Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines

Yu, Tsung; Vollenweider, Daniela; Varadhan, Ravi; Li, Tianjing; Boyd, Cynthia M.; Puhan, Milo A.

doi:10.1186/1741-7015-11-7

Cited by 30 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While there are many examples in other fields as well, the empirical evidence supporting these guidelines is not always clear and details on how to implement guidelines is often lacking. 9 A better evidence base for risk-based treatment recommendations might be available if clinical trials were analyzed using multivariable risk prediction methods, and it has recently been proposed that such methods be routinely applied. 10 This approach can provide better estimates of how the degree of benefit and/or harm of a medical intervention varies across patients in the trial based upon their overall risk of the study’s outcomes.…”

Section: Introductionmentioning

confidence: 99%

Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials

Burke

Hayward

Nelson

et al. 2014

Circ: Cardiovascular Quality and Outcomes

View full text Add to dashboard Cite

Background Recent proposals suggest that risk-stratified analyses of clinical trials be routinely performed to better enable tailoring of treatment decisions to individuals. Trial data can be stratified using externally developed risk models (e.g. Framingham risk score), but such models are not always available. We sought to determine whether internally developed risk models, developed directly on trial data, introduce bias compared to external models. Methods and Results We simulated a large patient population with known risk factors and outcomes. Clinical trials were then simulated by repeatedly drawing from the patient population assuming a specified relative treatment effect in the experimental arm, which either did or did not vary according to a subjects baseline risk. For each simulated trial, two internal risk models were developed on either the control population only (internal controls only, ICO) or on the whole trial population blinded to treatment (internal whole trial, IWT). Bias was estimated for the internal models by comparing treatment effect predictions to predictions from the external model. Under all treatment assumptions, internal models introduced only modest bias compared to external models. The magnitude of these biases were slightly smaller for IWT models than for ICO models. IWT models were also slightly less sensitive to bias introduced by overfitting and less sensitive to falsely identifying the existence of variability in treatment effect across the risk spectrum than ICO models. Conclusions Appropriately developed internal models produce relatively unbiased estimates of treatment effect across the spectrum of risk. When estimating treatment effect, internally developed risk models using both treatment arms should, in general, be preferred to models developed on the control population.

show abstract

Section: Introductionmentioning

confidence: 99%

Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials

Burke

Hayward

Nelson

et al. 2014

Circ: Cardiovascular Quality and Outcomes

View full text Add to dashboard Cite

show abstract

“…Socio-economic barriers are also responsible for non-adherence. For instance, a meta-analysis by Davies et al [83] on non-adherence to insulin therapy identified female gender, travelling, age and financial costs to be the reasons for non-adherence. On the other hand, the use of a pen device and better insurance cover for lowering costs, helped in improving adherence.…”

Section: Other 'Omics' Toolsmentioning

confidence: 98%

Genetics, Genomics and Personalized Medicine in Type 2 Diabetes: A Perspective on the Arab Region

Siddiqui

Tyagi

2015

Per. Med.

View full text Add to dashboard Cite

Type 2 diabetes (T2D) is a wide-spread, chronic metabolic disorder, affecting millions of people worldwide. The epidemic of diabetes has placed a huge strain on public health, longevity and economy. T2D occurs as a result of both genetic and environmental factors and is heterogeneous in its presentation across individuals. This review gives an overview of the genetic variations identified by genome-wide association studies which predispose individuals to T2D and those which are responsible for variable drug response across patients, and the necessity to adopt a personalized approach to diabetes management. We also include a perspective on diabetes in Arabs, given the high incidence of T2D and consanguineous marriages, and the need to understand associated genetic components in this vulnerable population.

show abstract

“…Also, prediction models are arguably most useful if they help guide the choice of further actions (e.g. secondary or tertiary prevention) 8,9 . However, prediction models alone cannot serve the purpose of risk-stratified management.…”

Section: Definition and Purpose Of Prediction Modelsmentioning

confidence: 99%

“…However, prediction models alone cannot serve the purpose of risk-stratified management. In order to identify different risk strata in which the benefit harm balance varies, quantitative benefit harm assessment or decision models are needed 8,9,11 . But once such risk strata are established, prediction models allow clinicians to assign individuals to these risk strata and thus guide the choice of interventions that provide more benefits than harms.…”

Section: Definition and Purpose Of Prediction Modelsmentioning

confidence: 99%

Prediction Models in Chronic Obstructive Pulmonary Disease - Informing Practice and Improving Research

Puhan¹,

Riet²

2018

BRN

View full text Add to dashboard Cite

REVIEW BRN Reviewswww.brn.cat BRN Rev. 2018;4(3): ABSTRACT Prediction models have the potential to advance personalised medicine through risk-stratified management, which optimises the benefits and harms of preventive and therapeutic interventions for individuals. Thereby, the risk that patients are either under-or over-treated can be minimised. In research, prediction models can be used to efficiently identify study subjects, to control for confounding and to study subgroup effects. In the area of chronic obstructive pulmonary disease (COPD), many prediction models have been developed to predict the risk of early death or exacerbations, the course of disease or for other outcomes. A prerequisite for their use is a careful development and external validation, which is, however, often lacking. This article describes the purposes of prediction models as well as their development and validation including novel approaches like multiple score comparison meta-analysis. It also describes research needs like the need for developing accurate and valid prediction models for exacerbations that support risk-stratified prevention of exacerbations. (BRN Rev. 2018;4(3):156-68)Corresponding author: Milo Puhan, miloalan.puhan@uzh.ch

show abstract

Support of personalized medicine through risk-stratified treatment recommendations - an environmental scan of clinical practice guidelines

Cited by 30 publications

References 31 publications

Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials

Using Internally Developed Risk Models to Assess Heterogeneity in Treatment Effects in Clinical Trials

Genetics, Genomics and Personalized Medicine in Type 2 Diabetes: A Perspective on the Arab Region

Prediction Models in Chronic Obstructive Pulmonary Disease - Informing Practice and Improving Research

Contact Info

Product

Resources

About