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Multi-period Fuzzy Regression Discontinuity

Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time peri

2 zdrojů2001

Multi-period Interrupted Time Series

Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention whi

Multi-period Inverse Probability Weighting

Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population whe

Multi-period Matching Estimator

The multi-period matching estimator extends the standard matching framework to settings with multiple time periods, pairing each treated unit to similar untreated units based on pre-treatment covariates or propensity scores, then using within-pair before-after differences to estimate the average treatment effect on the

2 zdrojů2005

Multi-period Propensity Score Weighting

Multi-period propensity score weighting extends the standard propensity score weighting framework to settings with repeated measurements and time-varying treatments. It constructs stabilised inverse probability weights (IPW) at each time point so that the weighted sample resembles a sequence of randomised experiments,

Multi-period Regression Discontinuity Design

Multi-period Regression Discontinuity Design extends the classic RDD to settings where a cutoff-based treatment is applied in multiple waves, across repeated time periods, or with varying thresholds. By pooling or comparing period-specific discontinuity estimates, researchers gain statistical precision and can examine

2 zdrojů2010

Multi-period Synthetic Control Method

The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatment window. It constructs a weighted combination of untreated units that reproduc

2 zdrojů2010

Multicenter case report

A multicenter case report is a structured clinical document describing one or a very small number of unusual patients observed across two or more independent healthcare institutions. By pooling observations from multiple sites, it overcomes the rarity barrier that prevents any single center from documenting an unusual

2 zdrojů2013

Multicenter case series

A multicenter case series is an observational descriptive study in which consecutive or selected patients sharing a defined clinical condition are enrolled and followed at two or more independent clinical sites. By pooling cases across institutions, researchers achieve larger sample sizes and greater demographic and cl

Multicenter Case-Control Study

A multicenter case-control study is an observational design that identifies individuals who have developed a disease (cases) and disease-free comparators (controls) across two or more study sites simultaneously. By pooling recruitment across hospitals, clinics, or geographic regions, the design achieves larger sample s

Multicenter Case-Crossover Design

The multicenter case-crossover design is an observational epidemiological method that investigates whether brief, transient exposures trigger acute health events by comparing each case's exposure just before the event to their own exposure during matched control periods — with data collected from two or more independen

2 zdrojů1991

Multicenter cohort study

A multicenter cohort study follows defined groups of participants at two or more geographically or institutionally distinct sites over time to estimate incidence, identify risk factors, and quantify associations between exposures and outcomes. By pooling data from multiple centers, it achieves statistical power and pop

Multicenter Diagnostic Accuracy Study

A multicenter diagnostic accuracy study evaluates how well an index test (e.g., a biomarker, imaging modality, or clinical prediction rule) identifies a target condition when conducted across two or more independent clinical sites. By recruiting patients from diverse settings, it produces estimates of sensitivity, spec

2 zdrojů2003

Multicenter Dose-Response Analysis

Multicenter dose-response analysis estimates the quantitative shape of the relationship between a graded exposure and a health outcome by pooling data or effect estimates across two or more study centers. Using flexible regression tools such as restricted cubic splines or fractional polynomials within a two-stage meta-

2 zdrojů1992

Multicenter Ecological Study

A multicenter ecological study is an observational epidemiological design in which the units of analysis are groups — such as cities, regions, or countries — rather than individuals, and data are pooled from two or more distinct centers or geographic areas. The approach links aggregate exposure measures (e.g., average

2 zdrojů1980

Multicenter Nested Case-Control

A multicenter nested case-control study embeds a case-control analysis within two or more geographically or institutionally distinct prospective cohorts. Cases who develop the outcome of interest are identified across all participating sites, then matched to controls sampled from the same risk sets, enabling pooled est

2 zdrojů1990

Multicenter Phase I Clinical Trial

A multicenter Phase I clinical trial is the first systematic administration of an investigational agent to humans, conducted simultaneously across two or more clinical sites. Its primary objectives are to characterize the safety and tolerability profile of the intervention, determine the maximum tolerated dose (MTD), a

Multicenter phase II clinical trial

A multicenter phase II clinical trial is an interventional study conducted at two or more independent clinical sites to evaluate the preliminary efficacy and safety of a new treatment in a defined patient population, following demonstrated tolerability in phase I. By pooling patients across sites, the design achieves t

Multicenter Phase III Clinical Trial

A multicenter Phase III clinical trial is the definitive confirmatory study that tests whether a new intervention produces a clinically meaningful benefit over a comparator in a large, representative patient population enrolled at two or more independent research sites. It is the primary evidence basis for regulatory a

2 zdrojů1940

Multicenter Phase IV Study

A multicenter Phase IV study is a post-marketing surveillance investigation conducted simultaneously at two or more clinical or research sites after a drug, device, or intervention has received regulatory approval. By pooling real-world data from diverse patient populations and geographic regions, it detects rare adver

2 zdrojů1980

Multicenter Randomized Clinical Trial

A multicenter randomized clinical trial (RCT) is an experimental study in which eligible participants are randomly assigned to intervention or control arms simultaneously across two or more clinical sites. By combining the rigor of randomization with enrollment from geographically or institutionally diverse centers, th

Multicenter Screening Test Evaluation

A multicenter screening test evaluation measures the diagnostic accuracy of a screening test — its sensitivity, specificity, predictive values, and ROC-curve area — by enrolling participants across two or more independent clinical sites. Conducting the study at multiple centers broadens the patient spectrum, tests gene

2 zdrojů1976

health behavior

Multidimensional Health Locus of Control Scale

The Multidimensional Health Locus of Control Scale (MHLC) is an 18-item measure developed by Wallston, Wallston, and DeVellis (1978) to assess individual differences in health-related beliefs about the locus of control—that is, to whom or what people attribute responsibility for their health. The MHLC measures three di

1 zdroj1978

scientometrics

Narrative Review

A narrative review is a broad, author-directed synthesis of published literature on a topic, written to summarize, interpret, and contextualize existing knowledge without following the rigorous, pre-registered search and selection protocols that characterize systematic reviews. It draws on the author's expertise to wea

health education

NCCS

The NCCS is a multidimensional self-assessment and clinician-rated instrument measuring nursing students' perceived and observed clinical competence across technical, interpersonal, and cognitive domains. Developed by Walt and van der Walt in 2009, the scale evaluates students' mastery of fundamental nursing skills, cr

2 zdrojů2009

Nested case-control

A nested case-control study is an efficient observational design embedded within a defined cohort. For each participant who develops the outcome of interest (a case), a small number of matched controls are sampled from those still at risk at the same point in time. This density-sampling strategy yields odds ratios that

2 zdrojů1973

network analysis

Network Diffusion Models

Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions

2 zdrojů1927

evidence synthesis

Network Meta-Analysis

Network meta-analysis (NMA) is a systematic method for comparing multiple interventions simultaneously within a single analytical framework, incorporating both direct evidence (head-to-head trials) and indirect evidence (comparisons via common comparators). First formalized by Lumley in 2002, NMA allows researchers to

3 zdrojů2002

scientometrics

Network-based Meta-analysis

Network-based Meta-analysis (NMA) extends conventional pairwise meta-analysis by simultaneously synthesizing evidence across a network of two or more competing treatments, including pairs that have never been compared head-to-head in a single trial. By combining direct and indirect evidence within a coherent statistica

2 zdrojů2002

public health nutrition

NLAI

The NLAI is a 26-item validated instrument measuring nutrition literacy—the ability to understand nutrition information and use it to make healthy food choices. Developed by Diamond and refined through validation studies by Rothman and colleagues, the NLAI evaluates comprehension of nutrition labels, understanding of p

2 zdrojů2007

health informatics

Nomophobia Questionnaire

The Nomophobia Questionnaire measures 'nomophobia'—the fear of being without one's mobile phone—a contemporary form of technology-related psychological distress emerging with smartphone ubiquity. Developed by Yildirim and Correia (2015), the 20-item NMP-Q captures anxiety, compulsive checking, communication apprehensio

2 zdrojů2015

implementation science

Normalization Process Theory

Normalization Process Theory (NPT) is a sociological framework developed by Carl May and colleagues to explain how new interventions become routinely embedded ('normalized') in organizational and clinical practice. Unlike efficiency-focused frameworks that measure adoption and fidelity, NPT explains the social processe

3 zdrojů2006

NOTEARS

NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization probl

1 zdroj2018

implementation science

NPT

Normalization Process Theory (NPT) is a framework developed by May, Murray, and colleagues (2009) to explain how new practices, technologies, and innovations become embedded and sustained in everyday organizational and clinical work. Rather than viewing implementation as a one-time adoption event, NPT conceptualizes im

2 zdrojů2009

health services

Numeric Rating Scale for Pain

The Numeric Rating Scale (NRS) is a single-item, self-report measure of pain intensity developed by Jensen and colleagues in 1986. Patients rate their pain on an 11-point scale (0-10) where 0 represents no pain and 10 represents the worst pain imaginable. The NRS is among the most widely used pain severity measures in

3 zdrojů1986

health informatics

Online Social Support Scale

The Online Social Support Scale measures the perceived availability and quality of emotional, informational, and practical support received through digital channels—social media, online communities, forums, messaging apps, and digital platforms. Developed by Vilelas and Tomás (2011) for patients with chronic illness an

2 zdrojů2011

psychometrics

Ordinal Content Validity

Ordinal content validity replaces the traditional binary (yes/no) expert relevance judgment with a graded, Likert-type rating scale, allowing richer expert opinion to be captured when evaluating whether scale items adequately represent the intended construct domain.

2 zdrojů2003

psychometrics

Ordinal IRT

Ordinal item response theory (ordinal IRT) comprises a family of probabilistic models — most notably the Graded Response Model and the Partial Credit Model — that relate a respondent's standing on a latent trait to the probability of choosing each ordered response category on a polytomous item. It extends classical IRT

2 zdrojů1969

implementation science

ORIC

The Organizational Readiness for Implementing Change (ORIC) is a 12-item self-report measure that assesses organizational readiness to implement evidence-based practices and innovations. Developed by Shea and colleagues in 2014, the ORIC measures two critical dimensions of organizational readiness: Change Commitment (t

1 zdroj2014

health services

Oxford Hip Score

The Oxford Hip Score (OHS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford beginning in 1996 to measure outcomes following hip replacement surgery. The OHS comprises 12 items assessing hip pain, hip-related functional limitations, and quality of life in pat

3 zdrojů1996

health services

Oxford Knee Score

The Oxford Knee Score (OKS) is a brief, validated self-report questionnaire developed by Murray and colleagues at the University of Oxford in 1998 to measure outcomes following knee replacement surgery. The OKS comprises 12 items assessing knee pain, knee-related functional limitations, and quality of life in patients

3 zdrojů1998

public health

Pandemic Fatigue Scale

The Pandemic Fatigue Scale (PFS) measures psychological exhaustion and reduced motivation to maintain protective behaviors during prolonged pandemics. Developed by Restrepo and colleagues, it captures the phenomenon whereby individuals progressively abandon preventive measures (distancing, mask-wearing, testing) despit

2 zdrojů2020

public health

Pandemic Grief Scale

The Pandemic Grief Scale (PGS) is a brief screening instrument assessing grief reactions specific to death losses during COVID-19. Developed by Zisook and colleagues in 2021, it adapts the Inventory of Complicated Grief (ICG) items to pandemic bereavement contexts, measuring both typical grief responses and complicated

1 zdroj2021

Panel Data Causal Impact Analysis

Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-in

2 zdrojů2015

Panel Data Coarsened Exact Matching

Panel Data Coarsened Exact Matching applies the Coarsened Exact Matching (CEM) algorithm to repeated-measures panel data, matching treated and control units within the same coarsened covariate strata across multiple time periods. It balances pre-treatment characteristics before estimating a causal treatment effect, com

2 zdrojů2012

Panel Data Difference-in-Differences

Panel Data Difference-in-Differences extends the classic two-period DiD design to settings with multiple units observed across many time periods. By absorbing unit-level fixed effects and time fixed effects simultaneously, it isolates the causal effect of a treatment or policy change while controlling for both time-inv

2 zdrojů1985

Panel Data Entropy Balancing

Panel data entropy balancing extends Hainmueller's (2012) entropy balancing method to longitudinal settings. It computes unit-level weights for control observations so that their covariate moments exactly match those of the treatment group across panel periods, then plugs these weights into a weighted panel regression

2 zdrojů2012

Panel Data Fuzzy Regression Discontinuity

Panel Data Fuzzy Regression Discontinuity Design (Panel FRD) extends the fuzzy RDD framework to settings where multiple observations per unit are available over time. It exploits a probabilistic — rather than deterministic — threshold-crossing rule to identify a local average treatment effect (LATE) while controlling f

2 zdrojů2001

Panel Data Instrumental Variables

Panel data instrumental variables combines the bias-correcting power of instrumental variables (IV) with the within-unit variation exploited by panel data methods. It addresses endogeneity — omitted variables, reverse causation, or measurement error — in longitudinal settings where observations are repeated across unit

2 zdrojů1978

Panel Data Interrupted Time Series

Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detect

Panel Data Inverse Probability Weighting

Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings whe

Panel Data Marginal Structural Model

A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conv

Panel Data Matching Estimator

The panel data matching estimator identifies causal treatment effects by pairing each treated unit with one or more control units that share similar covariate histories in the pre-treatment periods. By exploiting the longitudinal structure of panel data, it controls for both observed time-varying confounders and stable

2 zdrojů1997

Panel Data Propensity Score Matching

Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the

2 zdrojů1997

Panel Data Propensity Score Weighting

Panel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which

Panel Data Regression Discontinuity Design

Panel data regression discontinuity design (Panel RDD) combines the sharp local identification of a regression discontinuity with the within-unit variation available in repeated-observation panel data. Units are observed across multiple periods, and treatment is assigned based on whether a running variable crosses a kn

2 zdrojů1960

Panel Data Synthetic Control Method

The panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treat

2 zdrojů2010

Panel Event Study

A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged fr

2 zdrojů1990