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248
Natural Sciences236
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MetodăStatistică1,836IA și învățare automată1,661Științele deciziei932Metode de cercetare1,354Măsurare1,745Cauzalitate și dovezi532Practica cercetării118
75 metode în Health & Medicine · StatisticăȘterge
Metodele aflate la intersecția celor două filtre ale tale.
SorteazăPopularitateA–ZZ–ACele mai noi
survival

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 sursă2008
survival

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 surse1981
epidemiology

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 surse2013
epidemiology

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 surse1978
epidemiology

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 surse1972
epidemiology

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 surse1980
epidemiology

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 surse1958
epidemiology

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 surse1999
epidemiology

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 surse1972
epidemiology

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 surse2001
epidemiology

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 surse1972
survival

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 sursă2002
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 sursă1988
survival

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 sursă1972
survival

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 sursă1951
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