EBP Flashcards Preview

GIS + MSS + HNS + HIS + ERS Practicals > EBP > Flashcards

Flashcards in EBP Deck (74)
Loading flashcards...
1
Q

Prevention

A

Action that prevent disease occurrence / slow disease progression / minimise impact of disease

Examples of clinical preventive interventions:

  1. Immunisation
  2. Behavioural counselling e.g. smoking cessation
  3. Screening
  4. Chemoprevention e.g. statins for CVS disease prevention
2
Q

Levels of prevention

A

Primary:

  • aim to ↓ incidence
  • targets ***risk factors / causes / disease in populations / individuals
  • e.g. immunisation

Secondary:

  • aim to ↓ prevalence
  • targets individuals with ***early disease
  • e.g. screening

Tertiary:

  • aim to ↓ complications
  • targets individuals with ***established disease
  • e.g. stroke rehabilitation

Primordial:

  • targets underlying ***social, economic, environmental determinants of health
  • accomplished through **public policies and **intersectoral action
  • e.g. control of air pollution

Quaternary:

  • identifies individuals and groups at risk of ***over-diagnosis / over-treatment
  • accomplished through actions that prevent iatrogenesis
  • e.g. evidence-based medicine
3
Q

Individual vs Population approach

A

Individual (high-risk) approach (only target individuals at high risk):

  • Intervention appropriate to individual —> high motivation for physician and subject
  • Screening has inherent diffulcties and costs —> only palliative, not radical
  • Limited potential for populations —> large number of people at small risk may outweigh small number of people at high risk

Population approach (lower risk for whole population):

  • Large potential for populations
    —> aims to shift whole distribution of exposure in a favourable direction (i.e. lower prevalence of disease of whole population: shifting the normal distribution curve)
    —> behaviourally appropriate; changes norms
  • Limited by ***prevention paradox
    —> individuals perceive little benefit from intervention even though measure may benefit population
    —> lower physician, subject motivation
4
Q

Screening

A

Presumptive identification of an unrecognised disease/defect by application of tests/examinations/other procedures which can be applied rapidly

  • Sort out apparently well (asymptomatic) people who probably have a disease from those who probably do not
  • NOT intended to be diagnostic
5
Q

Goals of screening

A

Early detection —> Early intervention —> Reduction in morbidity / mortality

  1. Reduction in morbidity / mortality from the newly identified disease among screened individuals due to ***early treatment
  2. Reduce disease burden for the population
  3. Provide cost-effective screening to target population
6
Q

Wilson and Jungner screening criteria

A

Knowledge of diseases:

  1. Condition should be ***important health problem
  2. There should be a ***recognisable latent / early symptomatic stage
  3. ***Natural history of condition, including development from latent to declared disease, should be adequately understood

Knowledge of test:

  1. ***Suitable tests / examinations
  2. Test ***acceptable to the population
  3. Case-finding should be ***continuous (NOT “once and for all”)

Treatment of disease:

  1. ***Accepted treatment for patients with recognised disease
  2. ***Facilities for diagnosis and treatment should be available
  3. ***Agreed policy on whom to treat as patients

Cost considerations:
1. Cost of case-finding (including diagnosis and treatment of patients diagnosed) should be ***economically balanced in relation to possible expenditure on medical care as a whole

7
Q

Assessing a screening test: Reliability and Validity

A

Reliability: degree to which results can be replicated
- depends on
—> Variability between people
—> Measurement error

Validity: degree to which results obtained by a test are true

  • Sensitivity
  • Specificity
8
Q

***Sensitivity and Specificity

A

Used to decide whether a test should be used

Sensitivity (probability of true positive):
With disease + Positive test / Number of people with disease

Specificity (probability of true negative):
Without disease + Negative test / Number of people without disease

There will be trade-off between Sensitivity and Specificity
—> ↑ Sensitivity —> ↓ Specificity

Receiver operator characteristic (ROC) curve

  • used to help decide best cut-off point
  • Sensitivity plot against Specificity
  • maximising **True positive (Sensitivity), minimise **False positive (1-Specificity)
  • cut-off point usually at “shoulder” of ROC curve
  • ***Area under ROC curve: used to compare overall accuracy of tests
9
Q

***Predictive values

A

Predictive values:

  • used to determine how likely a patient has / not has disease
  • ***Post-test probability
PPV = True positive / All positive —> ***↑ with prevalence of disease (given constant SN, SP) (∵ ↑ true positive)
NPV = True negative / All negative —> ***↓ with prevalence of disease (given constant SN, SP) (∵ ↓ true negative)

Sensitivity = True positive / All disease
—> ability to pick up all diseased people
—> high sensitivity
—> high NPV (very few false negative) (無就一定無, 有病就一定可以pick up到)
—> low type II error

Specificity = True negative / All healthy
—> ability to exclude normal people (無就一定無)
—> high specificity
—> high PPV (very few false positive) (有就一定有, 所有無病一定exclude)
—> low type I error

Use of sensitive test: correctly identify people WITH disease, rule out dangerous conditions (negative result tends to rule out diagnosis)
Use of specific test: correctly identify people WITHOUT disease, false positive results are highly undesirable (positive result tends to rule in diagnosis)

10
Q

***Bayes’ theorem

A

Prevalence of disease:
- reflects Pre-test probability (probability of disease before diagnostic test result is known)

Predictive values:
- reflects Post-test probability (probability of disease after diagnostic test)
—> ***influenced by Prevalence (Pre-test probability)

SN, SP:

  • ***Inherent characteristics of test
  • ***Independent of Pre-test portability / prevalence

Implications of Predictive values dependent on Prevalence:
1. Better estimate prevalence by using best available information sources
—> high quality surveillance data
—> epidemiological studies with minimal bias

  1. Increase Pre-test probability may increase PPV
    —> Apply diagnostic tests to more susceptible patients (use sparingly in primary care)
    —> Interpret results of studies with care (tertiary care have higher disease prevalence, cannot be applied to primary / secondary settings)
11
Q

Risks of screening

A
  1. Discomfort and inconvenience
  2. Psychosocial consequences
    - Anxiety
    - Family discord on further procedures
  3. Adverse health outcomes
    - False-positives: possibly invasive diagnostic procedures
    - False-negatives: undetected disease
12
Q

Screening for colorectal cancer

A

Asymptomatic Hong Kong residents 50-75

- Faecal occult blood test every 1 to 2 years
OR
- Flexible sigmoidoscopy every 5 years
OR
- Colonoscopy every 10 years
13
Q

PICO question

A

Patient/Population in question
Intervention being considered
Comparison group
Outcome

14
Q

***Biases associated with screening

A
  1. Lead-time bias
    - Lead-time: Time between detection by screening and usual diagnosis
    - Apparently improved survival time with no change in time from disease onset to death
    - To avoid: Use ***mortality rather than survival rates
  2. Length-time bias
    - Slow-growing lesions are more likely to be detected by screening than fast-growing lesions
    - Screening tends to pick up slower growing lesions which are associated with better prognoses
    - To avoid: Compare outcomes in ***RCT
  3. Compliance bias
    - Individuals who are adherent to interventions usually have better prognosis than non-compliant individuals
    - tend to be more health conscious and higher socioeconomic position
    - To avoid: Compare outcomes in ***RCT
15
Q

Why studies showed that screening cannot save lives?

A
  1. Studies may be underpowered to detect small overall mortality benefit
  2. Any reductions in mortality may be offset by deaths due to ***downstream effects of screening
    - e.g. PSA screening yields numerous false-positive —> numerous prostate biopsies —> unnecessary harms and deaths

Misuse and Overuse of unnecessary tests/interventions:
Misuse: Interventions more harmful than beneficial
Overuse: Interventions that yield little useful information

16
Q

Appraising a study

A

Relevance:

  1. Is population similar to the patient?
  2. Intervention feasible?
  3. Comparison group reasonable?
  4. Outcome patient-oriented?

Validity (3 factors affecting):

  1. Random error
  2. Systematic error / bias
    - Selection bias
    - Information bias
  3. Confounding

Variations in responses:

  1. Participant variability
  2. Result of errors

Validity:

  1. Study sample
    - Selection bias (representative of target population?)
    —> inclusion / exclusion criteria reasonable?
    —> where were patients recruited?
    —> enrolled consecutively?
    —> response rates reasonable?
  2. Study design
    - Systematic reviews > individual studies
    - RCT for effectiveness of intervention
    - Prospective cohort studies for prognosis / etiology

For assessing prognosis:

  • is there an inception cohort? (group of patients assembled at onset of disease)
  • at the same point / stage in their illness?
  1. Study conduct
    - Selection bias (allocation to groups concealed? follow-up reasonably complete? drop-out rate differ between groups?)
    —> loss to follow-up —> reduce statistical power
    —> need >=70-80% patients follow up
    - Information bias (observer bias, recall bias)
    —> measurement of outcomes differ by exposure status?
    - Confounding (adjust for important potential confounders)
  2. Study results
    - Statistically significant (confidence intervals, P values, adequate power: 0.8 (sample size) for null associations)
    —> bigger effect size —> smaller sample size needed
    - Clinically significant (effects in clinical practice, relative risk reduction, absolute risk reduction, NNT)
    - How to apply results to patient
    - Adjusted for confounders
  3. Other factors that could affect interpretation of results
    - STEPS (Safety, Tolerability, Effectiveness, Price, Simplicity)
    - Conflicts of interest
17
Q

Bias

A
  1. Information bias
    —> Observer bias (by investigators)
    —> Recall bias (by subjects, particular problem if public is aware of exposure-disease association)
    - reduce bias:
    —> Blind assessment of outcome status (investigator blinded)
    —> Prospective assesssment of outcome (no need to recall)
    —> Use objective measures
  2. Selection bias:
    - systematic difference between subjects and target population
    - subjects not representative of target population —> affects EXTERNAL validity
    - subjects groups are not comparable —> affects INTERNAL validity
    - reduce bias:
    —> random sample
    —> maximise response rate
    —> minimise attrition rate
    —> select case and control from same underlying population
18
Q

***Relative risk (RR)

A

Risk of benefit / harm of intervention compared to another
- depend only on relative difference and does not account for risk of no treatment (absolute risk at baseline)

EER (experimental event rate) = treated with effect / total treated
CER (control event rate) = control with effect / total control

Relative risk reduction (RRR) = (1-RR) x 100% OR ***CER-EER / CER

Absolute risk reduction (ARR) = Incidence in control group - Incidence in intervention group (i.e. CER-EER)

Number needed to treat (NNT) = 1 / ARR

19
Q

***Number needed to treat (NNT)

A

NNT = 1 / ARR

  • Number of patients need to be treated for one patient to receive benefit/prevent disease
  • Ideal NNT = 1
  • take into account that not everyone benefit from an intervention
  • may be more useful than RR
    —> combines relative benefit with background risk of patients
20
Q

Prognosis

A

Likely course and outcome of disease / condition over time

Clinical course = prognosis of a condition that has received intervention
Natural history = prognosis of a condition that has NOT received intervention (determines when to intervene, disease staging etc.)

Types:

  1. Average / Overall prognosis: “most likely course of a condition”
  2. Prognostic factors: factors associated with outcome
  3. Risk prediction models: risk groups who are likely to have worse outcomes

Finding out:
1. Clinical experience (prone to bias)

(2. Systematic review of cohort studies)

  1. Cohort studies (exposure睇outcome)
    - **better evidence on prognosis
    - recruit based on **
    exposure characteristic:
    —> prognostic factor: exposed (e.g. smoking) vs not exposed (e.g. no smoking)
    - prospective —> look for ***outcome (morbidity / mortality)
  2. (Control arm of) RCT
    - treatment arm —> clinical course
    - control arm —> natural history
    - problem: participants may not be representative of population of interest
    - may give information on prognosis but ***participants may not be representative of population of interest
  3. Case-control studies (outcome睇exposure)
    - recruit based on **outcome:
    —> Cases
    —> Control
    - retrospective —> look for prognostic factors / **
    exposure
    - problem: does not provide information on outcome rates (∵ you select cases)
  4. Cross-sectional studies
    - cannot know temporal sequence
21
Q

Causation

A

Necessary cause: outcome would not occur without exposure
Sufficient cause: exposure guarantees cause will occur (very few in medicine)

E.g.

  • HIV is a necessary cause of AIDS (must have HIV to have AIDS)
  • but not a sufficient cause for AIDS (not everyone has HIV develop AIDS)

Finding cause:

  • Experimental studies e.g. RCT
  • Observational studies only tells ***associations

Bradford Hill’s Criteria

  1. Consistency (on replication)
  2. Strength (of association)
  3. Specificity
  4. Dose response relationship
  5. Temporality
  6. Biological plausibility
  7. Coherence
22
Q

OA risk factors:

A

Non-modifiable:

  • age
  • sex
  • genetics
  • bone deformities

Modifiable:

  • obesity
  • joint injuries
  • occupational exposure
23
Q

Dealing with confounding

A
  1. Multivariable analysis
    - adjust / control for confounders
    - Regression analysis

Regression analysis:
- mathematical model to describe relationship between Dependent variable and ***>=1 Independent variables
- allow estimation of a range of exposures, each one adjusted for potential confounding effect of others
- describe relationship between exposure and outcome adjusted for effects of potential confounders
—> “all else being equal, what is effect of X on Y?”

Types of regression analysis:

  1. Logistic regression —> Binary outcome
  2. Linear regression —> Quantitive outcome
  3. Cox regression —> Time to occurrence of an event (e.g. survival)
    - ***effects of several variables on time to occurrence of an event
    - Hazard: instantaneous rate of the event at time t
    - Cox proportional hazards model assume that Hazard Ratios comparing different exposure groups remain constant over time
24
Q

Hazard ratio

A

Ratio of hazards between 2 groups
- interpreted in the same ways as risk ratio

HR>1: high hazard than comparison group
HR=1: no difference in hazard from comparison group
HR<1: lower hazard than comparison group

25
Q

Survival curve vs Survival rate

A

Survival curve:

  • Probability of survival starts at 100%, decreases over time
  • Shows proportion of study population that is still alive at successive time points
  • Patients are censored when lost from study

Survival rate:

  • alternative to survival curve
  • survival rates at different time points (1 year, 5 years), median survival rate, disease-free survival rate etc.
26
Q

Mortality

A

All-cause mortality

  • includes all causes of mortality, even those seemingly unrelated to study exposure
  • ***less prone to misclassification
Disease-specific mortality
- heavily relies on accuracy and completeness of death certification
—> immediate cause
—> intervening cause
—> underlying cause
- ***more prone to misclassification

—> intervention may just reduce one of the above (All-cause / Disease-specific mortality)

Doctors when issuing death certificates, causes of death must be consistent with those in ICD-10 (classification system by WHO)

Standardised mortality ratio (SMR):

  • Mortality of study group to Mortality of standard population
  • SMR =1: same mortality rate as standard population
  • SMR >1: higher mortality rate in study group
  • SMR <1: lower mortality rate in study group

(Sometimes 100 is used rather than 1)

27
Q

Diagnosis

A

Diagnostic tests:

  • Laboratory tests
  • Imaging
  • Endoscopy
  • History + Physical examination
  • combination of above

Diagnosis is a ***Probability based on:

  1. Frequency of disease:
    - Prevalence (more commonly available)
    - Incidence
  2. Index of suspicion:
    - Risk factors / risk predictors: age, smoking status etc.
    —> Effect measures: Odds ratios, Risk ratios
    - Careful history and clinical judgement
  3. Accuracy and validity of diagnostic tests
28
Q

Screening vs Diagnosis

A

Screening:
- for Asymptomatic patients with early disease
—> secondary prevention
—> lower prevalence of disease
- Aims to identify people with high risk of disease where interventions may reduce morbidity / mortality
- NOT intended to diagnose

Diagnosis:
- for Symptomatic patients
—> higher prevalence of disease
- Aim: make a definitive diagnosis / narrow down DDx
- NOT intended to prevent disease
29
Q

Issues with determining validity

A
  1. An appropriate “gold standard” (reference standard)
  • best available test to determine presence / absence of disease
    —> new test should not be compared to imperfect test
  • sometimes too invasive / costly
    —> simpler tests should only be used as proxies when risk of misclassification is low
  • disease may lack objective standards for diagnosis e.g. mental disorders
  1. Lack of information on negative tests
    - clinicians reluctant to further test when initial test is negative
30
Q

***Likelihood Ratio (LR)

A

Describes performance of a test:
LR = probability (test results if disease is present) / probability (test result if disease is absent)

LR+ = P(+ve test in diseased) / P(+ve test in normal people)
—> Sensitivity / 1-Specificity
—> ***high LR+ —> better the positive test can rule in disease

LR- = P(-ve test in diseased) / P(-ve test in normal people)
—> 1-Sensitivity / Specificity
—> ***lower LR- —> better the negative test can rule out disease

LR = 1
—> no change in likelihood of disease —> not a useful test

Use:
1. Allow application to individuals with different **pre-test probabilities:
- ∵ Predictive values are not transferable with different prevalence, but LR still applicable even though prevalence is low
- **
Pre-test odds x LR = Post-test odds
(need to convert probability to odds)

  1. Summarise test performance for a range of possible ***test values
    - rather than SN and SP defined by a single cut-off point
    - disease should be more likely for extreme than borderline test results
31
Q

Multiple tests

A

Parallel testing (囊括越多人) —> ↓ False negative:

  • ↑ SN and NPV
  • but may ↑ false positive
  • clinical prediction rules may be used to combine multiple test results and pre-test probabilities

Serial testing (逐漸縮窄範圍) —> ↓ False positive:

  • ↑ SP and PPV
  • but may ↑ false negative
  • serial LR may be calculated
32
Q

Potential harms of diagnostic tests

A
  1. Diagnosis does not necessarily lead to improvement in outcomes
  2. Hazards of inappropriate diagnosis
    - false positive results
    - false negative results
    - overdiagnosis
  3. Overuse / Misuse of diagnostic test is waste of resources
33
Q

Therapy / treatment

A

Any intervention intended to improve course of established disease

May include:

  • medications
  • surgery
  • changes in organisation / financing of health care
34
Q

Observational vs Interventional studies

A

Observational studies:

  • Examine distributions and determinants of outcomes (observe exposed vs not exposed)
  • Without any attempt to influence them
  • Easier to conduct, less expensive
  • ***Residual confounding problem —> should equally allocate confounders on 2 side
    1. Descriptive (cross-sectional, cohort)
    2. Analytical (cross-sectional, cohort, case-control)

Interventional / Experimental studies (e.g. clinical trials):

  • Modify an exposure within a population (observe intervention group vs control group)
  • Examine its effect on outcome
  • More time-consuming, costly
  • ***Randomisation minimise confounding
  • ***Selection bias, Information bias problem

RCT:

  • gold standard study design to provide evidence for effectiveness due to randomisation
  • participants randomly allocated to different intervention group to minimise confounding
  • followed up prospectively for assessment of outcomes
35
Q

Level of evidence

A

Highest —> Lowest:

  • Evidence summaries
  • Meta-analysis / Systematic review
  • RCT
  • Cohort studies
  • Case-control / Cross-sectional / Ecological studies
  • Expert opinion
36
Q

Elements of clinical trials

A
  1. Ethics
  2. Intervention
  3. Control / Comparison
  4. Outcomes / End-points
37
Q
  1. Ethical principles
A
  1. Equipoise:
    - randomisation is ethical when either of the allocated interventions is better than the other
  2. Respect for rights of individual participants:
    - life, health, dignity, integrity, self-determination, privacy, confidentiality
    - informed consent
    - voluntary participation
    - specific consideration for vulnerable groups
  3. Minimising risks:
    - measures to assess, monitor, minimise risk to human subjects
  4. Research ethics committee:
    - research protocol should be submitted for consideration and approval
    - right to monitor ongoing studies, esp. for serious adverse events
  5. Reporting and dissemination of results:
    - registration in publicly available database
    - full reporting + dissemination of results
38
Q
  1. Intervention considerations
A
  1. Feasibility:
    - some interventions may not be available in all settings
  2. Complexity:
    - treatment plan usually involve multiple interventions (will an intervention work with others?)
  3. Effect size:
    - relative effects may exaggerate absolute effects (does the intervention has a clinically significant effect)
39
Q
  1. Control group
A
  1. No treatment
    - Hawthorne effect: participants may change their behaviours even without interventions (modify an aspect of their behavior in response to their awareness of being observed)
  2. Placebo
    - intervention intended to be indistinguishable from active treatment
    - intended to have no active effect
    - Placebo effect: report improvement even with placebo
  3. Usual care / another intervention
    - ***meaningful if this is the most effective treatment available (i.e. “gold standard”)
40
Q
  1. Outcomes / End-points
A
  1. Primary
    - outcome investigators are most interested in
    - basis for ***hypothesis testing + sample size calculation
  2. Secondary
    - additional outcomes of interest
    - associations are only ***hypothesis-generating as may have occurred by chance
    —> study may not have enough power to detect these associations
    —> interpret with caution
    - e.g. SE, differences by subgroups
  3. Hard
    - defined according to **objective criteria, involves **no subjective assessment
    - usually preferred over soft outcomes
    - e.g. All-cause mortality
  4. Soft
    - require subjective assessment by investigator / participants
    - potential ***information bias —> blinding is important
    - e.g. Cause-specific mortality
  5. Patient-oriented
    - focus on helping patients live longer and better
    - ***preferred over disease-oriented outcomes
    - e.g. mortality, morbidity, QOL
  6. Disease-oriented
    - focus on pathophysiological mechanisms / markers of disease
    - not preferred but ***easier to measure
41
Q

Types of clinical trials

A

Function:
- Prevention / Diagnosis / Treatment

Design:

  • Cluster vs Individual
  • Randomised vs Non-randomised
  • Parallel-group vs Cross-over
  • Superiority vs Non-inferiorly / Equivalence

Objective:

  • Explanatory (efficacy) vs Pragmatic (effectiveness)
  • Phases 1 to 4
42
Q

Superiority vs Non-inferiority trials

A

Superiority trials

  • aim to establish that a new treatment is BETTER than another
  • e.g. a new drug more effective than usual care

Non-inferiority trials
- aim to establish that a new treatment is NOT WORSE (not clinically inferior) than current / control treatment by more than a non-inferiority margin
—> smallest difference in effect considered to be clinically important
—> control treatment must be more effective than placebo
- one-sided
- Equivalence trials: 2-sided (new treatment is not better not worse than current treatment)
- e.g. a new drug with similar effectiveness is safer, cheaper, easier to administer
- conducted when new treatment is ***not expected to be better than control in terms of primary health outcomes, but may offer other benefits

***Non-inferiority is only shown if 95% CI for different between test and reference treatments are > -Δ (過左-Δ —> 代表同之前一樣; 過曬-Δ —> 差過之前)

Non-inferiority: CI well below non-inferiority threshold
Superiority: CI well above non-inferiority threshold

43
Q

Explanatory (efficacy) vs Pragmatic (effectiveness) trials

A

Explanatory (efficacy) trials:
- Can treatment help under **ideal circumstances (e.g. RCT)?
- include highly selective and homogeneous patients who are adherent
—> higher internal validity
—> may have lower external validity / generalisability
—> limitations: **
selection bias

Pragmatic (effectiveness) trials:
- Can treatment help under ***ordinary circumstances?
- done after efficacy is established
- done in routine health care settings
- include more diverse patients
—> participants not followed as closely to ensure adherence
—> ***higher external validity
—> may have lower internal validity
44
Q

Clinical trial phases

A

Phase 1:

  • safety + dosage
  • 20-100 participants over several months
  • “is it safe in health people?”

Phase 2:

  • efficacy + SE (i.e. explanatory trials)
  • several hundred participants over months - 2 years
  • “does it work?”

Phase 3:

  • efficacy / effectiveness + adverse reactions (i.e. pragmatic trials)
  • hundred to thousands of participants over 1-4 years
  • “is it any better than current treatment?”

Phase 4:

  • post-marketing surveillance of long-term benefits, risks
  • several thousand participants
  • “will it have any long term results?”
45
Q

***Limitations of clinical trials

A
  1. Confounding
    - ***Randomisation —> randomly distribute confounders in intervention / control groups
  2. Selection bias
    - Healthy volunteer bias
    —> participants may be healthier than average patients —> need to note how ***sampling was done
  • Cherry-picking participants
    —> assign healthier participants to desired treatment group —> need to ensure ***allocation concealment
  • Attrition
    —> participants who drop out may not be representative of all enrolled participants —> need to note follow-up rates and whether ***intention to treat analysis was done
  1. Information bias
    - ***Blinding
  2. Practical limitations
    - time-consuming
    - cost
    - not feasible / ethical interventions
46
Q

Randomisation

A
  • Randomly distribute confounders in intervention / control groups
  • Bad examples:
    —> assignment based on day of admission (not random)
    —> assignment based on day of birth (not concealed)
  • Non-randomised studies: overestimate / underestimate effect of intervention
  • ***Check if baseline characteristics of participants are similar across groups
    —> if sample size is small —> baseline differences are even exaggerated
    —> significance tests of baseline differences (giving P values) may not be very useful

Types of randomisation

  1. Unrestricted (simple) randomisation
    - no constraints in generation of random allocation sequence (e.g. a table of random numbers)
  2. Restricted randomisation
    - ensure particular allocation ratios to treatment groups (e.g. 1:1)
47
Q

Sampling

A

Meet strict criteria for participation

Inclusion criteria:

  • diagnostic criteria to ensure patients have the condition under study
  • patients with unusual, mild / equivocal manifestations of disease may be excluded

Exclusion criteria:
- usually involve patients who have
—> comorbidities that may affect outcomes
—> limited life expectancy that may affect assessment of outcomes
—> contraindications to treatment
—> do not participant
—> do not cooperate in early stages of trial

However, problem of highly selective:
- Limit ***external validity / generalisability

Example of sampling:
Sampled population —> Invited to participate —> Able to contact and agreed to participate —> Eventually randomised

48
Q

Allocation concealment

A

Ensure investigators do not know / have no influence over which group participants are allocated to
—> minimise selection bias

Low risk of bias:

  • opaque sealed envelopes containing group assignment
  • central randomisation by a third party

High risk of bias:

  • allocation based on day of admission
  • allocation sequence posted on staff room wall

Inadequate allocation concealment yield ***larger effect estimates when differences in results did occur

49
Q

Incomplete outcome data

A

Missing outcome data may:

  1. Bias the observed effects
    - esp. when % missing differ across groups
    - patients who drop out / missing data may have worse prognosis
  2. Reduce statistical power
    - esp. when % missing is high

Attrition / Drop out because:

  • withdrawal (due to SE)
  • do not attend follow up (busy)
  • do not provide relevant data
  • cannot be contacted (migration / death)
  • later found to be ineligible
  • investigators ceased to follow up (inadequate funding)
50
Q

Non-adherence

A

Extent to which patients fail to follow treatment recommendations / take medications as prescribed

Patient factors: illness, perceived benefits and risks
Treatment factors: cost, complexity, duration, SE
Other factors: poor doctor-patient communication

Measuring adherence in clinical trials:
- cheaper but less accurate
—> self-report, diaries, pill counts, prescription refills, third party observations
- more accurate but more costly
—> measuring drug levels in plasma, directly observed therapy

Dealing with non-adherence:

  1. Exclude non-adherent patients during run-in period
    - patients given placebo and observed for adherence —> non-adherence patient then excluded before randomisation
  2. Intention-to-treat analysis
    - analyse study outcomes in all participants according ***original assigned treatment group

Advantages:
- perserves randomisation (reduces confounding)
—> provide more conservative estimate of effect
—> selection bias may still exist if outcome data incomplete

  • more accurately reflects what happens in reality
  • **different from per protocol analysis: include participants only if they received the intended intervention in accordance with the study protocol
    —> may **
    overestimate treatment effect due to confounding (lose randomisation)
    —> may be done alongside ITT analysis to investigate influence of missing data
51
Q

Blinding

A

Keeping people unaware of which intervention is administered to which participants
—> prevent ***information bias

Done on various levels

  1. Participants
  2. Care providers
  3. Assessors of outcomes (most important, always feasible)
    - non-blinded outcome assessors tend to ***overestimate effect of intervention

Types of blinding
1. Single-blind, double-blind, triple-blind
2. Open-label
—> usually assumes no attempt at blinding
3. Placebo-controlled
—> usually assumes that participants are blinded

Lack of blinding may have more influence on subjective outcomes than objective outcomes

52
Q

Allocation concealment vs Blinding

A

Occur at different timeline

Allocation concealment: Prevent **selection bias
Blinding: Prevent **
information bias

53
Q

CONSORT statement

A

Consolidation of Standards of Reporting Trials
- specifies content to be covered when reporting RCT e.g. Full trial protocol, to be repeated
—> help us assess quality of reporting
- consist of:
1. Checklist
2. Flow diagram (showing passage of subjects through RCT)
- enrolment —> allocation —> follow-up —> analysis

54
Q

Harm to patient safety in medical practices

A
  • Only 1/3 medical practices = Beneficial / Likely to be beneficial
  • ~20% expenditure wasted on unnecessary diagnostic tests / treatments

Overuse: interventions that yield little useful information
Misuse: interventions that may be more harmful than beneficial

Hospital:

  1. Unsafe use of medication (Leading cause)
  2. Healthcare-associated infections
  3. Surgical complications

Primary care:

  1. Administrative errors
  2. Diagnostic errors

Combatting Overuse / Misuse:

  1. Choose wisely
  2. Prudent healthcare
  3. NICE
55
Q

Measures of patient safety

A

Hospital-acquired infection:

  • Ventilator pneumonia
  • Wound infection
  • Decubitus ulcer (i.e. Bedsore)

Operative / Post-op complications:

  • Complication of anaesthesia
  • Post-op sepsis
  • Post-op DVT

Sentinel events:

  • Transfusion reaction
  • Wrong surgical site
  • Foreign body left

Obstetrics:
- Birth trauma

Other care-related adverse effects:

  • Falls
  • Fractures
56
Q

Unsafe care

A
Structures
- Resources and organisational arrangements in place that influence care
—> Safety culture
—> Inadequate Human Resources
—> Stress and fatigue
Processes
- Activities of physicians and other providers of healthcare
—> Misdiagnosis
—> Poor test follow-up
—> Inadequate measures of patient safety

Outcomes
- Results / Consequcnes of clinical activities
—> Adverse events due to medications
—> Surgical complications

Example:
Adverse events to drug treatment
1. Health system level:
- lack necessary information for safe use of drug
- error-prone conditions: look-alike / sound-alike medication

  1. Provider level:
    - lack of knowledge about drug
    - lack of follow-up
    - fail to recognise drug SE
    - fail to prescribe drug
57
Q

Types of medical errors

A
  1. Preventive
    - fail to provide prophylactic treatment
    - inadequate monitoring to follow-up
  2. Diagnostic
    - delay in diagnosis
    - fail to use indicated tests
    - use of inappropriate tests
    - fail to act on test results
  3. Treatment
    - error in administering drug
    - error in procedure / operation
    - delay in treatment
    - inappropriate treatment
58
Q

Progress in patient safety

A
  • Little evidence that patient outcomes improved despite research
  • Investment in safety research is much lower than magnitude of problem
  • Medical errors are caused by faculty system, processes / conditions that lead people to make mistakes rather than result from individual recklessness
59
Q

Ethical issues in clinical research

A
  1. Respect for rights of individuals participants
    - informed consent
    - confidentiality
    - specific consideration for vulnerable groups
  2. Minimising risk
    - measures in place to minimise risk
  3. Research ethics committee
    - approve research protocol
    - monitor ongoing studies esp. adverse effects
  4. Reporting and dissemination fo results
    - registration in publicly available database
    - full reporting and dissemination of results

Ethical issues concerning patient safety research:

  1. Subject may be system / healthcare provider rather than a patient
  2. Tensions may arise between a researcher’s duty of care and aims of research
  3. Liability of health care providers when health care failures are documented
  4. Patient confidentiality
  5. Dissemination of findings
60
Q

Study designs

A

Observational (e.g. Cohort studies):
- Examine outcome without attempt to influence them —> too see Prognosis

Interventional (e.g. RCT):
- Modify an exposure and examine outcome —> measure effectiveness

61
Q

Hierarchy of evidence

A
Evidence summaries
—> Systematic reviews / Meta-analysis
—> RCT
—> Cohort studies
—> Case-control studies
—> Expert option / Consensus
—> Pathophysiologic reasoning
62
Q

Systematic reviews (i.e. Synthesis review)

A
  • Review of all available evidence on a specific topic
  • Apply **Systematic, **Explicit strategies that limit bias
    —> Assembly, Critical appraisal and Synthesis of evidence of all relevant studies
    —> provide more reliable evidence than individual studies
    —> identify gaps in existing evidence
63
Q

Process of systematic reviews

A

Literature search, Selection of studies, Data extraction
—> done by **>=2 **independent reviews to limit ***selection bias
—> should declare conflicts of interests (financial / intellectual conflicts of interest)

Steps:
1. Ask an answerable clinical question
2. Define inclusion + exclusion criteria
3. Develop search strategy + Identify relevant information sources
- Search strategy:
—> Determined by Eligibility criteria of studies for a specific research question —> PICO (Population, Intervention, Comparison, Outcome)
—> Information sources: Databases (PubMed), Grey literature (Reports by government agencies), Bibliography
—> Search terms: plus different combination
—> Search filters: time period, language, document format

  1. Select relevant studies
  2. Extract relevant data
  3. Explicitly and systematically assess quality (i.e. risk of bias) of individual studies:
  4. Synthesise results from individual studies
    - Meta-analysis
64
Q

Systematic reviews vs Narrative reviews

A

Systematic review:

  • Systematic + Explicit methods to identify, appraise and synthesise evidence
  • 2 independent reviewers —> less selection bias
  • May involve Meta-analysis

Narrative review (Summary review):

  • NO systematic / explicit methods to identify, appraise and synthesise evidence —> ***Uncertain validity
  • **Content experts who pick and choose relevant studies —> **prone to selection bias
  • NOT involve Meta-analysis
65
Q
  1. Explicitly and systematically assess quality (i.e. risk of bias) of individual studies
A
1. Use Explicit criteria
should include:
- Confounding
- Selection bias
- Information bias

Checklists:
- Jadad score
—> evaluate **quality of reporting of RCT (評核三方面)
—> Randomisation
—> Blinding
—> Account of all withdrawals and dropouts
- **
NOT include allocation concealment

  • Newcastle-Ottawa Quality assessment scale:
    —> for Case-control studies, Cohort studies
    —> Selection, Comparability, Exposure
  1. Sensitivity analysis
    - to assess potential influence of studies at high risk of bias —> repeating analysis by excluding them
    - many arbitrary assumptions are involved in a meta-analyses:
    —> Case definitions
    —> Study designs
    —> Study quality
    —> Type of analysis etc.
    - involve testing of different assumptions —> ***robustness of findings to different assumptions suggests that results from the meta-analysis are valid
66
Q

EQUATOR network

A

Reporting guidelines for research studies according to study design

  • recommend ***required reporting items for systematic reviews
  • can be used as a framework to evaluate ***quality of reporting
  1. PRISMA: systematic reviews
    - Identification
    - Screening
    - Eligibility
    - Included
  2. CONSORT: RCT
  3. STROBE: observational studies
  4. STARD: diagnostic / prognostic studies
  5. AGREE: clinical practice guidelines
67
Q

Reporting biases

A

When dissemination of research findings is influenced by nature of direction of results

  1. Publication bias —> positive results more likely to be published
  2. Time lag bias —> positive results published sooner
  3. Language bias —> English studies more likely found
  4. Citation bias —> positive results more likely to be cited
  5. Selective outcome reporting bias —> outcomes with favourable associations more likely to be reported

解決方法: Funnel plot

68
Q

Funnel plot

A

Scatter plot of **effect estimates (OR, RR) from individuals studies against **measure of study size

  • Dotted vertical line: Combined effect
  • Solid vertical line: Null effect
  • Diagonal lines: Triangular region with which 95% of studies are expected to lie without bias / heterogeneity

Precision of effect estimate ***↑ with larger sample sizes

  • smaller studies scatter more widely around bottom of funnel
  • “small-study effect”: tendency for smaller studies to show larger effects

Interpretation:
Plot should be symmetrical in the absence of bias / heterogeneity between studies
—> 越高(越大sample size) —> 結果(effect estimate)應該越中庸之道 —> 三角形
—> scatter of studies only due to ***sampling variation

  • Asymmetry could be due to
    1. **
    Reporting bias
    2. **
    Heterogeneity
    3. Chance —> statistical tests (e.g. Egger test) help to determine likelihood

Funnel plot ONLY suggest potential biases but do NOT address them

69
Q
  1. Synthesise results from individual studies: Meta-analysis
A

Statistical method to **quantitatively **combine results from individual studies
- Usually a ***weighted average of estimates from individual studies

Increase overall ***statistical power of individual studies
—> increases chance of detecting small but potentially important effects

Should only be done when studies are reasonably ***homogeneous (i.e. results from individual studies are reasonably similar / consistent)

70
Q

Forest plot

A
  • Graphical summary of results from individual studies and meta-analysis
  • Aid in ***identification of heterogeneity
  • Solid vertical line: Null line
  • Horizontal line: 95% confidence interval
  • Square:
    —> Centre: **Mean effect
    —> Size: Proportional to **
    % weight of study
  • Diamond (Only一粒): **Combined result
    —> Centre + Dotted vertical line: **
    Pooled estimate
    —> Horizontal tips: ***95% confidence interval

How to identify heterogeneity:
—> Use statistical tests —> when there is poor overlap of confidence intervals
1. I^2 statistic
- estimated proportion of variability that is explained by heterogeneity rather than sampling variation
- ***I^2 >30-40% usually suggests significant heterogeneity

  1. Cochran’s Q (Chi^2) statistic
    - weighted sum of squared differences between individual study effects and pooled effect
    - has low power; ***P value <0.1 suggests significant heterogeneity
  2. ***Identify potential sources of heterogeneity
71
Q

Heterogeneity

A
  1. Clinical diversity
    - variability in participants, interventions, outcomes studied
  2. Methodological diversity
    - variability in study design / risk of bias
72
Q

Dealing with heterogeneity

A
  1. Meta-analysis may not be appropriate when studies too heterogeneous
    —> combined estimate not a meaningful representation of studies
  2. ***Random effects model in meta-analysis
    —> assumes that there are different underlying effects between studies other than sampling variation
    —> Fixed effect model: assume that one true effect underlie all studies and that all differences in observed effects are due to sampling error (does not account for heterogeneity between studies)
73
Q

Strengths and Limitations of Systematic reviews

A

Strengths:

  1. Increase statistical power of individual studies to detect small but important effects
  2. Explicit and more objective summary of evidence compared to narrative reviews

Limitations:

  1. Limited by quality of individual studies
  2. Reporting biases
  3. Meta-analysis may not be appropriate when studies are too heterogenous
74
Q

Summary

A
  1. Relevance:
    - PICO
  2. Validity:
    - study design —> Observation / RCT
    - study conduct —> Search strategy, Selection bias, Validity / Risk of bias (Confounding, Selection bias, Information bias), Reporting bias (Asymmetry), Meta-analysis (Heterogeneity)
    - study results —> Statistically significant, Clinically significant, STEPS, Conflict of interest

STEPS:

  1. Safety
  2. Tolerability
  3. Effectiveness
  4. Price
  5. Simplicity