Helpful Resources

Contact us to join your grant team as co-investigators or consultants for instrument development, validation, expansion, analysis, and application proposals.

For Grant Proposals

Measures strongly influence the quality and effectiveness of data collected in a research study. Review factors to consider when choosing a measure for your research study.

Recognizing the value of research data that can be shared, compared and combined across studies, the National Institutes of Health (NIH) encourages investigators to use common data elements (CDEs) in basic, clinical, and applied research.

CDEs: Data elements that have been identified and defined for use in multiple data sets across different studies.

The NIH Resource Portal can help investigators identify NIH-supported CDEs--including PROMIS®, Neuro-QoL, and NIH Toolbox®--for use in protocols, case report forms, and other data collection instruments. The Portal includes tables showing CDEs for specific subject areas (e.g., neurology) and guidance for their use.
Currently, some NIH Funding Opportunity Announcements (FOAs) encourage investigators to consult the Portal and describe in applications how they will use NIH-supported CDEs.

Currently, the NIH scored review criterion “Approach” specifies the question, “Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project?” This criterion includes evaluating whether a measure is appropriate, and hence could influence the overall impact score of a grant or cooperative agreement proposal.

An NIH Institute & Center may sponsor a funding announcement (e.g., an RFA or PAR) and specify additional “review criteria” requiring that NIH peer reviewers evaluate if the grant or cooperative agreement proposal has been responsive to the use of an NIH-supported common data element (CDE) or a particular measure. The National Institute on Aging (NIA), for example, encourages the use of all four batteries of the NIH Toolbox in funded studies. If a proposal is not responsive to such specifications, this could influence the overall impact score.

Making the case for using a HealthMeasure

If you are considering a HealthMeasure for a study, you will have substantial evidence to evaluate. The questions you ask about that evidence can also serve as the framework for supporting your choice to others (e.g., granting agency). Here are some questions you should consider:

  • Why is it important to measure this construct or these constructs in my study? Describe the relevance of the symptom or outcome to the population of interest.
  • What psychometric evidence has accumulated when this measure was used in my targeted population? If you are not able to find a study in your population, weigh the evidence that exists for the measure across populations.
  • It is appropriate to consider evidence gathered about the validity of a measure, even if a different assessment strategy was used (e.g., one short form versus another, CAT versus short form). You should keep in mind that shorter short forms sacrifice some reliability for reduction in response burden.
  • What are the alternatives to the HealthMeasure? State clearly why you believe a HealthMeasure is a good choice, particularly for your population and for you particular purpose. Remember, validity resides in the use of the scores.

How to describe psychometric evidence for a grant proposal

Below is an example of reporting the psychometric properties of a PROMIS measure—PROMIS Sleep Disturbance. Remember, however, that the tone, length, and focus are context dependent. Consider the audience when describing the properties of a measure.

PROMIS Sleep Disturbance (PROMIS–SD)

The PROMIS-SD items assess self-reported perceptions of sleep quality, sleep depth, and restoration associated with sleep. This includes perceived difficulties and concerns with getting to sleep or staying asleep, as well as perceptions of the adequacy of, and satisfaction with, sleep. Sleep Disturbance does not focus on symptoms of specific sleep disorders; those symptoms are addressed in the PROMIS Sleep Related Impairment bank. The PROMIS-SD has demonstrated excellent validity as evidenced in associations with disease activity, depression, female sex, smoking, and use of corticosteroids or narcotics (N=3173; inflammatory bowel disease) (Ananthakrishnan, 2013), ability to distinguish among those with and without sleep disorders (Buysse, 2010), and prediction (along with negative affect) of global ratings of improvement in back pain (Karp, 2014). PROMIS-SD scores predicted return of active disease in a subsample of patients with Crohn’s disease (N=1291) in remission at baseline (Ananthakrishnan, 2013). Those with sleep disturbance, as measured by the PROMIS-SD, had a 2-fold increase in risk of active disease at six months (adjusted odds ratio, 2.00). The PROMIS-SD has been tested and exhibited validity evidence (e.g., expected associations, discrimination among known groups) in a wide range of populations including, but not limited to, parents in neonatal ICU (Busse, 2013), individuals with neurological conditions (Cook, 2012), patients with pelvic pain (Fenton, 2011), and head and neck cancer (Stachler, 2014).

Most domains in the HealthMeasures systems have manuscripts with detailed descriptions of instrument development (e.g., see development paper for smoking banks). Manuscripts also have been written describing the cross-domain instrument development and methodology. Review the Measure Development & Research pages for each measurement system on this site for guidance.