Medical statistics

Medical statistics is a branch of statistics that deals with the collection, analysis, interpretation, presentation, and organization of data in the context of health and medicine. It plays a crucial role in the design and evaluation of clinical trials, epidemiological studies, health surveys, and other research aimed at understanding health-related issues.

Evidence-based medicine (EBM) is an approach to medical practice that emphasizes the use of the best available research evidence to make decisions about the care of individual patients. It integrates clinical expertise, patient values, and the best available evidence from systematic research. The key components of EBM include: 1. **Best Available Evidence**: This refers to the most current and relevant scientific research, often derived from well-designed clinical trials, systematic reviews, and meta-analyses.

Health surveys are systematic collections of information about the health status, behaviors, and access to healthcare of individuals within a specific population. They are tools used by researchers, healthcare providers, and policymakers to gather data on various health-related topics, including physical health, mental health, lifestyle factors, disease prevalence, and healthcare usage.

Medical datasets are collections of health-related data used for various purposes in research, clinical practice, and healthcare management. These datasets can include information from various sources, such as hospitals, laboratories, clinical trials, and electronic health records (EHRs). Medical datasets can be used for different applications, including but not limited to: 1. **Clinical Research**: Researchers use medical datasets to study diseases, treatments, and patient outcomes. This includes observational studies, clinical trials, and epidemiological studies.

Pharmaceutical statistics is a specialized branch of statistics that focuses on the design, analysis, and interpretation of data related to pharmaceuticals and drug development. It plays a critical role throughout the entire lifecycle of a drug, from preclinical research to clinical trials and post-marketing surveillance. Here are some key aspects of pharmaceutical statistics: 1. **Clinical Trial Design**: Pharmaceutical statisticians help design clinical trials, determining factors such as sample size, randomization methods, and endpoint selection.

The Armitage–Doll multistage model of carcinogenesis is a theoretical framework developed by British statisticians Sir Richard Doll and Sir Austin Bradford Hill in the 1950s. This model aims to describe the process through which cancer develops in an organism, specifically emphasizing that cancer is not the result of a single event but rather a series of cumulative genetic changes or mutations.

The attack rate is an epidemiological measure used to describe the proportion of a population that becomes infected with a disease during a specified time period. It is often used in outbreak investigations to help quantify the spread of an infectious disease and assess the impact of the outbreak.

The Average Treatment Effect (ATE) is a fundamental concept in causal inference and statistics that quantifies the effect of a treatment or intervention on an outcome of interest across a population. Specifically, ATE measures the average difference in outcomes between individuals who receive the treatment and those who do not.

The Barber–Johnson diagram is a graphical representation used in materials science and engineering, particularly in the context of phase transformations in alloys. It is used to illustrate the relationships between temperature, composition, and phase stability of particular alloy systems. The diagram helps to visualize regions where different phases exist, such as solid solutions, liquid phases, and various eutectic or peritectic points.

Berkson's paradox is a statistical phenomenon that arises in epidemiological studies and other research settings. It refers to a situation where a statistical association between two variables is reversed or obscured when looking at a specific population or subgroup that is selected based on a third variable. The paradox was named after the statistician Joseph Berkson, who pointed out that in certain circumstances, conditioning on a variable can lead to misleading conclusions about the relationship between two other variables.

The "birthday effect" is a term that can refer to a few different concepts depending on the context, but it is most commonly associated with two interpretations: 1. **Statistical Phenomenon**: In probability theory, the term often relates to the "birthday paradox," which refers to the counterintuitive result that in a group of just 23 people, there is about a 50% chance that at least two individuals share the same birthday.

The Bland–Altman plot, also known as the difference plot, is a graphical method used to assess the agreement between two different measurement techniques or methods. It helps to visualize the agreement between the two methods by plotting the differences between the measurements against the averages of those measurements. ### Key Components of a Bland–Altman Plot: 1. **X-axis**: This axis represents the average of the two measurements. For each pair of measurements, you calculate the mean of the two values.

A blinded experiment is a type of experimental design used to reduce bias in research studies. In a blinded experiment, information that could influence the participants' behavior or the results of the study is concealed from one or more parties involved. The primary goal is to prevent bias from affecting the outcomes of the experiment.

A cancer cluster refers to a situation in which a higher-than-expected number of cancer cases occurs within a specific geographic area or among a specific group of people over a defined period of time. These clusters may raise concerns about potential environmental, occupational, or genetic factors that might contribute to the increased incidence of cancer. Cancer clusters can sometimes attract public attention or investigation, especially if they appear to be linked to certain locations, such as near industrial sites, landfills, or other sources of potential environmental exposure.

The ceiling effect in statistics refers to a situation where a measurement instrument or scale has an upper limit that restricts the ability to measure higher values effectively. This often results in a clustering of scores at the high end of the scale, which can limit the variability of the data and impact the validity of conclusions drawn from the analysis.

A clinical endpoint is an event or outcome measured in a clinical trial that is used to determine the efficacy or safety of a treatment. It represents a defined point in the study at which outcomes are assessed to evaluate the effect of an intervention, such as a drug or a therapeutic procedure.

Clinical study design refers to the systematic planning and structuring of a clinical trial, which is a research study conducted with human participants to evaluate the efficacy, safety, and overall impact of medical interventions (such as drugs, devices, or therapies). Effective study design is crucial to ensuring that the results of the trial are valid, reliable, and applicable to real-world settings.

A clinical trial is a research study conducted to evaluate the effects, efficacy, and safety of medical interventions, such as drugs, devices, therapies, or procedures, in humans. These trials are crucial for advancing medical knowledge and improving patient care. Clinical trials typically follow a structured protocol and are conducted in phases: 1. **Phase I**: Focuses on assessing the safety, dosage, and potential side effects of a new treatment in a small group of participants.

The Cochran–Mantel–Haenszel (CMH) statistics refer to a family of statistical methods used to analyze stratified categorical data. These methods are particularly useful when researchers want to examine the association between two categorical variables while controlling for the potential influence of one or more additional categorical variables (strata).

Cohen's h is a measure of effect size used in the context of comparing two proportions, such as in studies involving binary data or two independent samples. Specifically, it quantifies the difference between two proportions in terms of standard deviation units, providing a way to interpret the magnitude of the difference in a standardized manner.

The cohort effect refers to the differences in attitudes, behaviors, and experiences that arise from individuals being part of a specific group that experiences particular historical, social, or cultural events at the same time. These groups, known as cohorts, can be defined by various factors such as age, year of birth, or a specific life event that they collectively experience.

Companion diagnostics are medical devices or tests that provide information essential for the safe and effective use of a corresponding therapeutic product, often a drug. These diagnostics help identify patients who are most likely to benefit from a specific treatment or who may be at increased risk for serious side effects due to their unique biological characteristics.

The Cuzick-Edwards test is a statistical method often used to assess the relationship between an ordinal categorical variable and a continuous or count variable, particularly in the context of epidemiological and clinical research. This test is typically applied when researchers are interested in testing for trend effects across ordered categories. One of its primary applications is in survival analysis and longitudinal studies, where researchers may want to evaluate whether there is a systematic increase or decrease in an outcome measure as an ordinal predictor variable increases.

Decision curve analysis (DCA) is a statistical method used to evaluate the clinical utility of predictive models, particularly in the context of medical decision-making. It helps to assess the net benefits of using a specific predictive tool (such as a risk score or diagnostic test) by evaluating the trade-offs between true positive rates (sensitivity) and false positive rates (1-specificity) across a range of threshold probabilities.

The term "design effect" typically refers to the impact of a study's design on its statistical properties, particularly in the context of complex surveys. It is often used in the field of statistics and research methodology to describe how certain sampling designs can affect the variance of estimates compared to simple random sampling.

The Diagnostic and Statistical Manual of Mental Disorders, commonly referred to as the DSM, is a comprehensive classification system that provides standardized criteria for the diagnosis of mental health disorders. The DSM is published by the American Psychiatric Association (APA) and is widely used by clinicians, researchers, and public health professionals in the United States and around the world.

The Diagnostic Odds Ratio (DOR) is a measure used in medical statistics to assess the performance of a diagnostic test. It combines the test's sensitivity and specificity into a single number that reflects how much more likely patients with the condition are to have a positive test result compared to those without the condition.

Economic epidemiology is an interdisciplinary field that combines principles from economics and epidemiology to study the economic aspects of health and disease. It focuses on understanding how economic factors influence health outcomes and disease prevalence, as well as how health-related interventions can impact economic variables. Key areas of focus in economic epidemiology include: 1. **Cost-Effectiveness Analysis**: Assessing the economic efficiency of health interventions or programs. This involves comparing the costs of an intervention to its health outcomes (e.g.

The "Effect Model Law" or "Model Law" typically refers to the legislative framework established by the United Nations Commission on International Trade Law (UNCITRAL) for the recognition and enforcement of foreign judgments. While "Effect Model Law" may not be a formally recognized term, it likely relates to this context.

Effect size is a quantitative measure that describes the strength or magnitude of a phenomenon, typically the difference or relationship between groups or variables in a study. It provides a way to assess the practical significance of research findings, going beyond just statistical significance (e.g., p-values). There are several types of effect sizes, including: 1. **Cohen's d**: Used to measure the standardized difference between two means.

The term "experimental event rate" typically refers to the frequency or proportion of a specific event occurring during an experimental study or clinical trial. This can include various types of events, such as outcomes, side effects, or any other significant occurrences that researchers measure to assess the effectiveness or safety of an intervention.

The Fragility Index is a statistical measure used primarily in the field of clinical research and evidence-based medicine to assess the robustness of the results of clinical trials, particularly in relation to binary outcomes (e.g., yes/no, success/failure). It quantifies how many patients would need to be reassigned to the opposite treatment group in order for the trial results to become statistically non-significant.

Generation R, or Generation Resilient, is a term often used to describe people born from the mid-1990s to the early 2010s. This generation is characterized by a unique set of experiences and traits, shaped significantly by the rapid advancement of technology, increased access to information, and a dynamic global landscape.

Guidance for statistics in regulatory affairs refers to documents and recommendations provided by regulatory agencies to help ensure the proper application of statistical methods in the development, approval, and monitoring of products, particularly in the pharmaceutical and biotechnology sectors. These guidelines aim to provide clarity on best practices in statistical design, analysis, and interpretation of data submitted for regulatory approval.

The hazard ratio (HR) is a statistical measure used primarily in survival analysis to compare the risk of an event (such as death, failure, or relapse) occurring at any given time in two different groups. It quantifies the relationship between the rate of the event occurring in a treatment group and a control group over a specified time period. ### Key Points about Hazard Ratio: 1. **Definition**: The hazard ratio compares the hazard (the instant risk of the event happening) between two groups.

Healthcare analytics refers to the systematic use of data analysis and statistical methods to extract insights from healthcare data, which can be used to improve patient care, enhance operational efficiencies, and support decision-making within healthcare organizations. It typically involves the collection, processing, and analysis of various types of data, including clinical, administrative, financial, and patient-generated data.

A health indicator is a measurable characteristic or variable that provides insights into the health status of individuals, populations, or communities. Health indicators can be used to assess health outcomes, risks, and behaviors, as well as the effectiveness of health interventions and policies. They serve as vital tools for public health monitoring, research, and decision-making.

Healthy user bias refers to a type of selection bias that occurs in epidemiological studies and health research when the individuals who participate in the study are generally healthier than the general population. This bias can distort the findings of such studies and lead to overestimations of the effects of an exposure or treatment, or underestimations of the risks associated with certain behaviors or conditions.

Heart rate variability (HRV) is the measure of the variation in time intervals between consecutive heartbeats. It reflects the autonomic nervous system's (ANS) regulation of the heart and is an important indicator of cardiovascular health, stress levels, and overall physiological resilience. The heart does not beat at a consistent rate; rather, the time intervals between beats can vary. These variations are influenced by several factors, including breathing, physical activity, emotional states, and even time of day.

In epidemiology, "incidence" refers to the number of new cases of a disease or health condition that occur within a specific population during a defined period of time. It is a measure used to assess the frequency or risk of a disease and is crucial for understanding how diseases spread within populations.

Interim analysis refers to the evaluation of data collected from a clinical trial or study before the trial is officially completed. This analysis occurs at specified points during the study and allows researchers to assess various aspects of the trial, such as the efficacy and safety of the treatment being tested.

Lead time bias is a phenomenon that occurs in medical research and public health when evaluating the effectiveness of screening tests or early detection methods. It refers to the apparent prolongation of survival time due to the earlier diagnosis of a disease, rather than a true extension of life. Here's how it works: 1. **Early Detection**: When a disease like cancer is detected earlier through screening, patients often have a longer time between diagnosis and death, simply because the diagnosis is made sooner.

Length time bias is a phenomenon that can occur in the evaluation of medical screening methods or tests, particularly in the context of cancer screening. It occurs when the screening process disproportionately identifies slower-growing, less aggressive forms of a disease compared to more aggressive forms that may present differently. This can give a misleading impression of the effectiveness of the screening program and the overall prognosis of patients whose diseases were detected through screening.

Likelihood ratios (LR) are statistical measures used in diagnostic testing to evaluate the performance of a test in distinguishing between two conditions, usually the presence or absence of a disease. They provide a way to quantify how much a test result changes the odds of a condition being present. There are two types of likelihood ratios: 1. **Positive Likelihood Ratio (LR+)**: This represents the likelihood that a positive test result occurs in individuals with the disease compared to those without the disease.

The "List of Guidances for Statistics in Regulatory Affairs" typically refers to a compilation of documents and guidelines provided by regulatory agencies that address statistical methods and best practices for the design, analysis, and interpretation of clinical trials and other research studies in the context of drug and device approval. These guidelines are essential to ensure that statistical analyses meet the necessary standards to support regulatory submissions.

In statistics, "matching" refers to a technique used in observational studies and experiments to control for confounding variables when estimating causal effects. The main goal of matching is to create comparable groups that differ only in the treatment or intervention of interest, thus reducing bias in the estimation of treatment effects. There are several common forms of matching: 1. **Propensity Score Matching (PSM):** This is one of the most widely used methods.

Mathematical modeling of infectious diseases is a method used to understand and predict the dynamics of disease transmission in populations using mathematical equations and concepts. These models help researchers and public health officials analyze how diseases spread, identify potential outbreaks, and evaluate the impact of interventions such as vaccinations, social distancing, or treatment strategies. ### Key Components of Mathematical Models 1. **Population Segments**: - **Susceptible (S)**: Individuals who are not infected but can contract the disease.

Meadow's Law, often referred to in the context of medical and forensic science, states that "every time there is a fatal case of child abuse, there is at least one prior injury." This principle emphasizes the patterns of injury that often precede a fatal case of child abuse, suggesting that such incidents are rarely isolated and are typically preceded by a history of prior abuse or maltreatment.

The Minimal Important Difference (MID) is a concept used in health-related research to define the smallest change in a treatment outcome that a patient would perceive as important. It is particularly significant in the fields of clinical trials, patient-reported outcomes, and health economics. The MID helps researchers and clinicians determine whether a treatment has a meaningful effect on a patient's health or quality of life, rather than just a statistically significant effect.

The Multiple Deprivation Index (MDI) is a composite measure used to assess and compare levels of deprivation across different geographical areas. It aggregates various indicators related to socio-economic factors to provide a comprehensive picture of deprivation within a specific locality. The key features of the MDI typically include: 1. **Dimensions of Deprivation**: The index often encompasses multiple dimensions of deprivation, such as income, employment, health, education, housing, and access to services.

A multiple of the median refers to a value that is obtained by multiplying the median of a dataset by a certain factor or integer. The **median** is the middle value of a sorted dataset, and if the dataset has an even number of observations, the median is the average of the two middle values. For example, if the median of a dataset is 10, then: - A multiple of the median for factor 2 would be \(2 \times 10 = 20\).

The "Number Needed to Harm" (NNH) is a statistical measure used in clinical studies to quantify the risk of a harmful event resulting from a particular treatment or exposure. It represents the number of patients who need to be exposed to the treatment or intervention for one additional person to experience a harmful outcome compared to a control group.

The Number Needed to Treat (NNT) is a statistical measure used in healthcare to estimate the effectiveness of a treatment. It indicates the number of patients that need to be treated with a particular intervention in order for one patient to benefit from that treatment compared to a control group (usually receiving a placebo or standard care). The NNT is calculated from the absolute risk reduction (ARR), which is the difference in the event rate (e.g.

The "Number Needed to Vaccinate" (NNV) is a public health metric used to estimate the number of individuals who need to be vaccinated to prevent one case of a disease. It is a useful measure for evaluating the effectiveness of vaccination campaigns and helps in understanding the impact of vaccines on community health.

The odds ratio (OR) is a statistic that quantifies the strength of the association between two events, commonly used in epidemiology and various fields of research. It compares the odds of an event occurring in one group to the odds of it occurring in another group. Here's how it works: 1. **Definition of Odds**: The odds of an event is the ratio of the probability that the event occurs to the probability that it does not occur.

Passing-Bablok regression is a non-parametric statistical method used to assess the agreement between two different measurement methods or instruments. It is particularly useful in situations where the data may not meet the assumptions of normality or homoscedasticity required by traditional linear regression methods.

Post hoc analysis refers to the examination of data after an experiment or study has been conducted, particularly when looking for patterns or relationships that were not specified in advance. The term "post hoc" is derived from the Latin phrase "post hoc, ergo propter hoc," which means "after this, therefore because of this.

Pre-test and post-test probabilities are concepts used primarily in clinical medicine and diagnostic testing to evaluate the likelihood of a condition before and after a diagnostic test is performed. ### Pre-Test Probability - **Definition**: Pre-test probability refers to the probability that a patient has a particular condition or disease before any diagnostic tests are performed. It is based on clinical judgment, patient history, risk factors, and, in some cases, prior epidemiological data.

Predictive informatics refers to the use of data analysis techniques, algorithms, and statistical models to forecast outcomes and trends based on historical data. It combines elements of information science, statistics, machine learning, and data mining to extract insights and predict future events or behaviors. Key components of predictive informatics include: 1. **Data Collection and Management**: Gathering relevant datasets from various sources, which may include structured data (like databases) and unstructured data (like text and images).

Prevalence is a statistical measure used in epidemiology and public health to indicate the proportion of a population that has a specific characteristic, condition, or disease at a given point in time or over a specified period. It is typically expressed as a percentage or per a certain number of individuals (e.g., per 1,000 or 100,000 people).

The "Preventable fraction among the unexposed" (also known as the "attributable fraction among the unexposed") is a measure used in epidemiology to quantify the proportion of disease in a population that can be attributed to a specific risk factor within the group of individuals who are not exposed to that risk factor.

The preventable fraction, also known as the population attributable fraction (PAF), is a measure used in epidemiology to estimate the proportion of disease cases in a population that can be attributed to a specific risk factor or exposure. It provides insight into the potential impact of removing or reducing a risk factor on the health of a population.

The Proportional Reporting Ratio (PRR) is a statistical measure used in pharmacovigilance to assess the strength of a signal regarding adverse drug reactions (ADRs) associated with a specific drug. It helps to compare the frequency of reported adverse effects for a particular drug to the frequency of those effects for other drugs or across the overall population of reported cases.

A protective factor is a variable or condition that reduces the likelihood of negative outcomes or helps mitigate the impact of risk factors. In various fields such as psychology, public health, and social work, protective factors are identified to enhance resilience and promote positive development, well-being, and health. For instance, in the context of mental health, protective factors might include: - **Strong social support:** Having friends, family, or community connections that provide emotional and practical assistance can help individuals cope with stress and adversity.

The "rare disease assumption" typically refers to certain underlying principles or guidelines that govern the research, diagnosis, treatment, and policy-making surrounding rare diseases. In a general context, a rare disease is often defined as one that affects a small percentage of the population, with specific thresholds varying by country.

Real-time outbreak and disease surveillance refers to the systematic collection, analysis, and interpretation of health-related data in real time to monitor, detect, and respond to public health threats, particularly infectious disease outbreaks. This approach leverages technology and data analytics to provide timely information that can help public health officials make informed decisions, implement interventions, and allocate resources effectively.

The Relative Index of Inequality (RII) is a measure used in public health, social sciences, and economics to evaluate and compare the distribution of resources, health outcomes, or other variables of interest across different socio-economic groups. It is particularly useful for assessing health disparities. The RII is calculated based on the cumulative distribution of a population arranged by socio-economic status, often measured through income, education level, or social class.

Relative risk (RR) is a measure used in epidemiology to compare the risk of a particular event (such as developing a disease) occurring in two different groups. It quantifies the likelihood of an event happening in one group relative to another group, typically comparing those exposed to a risk factor with those who are not.

Relative Risk Reduction (RRR) is a statistical measure used in epidemiology and medical research to express the reduction in risk of a certain event (such as developing a disease) in a treatment group compared to a control group. It is often used to assess the efficacy of a treatment or intervention in clinical trials.

Relative survival is a statistical measure used in epidemiology and public health to assess the survival of individuals diagnosed with a particular disease, typically cancer, in comparison to the survival of a comparable group from the general population who do not have the disease. The relative survival rate is calculated by taking the observed survival rate of patients with the disease and dividing it by the expected survival rate of the general population, adjusted for factors such as age, sex, and time period.

The Risk Adjusted Mortality Rate (RAMR) is a statistical measure used to assess and compare the mortality rates across different populations or patient groups while taking into account the underlying health status and risk factors of those populations. It aims to provide a more accurate representation of the quality of care by controlling for variables that could affect mortality, such as age, sex, pre-existing health conditions, and other socio-economic factors. **Key points about RAMR:** 1.

Risk difference, also known as the absolute risk difference, is a measure used in epidemiology and clinical studies to quantify the difference in the risk of an event occurring between two groups. It is calculated by subtracting the risk (probability) of the event in one group from the risk in another group.

A **risk factor** is any characteristic, condition, or behavior that increases the likelihood of developing a disease, injury, or other negative health outcome. Risk factors can be biological, environmental, or lifestyle-related, and they can influence individual and population health in various ways. For example, in the context of cardiovascular disease, common risk factors include: - **Biological risk factors**: Age, gender, family history of heart disease, and genetics.

The risk–benefit ratio is a comparative assessment used to evaluate the potential risks and benefits associated with a particular action, decision, treatment, or intervention. This ratio helps individuals, organizations, and policymakers determine if the expected benefits outweigh the risks involved, and whether it is justifiable to proceed with a particular course of action. ### Key Components: 1. **Risk**: Refers to the potential negative outcomes or hazards associated with an action.

The "Rule of Three" in statistics is a principle used to estimate the confidence intervals for rare events or to determine the number of occurrences of an event within a given sample size.

A smart thermometer is a digital device that measures body temperature and offers additional features beyond traditional thermometers. These devices often connect to smartphones or tablets via Bluetooth or Wi-Fi, allowing users to track temperature readings over time, receive alerts, and share data with healthcare providers. Key features of smart thermometers may include: 1. **Digital Display**: They typically have an easy-to-read digital display that shows temperature readings quickly and accurately.

Spectrum bias refers to a phenomenon in diagnostic research where the performance of a diagnostic test or a medical screening tool varies depending on the characteristics of the population being tested. Specifically, this bias can occur when the sample of patients evaluated in a study does not accurately represent the broader population that will ultimately undergo testing in clinical practice. There are a few key factors that contribute to spectrum bias: 1. **Patient Selection**: If a study includes patients with certain characteristics (e.g.

The Standardized Mortality Ratio (SMR) is a measure used in epidemiology to compare the mortality rates of a specific population to a standard or reference population. It is often used to assess whether the mortality rate in a population (such as a certain geographic region or a specific group) is higher or lower than what would be expected based on the rates in a standard population, typically adjusted for age and sometimes other factors.

Subgroup analysis is a statistical method used in research and clinical trials to examine the effects of an intervention or treatment across different subsets or groups within a larger population. By breaking down the population into subgroups, researchers can identify whether the treatment is more or less effective in specific segments of the population based on characteristics such as age, gender, ethnicity, baseline health status, or other relevant factors.

A surrogate endpoint is a biomarker or a clinical measure that is used as a substitute for a direct measure of how a patient feels, functions, or survives. In clinical trials, surrogate endpoints are often used to provide earlier or more immediate indications of treatment effectiveness. They can be particularly useful in situations where the actual outcomes are difficult to measure, take a long time to manifest, or require large numbers of patients to demonstrate statistical significance.

The therapeutic effect refers to the beneficial or positive outcomes achieved through medical treatment or intervention, which help alleviate symptoms, cure diseases, or improve health conditions. This effect can be observed in various forms, depending on the treatment used, such as medication, therapy, surgery, or lifestyle changes. Key points about the therapeutic effect include: 1. **Purpose**: It aims to restore health, enhance well-being, or manage symptoms of a medical condition.

Transmission risks and rates generally refer to the likelihood and frequency of transmission of a disease or condition from one individual to another, or from an environment to an individual. While the term can be applied to various contexts, it is most commonly associated with infectious diseases. Here’s a breakdown: ### Transmission Risks Transmission risk refers to the factors that affect the probability of disease spread.

Verification bias occurs in research, particularly in diagnostic studies, when there is a systematic difference in how the outcomes of a diagnostic test are verified based on the results it produces. Essentially, it arises when the methods used to confirm or validate the accuracy of a test depend on the test's initial results, leading to potentially distorted or skewed findings.