Qilu Yu

Senior Researcher, NIH

2 active projects

Duplicate of Hypothesis generation

The main focus is to study patterns and trends in individuals taking complementary medicines and interventions, as well as associations of these interventions with multi-health system outcomes. We hope to use study findings to generate hypotheses for planning of clinical…

Scientific Questions Being Studied

The main focus is to study patterns and trends in individuals taking complementary medicines and interventions, as well as associations of these interventions with multi-health system outcomes. We hope to use study findings to generate hypotheses for planning of clinical trials in whole-person health research.
Specific questions include:
1) What are the patterns and trends in complementary medicines such as yoga, acupuncture, mindfulness interventions etc.? What are the participants’ demographic characteristics and health/comorbidity conditions associated with combinations of interventions?
2) What are the associations between complementary medicines and behavioral interventions and health outcomes? Specifically, are there certain combinations of interventions associated with great improvement of multi-health system outcomes?
3) Are there latent traits in individuals associated with great multi-health system outcomes related to complementary medicines and interventions?

Project Purpose(s)

  • Population Health
  • Methods Development

Scientific Approaches

We plan to apply statistical methods for longitudinal data and machine learning to identify patterns, trends and associations. R will be used for all analyses. Specifically, we will:
1) Construct datasets with individuals that have measures of complementary medicines and behavioral interventions, and/or have provided answers to survey questions related to those interventions.
2) Use growth mixture modeling to identify sub-populations, to describe longitudinal change within each sub-population, and to examine differences in trends with respect to use of those interventions over time.
3) Use structural equation modeling to link identified sub-populations and trends to individual level health-related outcomes and study the associations.
4) Also use clustering techniques to explore cross-sectional data to identify combinations of individual characteristics associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.

Anticipated Findings

From this study we expect to:
1) Identify patterns and trends of complementary medicines and behavioral interventions applications.
2) Build constructs that characterize underlying traits of whole-person health.
3) Identify traits in individuals associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.
Study findings will provide important information in understanding the causal relationship between complementary interventions and health outcomes, and will be used to generate hypotheses of effectiveness of a combination of behavioral and other interventions on whole person health, which can be tested in future clinical trials.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care
  • Income Level

Research Team

Owner:

Hypothesis generation

The main focus is to study patterns and trends in individuals taking complementary medicines and interventions, as well as associations of these interventions with multi-health system outcomes. We hope to use study findings to generate hypotheses for planning of clinical…

Scientific Questions Being Studied

The main focus is to study patterns and trends in individuals taking complementary medicines and interventions, as well as associations of these interventions with multi-health system outcomes. We hope to use study findings to generate hypotheses for planning of clinical trials in whole-person health research.
Specific questions include:
1) What are the patterns and trends in complementary medicines such as yoga, acupuncture, mindfulness interventions etc.? What are the participants’ demographic characteristics and health/comorbidity conditions associated with combinations of interventions?
2) What are the associations between complementary medicines and behavioral interventions and health outcomes? Specifically, are there certain combinations of interventions associated with great improvement of multi-health system outcomes?
3) Are there latent traits in individuals associated with great multi-health system outcomes related to complementary medicines and interventions?

Project Purpose(s)

  • Population Health
  • Methods Development

Scientific Approaches

We plan to apply statistical methods for longitudinal data and machine learning to identify patterns, trends and associations. R will be used for all analyses. Specifically, we will:
1) Construct datasets with individuals that have measures of complementary medicines and behavioral interventions, and/or have provided answers to survey questions related to those interventions.
2) Use growth mixture modeling to identify sub-populations, to describe longitudinal change within each sub-population, and to examine differences in trends with respect to use of those interventions over time.
3) Use structural equation modeling to link identified sub-populations and trends to individual level health-related outcomes and study the associations.
4) Also use clustering techniques to explore cross-sectional data to identify combinations of individual characteristics associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.

Anticipated Findings

From this study we expect to:
1) Identify patterns and trends of complementary medicines and behavioral interventions applications.
2) Build constructs that characterize underlying traits of whole-person health.
3) Identify traits in individuals associated with great improvement of multi-health system outcomes related to complementary medicines and behavioral interventions.
Study findings will provide important information in understanding the causal relationship between complementary interventions and health outcomes, and will be used to generate hypotheses of effectiveness of a combination of behavioral and other interventions on whole person health, which can be tested in future clinical trials.

Demographic Categories of Interest

  • Race / Ethnicity
  • Age
  • Geography
  • Access to Care
  • Income Level

Research Team

Owner:

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