The Per-Protocol analysis evaluates a predefined analysis population that complied with the study protocol in the aspects essential to the research question. It examines the treatment effect within this population according to specified inclusion and exclusion rules. Which individuals belong to it must not be decided only after the results are known.<\/p>\n
A Per-Protocol population is not a uniform, predefined group for all studies. The protocol and, in particular, the statistical analysis plan must transparently specify which protocol-related criteria are relevant, how the treatment received is taken into account, and how missing endpoint data are handled. The criteria depend on the respective research question, endpoint, and design. They should be applied in a comprehensible manner and must not retrospectively undermine randomization through outcome-driven selection.<\/p>\n
Because participants may be excluded after randomization, a Per-Protocol analysis may promote bias. Reasons for inadequate adherence, discontinuation, or missing measurements may be associated with prognosis or treatment. Therefore, its interpretation is limited to the defined population and the underlying assumptions. It does not automatically provide the \u201ctrue\u201d effect or the effect that would apply under ideal conditions.<\/p>\n
The Intention-to-Treat analysis generally evaluates all randomized participants according to their assigned treatment group, irrespective of subsequent intake, switching, or protocol adherence. It thereby preserves the benefit of randomization. The Per-Protocol analysis, by contrast, narrows the group to be evaluated based on predefined criteria. The two approaches therefore do not necessarily answer the same question and may produce different results.<\/p>\n
ICH E9 describes the full analysis set as a set of randomized individuals that is as broad as possible and the Per-Protocol population as a narrower set meeting essential protocol-related requirements. Which analysis is primary and which serves as supportive or sensitivity analysis depends on the design and the question to be answered. In non-inferiority studies, the possible conclusions from the full analysis set and the Per-Protocol population should be considered together, because exclusions and non-adherence may influence the interpretation in different directions.<\/p>\n
Before the analysis, it must also be specified when the population will be finalized and which data will be used for this purpose. The persons responsible for classification need a controlled process that is separated from knowledge of comparative endpoint results. In addition to the Per-Protocol analysis, further sensitivity analyses may be required to assess whether assumptions concerning missing data, treatment switching, or the definition of the population substantially alter the conclusion.<\/p>\n
The reporting should therefore disclose both the size of each analyzed population and the number and type of exclusions. Only then can it be assessed whether the Per-Protocol results provide a robust complement to the analysis according to treatment assignment or are substantially shaped by selective withdrawals.<\/p>\n
A protocol deviation is an event during conduct in which a protocol requirement was not implemented as intended. A deviation or protocol violation classified as major may, for example, affect a person’s eligibility, the integrity of the treatment, or the validity of an endpoint. It is therefore a fact to be documented at the level of an event, an individual, or a study site process.<\/p>\n
The Per-Protocol analysis, by contrast, is a statistical evaluation of a population. It is neither the list of all deviations nor a synonym for them. Not every protocol deviation leads to exclusion from the Per-Protocol population; conversely, the rules for exclusion from the population must clearly define which deviations are material for the respective analysis. The terms protocol deviation and protocol violation therefore remain terms relating to study conduct and quality management.<\/p>\n
A robust Per-Protocol analysis begins long before database lock. Relevant deviations must be classified promptly and consistently and without knowledge of treatment outcomes. The analysis plan requires precise rules for data sources, time windows, treatment exposure, and endpoint availability. A comprehensible presentation of the excluded individuals and the reasons for exclusion is necessary to assess potential selection bias.<\/p>\n
Full-service CROs such as Mediconomics support the coordination of the protocol, deviation management, and statistical analysis plan. Monitoring and quality management document and assess deviations, data management provides verifiable datasets, and biostatistics programs prespecified analysis populations and sensitivity analyses. Medical writing can present the populations and their effects consistently in the clinical study report.<\/p>\n
An isolated consideration without this information is methodologically insufficient.<\/p>\n
It protects the professional interpretation against abbreviated and potentially misleading conclusions.<\/p>\n
Who defines the criteria for the Per-Protocol population?<\/strong><\/p>\n They are specified in advance in the protocol and\/or the statistical analysis plan and must be appropriate to the study question.<\/p>\n Is the Per-Protocol analysis always the primary analysis?<\/strong><\/p>\n No. Its role depends on the objective and design. It is often supplementary, but may play a central role in certain circumstances.<\/p>\n Does every protocol deviation lead to exclusion?<\/strong><\/p>\n No. Only predefined criteria that are material to the respective analysis determine membership in the Per-Protocol population.<\/p>\n The Per-Protocol analysis evaluates a predefined analysis population that complied with the study protocol in the aspects essential to the research question. It examines the treatment effect within this population according to specified inclusion and exclusion rules. Which individuals belong to it must not be decided only after the results are known. Analysis population and […]<\/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":"set","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-6966","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\/6966","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":2,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6966\/revisions"}],"predecessor-version":[{"id":7459,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary\/6966\/revisions\/7459"}],"wp:attachment":[{"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/media?parent=6966"}],"wp:term":[{"taxonomy":"glossary-cat","embeddable":true,"href":"https:\/\/mediconomics.com\/en\/wp-json\/wp\/v2\/glossary-cat?post=6966"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Regulatory references<\/h2>\n
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