{"id":6787,"date":"2026-05-13T12:19:10","date_gmt":"2026-05-13T10:19:10","guid":{"rendered":"https:\/\/mediconomics.com\/glossar\/regression\/"},"modified":"2026-05-13T12:19:10","modified_gmt":"2026-05-13T10:19:10","slug":"regression","status":"publish","type":"glossary","link":"https:\/\/mediconomics.com\/en\/glossar\/regression\/","title":{"rendered":"Regression"},"content":{"rendered":"

Regression<\/strong> is a class of statistical methods used to model the relationship between a target variable (outcome) and one or more predictors. In clinical research and real-world analyses, regression is used to estimate effects, control for confounders, and develop prognostic models, for example in efficacy, safety, or health economics. <\/p>\n

Core Concept: Modeling Relationships and Estimating Effects<\/h2>\n

At its core, regression describes how the outcome changes when a predictor changes, while other variables are held constant. This is particularly relevant when randomization is not feasible or when additional covariates need to be considered in randomized trials. The estimated coefficients can often be interpreted as effect sizes, such as mean differences, odds ratios, or hazard ratios. <\/p>\n

In clinical trials, regression models are often specified in the Statistical Analysis Plan to clearly define analysis populations, covariates, and sensitivity analyses.<\/p>\n

Common Regression Models in Clinical Practice<\/h2>\n

The choice of regression depends on the data type. For continuous outcomes, linear regression is often used. For binary outcomes (e.g., event yes\/no), logistic regression is common. For time-to-event data, survival analysis models such as the Cox proportional hazards model are employed. For count variables (e.g., number of exacerbations), Poisson or negative binomial models are considered. <\/p>\n

In many projects, mixed models are also used to account for repeated measurements per patient. This is particularly relevant for longitudinal endpoints and quality-of-life data. <\/p>\n

Assumptions, Validation, and Common Errors<\/h2>\n

Every regression model is based on assumptions. For linear regression, these include linearity, homoscedasticity, and approximate normality of residuals. For logistic regression, issues such as separation, multicollinearity, and events-per-variable must be considered. In survival analysis, the proportional hazards assumption must be verified. <\/p>\n

Common errors include overfitting, inappropriate variable selection, unjustified transformations, or ignoring missing data. Particularly in medical datasets, data preparation (coding of categories, handling of outliers) can influence the effect more strongly than the model itself. <\/p>\n

Regression for Confounding Control in Observational Studies<\/h2>\n

In non-interventional studies, regression is an important tool for controlling confounding. By adjusting for relevant covariates, the effect of an exposure (e.g., treatment) on the outcome can be better isolated. However, regression can only adjust for measured confounders; residual confounding remains possible. <\/p>\n

In practice, regression is frequently combined with complementary methods, such as propensity score approaches or sensitivity analyses, to increase robustness against model assumptions.<\/p>\n

Regulatory and Documentation Requirements<\/h2>\n

Regulatory authorities do not prescribe specific models, but they do require traceability. In clinical trials, model selection, covariates, handling of missing data, and all deviations from the Statistical Analysis Plan should be clearly documented. For real-world evidence analyses, regulatory agencies and HTA bodies expect transparent methods, reproducibility, and appropriate bias discussion. <\/p>\n

A common focus during inspections or audits is traceability: from raw dataset through data cleaning to final regression. Therefore, versioned datasets, documented programs, and a consistent review process in data management and biostatistics are essential. <\/p>\n

FAQ<\/strong><\/p>\n

When Is Linear Regression Inappropriate?<\/strong><\/p>\n

When the outcome is not continuous, strong nonlinearities are present, or model assumptions are severely violated. In such cases, logistic regression, count models, or nonparametric approaches are more appropriate. <\/p>\n

Is a Significant Regression Coefficient Automatically Causal?<\/strong><\/p>\n

No. Significance initially only indicates a statistical association within the model. Causality requires an appropriate study design, control of confounding, and a plausible clinical interpretation. <\/p>\n

Why Is Missing Data So Critical in Regression?<\/strong><\/p>\n

Because missing values can bias the sample and influence the estimate. Therefore, missing data mechanisms should be evaluated and appropriate methods specified in the analysis plan. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

In operational implementation, roles and responsibilities should be clearly defined: who records, who assesses, who approves, and who escalates. Clear documentation also facilitates trend analyses across sites and countries and supports risk-based quality management. <\/p>\n

Regulatory References (Selection)<\/h2>\n
    \n
  • ICH E9 (Statistical Principles for Clinical Trials): Principles for statistical planning and analysis.<\/li>\n
  • ICH E6(R3) Good Clinical Practice: Requirements for data integrity and traceable analyses.<\/li>\n
  • EU Regulation (EU) No 536\/2014: Quality and documentation requirements in the study context.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

    Regression is a class of statistical methods used to model the relationship between a target variable (outcome) and one or more predictors. In clinical research and real-world analyses, regression is used to estimate effects, control for confounders, and develop prognostic models, for example in efficacy, safety, or health economics. Core Concept: Modeling Relationships and Estimating […]<\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"template":"","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"glossary-cat":[],"class_list":["post-6787","glossary","type-glossary","status-publish","hentry"],"acf":[],"related_terms":"","external_url":"","internal_reference_id":"","_links":{"self":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6787","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary"}],"about":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/types\/glossary"}],"author":[{"embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/users\/10"}],"version-history":[{"count":0,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6787\/revisions"}],"wp:attachment":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/media?parent=6787"}],"wp:term":[{"taxonomy":"glossary-cat","embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary-cat?post=6787"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}