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6,398 методов11 областей7 семейств методов40 языков

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ОбластьHealth & Medicine716 Psychology570 Business & Finance410 Engineering330 Life Sciences263 Education261 Research Practice

Справочная библиотека исследовательских методов, где главное — содержание: что представляет собой каждый метод, как он работает и откуда происходит.

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248

Natural Sciences236

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Environment & Sustainability160

МетодСтатистика1,836 ИИ и МО1,661 Науки о решениях932 Методы исследования1,354 Измерение1,745 Причинность и доказательства532 Исследовательская практика118

75 методов в области Health & Medicine · СтатистикаОчистить

Методы на пересечении двух ваших фильтров.

СортировкаПопулярность А–Я Я–А Новейшие

Random Survival Forest

Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates c

1 источник2008

Recurrent Event Model

A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipme

2 источников1981

Retrospective Case Report

A retrospective case report is a detailed, structured narrative of a single patient's clinical presentation, diagnosis, management, and outcome, assembled from existing medical records after the clinical events have occurred. It is the most granular and accessible observational design in clinical medicine, serving prim

2 источников2013

Retrospective competing risks analysis

Retrospective competing risks analysis applies competing risks methodology to historical (already-collected) time-to-event data in which subjects can experience one of several mutually exclusive endpoints. It uses the cumulative incidence function and cause-specific or subdistribution hazard models to estimate the prob

2 источников1978

Retrospective Cox proportional hazards

Retrospective Cox proportional hazards regression applies Cox's (1972) semi-parametric survival model to time-to-event data extracted from existing records — medical charts, administrative databases, registries, or biobanks. It estimates covariate-adjusted hazard ratios (HRs) without specifying the underlying baseline

2 источников1972

Retrospective Ecological Study

A retrospective ecological study examines associations between exposures and outcomes using pre-existing aggregate data from defined populations or geographic units. Rather than following individual subjects, the unit of analysis is a group — a country, region, or time period — and all measurements come from historical

2 источников1980

Retrospective Kaplan-Meier Analysis

Retrospective Kaplan-Meier analysis applies the Kaplan-Meier product-limit estimator to time-to-event data drawn from existing records — medical charts, registries, or administrative databases — rather than from a prospectively followed cohort. The method estimates the probability of surviving (or remaining event-free)

2 источников1958

Risk-adjusted competing risks analysis

Risk-adjusted competing risks analysis extends classical survival analysis to settings where subjects can experience more than one type of terminal event, and where the occurrence of one event prevents the occurrence of another. By modelling cause-specific or subdistribution hazards while adjusting for measured confoun

2 источников1999

Risk-adjusted Cox Proportional Hazards

Risk-adjusted Cox proportional hazards regression extends the classical Cox (1972) survival model by simultaneously entering known confounders — age, sex, comorbidities, disease severity — into the model alongside the exposure of primary interest. This adjustment isolates the independent effect of the exposure on the h

2 источников1972

Risk-adjusted Kaplan-Meier analysis

Risk-adjusted Kaplan-Meier analysis combines the non-parametric Kaplan-Meier estimator with inverse probability of treatment weighting (IPTW) or similar risk-adjustment procedures to produce survival curves that are comparable across groups as if the groups had identical distributions of baseline confounders. It is the

2 источников2001

Risk-adjusted survival analysis

Risk-adjusted survival analysis estimates the time to an event of interest — such as death, relapse, or hospital readmission — while simultaneously accounting for baseline differences in patient characteristics (covariates). By incorporating confounders such as age, comorbidities, or disease severity, it produces hazar

2 источников1972

Royston-Parmar Model

The Royston-Parmar model, introduced by Royston and Parmar in 2002, is a modern parametric approach to survival analysis that replaces the rigid distributional assumptions of classical models with a restricted cubic spline fitted to the log-cumulative-hazard scale. It combines the interpretability of a fully parametric

1 источник2002

pharmacometrics

Therapeutic Drug Monitoring

Therapeutic Drug Monitoring (TDM) is a clinical pharmacokinetic practice in which drug concentrations are measured in a patient's blood to guide individualized dosing. It applies principally to drugs with narrow therapeutic windows—where the margin between efficacy and toxicity is small—such as aminoglycosides, vancomy

1 источник1988

Time-Dependent Cox Regression

Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the

1 источник1972

Weibull Regression

Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express h

1 источник1951

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