HTA M2 START

• Health Outcome Variables


  Measure the safety, efficacy and effectiveness of health care technologies
• Main categories of health outcomes

  • Mortality (death rate)
  • Morbidity (disease rate)
  • Adverse health events (e.g., harmful side effects)
  • Quality of life
  • Functional status
  • Patient satisfaction
• Examples of health outcome variables

  • Cancer treatment: five-year survival rate
  • Coronary artery disease: incidence of fatal and nonfatal acute myocardial infarction (heart attack) and recurrence of angina pectoris (chest pain due to poor oxygen supply to the heart)
• Although mortality, morbidity, and adverse events are usually the outcomes of greatest interest, other types of outcomes are often important as well to patients and others
• Many technologies affect patients, family members, providers, employers, and other interested parties in other important ways; this is particularly true for many chronic diseases
• Biomarkers


  Objectively measured variable or trait that is used as an indicator of a normal biological process, a disease state, or effect of a treatment
• Intermediate Endpoint


  A non-ultimate endpoint (e.g., not mortality or morbidity) that may be associated with disease status or progression toward an ultimate endpoint such as mortality or morbidity
• Surrogate Endpoints


  Used as a substitute for a clinical endpoint of interest, such as morbidity and mortality
• Surrogate endpoints should be highly correlated with changes in the clinical endpoint
• Health-related quality of life measures or indexes


  Used to assess efficacy and effectiveness, providing a more complete picture of the ways in which health care affects patients
• Dimensions or domains of health-related quality of life measures

  • Physical function
  • Social function
  • Cognitive function
  • Anxiety/distress
  • Bodily pain
  • Sleep/rest
  • Energy/fatigue
  • General health perception
• Examples of widely used generic health-related quality of life measures

  • CAHPS (formerly Consumer Assessment of Healthcare Providers and Systems)
  • EuroQol (EQ-5D)
  • Health Utilities Index
  • Nottingham Health Profile
  • Quality of Well-Being Scale
  • Short Form (12) Health Survey (SF-12)
  • Short Form (36) Health Survey (SF-36)
  • Sickness Impact Profile
• Considerable advances have been made in the development and validation of generic and disease-specific quality of life measures since the 1980s
• Quality of life measures are increasingly used by health product companies to differentiate their products from those of competitors
• Health-Adjusted Life Years (HALYs)


  Recognize that changes in an individual's health status or the burden of population health should reflect not only the dimension of life expectancy but a dimension of quality of life or functional status
• Main types of HALYs

  • Quality-adjusted life years (QALYs)
  • Disability-adjusted life years (DALYs)
  • Healthy-years equivalents (HYEs)
• Quality-Adjusted Life Years (QALYs)


  A unit of health care outcome that combines gains (or losses) in length of life with quality of life
• QALYs provide a common unit for multiple purposes, including: estimating the overall burden of disease; comparing the relative impact on personal and population health of specific diseases or conditions, comparing the relative impact on personal and population health of specific technologies; and making economic comparisons, such as of the cost-effectiveness
• Disability-Adjusted Life Years (DALYs)


  Measure of something 'lost' rather than something 'gained', and the burden of disability depends on one's age
• The origins of quality of life weights and disability weights are different
• Screening and Diagnostic Tests

  Provide information about the presence of a disease or other health condition, and must be able to discriminate between patients who have a particular disease or condition and those who do not have it
• Uses of tests for asymptomatic and symptomatic patients

  • Asymptomatic patients: Susceptibility, Presence of (hidden or occult) disease
  • Symptomatic patients: Diagnosis, Differential diagnosis, Staging, Prognosis, Prediction, Surveillance, Monitoring
• Factors affecting the performance of screening and diagnostic tests can have a great effect on the probability that the test result truly indicates whether or not a patient has a given disease or other health condition
• Diagnosis


  Presence of a particular disease or condition (e.g., thyroid tests for suspected hyperthyroidism)
• Differential diagnosis


  Determine which disease or condition a patient has from among multiple possible alternatives (e.g., in a process of elimination using a series of tests to rule out particular diseases or conditions)
• Staging


  Extent or progression of a disease (e.g., imaging to determine stages of cancer)
• Prognosis


  Probability of progression of a disease or condition to a particular health outcome (e.g., a multi-gene test for survival of a particular type of cancer)
• Prediction


  Probability of a treatment to result in progression of a disease or condition to a particular health outcome (e.g., a genetic test for the responsiveness of colorectal cancer to a particular chemotherapy)
• Surveillance


  Periodic testing for recurrence or other change in disease or condition status
• Monitoring


  Response to treatment (e.g., response to anticoagulation therapy)
• Factors affecting technical performance of test

  • Precision and accuracy
  • Observer variation
  • Disease and the designated cut-off level
  • Detect disease when it is present and not detect disease when it is not present
• Technical performance


  Based on the probabilities of the four possible types of outcomes of a test
• Sensitivity


  Measures the ability of a test to detect a particular disease (e.g., a particular type of infection) or condition (a particular genotype) when it is present
• Specificity


  Measures the ability of a test to correctly exclude that disease or condition in a person who truly does not have that disease or condition
• Sensitivity and specificity are independent of the true prevalence of the disease or condition in the population being tested
• Biomarker


  A certain cut-off level of one or more variables for certain diseases or conditions
• Examples of variables used for biomarkers

  • Systolic and diastolic blood pressure for hypertension
  • HbA1c level for type 2 diabetes or NIDDM
  • Coronary calcium score for coronary artery disease
  • High-sensitivity cardiac troponin T for acute myocardial infarction
• Biomarkers used to detect diseases have distributions in non-diseased as well as in diseased populations
• Cut-off point


  A point set to detect more true positives will also yield more false positives; a cut-off point set to detect more true negatives will also yield more false negatives
• There are various statistical approaches for determining "optimal" cut-off points, and the selection should consider the acceptable risks of false positives vs. false negatives