Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
In survival analysis, the hazard ratio (HR) is the ratio of the hazard rates corresponding to the conditions characterised by two distinct levels of a treatment variable of interest. For example, in a clinical study of a drug, the treated population may die at twice the rate per unit time of the control population. The hazard ratio would be 2, indicating higher hazard of death from the treatment.
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run, and a dataset of unknown data against which the model is tested. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset.
Survival analysis is a branch of statistics for analyzing the expected duration of time until one event occurs, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modelling in economics, and event history analysis in sociology. Survival analysis attempts to answer certain questions, such as what is the proportion of a population which will survive past a certain time? Of those that survive, at what rate will they die or fail? Can multiple causes of death or failure be taken into account? How do particular circumstances or characteristics increase or decrease the probability of survival?
In healthcare, a differential diagnosis (DDx) is a method of analysis of a patient's history and physical examination to arrive at the correct diagnosis. It involves distinguishing a particular disease or condition from others that present with similar clinical features. Differential diagnostic procedures are used by clinicians to diagnose the specific disease in a patient, or, at least, to consider any imminently life-threatening conditions. Often, each individual option of a possible disease is called a differential diagnosis.
Clinical endpoints or clinical outcomes are outcome measures referring to occurrence of disease, symptom, sign or laboratory abnormality constituting a target outcome in clinical research trials. The term may also refer to any disease or sign that strongly motivates withdrawal of an individual or entity from the trial, then often termed a humane (clinical) endpoint.
In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables.
Overdiagnosis is the diagnosis of disease that will never cause symptoms or death during a patient's ordinarily expected lifetime and thus presents no practical threat regardless of being pathologic. Overdiagnosis is a side effect of screening for early forms of disease. Although screening saves lives in some cases, in others it may turn people into patients unnecessarily and may lead to treatments that do no good and perhaps do harm. Given the tremendous variability that is normal in biology, it is inherent that the more one screens, the more incidental findings will generally be found. For a large percentage of them, the most appropriate medical response is to recognize them as something that does not require intervention; but determining which action a particular finding warrants can be very difficult, whether because the differential diagnosis is uncertain or because the risk ratio is uncertain.
Proportional hazards models are a class of survival models in statistics. Survival models relate the time that passes, before some event occurs, to one or more covariates that may be associated with that quantity of time. In a proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. For example, taking a drug may halve one's hazard rate for a stroke occurring, or, changing the material from which a manufactured component is constructed may double its hazard rate for failure. Other types of survival models such as accelerated failure time models do not exhibit proportional hazards. The accelerated failure time model describes a situation where the biological or mechanical life history of an event is accelerated.
The five-year survival rate is a type of survival rate for estimating the prognosis of a particular disease, normally calculated from the point of diagnosis. Lead time bias from earlier diagnosis can affect interpretation of the five-year survival rate.
Survival rate is a part of survival analysis. It is the proportion of people in a study or treatment group still alive at a given period of time after diagnosis. It is a method of describing prognosis in certain disease conditions, and can be used for the assessment of standards of therapy. The survival period is usually reckoned from date of diagnosis or start of treatment. Survival rates are based on the population as a whole and cannot be applied directly to an individual. There are various types of survival rates. They often serve as endpoints of clinical trials and should not be confused with mortality rates, a population metric.
In statistics, censoring is a condition in which the value of a measurement or observation is only partially known.
The logrank test, or log-rank test, is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate to use when the data are right skewed and censored. It is widely used in clinical trials to establish the efficacy of a new treatment in comparison with a control treatment when the measurement is the time to event. The test is sometimes called the Mantel–Cox test. The logrank test can also be viewed as a time-stratified Cochran–Mantel–Haenszel test.
In the statistical area of survival analysis, an accelerated failure time model is a parametric model that provides an alternative to the commonly used proportional hazards models. Whereas a proportional hazards model assumes that the effect of a covariate is to multiply the hazard by some constant, an AFT model assumes that the effect of a covariate is to accelerate or decelerate the life course of a disease by some constant. This is especially appealing in a technical context where the 'disease' is a result of some mechanical process with a known sequence of intermediary stages.
Plasma cell dyscrasias are a spectrum of progressively more severe monoclonal gammopathies in which a clone or multiple clones of pre-malignant or malignant plasma cells over-produce and secrete into the blood stream a myeloma protein, i.e. an abnormal monoclonal antibody or portion thereof. The exception to this rule is the disorder termed non-secretory multiple myeloma; this disorder is a form of plasma cell dyscrasia in which no myeloma protein is detected in serum or urine of individuals who have clear evidence of an increase in clonal bone marrow plasma cells and/or evidence of clonal plasma cell-mediated tissue injury. Here, a clone of plasma cells refers to group of plasma cells that are abnormal in that they have an identical genetic identity and therefore are descendants of a single genetically distinct ancestor cell.
The epidemiology of cancer is the study of the factors affecting cancer, as a way to infer possible trends and causes. The study of cancer epidemiology uses epidemiological methods to find the cause of cancer and to identify and develop improved treatments.
Pre-test probability and post-test probability are the probabilities of the presence of a condition before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test or a negative test, respectively. In some cases, it is used for the probability of developing the condition of interest in the future.
The survival function is a function that gives the probability that a patient, device, or other object of interest will survive past a certain time. The survival function is also known as the survivor function or reliability function. The term reliability function is common in engineering while the term survival function is used in a broader range of applications, including human mortality. The survival function is the complementary cumulative distribution function of the lifetime. Sometimes complementary cumulative distribution functions are called survival functions in general.
In statistics, a zero-inflated model is a statistical model based on a zero-inflated probability distribution, i.e. a distribution that allows for frequent zero-valued observations.
Recurrent event analysis is a branch of survival analysis that analyzes the time until recurrences occur, such as recurrences of traits or diseases. Recurrent events are often analysed in social sciences and medical studies, for example recurring infections, depressions or cancer recurrences. Recurrent event analysis attempts to answer certain questions, such as: how many recurrences occur on average within a certain time interval? Which factors are associated with a higher or lower risk of recurrence?
