Median follow-up

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In statistics, median follow-up is the median time between a specified event and the time when data on outcomes are gathered. The concept is used in cancer survival analyses.

Statistics study of the collection, organization, analysis, interpretation, and presentation of data

Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation. In applying statistics to, for example, a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments. See glossary of probability and statistics.

Median quantile

The median is the value separating the higher half from the lower half of a data sample. For a data set, it may be thought of as the "middle" value. For example, in the data set {1, 3, 3, 6, 7, 8, 9}, the median is 6, the fourth largest, and also the fifth smallest, number in the sample. For a continuous probability distribution, the median is the value such that a number is equally likely to fall above or below it.

Cancer survival rates vary by the type of cancer, stage at diagnosis, treatment given and many other factors, including country. In general survival rates are improving, although more so for some cancers than others. Survival rate can be measured in several ways, median life expectancy having advantages over others in terms of meaning for people involved, rather than as an epidemiological measure.

Many cancer studies aim to assess the time between two events of interest, such as from treatment to remission, treatment to relapse, or diagnosis to death. This duration is generically called survival time, even if the end point is not death. [1]

Cancer research Research into cancer to identify causes and develop strategies for prevention, diagnosis, treatment, and cure

Cancer research is research into cancer to identify causes and develop strategies for prevention, diagnosis, treatment, and cure.

Cancer can be treated by surgery, chemotherapy, radiation therapy, hormonal therapy, targeted therapy and synthetic lethality. The choice of therapy depends upon the location and grade of the tumor and the stage of the disease, as well as the general state of the patient. A number of experimental cancer treatments are also under development. Under current estimates, two in five people will have cancer at some point in their lifetime.

In medicine, relapse or recidivism is a recurrence of a past condition. For example, multiple sclerosis and malaria often exhibit peaks of activity and sometimes long periods of dormancy, followed by relapse or recrudescence.

Time-to-event studies must have sufficiently long follow-up durations to capture enough events to reveal meaningful patterns in the data. A short follow-up duration is appropriate for studying very severe cancers with poor prognoses, whereas a long follow-up duration is better suited to studying less-severe disease, or participants with good prognoses. [1]

Prognosis is a medical term for predicting the likely or expected development of a disease, including whether the signs and symptoms will improve or worsen or remain stable over time; expectations of quality of life, such as the ability to carry out daily activities; the potential for complications and associated health issues; and the likelihood of survival. A prognosis is made on the basis of the normal course of the diagnosed disease, the individual's physical and mental condition, the available treatments, and additional factors. A complete prognosis includes the expected duration, function, and description of the course of the disease, such as progressive decline, intermittent crisis, or sudden, unpredictable crisis.

Median follow-up time is included in about half the survival analyses published in cancer journals, but of those, only 31% specify the method used to compute it. [2]

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The Scandinavian Simvastatin Survival Study is a multicenter clinical trial that was performed in the 1990s in Scandinavia and sponsored by Merck.

In survival analysis, the hazard ratio (HR) is the ratio of the hazard rates corresponding to the conditions described by two levels of an explanatory variable. For example, in a drug study, the treated population may die at twice the rate per unit time as the control population. The hazard ratio would be 2, indicating higher hazard of death from the treatment. Or in another study, men receiving the same treatment may suffer a certain complication ten times more frequently per unit time than women, giving a hazard ratio of 10.

Golden hour (medicine)

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Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, 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 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?

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In a clinical research trial, a clinical endpoint generally refers to occurrence of a disease, symptom, sign or laboratory abnormality that constitutes one of the target outcomes of the trial, but may also refer to any such disease or sign that strongly motivates the withdrawal of that individual or entity from the trial, then often termed humane (clinical) endpoint.

Overdiagnosis is the diagnosis of "disease" that will never cause symptoms or death during a patient's ordinarily expected lifetime. 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.

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Survival rate is a part of survival analysis. It is the percentage of people in a study or treatment group still alive for a given period of time after diagnosis. It is a method of describing prognosis in certain disease conditions. Survival rate can be used as yardstick for the assessment of standards of therapy. The survival period is usually reckoned from date of diagnosis or start of treatment. Survival rates are important for prognosis, but because the rate is based on the population as a whole, an individual prognosis may be different depending on newer treatments since the last statistical analysis as well as the overall general health of the patient. 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 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.

Progression-free survival (PFS) is "the length of time during and after the treatment of a disease, such as cancer, that a patient lives with the disease but it does not get worse". In oncology, PFS usually refers to situations in which a tumor is present, as demonstrated by laboratory testing, radiologic testing, or clinically. Similarly, "disease-free survival" is when patients have had operations and are left with no detectable disease.

Biovest International, Inc is a Minneapolis-based biotechnology company. Their active immunotherapy, BiovaxID, is a cancer vaccine whose first indication is intended to be consolidation/adjuvant therapy of follicular Non-Hodgkin's Lymphoma.

The survival function is a function that gives the probability that a patient, device, or other object of interest will survive beyond any given specified time.

References

  1. 1 2 Clark, M. J. Bradburn, S. B. Love and D. G. Altman (15 July 2003). "Survival Analysis Part I: Basic concepts and first analyses". British Journal of Cancer. PMC 2394262.
  2. Schemper and Terry L. Smith (1996). "A Note on Quantifying Follow-up in Studies of Failure Time". Elsevier (Department of Medical Computer Sciences, Vienna University, Austria).