Predictors of treatment effect

2019 ◽  
pp. 188-207
Author(s):  
Richard D Riley ◽  
Aroon Hingorani ◽  
Karel GM Moons
Keyword(s):  
Prognostic Factors ◽  
Treatment Effect ◽  
Interaction Effect ◽  
Specific Treatment ◽  
Health Condition ◽  
Effect Modification ◽  
Sign Test ◽  
Stratified Care ◽  
Multiple Trials ◽  
Using Data

A predictor of treatment effect is any factor or combination of factors (such as a patient characteristic, symptom, sign, test, or biomarker result) associated with the effect (benefit or harm) of a specific treatment in persons with a particular disease or health condition. Various terms are used across disciplines to refer to prediction of treatment effect, including treatment-predictor (treatment-covariate) interaction, effect modification, predictive (as opposed to prognostic) factors (in oncology), or moderation analysis. This chapter reviews principles of the design of studies of treatment effect predictors, such as exploration of treatment-predictor interactions in randomized trials and the importance of replication of such estimates using data from multiple trials. The application of predictors of treatment effect in practice for matching individuals or subgroups to specific treatments is introduced as one type of stratified care, and the need for impact studies to investigate whether stratified care leads to better outcomes and improved efficiency of healthcare is highlighted.

A framework for prognosis research

2019 ◽  
pp. 22-34
Author(s):  
Peter Croft ◽  
Richard D Riley ◽  
Karel GM Moons ◽  
Harry Hemingway
Keyword(s):  
Prognostic Factors ◽  
Clinical Practice ◽  
Treatment Effect ◽  
Statistical Models ◽  
Impact Evaluation ◽  
Health Condition ◽  
Prognostic Models ◽  
Prognosis Research

This chapter introduces the PROGRESS framework, which describes four types of prognosis research, each addressing different questions. The four types concern: studies of overall prognosis (the average outcome, or outcome risk, in people with a particular health condition, in the context of the nature and quality of current care); prognostic factors (characteristics associated with changes in the average outcome, or outcome risk, across individuals); prognostic models (development, validation, and impact evaluation of statistical models, incorporating multiple prognostic factors for use in clinical practice to predict an individual’s outcome value or to estimate their outcome risk); and predictors of treatment effect (characteristics that predict whether an individual responds to a particular treatment or not). Examples of each type are given to illustrate the framework.

Programming in Restrictive Housing: Considerations for Improving Outcome Evaluations

2018 ◽  
Vol 45 (8) ◽  
pp. 1174-1191 ◽  
Author(s):  
H. Daniel Butler ◽  
Starr Solomon ◽  
Ryan Spohn
Keyword(s):  
Treatment Effect ◽  
Successful Implementation ◽  
Inmate Misconduct ◽  
The Past ◽  
What Works ◽  
Different Types ◽  
Time Served ◽  
Using Data ◽  
Outcome Evaluations ◽  
Behavioral Programming

A number of studies have identified “what works” in regard to the successful implementation of correctional programming over the past several decades. Few studies, however, have examined the complexities associated with programming in restrictive housing. Using data from a Midwestern department of corrections, we examined whether the provision of programming in restrictive housing achieved desired outcomes (e.g., reductions in inmate misconduct). The findings revealed the amount of time served in restrictive housing and confinement in different types of restrictive housing may influence estimations of a treatment effect. As a growing number of states seek to reform the use of restrictive housing, the proper implementation of cognitive-behavioral programming may increase institutional security and safety.

scholarly journals Treatment of pulmonary hypertension associated with chronic obstructive pulmonary disease: a systematic review

2021 ◽  
pp. 00348-2021
Author(s):  
Ragdah Arif ◽  
Arjun Pandey ◽  
Ying Zhao ◽  
Kyle Arsenault-Mehta ◽  
Danya Khoujah ◽  
...  
Keyword(s):  
Systematic Review ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Hypertension ◽  
Pulmonary Disease ◽  
Treatment Effect ◽  
Specific Treatment ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Phosphodiesterase Type 5 Inhibitors ◽  
Clinical Benefits

Chronic obstructive pulmonary disease-associated pulmonary hypertension (COPD-PH) is an increasingly recognised condition which contributes to worsening dyspnea and poor survival in COPD. It is uncertain whether specific treatment of COPD-PH, including use of medications approved for pulmonary arterial hypertension (PAH), improves clinical outcomes. This systematic review and meta-analysis assesses potential benefits and risks of therapeutic options COPD-PH.We searched Medline and Embase for relevant publications until Sep 2020. Articles were screened for studies on treatment of COPD-PH for at least 4 weeks in 10 or more patients. Screening, data extraction, and risk of bias assessment were performed independently in duplicate. When possible, relevant results were pooled using the random effects model.Supplemental long-term O2 therapy (LTOT) mildly reduced mean pulmonary artery pressure (PAP), slowed progression of PH, and reduced mortality, but other clinical or functional benefits were not assessed. Phosphodiesterase type-5 inhibitors significantly improved systolic PAP (pooled treatment effect −5.9 mmHg; 95%CI −10.3, −1.6), but had inconsistent clinical benefits. Calcium-channel blockers and endothelin receptor antagonists had limited hemodynamic, clinical, or survival benefits. Statins had limited clinical benefits despite significantly lowering systolic PAP (pooled treatment effect −4.6 mmHg; 95% CI: −6.3, −2.9).This review supports guideline recommendations for LTOT in hypoxemic COPD-PH patients as well as recommendations against treatment with PAH-targeted medications, Effective treatment of COPD-PH depends upon research into the pathobiology, and future high-quality studies comprehensively assessing clinically relevant outcomes are needed.

Interazione: modificazione dell’associazione tra esposizione ed esito al variare di un terzo fattore

2019 ◽  
Vol 31 (1) ◽  
pp. 40-42
Author(s):  
Francesco Franco ◽  
Anteo Di Napoli
Keyword(s):  
Interaction Effect ◽  
Additive Model ◽  
Joint Effect ◽  
Effect Modification ◽  
Multiplicative Model ◽  
Total Effect ◽  
Expected Effect ◽  
Individual Effects ◽  
Independent Effects ◽  
The Individual

From a statistical perspective, interaction (effect modification) occurs when the effect of an exposure on an outcome depends on the level of another factor. In epidemiology, effect modification (interaction) occurs if the joint effect of two (or more) factors is different from the expected effect if considering only their independent effects. In an additive model, the effect of one exposure is added to effect of another exposure, and there is interaction if the joint effect of the two exposures together is greater than the sum of their individual effects. In a multiplicative model, the effect of the second exposure multiplies the effect of the first exposure, and there is interaction if the joint effect of the two exposures together is greater than the product of their individual effects. Interaction of two (or more) factors is synergic or antagonistic if the total effect is, respectively, greater or smaller than the sum of the individual effects of each factor. (Epidemiology_statistics)

scholarly journals Inference on treatment effect modification by biomarker response in a three-phase sampling design

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 545-560 ◽  
Author(s):  
Michal Juraska ◽  
Ying Huang ◽  
Peter B Gilbert
Keyword(s):  
Treatment Effect ◽  
Sampling Design ◽  
Causal Effect ◽  
Effect Modification ◽  
Finite Sample ◽  
Dengue Vaccine ◽  
Biomarker Response ◽  
Three Phase ◽  
Phase Sampling ◽  
Structural Risk

Summary An objective in randomized clinical trials is the evaluation of “principal surrogates,” which consists of analyzing how the treatment effect on a clinical endpoint varies over principal strata subgroups defined by an intermediate response outcome under both or one of the treatment assignments. The latter effect modification estimand has been termed the marginal causal effect predictiveness (mCEP) curve. This objective was addressed in two randomized placebo-controlled Phase 3 dengue vaccine trials for an antibody response biomarker whose sampling design rendered previously developed inferential methods highly inefficient due to a three-phase sampling design. In this design, the biomarker was measured in a case-cohort sample and a key baseline auxiliary strongly associated with the biomarker (the “baseline surrogate measure”) was only measured in a further sub-sample. We propose a novel approach to estimation of the mCEP curve in such three-phase sampling designs that avoids the restrictive “placebo structural risk” modeling assumption common to past methods and that further improves robustness by the use of non-parametric kernel smoothing for biomarker density estimation. Additionally, we develop bootstrap-based procedures for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses about the mCEP curve. We investigate the finite-sample properties of the proposed methods and compare them to those of an alternative method making the placebo structural risk assumption. Finally, we apply the novel and alternative procedures to the two dengue vaccine trial data sets.

scholarly journals Tavola Rotonda su: “Fattori prognostici e markers nel carcinoma della vescica”. Aspetti generali e introduttivi: Round Table on: “Prognostic factors and markers in bladder carcinoma”. General and introductory aspects

1995 ◽  
Vol 62 (2) ◽  
pp. 163-167
Author(s):  
G. Mobilio
Keyword(s):  
Prognostic Factors ◽  
Bladder Carcinoma ◽  
Predictive Value ◽  
Clinical Studies ◽  
Specific Treatment ◽  
Immunological Response ◽  
Round Table ◽  
End Points ◽  
The Individual ◽  
Bladder Tumours

Bladder tumours are generally grouped as superficial or invasive because of their different therapeutical regimens. Superficial tumours still cause uncertainties in choosing the best treatment due to their heterogeneity and different behaviour. In the prevision of these tumours it is important to consider all the factors that could influence the prognosis: tumoral characteristics, grading and staging mistakes, effects of the therapy and immunological response. Moreover it is important for clinical studies to have appropriate end points. Prognostic factors and markers with high predictive value can allow specific treatment for the individual cases to be planned.

scholarly journals Being Healthy, Being Sick, Being Responsible: Attitudes towards Responsibility for Health in a Public Healthcare System

2019 ◽  
Vol 12 (2) ◽  
pp. 145-157 ◽  
Author(s):  
Gloria Traina ◽  
Pål E Martinussen ◽  
Eli Feiring
Keyword(s):  
Social Responsibility ◽  
Eating Habits ◽  
Personal Responsibility ◽  
Strong Support ◽  
Health Condition ◽  
Political Orientation ◽  
Public Healthcare ◽  
Promotion Policies ◽  
Health Responsibility ◽  
Using Data

Abstract Lifestyle-induced diseases are becoming a burden on healthcare, actualizing the discussion on health responsibilities. Using data from the National Association for Heart and Lung Diseases (LHL)’s 2015 Health Survey (N = 2689), this study examined the public’s attitudes towards personal and social health responsibility in a Norwegian population. The questionnaires covered self-reported health and lifestyle, attitudes towards personal responsibility and the authorities’ responsibility for promoting health, resource-prioritisation and socio-demographic characteristics. Block-wise multiple linear regression assessed the association between attitudes towards health responsibilities and individual lifestyle, political orientation and health condition. We found a moderate support for social responsibility across political views. Respondents reporting unhealthier eating habits, smokers and physically inactive were less supportive of health promotion policies (including information, health incentives, prevention and regulations). The idea that individuals are responsible for taking care of their health was widely accepted as an abstract ideal. Yet, only a third of the respondents agreed with introducing higher co-payments for treatment of ‘self-inflicted’ conditions and levels of support were patterned by health-related behaviour and left-right political orientation. Our study suggests that a significant support for social responsibility does not exclude a strong support for personal health responsibility. However, conditional access to healthcare based on personal lifestyle is still controversial.

scholarly journals Quantification of Treatment Effect Modification on Both an Additive and Multiplicative Scale

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153010 ◽  
Author(s):  
Nicolas Girerd ◽  
Muriel Rabilloud ◽  
Philippe Pibarot ◽  
Patrick Mathieu ◽  
Pascal Roy
Keyword(s):  
Treatment Effect ◽  
Effect Modification
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