Dynamic Treatment Regimes for Managing Chronic Health Conditions
A Statistical Perspective
Bibliographic Data
| ID | 11026065 |
|---|---|
| Authors | Bibhas Chakraborty (0000-0002-7366-0478, Columbia University, corresponding author) |
| Year | 2011 |
| Volume | 101 |
| Issue | 1 |
| Pages | 40-45 |
| Publication date | 2011-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | American Journal of Public Health (JOURNAL) |
| Journal identifiers | ISSN: 0090-0036 • E-ISSN: 1541-0048 |
| Publisher | American Public Health Association (PUBLISHER • US) |
| DOI | 10.2105/ajph.2010.198937 |
| PMID | 21088260 |
| PMCID | PMC3000720 |
| OpenAlex | W2006132001 |
| Language | EN |
| Citations received | 1 |
| References cited | 21 |
Dynamic treatment regimes are an emerging and important methodological area in health research, particularly in the management of chronic health conditions. This paradigm encompasses the ideological shift in research from the acute care model to the chronic care model. It allows individualization of treatment (type, dosage, timing) at each stage of intervention. Constructing evidence-based dynamic treatment regimes requires implementation of cutting-edge design and analysis tools. Here I briefly discuss some of these modern tools, namely the sequential multiple assignment randomized trial (SMART) design and a regression-based analysis approach called Q-learning
Chronic disease · Data science · Economics · Health care · Ideology · Intensive care medicine · Intervention (counseling) · Management science · Perspective (graphical) · Political science · Research design · Risk analysis (engineering) · Social science · Sociology · Advanced Causal Inference Techniques · Artificial Intelligence · Computer Science · Health Policy Implementation Science · Health Systems, Economic Evaluations, Quality of Life · Medicine · Nursing · Psychology
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |