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DIT Phase II Chapters > Medicine in Society > Flashcards

Medicine in Society Flashcards

1
Q

Case-control study

A
  • Observational and retrospective
  • Compares group of people with disease to group without
  • ID’s risk factors
  • Asks, “what happened”
  • Odds ratio
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2
Q

Cohort study

A
  • Observational
  • Groups according to risk factors and sees what happens to them
  • Looks to see if exposure increases likelihood of disease
  • Asks, “What will happen?” Rate of exposed/rate of unexposed
  • Relative risk (RR)
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3
Q

Cross-sectional study

A
  • Observational
  • Gives snapshot of disease at one point in time
  • Disease prevalence
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4
Q

Twin concordance study

A
  • Compares the frequency with which both monozygotic twins or both dizygotic twins develop a disease
  • Measures heritability
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5
Q

Adoption study

A
  • Compares siblings raised by biologic vs. adoptive parents
  • Measures heritability and influence of environmental factors
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6
Q

Clinical trial

Phases

A
  • Experimental study involving humans
  • Compares therapeutic benefit of 2 or more treatments or of treatment and placebo
  • Highest quality when randomized, controlled, double blinded
  • Phase I - Is it safe - safety, toxicity, pharmacokinetcs
  • Phase II - Does it work - Efficacy, optimal dosing, adverse effects
  • Phase III - Does it work better - compares new treatment to current standard of care.
  • Phase IV - Are there rare adverse affects? Postmarketing surveillance trial of patients after approval.
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7
Q

Meta-analysis

A
  • The systematic process of using statistical methods to combine the results of different studies
  • systematic, organized, and structured evaluation of a problem using information, commonly in the form of statistical tables, from a number of different studies of a problem
  • Need strict inclusion criteria and selection bias may creep in
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8
Q

Diagnostic Tests

A
  • Sensitivity = A/(A+C)
  • Specificity = D/(D+B)
  • PPV = A/(A+B);
  • PPV - tells you the probability that a person who tests positive actually has the disease
  • NPV = D/(C+D)
  • NPV - tells you the probability that a person who tests negative is actually free of the disease
  • Sensitivity and Specificity are the vertical columns
  • PPV and NPV are the horizontal columns
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9
Q

Which diagnostic tests change with prevalence of disease?

A

Sensitivity and Specificity don’t change - static
PPV and NPV change depending on prevalence of disease in society.

  • ↑PPV with ↑prevalence of disease
  • ↓ prevalence will ↑ NPV
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10
Q

Prevalence vs. Incidence

A
  • Point prevalence = total cases in population at a given time/ total population at a given time
  • Incidence = new cases in population over a given time period/total population at risk during that time period
  • Incidence = new incidence
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11
Q

What is prevalence approximately equal to?

A

Prevalence ≈ incidence x disease duration

Prealence > Incidence for chronic diseases (diabetes)

Prevalence = incidence for acute disease (e.g., common cold)

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12
Q

Odds ratio vs. Relative risk

A

Odds ratio for case control studies:

  • Odds of having disease in exposed group divided by odds of having disease in unexposed group
  • Approximates relative risk if prevalence of disease is not too high

Relative risk (RR) for cohort studies:

  • Probability of getting a disease in the exposed group divided by the probability of getting a disease in the unexposed group
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13
Q

Attributable risk

A

AR = incidence of disease in the exposed group - incidence of disease in the unexposed group

Example: In a population of sexually-active people, 30% have HPV infection. In a population of people who are not sexually active, only 5% have HPV infection. The attributable risk of sexual activity to HPV is 25%.

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14
Q

Absolute Risk Reduction

A

The reduction in risk associated with a treatment as compated to placebo

ARR = C/(C+D) - A/(A+B)

Example:

People that got the disease on the drug = 5%
People that got the disease w/o drug = 20%

Absolute risk reduction is 15%

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15
Q

Number needed to treat

A
  • NNT = 1/absolute risk reduction
  • Number of patients you would need to treat in order to save/effect one life
  • Important number to help determine if a drug should be used or is cost effective
  • Example: If out of 10,000 patients that took t-PA during a STEMI, 100 were saved by the t-PA, then the NTT is 100. In other words, you would need to treat 100 patients in order to save/effect 1 life
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16
Q

Number needed to harm

A
  • NNH = 1/attributable risk
  • NNH=1/AR
  • (AR = incidence of disease in exposed group - incidence of disease in unexposed group)
17
Q

Precision vs. Accuracy

A

Precision is:

  • The consistency and reproducibility of a test (reliability)
  • The absence of random variation in a test
  • Reduced by random error

Accuracy is the truness of the test measurements (validity):

  • Reduced by systematic error
18
Q

Ways to reduce bias?

A
  • Blind studies (double-blind to limit influences of participants and researchers on interpretaiton of outcome
  • Placebo responses
  • Crossover studies (each subject acts as own control to limit confounding bias)
  • Randomization to limit selection bias and confounding bias
19
Q

The referral centers for a trial of a new anticancer drug have more patients with end stage disease than early stage, so more patients with end stage disease are referred for the trial than early stage disease.

A

Selection bias - nonrandom assignment to study group (e.g., Berkson’s bias, loss to follow-up)

20
Q

Studies performed on patients that have been hospitalized

A
  • Berkson’s bias - type of selection bias
  • The result is that two independent events become conditionally dependent (negatively dependent) given that at least one of them occurs
  • classic illustration involves a retrospective study examining a risk factor for a disease in a statistical sample from a hospital in-patient population. If a control group is also ascertained from the in-patient population, a difference in hospital admission rates for the case sample and control sample can result in a spurious association between the disease and the risk factor.
21
Q

Parents of autism patients having a more detailed recall of events and illnesses in theirchild’s first two years of life compared to parents of healthy controls.

A
  • Recall bias - knowledge of presence of disorder alters recall by subjects
22
Q

A study performed in China may not be generalizable to the US population.

A
  • Sampling bias - Subjects are not representative relative to general population
  • Results not generalizable
  • Sampe does not represent population
23
Q

Sending a survey out to people diagnosed with a fatal illness 5 years after diagnosis will preferentially sample those with a low grade disease (or few comorbidities)

A
  • **Late-look bias **- information gathered at an inappropriate time
  • e.g. using a survey to study a fatal disease (only those patients still alive will be able to answer the survey)
24
Q

The positive benefit of a new drug during a study simply may have been due to thefact that study participants were required to attend clinic monthly, where they received extra disease education and counseling compared with the controls.

A
  • Procedure bias - subjects in different groups are not treated the same
  • e.g. more attention is paid to treatment group, stimulating greater compliance
25
Q

Are asbestos miners more likely to have cancer because they mine asbestos or because they are more likely to smoke?

A
  • Confounding bias - occurs with 2 closely associated factors
  • the effect of 1 factor distorts or confuses the effect of the other
26
Q

While test PSA-xyz may detect prostate cancer before it is detected by a traditional PSA, early detection using PSA-xyz does not increase cancer survival compared to traditional PSA.

A
  • Lead-time bias - early detection confused with increased survival
  • Seen with imporved screening (natural history of disease is not changed, but early detection makes it seem as though survival increased.
27
Q

An orthopedic surgeon investigator who finds statistically significant benefit ofarthroscopic surgery when compared to non-invasive therapeutic strategies. A chiropractor-led study that finds significant benefit of the effects of cervical manipulation when compared to traditional medicine strategies.

A

Pygmalion effect - occurs when a researcher’s belief in the efficacy of a treatment changes the outcome of that treatment.

28
Q

When studying the effects that infection control education has on physicians, the investigator notes that both the experimental and the control groups improve their hand hygiene.

A

Hawthorne effect - occurs when the group being studied changes its behavior owing to the knowledge of being studied

  • “Dr. Hawthorne is watching you”
29
Q

Terms that describe statistical distribution

A
  • Normal = Gaussian = bell-shaed (mean = median = mode)
  • σ = standard deviation; n = sample size
  • SEM = standard error of the mean = σ/(n)^.5
  • SEM decreases as n increases
30
Q

Positive skew

Negative skew

A
  • Positive skew - mean>median>mode
  • Asymmetry with tail on right
  • Mode least affected by outliers in sample
  • Negative skew - mean<median></median>
  • Asymmetry with tail on left
31
Q

Statistical hypotheses

A
  • Null (Ho) - Hypothesis of no difference
  • Alternative (H1) - Hypothesis that there is some difference
32
Q

What is a p value?

What is an alpha level?

Type I error (alpha)

Type II error (beta)

A
  • P value = tells you how compatible data is with null hypothesis - probability that study results occurred by chance alone given that Null hypothesis is true
  • Alpha level - set by investigator at which p value is judged.
  • Type I error - “false-positive error” “saw” a difference that did not exist
  • Type II error - “false negative error” - B = Blind to a difference that did exist, B= probability of making a type II error - failing to reject the null hypothesis when it is in fact false
33
Q

Power (1-β)

A
  • Probability of rejecting null hypothesis when it is in fact false
  • likelihood of finding a difference if one in fact exists. It depends on:
    • Total number of end points experienced by population
    • Difference in compliance between treatment groups
    • Size of expected effect
34
Q

Standard deviations

A
  • 1 - 68%
  • 2 - 95%
  • 3 - 99.7%
35
Q

What does the shape of a positive skew graph look like?

A
  • asymmetry with curve shifted to left with tail on right; mean > median > mode
36
Q

Confidence interval

Z values

A
  • CI = mean ± Z(SEM)
  • range from [mean-Z(SEM)] to [mean + Z(SEM)]
  • Range of values in which a specified probability of the mean of repeated samples would be expected to fall
  • The 95% CI (corresponding to p = 0.05) is often used
    • For 90% CI, Z = 1.645
    • For 95% CI, Z = 1.96
    • For 99% CI, Z = 2.58
37
Q

t-test vs. ANOVA vs. χ2

A
  • T-test checks difference between means
  • ANOVA checks difference between the means of 3 or more variables
  • χ2 (chi-square) = compare percentages (%) or proportions
38
Q

Correlation coefficient (r)

A
  • Goodness of fit, how variables relate
  • always between -1 and +1

DIT Phase II Chapters (10 decks)

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