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שיטהסטטיסטיקה1,836בינה מלאכותית ולמידת מכונה1,661מדעי ההחלטה932שיטות מחקר1,354מדידה1,745סיבתיות וראיות532פרקטיקת מחקר118
75 שיטות בתחום Health & Medicine · סטטיסטיקהניקוי
שיטות בהצטלבות שני המסננים שלכם.
מיוןפופולריותא–תת–אהחדשות ביותר
epidemiology

Cox proportional hazards

The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 197

2 מקורות1972
survival

Accelerated Failure Time Model

The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how co

3 מקורות1992
epidemiology

Adaptive Competing Risks Analysis

Adaptive competing risks analysis combines the Fine-Gray subdistribution hazard framework — which models the cumulative incidence of one cause of failure in the presence of other mutually exclusive causes — with adaptive or group-sequential interim monitoring rules. This allows a clinical trial or observational study t

2 מקורות1999
epidemiology

Adaptive Cox Proportional Hazards

The Adaptive Cox Proportional Hazards model extends the classic Cox regression for time-to-event outcomes by adding adaptive LASSO (or related) penalization. It simultaneously estimates hazard ratios and performs variable selection, shrinking irrelevant covariate coefficients exactly to zero. This makes it especially v

2 מקורות2007
epidemiology

Bayesian Case Series

Bayesian case series is an observational epidemiological method that applies Bayesian inference to case series data — typically records of patients who experienced both a drug or vaccine exposure and an adverse health event. By incorporating prior evidence and computing posterior estimates of the incidence rate ratio w

2 מקורות1995
epidemiology

Bayesian Case-Control Study

A Bayesian case-control study applies Bayesian statistical inference to the classic case-control epidemiological design, formally combining prior knowledge about exposure-disease associations with observed case and control data to estimate posterior odds ratios and credible intervals. Rather than relying solely on obse

2 מקורות1990
epidemiology

Bayesian Case-Crossover Design

The Bayesian case-crossover design is a self-matched epidemiological method that estimates the transient effect of a time-varying exposure on the risk of an acute event. Each case serves as their own control, eliminating confounding by time-stable individual characteristics. Bayesian inference replaces or supplements t

2 מקורות1991
epidemiology

Bayesian Cohort Study

A Bayesian cohort study follows a defined group of individuals over time to estimate incidence, risk, or rate of outcomes, while using Bayesian statistical inference to incorporate prior knowledge and quantify uncertainty through posterior probability distributions rather than classical p-values and confidence interval

2 מקורות1990
epidemiology

Bayesian Competing Risks Analysis

Bayesian competing risks analysis is a time-to-event method for settings where subjects can fail from more than one mutually exclusive cause — such as death from cancer versus death from cardiovascular disease — and prior knowledge or small-sample uncertainty makes a Bayesian framework advantageous. It extends classica

2 מקורות1980
epidemiology

Bayesian Cox Proportional Hazards

The Bayesian Cox proportional hazards model combines Cox's classical semiparametric survival regression with Bayesian inference, replacing point estimates and p-values with full posterior distributions over regression coefficients. It handles right-censored time-to-event outcomes, quantifies uncertainty about hazard ra

2 מקורות1972
epidemiology

Bayesian Diagnostic Accuracy Study

A Bayesian diagnostic accuracy study evaluates how well a medical test distinguishes between people who have a condition and those who do not, using Bayesian statistical methods that formally incorporate prior knowledge into the estimation of sensitivity, specificity, and related measures. Unlike classical approaches t

2 מקורות1995
epidemiology

Bayesian Dose-Response Analysis

Bayesian dose-response analysis models the relationship between the level of exposure (dose) to a substance and the magnitude or probability of a biological response, embedding that model in a Bayesian probabilistic framework. Unlike frequentist approaches that yield a single point estimate with confidence intervals, t

2 מקורות1990
epidemiology

Bayesian Ecological Study

A Bayesian ecological study combines the group-level observational design of classical ecological epidemiology with Bayesian hierarchical modelling. Rather than treating disease rates as fixed quantities, it places prior distributions over latent spatial or temporal effects — commonly using the Besag-York-Mollié (BYM)

2 מקורות1991
epidemiology

Bayesian Kaplan-Meier analysis

Bayesian Kaplan-Meier analysis extends the classical Kaplan-Meier estimator by placing a prior distribution over the survival function and updating it with observed time-to-event data to obtain a full posterior distribution for the survival curve. This approach, rooted in Susarla and Van Ryzin's 1976 Dirichlet-process

2 מקורות1976
epidemiology

Bayesian nested case-control

A Bayesian nested case-control study embeds a case-control sampling scheme within a defined prospective cohort and then estimates exposure-outcome associations using Bayesian inference. Cases are individuals in the cohort who develop the outcome of interest; controls are sampled from the risk set at the time each case

2 מקורות1977
epidemiology

Bayesian Phase I clinical trial

A Bayesian Phase I clinical trial uses prior probability models and sequential Bayes updating to find the maximum tolerated dose (MTD) of a new agent. Unlike the traditional 3+3 rule-based escalation, the Bayesian approach revises a dose-toxicity curve continuously as each patient's outcome is observed, allowing faster

2 מקורות1990
epidemiology

Bayesian Phase II Clinical Trial

A Bayesian Phase II clinical trial applies Bayesian statistical inference to the standard Phase II objective of evaluating whether an experimental treatment shows sufficient early-phase efficacy to justify progression to a Phase III trial. By combining prior information with accumulating trial data, it enables principl

2 מקורות1990
epidemiology

Bayesian Phase III Clinical Trial

A Bayesian Phase III clinical trial is a large-scale, confirmatory randomized controlled trial that uses Bayesian statistical inference rather than conventional frequentist hypothesis testing to evaluate whether an experimental treatment meets pre-defined efficacy and safety thresholds. By combining prior evidence with

2 מקורות1990
epidemiology

Bayesian Phase IV study

A Bayesian Phase IV study is a post-marketing research design that applies Bayesian statistical inference to accumulate evidence about a drug or device already approved for clinical use. By formally combining prior evidence from earlier development phases with emerging real-world data, it enables continuous, probabilis

2 מקורות1980
epidemiology

Bayesian Randomized Clinical Trial

A Bayesian randomized clinical trial (Bayesian RCT) combines the rigour of random treatment allocation with Bayesian statistical inference, allowing researchers to incorporate prior evidence and update beliefs continuously as trial data accumulate. Unlike the classical frequentist RCT, it yields direct probability stat

2 מקורות1980
epidemiology

Bayesian Screening Test Evaluation

Bayesian screening test evaluation applies Bayes' theorem to quantify how a screening test result changes the probability that an individual truly has a disease. Rather than reporting sensitivity and specificity in isolation, the approach centres on predictive values — the probability of disease given a positive or neg

2 מקורות1763
pharmacometrics

Bioequivalence Analysis

Bioequivalence Analysis is a regulatory-grade statistical framework used to determine whether a test drug formulation (generic or reformulated) delivers the active ingredient to the systemic circulation at a rate and extent comparable to a reference product. Introduced by Donald J. Schuirmann in 1987, the method operat

1 מקור1987
clinical psychology

Cambridge Depersonalisation Scale

The CDS is a 29-item self-report measure of depersonalisation and derealisation experiences, developed by Sierra and Berrios in 2000. It is the most widely used instrument for assessing dissociative symptom severity in both clinical and research settings, valuable for identifying depersonalisation disorder, monitoring

1 מקור2000
health outcomes

CAT

The COPD Assessment Test (CAT) is a simple, rapid, patient-centered measure of COPD symptom burden and functional impact. Developed by Paul Jones and colleagues in 2009, this 8-item questionnaire captures how COPD affects cough, sputum, chest tightness, breathing difficulty, activity limitation, confidence, sleep, and

3 מקורות2009
nursing

Clinical Frailty Scale

The Clinical Frailty Scale (CFS), developed by Kenneth Rockwood and colleagues in 2005, is a brief, validated tool for assessing frailty in older adults. Frailty—a syndrome of diminished physiologic reserve, increased vulnerability, and reduced functional ability—is recognized as a distinct clinical state that predicts

2 מקורות2005
psychotherapy research

Common Factors Questionnaire

The Common Factors Questionnaire (CFQ) is a structured client-report measure that quantifies the client's perception of therapeutic factors deemed common to effective psychotherapy across all modalities—including alliance, therapist empathy, client agency, goal clarity, and emotional expression. Based on Lambert's cont

2 מקורות1992
survival

Competing Risks Analysis

Competing risks analysis, formalized by Fine and Gray in 1999, is a survival analysis framework for settings where a subject can experience one of several mutually exclusive event types. The key quantity is the cumulative incidence function (CIF), which estimates the probability of a specific event occurring by time t

1 מקור1999
survival

Cox Regression

Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival ana

2 מקורות1972
survival

DeepHit

DeepHit is a deep neural network framework for survival analysis with competing risks. Introduced by Lee et al. in 2018, it extends DeepSurv to handle settings where multiple, mutually exclusive events can occur, such as disease-specific mortality versus death from other causes. DeepHit solves the challenge of personal

3 מקורות2018
survival

DeepSurv

DeepSurv is a deep neural network approach to survival analysis that learns personalized survival distributions directly from data. Introduced by Katzman et al. in 2018, it extends the Cox proportional hazards model using deep learning to capture complex, nonlinear relationships between covariates and survival outcomes

3 מקורות2018
epidemiology

Ecological Study

An ecological study is an observational epidemiological design in which the unit of analysis is a group or population — a country, region, city, or time period — rather than an individual. Exposures and outcomes are measured as aggregates (rates, proportions, or means) and then correlated across groups to generate or e

2 מקורות1854
pharmacometrics

Emax Model

The Emax model is a nonlinear pharmacodynamic model that describes the relationship between drug concentration and biological effect. Introduced by Holford and Sheiner in 1981, it characterizes dose-response curves using three fundamental parameters: the maximum achievable effect (Emax), the concentration producing hal

1 מקור1981
survival

Frailty Model

The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (pati

2 מקורות1979
health outcomes

HIT-6

The HIT-6 is a brief, validated measure of headache impact on daily functioning and quality of life. Developed by Mark Kosinski and colleagues in 2003, this 6-item questionnaire quantifies how headache (migraine or other types) affects work, social activities, sleep, and emotional well-being. It is widely used in heada

3 מקורות2003
pharmacology

In Vitro-In Vivo Correlation

IVIVC is a mathematical relationship between in vitro and in vivo properties of a drug, developed to predict oral bioavailability from dissolution data. Introduced by Amidon and colleagues in the 1995 Biopharmaceutics Classification System, it bridges laboratory measurements and clinical outcomes to streamline drug dev

2 מקורות1995
survival

Joint Model for Longitudinal and Survival Data

The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through share

2 מקורות2004
survival

Kaplan-Meier

The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.

2 מקורות1958
epidemiology

Kaplan-Meier Analysis

Kaplan-Meier (KM) analysis is a nonparametric method for estimating the survival function from time-to-event data. Introduced by Kaplan and Meier in 1958, it produces the classic step-function survival curve that shows the probability of surviving beyond each observed event time, correctly accounting for censored obser

2 מקורות1958
survival

Landmark Analysis

Landmark analysis, introduced by Anderson, Cain, and Gelber in 1983, estimates conditional survival probabilities for subjects who are still at risk at a pre-specified point in time — the landmark — rather than at study entry. It was developed explicitly to avoid immortal time bias that arises when subjects are grouped

2 מקורות1983
survival

Log-Rank Test

The log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statis

2 מקורות1966
epidemiology

Matched Competing Risks Analysis

Matched competing risks analysis combines subject-level matching (e.g., propensity-score matching) with competing risks survival methods to estimate the cause-specific or subdistribution hazard of an event of interest while accounting for competing events that preclude the occurrence of that event. It is widely used in

2 מקורות1999
epidemiology

Matched Cox Proportional Hazards

Matched Cox proportional hazards is a survival analysis method that extends the Cox regression model to appropriately handle data arising from matched study designs — matched cohorts or matched case-control studies with time-to-event outcomes. By stratifying the partial likelihood by matched set, the method eliminates

2 מקורות1972
epidemiology

Matched Kaplan-Meier Analysis

Matched Kaplan-Meier analysis estimates and compares survival functions in groups that have been pre-balanced through individual or propensity-score matching. By applying the Kaplan-Meier product-limit estimator to matched cohorts or matched pairs, investigators can visualize time-to-event outcomes while controlling fo

2 מקורות1958
epidemiology

Matched Survival Analysis

Matched survival analysis combines a matching design — typically propensity score matching or exact matching on key covariates — with time-to-event methods such as Kaplan-Meier estimation and the Cox proportional hazards model. By pairing treated and control subjects who are similar on observed confounders before estim

2 מקורות1983
epidemiology

Meta-analytic competing risks analysis

Meta-analytic competing risks analysis pools results from multiple primary studies that each used a competing risks framework, allowing summary estimates of cause-specific or subdistribution hazard ratios and cumulative incidence functions. Because standard meta-analytic methods may misrepresent competing events, speci

2 מקורות2000
epidemiology

Meta-analytic Cox proportional hazards

Meta-analytic Cox proportional hazards is a quantitative synthesis technique that pools log hazard ratios from multiple Cox regression survival analyses into a single, more precise estimate of the association between an exposure or treatment and a time-to-event outcome. It combines the inferential power of survival ana

2 מקורות1998
epidemiology

Meta-analytic Kaplan-Meier analysis

Meta-analytic Kaplan-Meier analysis synthesizes time-to-event data across multiple studies by pooling Kaplan-Meier survival estimates, either from reconstructed individual patient data or from summary statistics extracted from published curves. It produces a pooled survival function with confidence bands and enables fo

2 מקורות2007
epidemiology

Meta-analytic Phase I clinical trial

A meta-analytic Phase I clinical trial formally pools evidence from prior Phase I studies — using Bayesian or frequentist meta-analysis — to construct an informative prior (or summary estimate) for dose-toxicity relationships before or during a new first-in-human or early-phase study. The approach increases statistical

2 מקורות2000
rehabilitation

Mini-BESTest Balance Evaluation

The Mini-Balance Evaluation Systems Test (Mini-BESTest) is a brief performance-based measure of balance impairment designed to identify the underlying sensory, motor, and cognitive contributions to balance deficits. Developed by Franchignoni and colleagues in 2010 as a shortened version of the comprehensive BESTest, Mi

2 מקורות2009
survival

Mixture Cure Model

The mixture cure model, first proposed by Boag in 1949 for cancer survival data, is a parametric survival model that explicitly accounts for a fraction of subjects who will never experience the event of interest — the so-called cured or immune fraction. It is the appropriate tool whenever the Kaplan-Meier curve levels

1 מקור1949
rehabilitation

Montreal Cognitive Assessment

The Montreal Cognitive Assessment (MoCA) is a brief 10-minute cognitive screening test designed to detect mild cognitive impairment (MCI) in older adults. Developed by Nasreddine and colleagues in 2005 at McGill University, MoCA is more sensitive to cognitive impairment than the Mini-Cog or MMSE, particularly for detec

2 מקורות2005
survival

Multi-State Model

The multi-state model is a generalised survival framework, formalised in the work of Andersen and Keiding and brought to wide biostatistical practice by Putter, Fiocco and Geskus (2007), that models individuals moving through multiple distinct health states — for example, healthy, ill and dead — over time. A separate h

2 מקורות1978
epidemiology

Multicenter Competing Risks Analysis

Multicenter competing risks analysis is a time-to-event method applied across multiple clinical centers to estimate the probability of a specific event of interest when other mutually exclusive events — competing risks — can preclude its occurrence. By pooling data from diverse sites, it achieves the sample sizes neede

2 מקורות1999
epidemiology

Multicenter Cox proportional hazards

Multicenter Cox proportional hazards regression extends the classic Cox PH model to studies conducted at two or more clinical sites or centers. It estimates the effect of predictors on time-to-event outcomes while explicitly accounting for clustering within centers, between-center heterogeneity, and potential differenc

2 מקורות1972
epidemiology

Multicenter Kaplan-Meier analysis

Multicenter Kaplan-Meier analysis applies the Kaplan-Meier nonparametric estimator to time-to-event data collected from two or more clinical centers. By pooling or stratifying data across sites, it estimates survival functions and compares them between treatment groups while accounting for potential center effects, ena

2 מקורות1958
survival

Nelson-Aalen Estimator

The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to t

2 מקורות1972
pharmacometrics

Population Pharmacokinetics

Population Pharmacokinetics (PopPK) is a nonlinear mixed-effects modeling framework that characterizes how drugs are absorbed, distributed, metabolized, and eliminated across a patient population, estimating both typical population parameters and the magnitude of between-subject variability. Introduced by Sheiner, Rose

1 מקור1977
epidemiology

Pragmatic Kaplan-Meier analysis

Pragmatic Kaplan-Meier analysis applies the non-parametric Kaplan-Meier product-limit estimator to time-to-event data collected under real-world or pragmatic conditions — diverse populations, routine clinical care, minimal exclusions, and standard-of-care comparators. Unlike explanatory trials designed to isolate a tre

2 מקורות1958
epidemiology

Prospective Competing Risks Analysis

Prospective competing risks analysis is an observational study design that follows participants forward in time from a well-defined starting point, recording all events — including those that prevent the primary event from occurring — and then estimates cause-specific incidence while correctly accounting for competing

2 מקורות1978
epidemiology

Prospective Cox proportional hazards

Prospective Cox proportional hazards regression combines a forward-looking cohort design — in which participants are enrolled before outcomes occur and followed over time — with Cox's semi-parametric survival model. The method estimates how baseline covariates measured at enrollment influence the rate at which particip

2 מקורות1972
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