed with a cancer are still alive after a set period, usually five years. It does not tell you...
- How to Read a Kaplan-Meier Curve: What the Steps, the Spread, and the Censoring Marks Mean
A Kaplan-Meier curve is a running estimate of the share of a group that has not yet had the event being tracked, plotted against time since each person entered the...
- Judging Whether a Subgroup Effect Is Real: The ICEMAN Approach
A subgroup analysis asks whether a treatment's effect differs across groups of patients, such as by age, sex, or disease severity. Many reported subgroup effects...
- The Minimal Clinically Important Difference: When a Real Change Is Big Enough to Matter
The minimal clinically important difference, or MCID, is the smallest change in a symptom or quality-of-life score that patients experience as meaningful, rather...
- Missing Data in a Trial: Why How It Is Handled Can Change the Result
Missing data are almost never truly random, so the method used to handle them can move a trial's result in either direction. The strongest analyses prevent missing...
- Testing Many Outcomes at Once: How Trials Keep False Positives in Check
Each additional statistical test in a trial, whether an extra endpoint, a subgroup, or an interim peek at the data, adds another chance for a random finding to...
- Number Needed to Harm, and How to Read It Against Number Needed to Treat
Number needed to harm is the count of people who would have to take a treatment before one of them experiences a particular harm from it. If a drug causes one extra...
- Number Needed to Treat: How Many People to Help One
Number needed to treat is the count of people who must take a treatment, for a defined period, for one of them to get the benefit being measured. A number needed to...
- P-Hacking: The Hidden Choices That Manufacture Significance
P-hacking is what happens when the many small, defensible choices inside a study (which outcome to report, when to stop collecting data, which cases to exclude,...
- Precision vs Accuracy in Measurement: Repeatability Is Not the Same as Truth
Precision is how tightly your measurements agree with each other, and accuracy is how close they land to the true value, so a device can be highly precise and still...
- How to Read Antidepressant Effect Sizes Without Overclaiming
The short answerWhen you read that antidepressants beat placebo with a standardized mean difference (SMD) of about 0.30, the honest interpretation is this: on...
- Reclassification Metrics NRI and IDI, and How They Mislead
Net reclassification improvement (NRI) and the integrated discrimination improvement (IDI) were built to measure how much a new marker adds to an existing risk...
- Relative Risk Versus Odds Ratio, and Why They Stop Agreeing When an Outcome Is Common
A relative risk compares the chance of an event between two groups, while an odds ratio compares the odds of that event between the same groups, and those are not...
- Responder Analyses: What You Gain and Lose by Turning a Score Into Yes or No
A responder analysis takes a continuous outcome, such as a pain score or the percent improvement on a symptom scale, and collapses it into two categories, responder...
- Restricted Mean Survival Time: A Way to Read a Trial When the Hazard Ratio Breaks Down
Restricted mean survival time (RMST) measures the average amount of event-free time a group accumulates from the start of a study up to a chosen time horizon, read...
- How Many Patients It Takes to Build a Prediction Model
The familiar rule of ten outcome events per candidate variable is neither necessary nor sufficient for building a reliable prediction model. Modern sample-size...
- Statistical Significance Versus Clinical Importance: Why a Small P-Value Does Not Mean a Result Matters
Statistical significance and clinical importance answer two different questions, and a study can score high on one while scoring near zero on the other....
- The Fragility Index: How Many Events Separate a Positive Trial From a Null One
The fragility index is the smallest number of patients who would have to switch from a non-event to an event (or the reverse) for a statistically significant trial...
- Understanding Competing Risks, and Why They Change What a Survival Curve Means
A competing risk is any event that makes the outcome you are tracking impossible once it happens. If you are studying death from kidney disease and a patient dies...
- Understanding Hazard Ratios, and How They Differ From Risk Ratios
A hazard ratio compares how fast an event is arriving in one group against another, moment by moment, across the whole stretch of follow-up. It is a ratio of rates,...
- Multiple Testing: Why Twenty Questions of One Dataset Produce a False Positive
Ask one dataset enough questions and one of them will answer yes by luck alone, even when nothing real is there. That is the multiple-comparisons problem in a...
- Standardized Mean Differences: Comparing Studies That Used Different Scales
A standardized mean difference, or SMD, divides the gap between two group means by a standard deviation, turning a result measured in the units of one questionnaire...
- What a Confidence Interval Is Not, and How to Read It as a Measure of Precision
A confidence interval is a range of values compatible with your data, and its width tells you how precisely you measured the thing you set out to measure. A 95...
- What a P-Value Really Means, and the Four Things People Think It Means but It Does Not
A p-value answers one narrow question: if nothing real were going on, how surprising would data this extreme be? Formally, it is the probability of observing a...
- What a Subgroup Analysis Shows, and Why Most Are Fragile
A subgroup analysis asks whether a treatment worked differently in one slice of a trial than in another, such as older versus younger participants, or men versus...
- The Win Ratio: Ranking What Matters Most in a Composite Endpoint
The win ratio is a way to analyze a composite endpoint that ranks the outcomes by importance instead of treating them as equal. Patients in the two groups are...
Bias, confounding and causation (20)
- Allocation Concealment Versus Blinding: Two Safeguards People Keep Confusing
Allocation concealment and blinding are different safeguards that act at different moments. Concealment keeps whoever enrolls a patient from foreseeing the next...
- When a Trial Is Open-Label but the Endpoint Is Blinded: Reading PROBE
PROBE stands for Prospective Randomized Open Blinded End-point: patients and clinicians know the assigned treatment, but an independent committee judges the...
- Confounding and Causation: Why a Real Association Can Still Lie to You
Two things can move together for years without one causing the other, and the usual reason is a third thing quietly steering both. That third thing is a confounder,...
- Directed Acyclic Graphs: How to Read a Causal Map Before You Trust the Adjustment
A directed acyclic graph, or DAG, is a simple diagram in which arrows point from causes to effects, drawn from knowledge before any data are analyzed. Its purpose...
- The E-Value: Asking How Strong a Hidden Confounder Would Have to Be
The E-value is a number that summarizes how robust an observational finding is to unmeasured confounding. It states the minimum strength of association, on the...
- The Ecological Fallacy: Why Group Averages Cannot Tell You About Individuals
The ecological fallacy is the mistake of assuming that a relationship seen between whole populations also holds for the individuals inside them. Studies that...
- Mediation Analysis: How a Study Tries to Show the Pathway, Not Just the Effect
Mediation analysis tries to open the black box between a cause and an effect by asking how much of the effect travels through a specific intermediate step, the...
- Propensity Scores: What They Balance, What They Miss, and How to Read One
A propensity score is the estimated probability that a person receives the treatment being studied, given their measured characteristics. Researchers use it to make...
- Regression to the Mean: Why Extreme Measurements Drift Back, and How It Fools Us
When you measure something at its most extreme moment and measure it again later, the second reading tends to sit closer to the average, even if nothing was done in...
- Target Trial Emulation: Using Observational Data Without Fooling Yourself
Target trial emulation is a method for using observational data to answer a cause-and-effect question by first writing down the randomized trial you wish you could...
- Target Trial Emulation: How an Observational Study Imitates the Trial You Wish You Had
Target trial emulation is a discipline for observational research. Before touching the data, the researchers write down the full protocol of the randomized trial...
- The Counterfactual Idea: What Cause Means in a Study You Can Run Only Once
The counterfactual idea says that a causal effect is a comparison between two outcomes for the same person or group: the outcome under the treatment and the outcome...
- The Table 2 Fallacy: Why Not Every Number in a Regression Is a Causal Effect
A multivariable regression is built to answer one causal question at a time, so only one number in the table, the coefficient for the primary exposure, is designed...
- Confounding by Indication: Why the Reason for Treatment Distorts Drug Studies
Confounding by indication is the bias that arises when the reason a treatment was prescribed is itself linked to the outcome being studied. Sicker people tend to...
- Immortal Time Bias: Why a Treatment Can Look Better Than It Is
Immortal time bias is the illusion that a treatment helps people live longer, when part of the apparent benefit comes from a stretch of time in which the studied...
- Instrumental Variables: Estimating a Cause When You Cannot Measure Every Confounder
An instrumental variable is a factor that nudges the exposure you care about, has no other route to the outcome, and shares no common cause with it. When such a...
- Lead-Time Bias: Why Earlier Detection Can Make Survival Look Longer Without Saving Anyone
Lead-time bias is the illusion that finding a disease earlier makes people live longer, when in truth you have only started the clock sooner. Catching a condition...
- Understanding Selection Bias, the Quiet Distorter
Selection bias is what happens when the people in a study, or the data in an analysis, are not representative of the group the conclusion is meant to cover, so the...
- Negative Controls: Catching Bias by Looking Where There Should Be No Effect
A negative control is a deliberate check placed where the honest answer should be "no effect," so that any effect you find there is a warning about your method...
- Why Cognitive Shortcuts Cause Diagnostic Errors
The short answerCognitive shortcuts cause diagnostic errors when a fast, pattern-based impression forms early and then fails to update as new information arrives....
Diagnosis, screening and testing (19)
- How Doctors Decide Which Test to Order for Chest Pain
Doctors do not order the same test for everyone with chest pain. Before choosing any scan, the physician estimates how likely it is that the pain comes from...
- How a Clinical Prediction Model Earns Trust: Reading It Through TRIPOD
A clinical prediction model is not trustworthy just because it fit its own data well. It has to show both discrimination and calibration, survive internal...
- How Decision Rules Rule Out Pulmonary Embolism Without a Scan
How can a doctor rule out a blood clot in the lung without imaging?Clinical decision rules rule out pulmonary embolism when a structured estimate of risk, combined...
- How Overdiagnosis Is Actually Measured
Overdiagnosis, the detection of a cancer that would never have caused symptoms or death, cannot be observed in an individual, because once a cancer is treated its...
- How the USPSTF Built Its Adult Depression Screening Recommendation
The short versionIn its June 20, 2023 recommendation statement, the US Preventive Services Task Force (USPSTF) assigned a Grade B to screening for depression in...
- How the USPSTF Built Its First Adult Anxiety Screening Recommendation
The short versionIn June 2023 the United States Preventive Services Task Force issued its first recommendation to screen adults for anxiety disorders. It landed as...
- Length-Time Bias: Why Screen-Detected Cancers Look More Survivable
Length-time bias, also called length-biased sampling, is the tendency of screening to catch the slow-growing, indolent tumors that sit in a detectable state for a...
- Why Pretest Probability Decides What a Test Result Means
A test result is not a verdict. It is an update to what you already believed. A likelihood ratio tells you how strongly a given result should move your estimate of...
- Why a Diagnostic Test's Accuracy Shifts With the Patients It Sees
Sensitivity and specificity are often treated as fixed properties of a test, but they shift with the mix of patients being tested. This is the spectrum effect: when...
- When There Is No Perfect Gold Standard: Reading Around an Imperfect Reference Test
Measuring a test's accuracy requires a reference standard we agree to treat as the truth. But many reference standards are themselves imperfect, and when they are,...
- How to Read a Diagnostic Accuracy Study Without Being Fooled by the Headline Numbers
To judge a diagnostic accuracy study, look past the reported sensitivity and specificity and ask three questions first: who was enrolled and how, what the test was...
- Understanding Sensitivity and Specificity, and Why a Test's Real Usefulness Depends on Who Is Being Tested
Sensitivity is how good a test is at catching people who truly have the condition. Specificity is how good it is at clearing people who truly do not. Both are...
- Verification Bias: When the Reference Standard Depends on the Test
Verification bias, sometimes called workup bias, arises when whether a patient gets the reference standard depends on the index test result. If only test-positives...
- What a Normal Lab Reference Range Actually Means
What a reference range really isA laboratory reference range is not a border between healthy and sick. It is a statistical summary of where most results from a...
- What Makes a Screening Program Worthwhile, and When Testing Healthy People Does Harm
A screening program earns its place only when catching a condition earlier in a healthy person leads to a better life than waiting for symptoms would, and that bar...
- What Procalcitonin and CRP Can and Cannot Decide About Antibiotics
Procalcitonin and C-reactive protein are supporting signals, not verdicts. The best trial evidence shows procalcitonin can help clinicians safely shorten a course...
- Why a Good Screening Test Can Mislead in the Wrong Population
A screening test is only as useful as the population you point it at. The same test, with the same accuracy printed on the box, can be a sharp tool in one group and...
- Why Ordering More Tests Is Not Automatically Safer
Does ordering more tests make care safer?Not automatically, and often the opposite. Every test has a false-positive rate, and when the chance a person actually has...
- Why Suicide Risk Screening Received an Insufficient Evidence Statement
In June 2023, the U.S. Preventive Services Task Force (USPSTF) published its recommendation on screening for depression and suicide risk in adults and gave...
Evidence synthesis and reporting (38)
- How to Tell Whether a Systematic Review and Meta-Analysis Is Trustworthy
To judge whether a systematic review and meta-analysis is trustworthy, read it in this order: was the question precise and the search wide enough to find...
- GRADE Imprecision and the Optimal Information Size
Imprecision is one of the domains GRADE uses to decide how much to trust a body of evidence, and it asks whether there is simply enough data to guide a decision....
- How Choosing Wisely Decides a Test Is Low Value
Choosing Wisely does not use a single scoring formula to declare a test low value. Instead, each participating medical specialty society writes its own list of...
- How to Read a Conflict of Interest Disclosure
A conflict of interest disclosure is the short block of text, usually near the end of a paper, where authors report the financial and personal relationships that...
- How GRADE Turns Evidence Into a Recommendation
What does GRADE actually do?When a guideline tells you a treatment is recommended, GRADE is often the machinery behind that sentence. It does two separate jobs that...
- How Preregistration and Registered Reports Curb Bad Science
Preregistration timestamps a study's hypotheses and analysis plan in a public archive before any data are collected, and Registered Reports go one step further by...
- How to Read a Forest Plot Without the Jargon
A forest plot is the picture at the heart of a meta-analysis, and you can learn to read it in a few minutes: each row is one study, the line shows how uncertain...
- How to Read a PRISMA Flow Diagram in a Systematic Review
A PRISMA flow diagram is the map of how a systematic review went from thousands of database hits to the handful of studies it actually analyzed. Read it top to...
- How to Spot a Predatory Journal
A legitimate open-access journal earns its standing through verifiable membership in shared registries, not through a polished website or a flattering invitation...
- How to Spot Spin in a Study Abstract or Press Release
Spin is the gap between what a study actually found and how that finding gets sold to you. It rarely involves false numbers. More often the numbers are correct and...
- Individual Participant Data Meta-Analysis: Why Raw Data Beats Published Summaries
An individual participant data meta-analysis gathers the original line-by-line records from every eligible trial and reanalyzes them together, rather than combining...
- Who Gets to See the Raw Trial Data? IPD Sharing Explained
Since July 1, 2018, journals that follow the International Committee of Medical Journal Editors (ICMJE) have required every clinical trial to publish a data-sharing...
- When a Systematic Review Goes Stale: Living Reviews and How to Judge Currency
A systematic review is a photograph, not a live feed. The moment it is published it begins to age against evidence that keeps moving. To judge whether one still...
- Living Systematic Reviews: How a Review Keeps Up With New Evidence
A living systematic review is a review that stays continually up to date. Instead of being finished once and slowly going stale, the team searches for new studies...
- What a Meta-Analysis Can and Cannot Tell Us in Diabetes Research
A meta-analysis can tell you what the weight of the existing evidence says when you pool many studies together, smoothing out the noise of any single small trial....
- Meta-Regression and Ecological Bias: Explaining Heterogeneity Without Being Misled
Meta-regression tries to explain why trials in a meta-analysis disagree by relating each trial's effect to a trial-level characteristic such as average age, dose,...
- Outcome Switching: Why the Registered Protocol Is the Receipt
Outcome switching is when a trial reports a different primary outcome than the one it registered in advance, letting a post hoc finding pose as a planned result....
- Prediction Intervals in Meta-Analysis: The Range a Confidence Interval Hides
In a random-effects meta-analysis, the confidence interval tells you how precisely the average effect across studies has been estimated. The prediction interval...
- How Selective Publication Inflated Antidepressant Efficacy
Selective publication made a class of antidepressants look more effective than the complete evidence supported. When Erick Turner and colleagues compared the trials...
- QUADAS-2: How Reviewers Judge Whether a Diagnostic Study Can Be Trusted
QUADAS-2 is the tool systematic reviewers use to judge how much a diagnostic accuracy study can be trusted. It works through four domains, patient selection, the...
- Quantifying Heterogeneity: What I-Squared and Tau-Squared Each Really Mean
When trials in a meta-analysis disagree, reviewers reach for statistics to describe that disagreement, and the two most common ones answer different questions....
- Reading a Network Meta-Analysis and the Transitivity Assumption
A network meta-analysis compares several treatments at once by combining head-to-head trials with indirect comparisons made through a shared comparator. The whole...
- How Trials Report Harms, and Why the Safety Half Is Often Thin
Randomized trials usually report benefits in more detail than harms, so the safety half of a paper is often thin. The CONSORT harms guidance sets out how adverse...
- Reading the Methods Section Like a Peer Reviewer
A peer reviewer decides whether to believe a result by reading the methods section first, before the abstract's conclusion sets expectations. The methods tell you...
- Reporting Guidelines: How CONSORT and EQUATOR Keep Trials Honest
Reporting guidelines are structured checklists that spell out what a published study must disclose so a reader can judge whether it was conducted and reported...
- Reproducibility in Diabetes Research: What It Means and How We Protect It
What does reproducibility mean in diabetes research?Reproducibility means that an independent team, given your methods and your data, can run the same analysis and...
- ROBINS-I: How Reviewers Judge Whether a Non-Randomized Study Can Be Trusted
ROBINS-I is a structured tool for judging how much bias threatens the result of a study that did not randomize its groups. Its central move is to picture the ideal...
- Small-Study Effects: What the Egger Test and Trim-and-Fill Can and Cannot Show
Small-study effects describe a common pattern in meta-analysis where smaller trials report systematically larger effects than big ones. Funnel plots display it,...
- The File Drawer Problem: Why Unpublished Negative Studies Quietly Distort What We Know
The file drawer problem is the tendency for studies that find nothing to never get published, so the literature fills up with positive results and underrepresents...
- The Hierarchy of Evidence Explained: From Case Reports to Systematic Reviews
The hierarchy of evidence is a rough ranking of study designs by how much they protect a result from being fooled, running from a single patient's story at the...
- The Role of Registries in Medicine: How a Public Record Keeps Studies Honest
A registry is a public record of what a study intended to do, filed before the results are known, and it is one of the simplest tools we have for telling honest...
- Umbrella Reviews and Overviews of Reviews: How to Read a Review of Reviews
An umbrella review, also called an overview of reviews, is a systematic review whose building blocks are other systematic reviews rather than primary studies, used...
- What a Data and Safety Monitoring Board Does, and Why It Watches in the Dark
A Data and Safety Monitoring Board, often called a DSMB or a Data Monitoring Committee, is a small group of independent experts who periodically review the...
- What a Funnel Plot Shows, and What It Cannot
A funnel plot is a scatter graph that a meta-analysis uses to ask one quiet question: are some of the studies that should exist missing from the picture? It plots...
- What a Meta-Analysis Cannot Fix
A meta-analysis cannot fix three things, and pretending otherwise is how careful readers get fooled by careful arithmetic. It cannot turn biased or low-quality...
- What Peer Review Can and Cannot Catch
Peer review reliably catches the things a careful reader can verify from the manuscript: muddled writing, claims the data do not support, missing analyses, ignored...
- When Not to Pool: Deciding a Meta-Analysis Would Mislead
Not every systematic review should end in a single pooled number. When the studies differ too much in who they enrolled, what they did, or how they measured...
- Why Retractions Are Rising and What a Retraction Actually Means
Retractions reached a record in 2023, when more than 10,000 papers were pulled from the scientific literature, according to an analysis by Richard Van Noorden in...
Reading the evidence in practice (35)
- How Researchers Estimate Antidepressant Discontinuation Symptoms
The short answerPublished estimates of how often people develop discontinuation symptoms after stopping an antidepressant range from roughly 15% to well over 40%,...
- Why Guidelines Stopped Recommending Aspirin for Most Healthy Adults
Guidelines stopped recommending routine daily aspirin for most healthy adults because a wave of large randomized trials showed the same thing: in people who have...
- How Tumor Response Is Measured and Where RECIST Falls Short
The short answerTumor response is measured by tracking a handful of representative lesions on serial CT or MRI scans and comparing their combined size against the...
- Does Advance Care Planning Improve End-of-Life Care?
On the best available evidence, advance care planning as it is commonly practiced, a documented conversation about future treatment preferences, does not reliably...
- Does the Type of Talk Therapy Matter? Reading the Comparative Evidence
The short answerThe type of talk therapy usually matters less than the marketing around it suggests. When researchers compare established, well-delivered therapies...
- How a Perioperative Cardiac Risk Score Is Derived and Validated
A perioperative cardiac risk score is built the way any honest prediction tool is built: investigators follow a large group of patients, record who suffers the...
- How Clinical Guidelines Get Updated, and How to Read the Change
A clinical guideline gets updated when a panel decides the standing advice no longer matches the standing evidence, and the honest ones tell you both when they last...
- How Depression Rating Scales Are Scored and Why the Cutoffs Are Debated
The short answerDepression rating scales convert a set of symptoms into a single number by scoring individual items and adding them up. The two most common in...
- How to Read a Cardiology Guideline: Class of Recommendation and Level of Evidence
Every recommendation in a modern American College of Cardiology and American Heart Association (ACC/AHA) guideline carries two separate labels. The Class of...
- Relative Risk, Absolute Risk, and Number Needed to Treat: How to Read a Statin Trial
Why does the same statin result sound big and small at once?A headline like "statins cut heart attacks by a quarter" is reporting a relative risk reduction, and a...
- Measuring Insulin Sensitivity and Insulin Response: Why You Read Them Together
Measure insulin sensitivity by itself and you have half a sentence. Measure insulin response alone and you have the other half. A person can look reassuringly...
- Why Placebo Response Is So Large in Depression Trials
The short answerPlacebo response is large in depression trials because the placebo arm captures far more than the effect of an inert pill. When a person in the...
- Prebunking: The Science of Inoculating People Against Health Misinformation
Prebunking means showing people a weakened, clearly labeled example of a misleading tactic before they encounter the real version, so they recognize and resist it...
- A Prediction Model Can Be Accurate and Still Not Help: The Case for Impact Studies
Showing that a prediction model discriminates and calibrates well tells you it makes accurate estimates, not that using it helps anyone. A model earns its place...
- Predictive Versus Prognostic Cancer Biomarkers Explained
A prognostic biomarker tells you where a patient is likely headed. A predictive biomarker tells you whether a particular drug will change that trajectory. Put...
- Why the Heart Risk Calculator Changed From Pooled Cohort to PREVENT
The short answerThe American College of Cardiology and American Heart Association replaced the 2013 Pooled Cohort Equations with the 2023 PREVENT equations because...
- How to Read a Diabetes Study Without Getting Fooled
When you read that a new diabetes treatment "significantly lowered blood sugar," the first question to ask is not whether the result is true, but what it actually...
- Reading a Trial That Missed Its Endpoint: The Lactate Question in Septic Shock
A clinical trial that misses its primary endpoint has not proven the treatment worthless; it has usually shown that the answer is less certain than a single...
- How a Patient-Reported Outcome Measure Earns Trust: Reading It Through COSMIN
A patient-reported outcome measure is a questionnaire that turns how a patient feels or functions into a number, and that number is trustworthy only if the...
- Reading an Objective Response Rate in a Cancer Study
An objective response rate, or ORR, reports the fraction of patients in a trial whose tumors shrank by a predefined amount within a set window. Duration of response...
- Reading the Esketamine Evidence for Treatment-Resistant Depression
The short answerEsketamine helps some people with treatment-resistant depression, but the benefit is modest and the evidence carries real limits. When Johnson &...
- When Real-World Evidence Helps and When It Misleads
Real-world evidence is trustworthy when the question was specified before anyone looked at the data, when the groups being compared would have been clinically...
- Risk-Enhancing Factors: The Tiebreakers in a Statin Decision
Risk-enhancing factors are a defined set of clinical features and laboratory markers that the 2018 American Heart Association and American College of Cardiology...
- How a Diabetes Drug Class Became Heart Failure Therapy
A class of drugs designed to lower blood sugar became a cornerstone of heart failure treatment because large trials, run to prove the drugs were safe for the heart,...
- What the SPRINT Trial Showed About a Blood Pressure Target of 120
The Systolic Blood Pressure Intervention Trial (SPRINT) randomly assigned 9,361 adults at increased cardiovascular risk, but without diabetes or prior stroke, to...
- What Happens to a Treatment After Approval: The Job of Real-World Data
Approval is not the end of the evidence. It is the moment a treatment moves from a few thousand carefully chosen trial participants into the hands of millions of...
- What the USPSTF Statin Recommendation Says and How It Was Built
The 2022 US Preventive Services Task Force (USPSTF) recommendation on statins for primary prevention rests on two decisions: who gets treated and how strongly. For...
- What a Clinical Guideline Is, and What It Is Not
A clinical guideline is a panel's structured attempt to turn the best available evidence into advice a clinician can act on, expressed as graded recommendations...
- What ctDNA Minimal Residual Disease Testing Can and Cannot Do
The short answerCirculating tumor DNA (ctDNA) testing for minimal residual disease (MRD) is a genuinely powerful prognostic tool. When fragments of tumor DNA remain...
- What Good Clinical Evidence Actually Looks Like
Good clinical evidence has a recognizable shape: it answers a clear question, with a fair comparison, in the right people, measured honestly, and it holds up when...
- What Makes a Biomarker Actually Useful
A biomarker is useful only when it reliably measures something that matters and, crucially, when knowing it changes a decision. Plenty of biomarkers are accurate...
- What Real-World Data Sources Actually Look Like
Every source of real-world data was built to do a job, and almost none of those jobs was research. A registry tracks a defined group of patients, a health record...
- What the STAR*D Reanalysis Debate Teaches About Trial Fidelity
The number everyone quotes, and why it is contestedFor nearly two decades, one figure anchored how clinicians talked about treating depression: after up to four...
- Why Overall Survival Is the Gold Standard in Cancer Trials
Overall survival is the gold standard in cancer trials because it measures the one outcome that matters without ambiguity: whether a treatment helps people live...
- Why Psychotherapy Trials Are Hard to Blind and What That Does to the Evidence
The short answerA psychotherapy trial cannot be run the way a drug trial can, because in talk therapy both the patient and the therapist always know exactly what is...