When studying the effect of a treatment (or an intervention) on an outcome, we should keep in mind that it will probably not be the same for everyone. In other words, each person will likely experience a different effect of the same treatment — we say that the treatment has a heterogeneous effect. We can …
8 Types of Treatment Effects Explained (with Examples) Read More »
The instrumental variable approach is a method to identify the causal effect of a treatment on an outcome of interest by controlling for unobserved confounding between them. A valid instrumental variable, Z, is one that influences the outcome, Y, through the treatment, X, without being related to the confounding variable, C, as shown in the …
3 Real-World Examples of Using Instrumental Variables Read More »
I analyzed a random sample of 9,830 full-text research papers, uploaded to PubMed Central between the years 2016 and 2021, to check the popularity of different sampling methods and assess their correlation with the quality of research. I used the BioC API to download the data (see the References section below). Here’s a summary of …
Which Sampling Methods Are Most Commonly Used in Research? Read More »
In this article, we will cover 7 sampling methods, which we are going to divide into 2 types: probability sampling methods, and non-probability sampling methods. Probability sampling methods involve random selection of participants, and therefore tend to produce unbiased samples; Non-probability methods do not involve random selection of participants, and therefore are cheaper to apply, …
An association between 2 variables X and Y cannot be interpreted as causal if it can be attributed to an alternative mechanism. Confounding is an example of such mechanism that alters the relationship between X and Y, and therefore, leads to an over or underestimation of the true effect between them. In its simplest form, …
5 Real-World Examples of Confounding [With References] Read More »
Suppose we conducted an observational study to estimate the causal effect of some depression treatment on the quality of life of patients: The problem is that the relationship between the two is confounded by the severity of depression: The arrows in the diagram reflect causal associations: The arrow from “depression severity” to “treatment” reflects the …
Front-Door Criterion to Adjust for Unmeasured Confounding Read More »
Confounding can be controlled in the design phase of the study by using: Random assignment Restriction Matching Or in the data analysis phase by using: Stratification Regression Inverse probability weighting Instrumental variable estimation Here’s a quick summary of the similarities and differences between these methods: Study Phase Method Can easily control for multiple confounders Can …
A variable is a confounder if it satisfies one of the following conditions: It has been proven so in previous studies. Adjusting for it produces more than 10% change in the relationship between the exposure and the outcome. It is associated with both the exposure and the outcome, without being on the causal pathway between …
Suppose we are interested in studying whether smoking increases heart rate. Because it would not be ethical to randomly assign people to smoke, we are stuck with an observational design where we have to deal with bias and confounding ourselves. The questions that we are going to be concerned with in this article are: Which …
An Example of Identifying and Adjusting for Confounding Read More »
Bias is an error in the estimation of an association between an exposure and an outcome due to a flaw in the design or conduct of the study. Confounding on the other hand, is a real but non-causal association between the exposure and the outcome. Although their mechanisms are different, both bias and confounding can …
I analyzed the content of 98,709 randomly chosen research papers from PubMed to learn more about bias. Specifically, I wanted to do 2 things: Rank 64 types of biases by popularity, in order to determine on which ones professional researchers focus the most in practice. Test the hypothesis that addressing bias issues is a sign …
List of All Biases [Sorted by Popularity in Research Papers] Read More »
The Solomon four-group design is a type of experiment where participants get randomly assigned to either 1 of 4 groups that differ in whether the participants receive the treatment or not, and whether the outcome of interest is measured once or twice in each group. The four groups in this design are (see figure below): …
In a matched pairs design, treatment options are randomly assigned to pairs of similar participants, whereas in a randomized block design, treatment options are randomly assigned to groups of similar participants. The objective of both is to balance baseline confounding variables by distributing them evenly between the treatment and the control group. Matched pairs design …
A randomized block design differs from a completely randomized design by ensuring that an important predictor of the outcome is evenly distributed between study groups in order to force them to be balanced, something that a completely randomized design cannot guarantee. A Completely randomized design uses simple randomization to assign participants to different treatment options …
Randomized Block Design vs Completely Randomized Design Read More »
In an experimental study, random assignment is a process by which participants are assigned, with the same chance, to either a treatment or a control group. The goal is to assure an unbiased assignment of participants to treatment options. Random assignment is considered the gold standard for achieving comparability across study groups, and therefore is …
The pretest-posttest control group design, also called the pretest-posttest randomized experimental design, is a type of experiment where participants get randomly assigned to either receive an intervention (the treatment group) or not (the control group). The outcome of interest is measured 2 times, once before the treatment group gets the intervention — the pretest — …
Pretest-Posttest Control Group Design: An Introduction Read More »
The separate-sample pretest-posttest design is a type of quasi-experiment where the outcome of interest is measured 2 times: once before and once after an intervention, each time on a separate group of randomly chosen participants. The difference between the pretest and posttest measures will estimate the intervention’s effect on the outcome. The intervention can be: …
Separate-Sample Pretest-Posttest Design: An Introduction Read More »
The one-group pretest-posttest design is a type of quasi-experiment in which the outcome of interest is measured 2 times: once before and once after exposing a non-random group of participants to a certain intervention/treatment. The objective is to evaluate the effect of that intervention which can be: A training program A policy change A medical …
One-Group Pretest-Posttest Design: An Introduction Read More »
The posttest-only control group design is a basic experimental design where participants get randomly assigned to either receive an intervention or not, and then the outcome of interest is measured only once after the intervention takes place in order to determine its effect. The intervention can be: a medical treatment a training program an exposure …
Posttest-Only Control Group Design: An Introduction Read More »
A case report is the description of the clinical story of a single patient, whereas a case-control study compares 2 groups of participants differing in outcome in order to determine if a suspected exposure in their past caused that difference. Case Report Case-Control Study Participants involved A case report describes the medical case of 1 …
Case Report vs Case-Control Study: A Simple Explanation Read More »
A case report is the description of the clinical story of a single patient. A cross-sectional study involves a group of participants on which data is collected at a single point in time to investigate the relationship between a certain exposure and an outcome. Here’s a table that summarizes the relationship between a case report …
Case Report vs Cross-Sectional Study: A Simple Explanation Read More »
The static-group comparison design is a quasi-experimental design in which the outcome of interest is measured only once, after exposing a non-random group of participants to a treatment, and compared to a control group. The objective is to evaluate the effect of this treatment (or intervention) which can be: a medical treatment a training program …
Detection bias refers to systematic differences between groups of a study in how the outcome is assessed, while performance bias is introduced by unequal care between groups and has nothing to do with how the outcome is assessed. In other words, detection bias occurs when the patient’s characteristics influence the probability of detecting the outcome …
The one-group posttest-only design (a.k.a. one-shot case study) is a type of quasi-experiment in which the outcome of interest is measured only once after exposing a non-random group of participants to a certain intervention. The objective is to evaluate the effect of that intervention which can be: A training program A policy change A medical …
Suppose you developed a mobile application whose aim is to help diabetic patients control their blood glucose by providing them information and practical tips on how to behave in different situations. So you decided to design a study to figure out if this app does in fact help these patients control their blood glucose. Here’s …
Understand Quasi-Experimental Design Through an Example Read More »
The most important characteristics that you should look for to identify a cohort are the following: It is an observational study (the investigator is an observer and does not intervene) It follows participants over time (several months, or even years) It compares the incidence of the outcome (i.e. the number of participants who developed that …
How to Identify Different Types of Cohort Studies Read More »
In a cohort study, the researcher selects a group of exposed and another group of unexposed individuals and follows them over time to determine whether or not a particular outcome of interest will occur. The objective is to find out which group is more likely to develop the outcome (eg. disease) by comparing its incidence (i.e. …
Cohort vs Cross-Sectional Study: Similarities and Differences Read More »
A randomized block design is a type of experiment where participants who share certain characteristics are grouped together to form blocks, and then the treatment (or intervention) gets randomly assigned within each block. The objective of the randomized block design is to form groups where participants are similar, and therefore can be compared with each …
A matched pairs design is an experimental design where participants having the same characteristics get grouped into pairs, then within each pair, 1 participant gets randomly assigned to either the treatment or the control group and the other is automatically assigned to the other group. In other words, if we take each pair alone, the …
Here’s a table that summarizes the similarities and differences between an experimental and a quasi-experimental study design: Experimental Study (a.k.a. Randomized Controlled Trial) Quasi-Experimental Study Objective Evaluate the effect of an intervention or a treatment Evaluate the effect of an intervention or a treatment How participants get assigned to groups? Random assignment Non-random assignment …
Experimental vs Quasi-Experimental Design: Which to Choose? Read More »
In science, credit goes to the man who convinces the world, not to whom the idea first occurs. Francis Darwin Epidemiology certainly has much more contributors than can be described in a single article. So this will be a list of 12 of the most famous epidemiologists who had largely influenced the field. Note that …
Epidemiology is the study of health issues at the population level which can provide information not available at the individual level. The ultimate goal of epidemiology is to improve health — lower the risk of death and increase the quality of life — by refining preventive measures and treatments of diseases. The objectives of epidemiology …
Objectives of Epidemiology (With Real-World Examples) Read More »
Neyman’s bias, also known as prevalence-incidence bias, occurs when studying the relationship between an exposure and an outcome using prevalence of the outcome instead of incidence in cases where prevalence is a biased estimator of incidence. Reminder:Prevalence is the proportion of individuals who have the outcome/disease at a given time.Incidence (or risk) is the number …
Neyman’s [Prevalence-Incidence] Bias: A Simple Explanation Read More »
Protopathic bias occurs when an exposure is initiated (or stopped) in response to a symptom of the disease (outcome) which is not yet diagnosed. This leads to a false conclusion on the causal relationship between exposure and outcome. This bias is especially known in pharmacoepidemiological studies where: The exposure is the prescription of a medication …
Proxy bias occurs when the proxy variable used is systematically different from the variable of interest. A proxy or surrogate variable being a variable related enough to the variable of interest to be used as its substitute. But why use a proxy in the first place? One reason could be because the variable of interest …
Exposure suspicion bias occurs when the knowledge of the subject’s disease status influences the search for the exposure to the cause. For instance, when subjects who have the disease undergo a more rigorous search for the cause than those who do not have the disease, leading to an overestimation of the relationship between the risk …
Exposure Suspicion Bias: Simple Explanation + Example Read More »
Temporal bias occurs when we assume a wrong sequence of events which misleads our reasoning about causality. It mostly affects study designs where participants are not followed over time. The most common study designs that are subject to temporal bias are: Cross-sectional studies: Because information is collected at a single moment in time Case-control studies: …
A randomized controlled trial (RCT) is an experiment controlled by the researcher. A cohort study is an observational study where the researcher observes the events and does not control them. In short, If you want to prove a causal relationship between a treatment and an outcome, use a randomized controlled trial. If randomization is not …
Cohort vs Randomized Controlled Trials: A Simple Explanation Read More »
Performance bias occurs when there is unequal care between study groups. This can happen in 2 scenarios: If researchers provided, intentionally or unintentionally, unequal treatment/care to different groups in the study If patients in different groups behaved differently Performance bias affects the study validity since the observed outcome can now be attributed either: To the …
Here’s a table that summarizes the similarities and differences between risk and rate: (Note that the text below contains all the necessary details to understand this table) Risk Rate Definition Proportion of individuals who developed the disease over a specified period of time (the follow-up period) Proportion of individuals who developed the disease over …
Length time bias occurs when cases who were detected earlier by SCREENING seem to have survived longer than cases DIAGNOSED after symptoms appear just because screening tests tend to identify less aggressive cases of the disease more often than aggressive ones. When screening a population, we can imagine that the slower-developing cases of a disease …
A case report is a descriptive study that documents an unusual clinical phenomenon in a single patient. It describes in details the patient’s history, signs, symptoms, test results, diagnosis, prognosis and treatment. It also contains a short literature review, discusses the importance of the case and how it improves the existing knowledge on the subject. …
Lead time bias occurs when cases who were detected by screening seem to have survived longer than diagnosed cases just because the disease was detected earlier, not because death was delayed. For example: Consider the following 2 scenarios of a patient who suffers from dementia since the age of 65: Scenario 1: The patient was not diagnosed …
Prevalence is the proportion of individuals who have the disease at a given time. It is used to quantify the burden of disease in a population. Understanding what is going on in society at a certain point in time can help us plan a policy change and create the right health service. How to calculate …
Throughout this article we will use the following example: Suppose we conducted a study and found out that moderate consumers of red wine have a 10-year risk of heart disease of 0.9%, and non-consumers have a risk of 1.2%. Our objective is to find out whether red wine is good for the heart or not. So …