What does outcome measures mean in research?

What does outcome measures mean in research?

Outcome measures are a key part of the research. Experts carry out research by planning to evaluate the results of their studies using specific outcome measures. These measures could be:

  • Looking at results for tests.
  • Reviewing xyz (any disease) disease control questionnaires or xyz disease control test scores.
  • Measuring the number of xyz disease attacks, doctor’s office or emergency department visits a patient has in a set period of time.
  • Reviewing of a type of xyz disease, medication use or its symptoms.

There is a relatively new outcome measure called Patient-Reported Outcome Measures (PROMS). Patients-Reported Outcome Measures tell us about treatment outcomes reported by patients and caregivers.

PROMS focus on:

  • Quality of life of patients.
  • Impact of the interventions in the treatment of a patient and on patient?s life.

It is very challenging to develop outcome measures. Here we will discuss 5 pointers for making rapid advancement in developing outcomes measures for research purposes.

These 5 recommendations are:

1. Future research studies should clearly state the expected outcome of treatment, and how it will be measured:

For trials of medical efficacy, investigators should clearly specify what changes they expect from the treatments. Depression and anxiety-related examples include effects on the personal relationships of a patient?. These specific domains (s) of expected treatment-related change should clearly define the medical trial outcome(s). And it is quite important to provide some explanation as to why that particular intervention tool was used. The chosen intervention tool should also have been used in the past for responsiveness to the potential treatment-related change in the appropriate patient population. There should be well defined and clear guidelines for choosing the most appropriate intervention tool.

2. Future research studies should be able to differentiate between instruments developed to discriminate between patients and instruments developed to detect treatment-related change:

It is strenuous to design a questionnaire instrument that is both discriminative and evaluative. For a better understanding to illustrate with an example, personal relationships and communication difficulties, but only one of these two problems might respond to treatment (for example, psychiatric therapy for depression

Should improve personal relationships, but might not necessarily reduce communication difficulties). By taking the average score for these two different outcome domains could lead to compromise with the sensitivity of the composite score for measuring treatment-related change. Questionnaire instruments which have good statistical properties are able to successfully measure therapeutic benefits and enable investigators to interpret specific complaints, rather than non-specific complaints like a handicap or extreme conditions.

3. Future research studies should interpret whether the outcome of the treatment is clinically beneficial to the patient:

If a trial achieves a statistically significant benefit that does not necessarily mean that this trial will also be clinically beneficial. Statistics is all about figures. It is possible for a trial to show a significant change having a positive impact, albeit the size of that change is very small. For example, if a trial enrolls a large number of patients who have low variance in their scores, well in this case what is more important is whether the size of the change is significant from a clinical perspective. There are various research methods which assign numerical values to clinical meaning, often termed as the Minimal Important Difference (MID) or Minimal Clinically Important Difference (MCID). Only the most reliable methods take into account the perspective of a patient, which is very important.

4. Future research studies should tend to reduce the diversity of outcome instruments:

Recent reviews of instruments for measuring outcomes in case of depression and research on depression has shown weird reasons for leading a person to depression. For example, there are hundreds of different measurement tools for assessing depression issues. For investigators, it is not only confusing but it also makes the comparison between studies and reality impossible. Investigators can find solutions that are feasible to control, have good construct validity, and have good statistical properties, but it will have a widespread impact only by working together and following consensus guidelines.

5. Future research studies should properly describe both positive and negative outcomes:

In order to be in good books and to look great, everyone pays attention to highlighting only the benefits of an intervention and hardly pays any attention towards describing the negative impacts of an intervention. One way to reduce such biases is to always consider those outcomes which are related to the safety of patient since patient safety is just as important as those relating to clinical efficacy. These can include but are not totally restricted to, treatment-related serious reactions?adverse cases treatment adherence and withdrawals from the study?. Another way to reduce such biases of highlighting the positive outcomes and neglecting the negative outcomes is to publish clinical trial design and statistical analysis plan, meanwhile, the data are still being collected. This includes primary and secondary outcomes and stating how they can be analyzed. There are various public registries that provide such type of publishing services and nowadays it is becoming increasingly popular and common to publish a detailed clinical protocol in peer-reviewed journals.

Nothing is easy and so as developing good outcome measures, it requires a significant amount of time, effort, skills and proper understanding of the subject to develop good outcome measures, but surely the above mentioned 5 recommendations can guide and be helpful to future research studies, and overcome current limitations.

The current limitations to overcome are:

  • Existence of diversity of outcome instruments.
  • Negative outcomes of an intervention are not highlighted properly.
  • Ignorance towards interpreting whether the outcome is clinically beneficial to the patient.
  • The importance is not given towards differentiating between instruments developed to discriminate between patients and those instruments developed to find out treatment-related change.
  • The expected outcome of treatment is not stated in prior and how to measure the outcome is also not clearly defined.

Hence, to overcome these current limitations, those 5 recommendations will be very helpful.

The outcome is the end result of a treatment which is monitored and analyzed during a study or research to measure the impact (positive, negative or negligible) that a given intervention or treatment has on the health of a given population. There are various measures in a research study to which patients and caregivers respond, these research measures include questionnaire, case studies, constructed situations, interview questions, survey questions. All these constructed questions should be related to the research purpose.

It is difficult to develop outcome measures for research but outcome measures are the most important instrument for research purpose. As an outcome measure within medical practice or research is used to assess the impact of an intervention or treatment, both positive and negative. For research it is very important to think critically about what you are trying to measure, first, we should state the outcome interest and clearly define the way of measuring the outcome.