CAPSL Human

What is CAPSL and how does it support human research studies?

The Carolina Pragmatic Trials Laboratory (CAPSL) is a specialized research entity that supports the design and statistical analysis of pragmatic trials testing interventions in real-world settings. CAPSL incorporates the expertise of senior academic and consulting biostatisticians, post-doctoral researchers, and advanced graduate students to contribute novel clinical and translational science solutions. The laboratory specifically focuses on translating scientific discoveries into new treatments and approaches to medical care while simultaneously addressing health equity concerns for historically marginalized groups .

CAPSL has two primary focus areas that are particularly relevant to human research studies:

• Cluster randomized trials with increasingly complex structures, including multiple periods, multiple levels, and multi-component interventions

• Marginal modeling methods for stepped wedge designs and other sophisticated research approaches

What fundamental variables must be defined in human research experimental design?

Effective experimental design requires clearly defined variables from the very beginning of the research process. Most studies examining relationships between phenomena must carefully define:

• Independent variable: The stimulus, condition, or intervention being manipulated and controlled by the researcher

• Dependent variable: The subsequent effect or measured outcome resulting from changes in the independent variable

Beyond these primary variables, rigorous human research experimental design must also account for:

• Control variables: Factors that must be held constant across conditions

• Confounding variables: Unintended factors that might influence the relationship between independent and dependent variables

• Extraneous variables: External factors that need to be controlled or accounted for in the analysis

Proper conceptualization of all variables during the planning stage is essential for designing valid experimental protocols that can produce meaningful, reliable results in human research contexts.

What defines human subjects research according to regulatory frameworks?

Under 45 CFR 46, human subjects research has specific definitions that determine when institutional review and protections are required:

"Research" is defined as a systematic investigation designed to develop or contribute to generalizable knowledge, including research development, testing, and evaluation.

A "Human Subject" is defined as a living individual about whom an investigator conducting research obtains:

• Data through intervention or interaction with the individual, or

• Identifiable private information

How can researchers effectively implement cluster randomized trials in human research?

Cluster randomized trials represent an increasingly important methodological approach in human research, particularly when interventions must be administered at a group level. Based on CAPSL's expertise, effective implementation involves addressing several key factors:

Common Design Types:

• Parallel cluster designs

• Cluster crossover designs

• Stepped wedge designs

Data Collection Approaches:

• Cohort samples (following the same participants)

• Repeated cross-sectional samples (different participants at each time point)

Implementation Settings:
Cluster randomized trials can be conducted in various "cluster" settings, including:

Statistical Challenges:
When implementing cluster randomized trials, researchers must address:

• Small numbers of clusters, which create challenges for valid design and statistical analysis

• Intracluster correlation, which reduces effective sample size

• The need for specialized analytic approaches that account for clustering effects

What approaches are recommended for handling data contradictions in human research?

Contradictions in data represent a significant challenge to research quality. A structured approach to identifying and addressing contradictions involves understanding their patterns and implementing appropriate assessment frameworks:

Contradictions in data quality are typically understood as impossible combinations of values in interdependent data items. While handling a single dependency between two data items is well established, more complex interdependencies require more sophisticated approaches .

A formal notation for contradiction patterns using three parameters (α, β, θ) has been proposed:

• α: the number of interdependent items

• β: the number of contradictory dependencies defined by domain experts

• θ: the minimal number of required Boolean rules to assess these contradictions

The implementation of a structured classification for contradiction checks allows:

• Scoping of different contradiction patterns across multiple domains

• Effective support for implementing generalized contradiction assessment frameworks

• Improved handling of multidimensional interdependencies within health data sets

What funding mechanisms support innovative human research studies?

Various funding mechanisms exist to support innovative human research, particularly in social, behavioral, and public health domains. For example:

The CAPS Research Awards (formerly CAPS-HIV Innovative Grants) provides support for innovative research with an award amount of $50,000. These awards support either:

• Innovative Pilot Study (non-mentored)

• Mentored Science Award in innovative research

Priority funding areas include social, behavioral, and public health-related approaches to:

• Reduce the incidence of disease

• Develop innovative strategies for next-generation therapies

• Address comorbidities, coinfections, and complications

• Make progress toward curative approaches

Special emphasis is placed on:

• Populations and regions experiencing disproportionate impact

• Vulnerabilities and protective factors associated with intersecting identities

• Translational research that ensures effective prevention and care strategies

What statistical methods does CAPSL recommend for analyzing pragmatic trials?

CAPSL specializes in statistical methods for pragmatic trials in real-world settings, with particular expertise in complex trial designs. Key methodological approaches include:

Marginal Modeling Methods:
CAPSL has advanced marginal modeling methods for analyzing data from stepped wedge designs and other complex trial structures. These methods are particularly valuable when working with:

• Multiple time periods

• Multiple levels of clustering

• Multi-component interventions

Specialty Expertise Available:
CAPSL's panel of statistical experts provides collaborative support in numerous specialized areas relevant to human research:

This range of expertise allows CAPSL to contribute novel clinical and translational science solutions for the statistical design and analysis of human research across diverse contexts and populations .

What review processes are required before initiating human subjects research?

Before any human subject research can commence, specific review processes must be completed:

All human subject research must undergo proper review and approval by authorized Human Research Protection Program (HRPP) personnel. This applies to all forms of research activity, including recruitment, consent, and data collection .

The review process typically follows these steps:

• Determination if the protocol meets the definition of research

• If deemed research, evaluation to determine if it meets the definition of human subjects research

• For protocols meeting both definitions, submission of a complete protocol packet for review

• Review by the appropriate Institutional Review Board (IRB)

As part of the determination process, the HRPP and IRB may:

• Recommend approval

• Require modifications

• Withhold approval

• Suspend research involving human subjects

The protocol packet must include comprehensive documentation as outlined by the specific research institution. For specific requirements, researchers should contact their institution's HRPP Point of Contact .

How are ethical considerations addressed in research with marginalized populations?

Ethical considerations for research involving marginalized populations require special attention to vulnerabilities, protections, and equitable benefits. Based on the available information:

CAPSL specifically aims to "achieve translational benefits, tackle translational roadblocks, and address health equity—all in support of TraCS-wide efforts to translate scientific discoveries into new treatments and approaches to medical care while reducing health disparities for historically marginalized groups" .

When designing research involving vulnerable populations, researchers should:

• Ensure equitable selection of research subjects

• Implement additional safeguards to protect rights and welfare

• Consider the intersection of identities based on race, sexual orientation, class, gender, mental health, geography, socioeconomic status, and other social factors

• Address potential power imbalances in the research relationship

• Ensure that benefits of research extend to the communities being studied

What advantages do capsule-based methodologies offer in human research studies?

Capsule-based methodologies provide several advantages in human research studies, particularly in pharmaceutical and biomedical contexts:

Methodological Advantages:

• Allow for uncompressed powders, enabling quicker dissolution and absorption compared to tablets, which may provide more accurate pharmacokinetic data

• Offer versatility to prepare precise doses for various administration routes (oral, inhalation, rectal, or to be diluted for vaginal, rectal, oral, or topical use)

• Can be designed to alter drug release rates, providing options for time-course studies

• Enable masking of unpleasant tastes, aromas, or appearance of drugs, potentially improving participant compliance and reducing bias in studies

Research Limitations to Consider:

• Subject to the effects of relative humidity and microbial contamination, requiring controlled storage conditions

• More expensive than alternative formulations, impacting research budgets

• May be difficult for some research participants to swallow

• Can be tampered with, requiring additional security measures in certain research contexts

For human research applications, these properties make capsules particularly valuable for studies requiring:

• Precise dose control

• Participant blinding

• Flexible administration routes

• Modified release profiles

How can structured contradiction checks improve data quality in human research?

A formal notation system using parameters (α, β, θ) helps researchers conceptualize and implement contradiction checks:

• α represents the number of interdependent items

• β represents the number of contradictory dependencies defined by domain experts

• θ represents the minimal number of required Boolean rules to assess these contradictions

Analysis of data quality assessment tools reveals that most existing packages implement only basic contradiction checks, classified as (2,1,1) - meaning they only check relationships between two variables using a single rule .

More complex contradiction patterns, particularly in biobank and COVID-19 research domains, demonstrate that the minimum number of Boolean rules required might be significantly lower than the number of described contradictions, allowing for more efficient implementations .

Benefits of implementing structured contradiction checks include:

• Improved data quality through systematic identification of impossible value combinations

• Enhanced efficiency in data cleaning and validation processes

• Better handling of complex multidimensional interdependencies

• Support for implementing generalized contradiction assessment frameworks across multiple domains