Chapter 14 - Biostatistics Flashcards
Editor’s role
The editor of the journal selects potential publications and sends them to experts in the topic area for peer review.
Peer review’s role:
Assess
- the research design and methods
- the value of the results and conclusions to the field of study
- how well the manuscript is written
- whether it is appropriate for the readership of the journal.
Reviewers role:
- The reviewers make a recommendation to the editor to either accept the article (usually with revisions) or reject it.
- Data that contradicts a previous recommendation, or presents new information, can change treatment guidelines.
What are the 6 STEPS TO JOURNAL PUBLICATION
1) Begin with a research question:
Write a null hypothesis to answer the research question, such as: New drug is not as effective as current drug.
2) Design the study:
Is it randomized, placebo-controlled, a case-control or other type of study?
3) Enroll the subjects:
Assign to a treatment group or control group, or identify subjects belonging to a cohort or other group.
4) Collect the data:
- Prospectively (going into the future for a set period of time) or
- Retrospectively (looking back in time using medical records).
5) Analyze the data: Enter the data into statistical software; assess the results (e.g., risk reductions, confidence intervals).
6) Publish
Types of study data:
1) Continuous Data:
- Interval Data: No meaningful 0 (0 Celsius)
- Ratio Data: Meaningful 0 (HR: 0 bpm)
2) Discrete (Categorical) Data
- Nominal (Arbitrary order): M/F or Y/N
- Ordinal (Logical order): Pain scale (4 is not double 2)
Quantity
Quality
Measures of central tendency: (Define)
■ Mean:
- Average value
- Add up the values & divid the sum by the # of values.
- Preferred for CONTINUOUS data that is NORMALLY distributed
■ Median:
- The value in the middle when the values are arranged from lowest to highest.
- When there are two center values, take the average of the two center values.
- Preferred for ORDINAL data or CONTINUOUS data that is SKEWED (not normally distributed).
■ Mode:
- Value that occurs most frequently.
- Preferred for NOMINAL data.
SPREAD (VARIABILITY) OF DATA
■ Range:
The difference between the highest and lowest values.
■ Standard deviation (SD):
Indicates how spread out the data is, and to what degree the data is dispersed away from the mean
A large number of data values close to the mean has a smaller SD.
Data that is highly dispersed has a larger SD.
GAUSSIAN OR NORMAL DISTRIBUTION
- Characteristics
- Bell-shaped distribution
- Large sample sets of CONTINUOUS data
- Curve is symmetrical
- Most of values closer to the middle
- Mean, Median, Mode are at the CENTER and SAME value
- 68% of the values fall within 1 SD of the mean
- 95% of the values fall within 2 SDs of the mean.
SKEWED DISTRIBUTIONS
- Curve is not symmetrical
- 68% of data do not fall under 1 SD from the mean
- Mean, median and mode are not the same value
- Small sample size &/or there’s outliers in data
- The MEDIAN is a better measure of central tendency
- As the number of values inc, the effect of outliers of the mean dec
Data is skewed towards the outliers
1) Positive (Right) skewed:
- Data is more to the LEFT
2) Negative (Left) skewed:
- Data is more to the RIGHT
What are the types of endpoints to the study’s variables
- Clinical endpoint: hospitalization, death…
- Intermediate (Surrogate) endpoint: measuring CrCl to assess renal fcn
What are the Dependent & Independent Variables?
- Dependent: The outcome (hosp, death…)
- Independent: Chosen by researcher (to determine whether it has an effect on dependent variable or outcome), characteristics of pts, inclusion/ exclusion criteria (age, gender…)
Null hypothesis Vs Alternative Hypothesis
Ho: null
- no statistical difference
- should be rejected
Ha: alternative
- statistical diff
- Should be accepted
They are complementary
What is the alpha level or standard for significance?
- Max permissible error margin
- Set by researchers
- Commonly 5% or 0.05 (should be <0.05)
- The threshold of rejecting the null hypothesis
- If they want to chose a lower alpha (1%); requires:
- More data –> more expensive
- Larger ttmt effect
- It correlates to the values in the tails when the data has a normal distribution
What is the P-value and how is it used in assessing statistical significance?
1) Researcher determine alpha
2) Statistical tests are done to compare data
3) P-value is calculated; i need p-value < alpha
- if alpha is 5%,
p-value <5 –> reject null hypothesis and stat sig
p-value >= 5 –> accept null hypothesis and no stat sig
What is the Confidence Interval?
It shows 2 main things, what are they?
- Shows significance and precision
- CI = 1 - Alpha
If a=0.05, and p-value turned out to be < 0.05, then
I am 95% confident that the conclusion is correct and there is 5% chance that it is not –> STAT SIG
The result is stat sig if the CI does not cross 0 (diff) or 1 (ratio)
- Narrow CI –> high precision (preferable)
- Specific pt types will cause wider distribution in data
(Pts with high TG levels will experience a higher drop of TG)
Type I Error (False Positive)
- The alternative hypothesis was accepted and the null hypothesis was rejected IN ERROR.
- The probability, or risk, of making a type I error is determined by ALPHA and it relates to the CONFIDENCE INTERVAL.
CI = 1 - Alpha
- When alpha is 0.05 and a study result is reported with P-value < 0.05, it is statistically significant and the probability of a type I error (making the wrong conclusion) is < 5%
- You are 95% confident (0.95 = 1- 0.05) that your result is correct and not due to chance.
Type II Error (False Negative)
- Denoted as beta
- Null hypothesis is accepted when it should have been rejected.
- Beta is set by the investigators during the design of a study.
- Set at 0.1 or 0.2, meaning the risk of a type II error is 10% or 20%.
- The risk of a type II error increases if the sample size is too small.
- To decrease this risk, a power analysis is performed to determine the sample size needed to detect a true difference between groups.
Study Power
- Power is the probability that a test will reject the null hypothesis correctly (i.e., the power to avoid a type II error).
Power = 1 - beta
- As the power increases, the chance of a type II error (Beta) decreases.
- Power is determined by the number of outcome values collected, the difference in outcome rates between the groups, and the significance (alpha) level.
- If beta is set at 0.2, the study has 80% power (there is a 20% chance of missing a true difference and making a type II error).
- If beta is set at 0.1, the study has 90% power. A larger sample size is needed to increase study power and decrease the risk of a type II error.
Risk
- Probability of an event to occur when the intervention is given
Relative risk or risk ratio
Risk = (Number of subjects in group with an unfavorable event) / (Total number of subjects in group)
RR = (Risk in treatment group)/ (Risk in control group) x100
- RR= 1 (or 100%) implies no difference in risk of the outcome between the groups.
- RR> 1 (or 100%)implies greater risk of the outcome in the treatment group.
- RR< 1 (or 100%) implies lower risk (reduced risk) of the outcome in the treatment group.
ex: RR of HF progression was 57%.
Patients treated with metoprolol were 57% as likely to have progression of disease as placebo-treated patients.
A placebo-controlled study was performed to evaluate whether metoprolol reduces disease progression in patients with heart failure (HF).
A total of 10,111patients were enrolled and followed for 12months.
What is the relative risk of HF progression in the metoprolol-treated group versus the placebo group?
Calculate the risk of HF progression in each group. Then calculate RR.
METOPROLOL
- N = 5,123
- HF progression: 823
CONTROL
- N = 4,988
- HF progression: 1,397
Metoprolol Risk:
823/ 5,123 = 0.16
Control Risk:
1,397/ 4,988 = 0.28
RR = R/R = 0.16/0.28 = 0.57
57%
The RR of HF progression was 57%.
Patients treated with metoprolol were 57% as likely to have progression of disease as placebo-treated patients.
Relative risk ratio
- Indicates how much the risk is reduced in the treatment group compared to the control group.
RRR = (% risk in control group - % risk in treatment group) / % risk in the control group
OR
RRR = 1 - RR (use RR in decimal)
ex: The RRR is 43%.
Metoprolol-treated patients were 43% less likely to have HF progression than placebo-treated patients.
RR + RRR =
1
RR as likely
RRR less likely
The RR and RRR provide relative (proportional) differences in risk between the treatment group and the control group; they have no meaning in terms of absolute risk.
Absolute Risk Reduction
- Absolute risk reduction is more useful because it includes the reduction in risk and the incidence rate of the outcome.
- If the risk of nausea is reduced, but the risk was small to begin with (perhaps the drug caused very little nausea), the large risk reduction has little practical benefit.
- It is best if a study reports both ARR and RRR
ARR = (% risk in control grp) - (% risk in treatment grp)
interpretation:
The ARR is 12%, meaning 12 out of every 100 patients benefit from the treatment.
Said another way, for every 100 patients treated with metoprolol, 12 fewer patients will have HF progression.
Number needed to Treat or Harm
NNT:
- NNT is the number of patients who need to be treated for a certain period of time (e.g., one year) in order for one patient to benefit (e.g.,avoid HF progression).
NNT = 1 / (Risk in control group - Risk in ttmt group)
NNT = 1 / ARR
Risk and ARR are expressed as decimals
Round up to the nearest 1
Interpretation:
For every 9 patients who receive metoprolol for one year, HF progression is prevented in one patient.
NNH:
- NNH is the number of patients who need to be treated for a certain period of time in order for one patient to experience harm.
- Same formula as NNT
- Round down to nearest one
Odds Ratio
- Odds are the probability that an event will occur versus the probability that it will not occur
- Case-control studies, are not suitable for relative risk calculations. In case-control studies, the odds ratio is used to estimate the risk of unfavorable events associated with a treatment or intervention. (an be used in cohort and cross-sectional studies.)
- The odds ratio (OR) is used to calculate the odds of an outcome occurring with an exposure, compared to the odds of the outcome occurring without the exposure.
exposure/ttmt——outcome present —-Absent
Present ——————A ———————-B
Absent——————–C————————-D
OR = AD / BC
OR = (# that have the outcome, with exposure) x (# without the outcome, without exposure) / (# without the outcome, with exposure) x (# that have the outcome, without exposure)
Hazard ratio
- Hazard Risk: if outcome is death or progression of disease (use instead of RR)
- Same formula as RR
Primary vs Composite outcome
1) Primary Endpoints (distinct and separate)
- Death from cardiovascular causes or
- Nonfatal stroke or
- Nonfatal Ml
2) Composite Endpoint (combined into one)
- Death from cardiovascular causes and
- Nonfatal stroke and
- Nonfatal Ml
Cautions in Composite endpoints
- All endpoints must be similar in magnitude and have similar, meaningful importance to the patient.
The value of the sum of the individual endpoints may not equal the value of the composite endpoint, since a patient can have more than one non-fatal endpoint during a trial.
What are the 2 types of distribution?
What are the types of statistical tests and when do you use them?
- Parametric: normal distribution
- Non-parametric: not normally distributed
T-test:
- Parametric Method
- Used in continuous data + normal distribution
One-sample t-test:
- When data from a single sample group is compared with known data from the general population.
Paired t-test
- If a single sample group is used for a pre-/post-measurement (i.e., the patient serves as their own control).
Student t-test
- Used when the study has two independent samples: the treatment and the control groups.
Anova (F-test):
- Analysis of variance (ANOVA), or the F-test, is used to test for statistical significance when using continuous data with 3 or more samples, or groups.
Chi-SquareTest
- For nominal or ordinal data, a chi-square test is used to determine statistical significance between treatment groups.
Correlation:
- What is it used to determine?
- When is the direction of the correlation positive?
- When is it negative?
- What tests do you use if the data is ordinal, ranked
- What tests do you use if the data is continuous
- Can you conclude from a correlation analysis that a change in variable causes the change in another variable?
- Can a + or - correlation prove a causal relationship?
- Used to determine if one variable changes, or is related to, another variable.
- When the independent variable (number of hospital days) causes the dependent variable (infections) to increase, the direction of the correlation is positive (increases to the right).
- When the independent variable causes the dependent variable to decrease, the direction of the correlation is negative (decreases to the right).
Different types of data require different tests for correlation.
1) Ordinal, ranked data:
- Spearman’s rank-order correlation, referred to as Rho.
2) Continuous data:
- Pearson’s correlation coefficient, denoted as r
- Is a calculated score that indicates the strength and direction of the relationship between two variables.
- The values range from -1 to +l
- It is not possible to conclude from a correlation analysis that the change in a variable causes the change in another variable.
- A correlation, whether positive or negative, does not prove a causal relationship.
- What is Regression?
- Where is it commonly used?
- What are the 3 typical types of regression?
- Used to describe the relationship between a dependent variable and one or more independent variables, or how much the value of the dependent variable changes when the independent variables changes.
- Common in observational studies where researchers need to assess multiple independent variables or need to control for many confounding factors.
There are three typical types of regressions:
1) Linear, for continuous data,
2) Logistic, for categorical data,
3) Cox regression, for categorical data in a survival analysis.
SENSITIVITY
- true + or true -ve?
- Define it
- Do we need higher or lower sensitivity
- How is it calculated?
- THE TRUE POSITIVE
- How effectively a test identifies patients with the condition.
- The higher the sensitivity, the better;
- A test with 100% sensitivity will be POSITIVE in ALL patients WITH the CONDITION.
- Sensitivity is calculated from the number of patients who test positive, out of those who actually have the condition (sensitivity is the percentage of “true-positive” results).
eza hada aando covid, the test akid ha ybayyen eno aando covid
bs eza ma aando covid, fi chance eno ybayyen eno either maao aw no
SPECIFICITY
- Is it true + or -?
- Define it
- How is it calculated?
- THE TRUE NEGATIVE
- How effectively a test identifies patients without the condition.
- The higher the specificity the better
- A test with 100% specificity will be NEGATIVE in all patients WITHOUT the CONDITION.
- Specificity is calculated from the number who test negative, out of those who actually do not have the condition (specificity is the percentage of “true-negative” results).
eza hada ma maao cancer, l test ha yfarje eno akid ma maao cancer
bs eza maao cancer, fi either yfarje eno maao aw no
A sensitivity of 28% means:
A specificity of 87% means:
1) A sensitivity of 28% means that:
- Only 28% of patients with the condition will have a positive result;
- The test is negative in 72% of patients with the disease (and the diagnosis can be missed).
2) A specificity of 87% means that:
- The test is negative in 87% of patients without the disease;
- 13% of patients without the disease can test positive (potentially causing an incorrect diagnosis).
Intention to treat Vs Per protocol
1) Intention-to-treat analysis:
- Includes data for all patients originally allocated to each treatment group (active and control) even if the patient did not complete the trial according to the study protocol (e.g., due to non-compliance, protocol violations or study withdrawal).
- Provides a conservative (real-world) estimate of the treatment effect.
2) A per protocol analysis:
- Population who completed the study according to the protocol (or at least without any major protocol violations).
- Provide an optimistic estimate of treatment effect since it is limited to the subset of patients who were adherent to the protocol.
non inferiority trials
- It is based on:
- Define
- Not less effective
- Based on the predefined non-inferiority (delta) margin.
- The delta margin is the minimal difference in effect between the two groups that is considered clinically acceptable based on previous research.
equivalence trials
same effect as old drug
These trials test for effect in two directions, for higher or lower effectiveness, which is called a two-way margin.
Forest Plot
- In what types of study could it be used?
- What do the boxes indicate?
- What does the diamond at the bottom represents?
- What about the horizontal lines?
- What about the vertical solid line?
- Can be used for a single study in which individual endpoints are pooled (gathered together) into a composite endpoint
- Used when the results from multiple studies are pooled into a single study, such as with a meta-analysis
The boxes:
- Effect estimate
- In a meta-analysis, the size of the box correlates with the size of the effect from the single study shown.
Diamonds (at the bottom of the forest plot):
- Represent pooled results from multiple studies.
The horizontal lines through the boxes:
- Length of the confidence interval for that particular endpoint (in a single study) or for the particular study (in a meta-analysis).
- The longer the line, the wider the interval, and the less reliable the study results.
- The width of the diamond in a meta-analysis serves the same purpose.
The vertical solid line:
- Is the line of no effect;
- A significant benefit has been reached when data falls to the left of the line; data to the right of the line indicates significant harm.
- Zero for difference data
- One for ratio data
- What is a Meta-analyses?
- What are its benefits?
- What are its limitations?
- Analyzes the results of multiple studies
- Develops a conclusion that has greater statistical power than is possible from the individual smaller studies.
Benefits
- Smaller studies can be pooled instead of performing a large, expensive study.
Limitations
- Studies may not be uniform (size, inclusion and exclusion criteria, etc).
- Validity can be compromised if lower quality studies are weighted equally to higher quality studies.
2) SYSTEMATIC REVIEW ARTICLE:
- Define
- Benefits
- Summary of the clinical literature that focuses on a specific topic or question
(e.g., treatment options for a condition) - Begins with a question followed by a literature search, then the information is summarized, and sometimes includes a meta-analysis to synthesize results.
Benefits
- Inexpensive (studies already exist).
3) Randomized controlled trials:
- Prospective or retrospective?
- Benefits?
- Limitations?
Differentiate between:
- Parallel RCT
- Crossover RCT (its benefit? limitation?)
- Factorial
- Prospective
- Compares an experimental treatment to a control
- Randomized (have an equal chance
of being assigned to the treatment or control group) - Sometimes blinded (double-blind design)
Benefits
- Preferred study type to determine cause and effect or superiority.
- Less potential for bias.
Limitations
- Time-consuming and expensive.
- May not reflect real-life scenarios (when rigorous exclusion criteria are used).
——————————
1) PARALLEL RCT (most common type)
Subjects are randomized to the treatment or control arm for the entire study.
——————————
2) CROSSOVER RCT
- Patients are randomized to one of two sequential treatments:
Group 1 - receive treatment A first, then crossover (change) to treatment B.
Group 2 - receive treatment B first, then crossover (change) to treatment A.
Benefits
- Patients serve as their own control; this minimizes the effects from confounders.
Limitations
- A washout period is needed to minimize the influence of the first drug during the second treatment.
——————————
3) FACTORIAL DESIGN
- Randomizes to more than the usual two groups to test a number of experimental conditions.
Benefits
- Evaluates multiple interventions (multiple drugs or dosing regimens) in a single experiment.
Limitations
- With each arm added, more subjects are needed to have adequate power.
4) Cohort Study
- Compares outcomes of a group of patients EXPOSED and NOT EXPOSED to a treatment;
- The researcher follows both groups PROSPECTIVELY (in the future) or RETROSPECTIVELY (less common) to see if they develop the outcome.
Benefits
- Good for looking at outcomes when the intervention would be unethical.
Limitations
- More time-consuming and expensive than a retrospective study.
- Can be influenced by confounders, which are other factors that affect the outcome (e.g.,smoking, lipid levels).
5) CASE-CONTROL STUDY
- Compares patients with a disease (cases) to those without the disease (controls).
- The outcome of the cases and controls is already known, but the researcher looks back in time (RETROSPECTIVELY) to see if a relationship exists between the disease (outcome) and various risk factors.
Benefits
- Data is easy to get from medical records.
- Good for looking at outcomes when the intervention is unethical
- Less expensive than a RCT.
Limitations
- Cause and effect cannot reliably be determined (associations may be proven to be non-existent).
6) CASE REPORT AND CASE SERIES
- Describes an adverse reaction or a unique condition that appears in a single patient (case report) or a few patients (case series).
- The outcome of the case in each of these is already known.
- A case series is more reliable than a case report.
Benefits
- Can identify new diseases, drug side effects or potential uses.
- Generates hypotheses that can be tested with other study designs.
Limitations
- Conclusions cannot be drawn from a single or few cases.
7) CROSS-SECTIONAL SURVEY
- Estimates the relationship between variables and outcomes (prevalence) at one particular time (cross-section) in a defined population.
Benefits
- Can identify associations that need further study (hypothesis-generating).
Limitations
- Does not determine causality (further studies needed if association found).
Pharmacoeconomic research identifies, measures and compares:
1)
a)
b)
c)
2)
a)
b)
c)
— of pharmaceutical products and services.
The costs
- direct,
- indirect
- intangible
and
The consequences
- clinical
- economic
- humanistic
Research methods used to determine the impact of the pharmaceutical product or service:
- What is critical for interpretation?
- Cost-effectiveness analysis
- Cost-minimization analysis
- Cost-utility analysis
- Cost-benefit analysis
the point of view of the analyst (study perspective) is critical for interpretation
What is the ECHO model?
What are the different outcomes?
- Provides a broad evaluative framework to assess the outcomes associated with diseases and treatments
■ Economic outcomes:
- Direct, (medical non medical)
- Indirect and
- Intangible (pain…) costs of the drug compared to a medical intervention.
■ Clinical outcomes:
- Medical events that occur as a result of the treatment or intervention.
■ Humanistic Qutcomes:
- Consequences of the disease or treatment as reported by the patient or caregiver (e.g., patient satisfaction, quality of life).
How do you calculate Average Cost-Effectiveness Ratios
Cost per outcome of one treatment independent of other treatment alternatives
Cost/Treatment success
50$/ 2 treated patients
Incremental Cost-Effectiveness Ratios
- When do you use it?
- How do you calculate it?
- How can you interpret it?
- The change in costs and outcomes when two treatment alternatives are compared.
- Calculated when evaluating COST and OUTCOMES between COMPETING ALTERNATIVES
- Represents the additional costs required to produce an additional unit of effect.
Incremental Cost Ratio =
(C2- C1) / (E2- E1)
ICR: 50$
Interpretation:
Drug B costs $50 more relative to Drug A for each additional treatment success
1 group + continuous + symmetrical (normal distribution)
1 sample t test
1 group + ordinal or discrete or skewed data
1 sample Wilcoxon sign rank
1 group + categorical (Nominal)
1 sample x2 test (or binomial exact)
chi square
2 groups (Independent design) + continuous + symmetrical
2 independent sample t test
2 groups (Independent design) + ordinal or discrete or skewed data
mann-whitney
wilcoxon sum rank
2 groups (Independent) + Categorical (nominal)
Pearson’s x2 test
or fisher’s exact
2 groups (matched) + continuous + symmetrical
2 paired samples t test
2 groups (matched) + ordinal or discrete or skewed data
2 paired samples wilcoxon sign rank
2 groups (Matched) + categorical (nominal)
McNemar x2 test
or binomial exact
What are the 4 PHARMACO ECONOMIC METHODOLOGIES?
What is the COST MEASUREMENT UNIT?
What is their OUTCOME UNIT?
1) Cost-Minimization Analysis:
- Demonstrated or assumed to be equivalent in comparative groups
2) Cost-Benefit Analysis
- Dollars
3) Cost-Utility Analysis
- Quality-adjusted-life year (QALY) or other utilities
4) Cost-Effectiveness Analysis
- Natural units (Life-years gained, mmHg blood pressure, % at treatment goal)
- COST MEASUREMENT UNIT: Dollar
Cost-Minimization Analysis
- When is it used?
- What does it measure?
- Cost-minimization analysis (CMA) is used when two or more interventions have demonstrated equivalence in outcomes, and the costs of each intervention are being compared.
- CMA measures and compares the input costs of treatment alternatives that have equivalent outcomes.
- This determination of equivalence is a key consideration in adopting this methodology.
- Ideally, evidence exists to support the clinical equivalence of the alternatives.
- In some instances, assumptions are made in the absence of relevant evidence.
For example, two ACE inhibitors, captopril and lisinopril, are considered therapeutically equivalent in the literature, but the acquisition cost (the price paid for the drug) and administrative costs may be different (captopril is administered TID and lisinopril is administered once daily).
- A CMA looks at “minimizing costs” when multiple drugs have equal efficacy and tolerability.
- Another example of CMA is looking at the same drug regimen given in two different settings (e.g., hospital versus home health care).
- CMA is considered the easiest analysis to perform, but use of this method is limited given its ability to compare only alternatives with demonstrated equivalent outcomes.
Cost-Benefit Analysis
- Cost-benefit analysis (CBA) is a systematic process for calculating and comparing benefits and costs of an intervention in terms of monetary units (dollars).
- CBA consists of identifying all the benefits from an intervention and converting them into dollars in the year that they will occur.
- The costs associated with the intervention are identified, allocated to the year when they occur, and then discounted back to their present day value.
- Given that all other factors remain constant, the program with the largest present day value of benefits minus costs is the best economic value.
- In CBA,it can be difficult to assign a dollar amount to a benefit (e.g., measuring the benefit of patient quality of life, which is difficult to quantify, and assigning a dollar value to it).
- One advantage to using CBA is the ability to determine if the benefits of the intervention exceed the costs of implementation.
- CBA can also be used to compare multiple programs for similar or unrelated outcomes, as long as the outcome measures can be converted to dollars.
Cost-Effectiveness Analysis
- Cost-effectiveness analysis (CEA) is used to compare the clinical effects of two or more interventions to the respective costs.
- The resources associated with the intervention are usually measured in dollars, and clinical outcomes are usually measured in natural health units (e.g., LDL values in mg/dL, % clinical cures, length of stay).
- The main advantage of this method is that the outcomes are easier to quantify when compared to other analyses, and clinicians are familiar with these types of outcomes since they are similar to outcomes seen in clinical trials and practice.
- CEA is the most common pharmaco economic methodology seen in biomedical literature.
- A disadvantage of CEAis the inability to directly compare different types of outcomes.
- For example, one cannot compare the cost-effectiveness of implementing a diabetes program with implementing an asthma program where the outcome units are different (e.g., blood glucose values versus asthma exacerbations).
- It is also difficult to combine two or more outcomes into one value of measurement (e.g., comparing one chemotherapeutic agent that prolongs survival, but has significant side effects, to another chemotherapeutic agent that has less effect on prolonging survival but fewer side effects).
Cost-Utility Analysis
- Cost-utility analysis (CUA) is a specialized form of CEA that includes a quality-of-life component of morbidity assessments, using common health indices such as quality- adjusted life years (QALYs) and disability-adjusted life years (DALYs)
- CEA can measure the quantity of life (years gained) but not the “quality” or “utility” of those years.
- In CUA, the intervention outcome is measured in terms of QALYs gained.
- QALY takes into account both the quality (morbidity) and the quantity (mortality) of life gained.
- CUA measures outcomes based on years of life that are adjusted by utility weights, which range from 1 for “perfect health” to O for “dead.”
- These weights can take into account patient and society preferences for specific health states.
- There is no consensus on the measurement, since both patient and society preferences can vary based on culture.
- An advantage of CUA is that different types of outcomes, and diseases with multiple outcomes of interest, can be compared (unlike CEA which can only compare one common unit).
