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The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies.

ABSTRACT: There has been limited success in identifying prognostic biomarkers in prostate cancer. A partial explanation may be that insufficient emphasis has been put on clearly defining what type of marker or patient category a biomarker study aims to identify and how different cohort characteristics affect the ability to identify such a marker. In this article, the authors put forth the ABC model of prostate cancer, which defines 3 groups of patients with localized disease that an investigator may seek to identify: patients who, within a given time frame, will not develop metastases even if untreated (category A), will not develop metastases because of radical treatment (category B), or will develop metastases despite radical treatment (category C). The authors demonstrate that follow-up time and prostate-specific antigen screening intensity influence the prevalence of patients in categories A, B, and C in a study cohort, and that prognostic markers must be tested in both treated and untreated cohorts to accurately distinguish the 3 groups. The authors suggest that more emphasis should be put on considering these factors when planning, conducting, and interpreting the results from prostate cancer biomarker studies, and propose the ABC model as a framework to aid in that process. Cancer 2017;123:1490-1496. © 2017 American Cancer Society.

SUBMITTER: Pettersson A

PROVIDER: S-EPMC5716345 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies.

Pettersson Andreas A Gerke Travis T Fall Katja K Pawitan Yudi Y Holmberg Lars L Giovannucci Edward L EL Kantoff Philip W PW Adami Hans-Olov HO Rider Jennifer R JR Mucci Lorelei A LA

Cancer 20170202 9

There has been limited success in identifying prognostic biomarkers in prostate cancer. A partial explanation may be that insufficient emphasis has been put on clearly defining what type of marker or patient category a biomarker study aims to identify and how different cohort characteristics affect the ability to identify such a marker. In this article, the authors put forth the ABC model of prostate cancer, which defines 3 groups of patients with localized disease that an investigator may seek ...[more]

PMID: 28152172

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Project description:BACKGROUND:Prognostic research has many important purposes, including (i) describing the natural history and clinical course of health conditions, (ii) investigating variables associated with health outcomes of interest, (iii) estimating an individual's probability of developing different outcomes, (iv) investigating the clinical application of prediction models, and (v) investigating determinants of recovery that can inform the development of interventions to improve patient outcomes. But much prognostic research has been poorly conducted and interpreted, indicating that a number of conceptual areas are often misunderstood. Recent initiatives to improve this include the Prognosis Research Strategy (PROGRESS) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) Statement. In this paper, we aim to show how different categories of prognostic research relate to each other, to differentiate exploratory and confirmatory studies, discuss moderators and mediators, and to show how important it is to understand study designs and the differences between prediction and causation. MAIN TEXT:We propose that there are four main objectives of prognostic studies - description, association, prediction and causation. By causation, we mean the effect of prediction and decision rules on outcomes as determined by intervention studies and the investigation of whether a prognostic factor is a determinant of outcome (on the causal pathway). These either fall under the umbrella of exploratory (description, association, and prediction model development) or confirmatory (prediction model external validation and investigation of causation). Including considerations of causation within a prognostic framework provides a more comprehensive roadmap of how different types of studies conceptually relate to each other, and better clarity about appropriate model performance measures and the inferences that can be drawn from different types of prognostic studies. We also propose definitions of 'candidate prognostic factors', 'prognostic factors', 'prognostic determinants (causal)' and 'prognostic markers (non-causal)'. Furthermore, we address common conceptual misunderstandings related to study design, analysis, and interpretation of multivariable models from the perspectives of association, prediction and causation. CONCLUSION:This paper uses a framework to clarify some concepts in prognostic research that remain poorly understood and implemented, to stimulate discussion about how prognostic studies can be strengthened and appropriately interpreted.

Project description:Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations.MethodsThe Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status.ResultsPreconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005).ConclusionsEmpirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities.

Project description:Eimeria infection impacts upon chicken welfare and economic productivity of the poultry sector. Live coccidiosis vaccines for chickens have been available for almost 70 years, but the requirement to formulate blends of oocysts from multiple Eimeria species makes vaccine production costly and logistically demanding. A multivalent vaccine that does not require chickens for its production and can induce protection against multiple Eimeria species is highly desirable. However, despite the identification and testing of many vaccine candidate antigens, no recombinant coccidiosis vaccine has been developed commercially. Currently, assessment of vaccine efficacy against Eimeria, and the disease coccidiosis, can be done only through in vivo vaccination and challenge experiments but the design of such studies has been highly variable. Lack of a "standard" protocol for assessing vaccine efficacy makes comparative evaluations very difficult, complicating vaccine development, and validation. The formulation and schedule of vaccination, the breed of chicken and choice of husbandry system, the species, strain, magnitude, and timing of delivery of the parasite challenge, and the parameters used to assess vaccine efficacy all influence the outcomes of experimental trials. In natural Eimeria infections, the induction of strong cell mediated immune responses are central to the development of protective immunity against coccidiosis. Antibodies are generally regarded to be of lesser importance. Unfortunately, there are no specific immunological assays that can accurately predict how well a vaccine will protect against coccidiosis (i.e., no "correlates of protection"). Thus, experimental vaccine studies rely on assessing a variety of post-challenge parameters, including assessment of pathognomonic lesions, measurements of parasite replication such as oocyst output or quantification of Eimeria genomes, and/or measurements of productivity such as body weight gain and feed conversion rates. Understanding immune responses to primary and secondary infection can inform on the most appropriate immunological assays. The discovery of new antigens for different Eimeria species and the development of new methods of vaccine antigen delivery necessitates a more considered approach to assessment of novel vaccines with robust, repeatable study design. Careful consideration of performance and welfare factors that are genuinely relevant to chicken producers and vaccine manufacturers is essential.

Dataset Information

The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies.

Publications

The ABC model of prostate cancer: A conceptual framework for the design and interpretation of prognostic studies.

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