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BCPS > Statistics > Flashcards

Statistics Flashcards

1
Q

Type I Error

A

alpha error = false positive

Statistical test shows that there is a difference when one does not exist

Cause by incorrect stat test or random error

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2
Q

Type II Error

A

beta error = false negative

Statistical test concludes there is no difference when one exists

Cause by insufficient power

Large sample size helps decrease chance of error

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3
Q

Ordinal data

A

Ranked data with order but no consistent magnitude between data points

Example: Likert Scale (Wong-Baker Faces Pain Rating Scale; Pain rated 0-10)

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4
Q

Interval/Ratio Data

A

Continuous data
Interval has no true 0 but ratio does

Ex: lab values, vital sign, age, pain on continuous line / VAS

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5
Q

Nominal Data

A

Data with mutually exclusive categories but no rank or order

Ex: presence of event/disease state (yes/no); gender, race

Often expressed as a %

Ex: Pain severity using descriptive terms (minimal, moderate, sharp, aching)

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6
Q

Random Error

A

Chance

Unavoidable, unidentifiable circumstance randomly introduced into a study

Minimize with statistical testing and increased sample size

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7
Q

Intention to Treat

A

Once randomized, then analyzed

Maintains integrity of randomization

Conservatively presents results to mimic real world conditions

Worst case scenario for superiority studies

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8
Q

Delta Margin

A

Minimum clinically acceptable difference based on previous research

Used in noninferiority trials

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9
Q

Noninferiority trial

A

Alternative design when unethical to use placebo

Aim: demonstrate intervention is no worse than control by delta margin

Large sample needed for adequate power

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10
Q

Practice-based Research

A

Evaluates value of program/service to improve clinical outcomes and/or decrease cost

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11
Q

Confidence Interval

A

Range of values that probably includes the true treatment effect

Large sample size = narrower, more precise confidence interval

Expressed as 95% CI (corresponding to alpha of 0.05)

Statistically significant if it does not cross “1” (not due to chance)

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12
Q

Absolute Risk Reduction/Increase

A

Difference in risk between control group and intervention group

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13
Q

Relative Risk Reduction/Increase

A

% reduction in risk in intervention group compared with control group

RRR = (1 - RR) * 100
RRI = (RR - 1) * 100

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14
Q

Relative Risk

A

Incidence of outcome in exposed group compared with unexposed group

Used in cohort studies

RR < 1: lower risk in exposed group
RR = 1: Risk is the same
RR > 1: higher risk in exposed group

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15
Q

Descriptive Study Design examples, pros, cons

A

Case Report, Case Series

Case report = patient/setting
Case series = group of patients

Shows experience or exposure to intervention

Pro: identifies potential therapies for rare disease, unusual ADRs
Describes innovative approach
Hypothesis generating

Con: Weakest form of evidence due to lack of study elements that reduce bias

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16
Q

Surrogate Marker

A

Outcome measure of a lab value, physical biomarker, or other intermediate measure instead of clinical outcome

Convenient

Example: surrogate marker for hypertension is blood pressure

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17
Q

Per protocol (final analysis)

A

Only patients completing the entire study included in final analysis

Presents results under controlled conditions = best case scenario for superiority studies

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18
Q

Log Rank Test (Mantel-Cox)

A

Survival analysis

Assesses differences between groups in survival rate

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19
Q

Cox Proportional-hazards (cox regression)

A

Survival analysis

Predict time to experience an event taking into account covariates

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20
Q

Kaplan-Meier Survival

A

Survival analysis

Reflects cumulative proportion of surviving participants and is recalculated every time an event occurs

21
Q

Crossover Clinical Trial

A

Subject serve as own control by receiving all interventions under investigation in a sequential order with washout period between different interventions

Do not use in diseases that are not curable

Do not use if patient cannot return to pretreatment status before each treatment

22
Q

P value

A

Probability that results are due to chance, not the intervention

23
Q

Parallel clinical trial

A

Each subject receives/is assigned to one intervention

Data from all subjects in specific group are pooled together and compared with data from other groups receiving different interventions
+ outcome
intervention – outcome

population
+ outcome
control – outcome

24
Q

Interventional Study Design

A

Randomized Controlled Trials

Aim: determine cause and effect by investigating whether differences exist and quantify differences between interventional & control groups

Need to employ methods to minimize risk or error, bias, confounding (ex: blinding, randomization, statistical analysis)

25
Q

Observational Study Design

A

Cross-sectional, Case-control, Cohort

Aim: demonstrate association (NOT causation) between exposure and outcome

Can be retrospective or prospective

Prospective cohort > retrospective cohort > case control > cross-sectional

26
Q

Systematic Error/Bias

A

Avoidable, identifiable, and non-randomly introduced into a study

Minimize with randomization, restriction, stratification, matching

27
Q

D4 Approach to Biostats

A

Design of study (independent/parallel or dependent/crossover)

Designated # groups (2 or >2)

Data types (Interval/Ratio, Ordinal, Nominal)

Distribution

28
Q

Number Needed to Harm

A

Number of patients needed to treat over a specified period for 1 to experience an adverse event

29
Q

Number Needed to Treat

A

Number of patients who would need to be treated over a specified period for 1 patient to be spared a harmful event or experience a beneficial event

NNT = 100/ARR (%) or 1/ARR (decimal)
ARR = Control - intervention (X-Y)

Calculate when there are significant results for primary outcome (nominal data)

30
Q

Case-control Study

A

Observational Study

Examines individuals with an outcome of interest to determine if there are exposures associated with development of the outcome

Retrospective. The outcome is known at the beginning of the study.

Use Odds Ratio for measure of association

Good for studying rare outcomes with multiple exposures.
Inexpensive, short duration.

Cannot determine incidence or prevalence or multiple outcomes
Study cases and controls may come from different populations

31
Q

Cross-sectional Study

A

Observational Study

Snapshot of prevalence of exposure(s) and outcomes

Measure of association: prevalence

-Provides epidemiology information
-include larger sample size compared with case report
-Include patients regardless of disease severity, access to care

-Cannot determine incidence of outcomes
-Not ideal for rare exposure, outcomes, or conditions

32
Q

Cohort Study

A

Observational Study

Examines whether differences in exposure or individual characteristics are associated with development of outcomes

Can be prospective or retrospective

Relative Risk = measure of association

-Study rare outcomes
-Determine incidence of outcomes
-Identify harmful & protective factors for disease/outcome
-Establish temporal relationship (prospective only)

-Not ideal for rare outcomes
-High cost, long study duration

33
Q

Selection bias

A

Differences in probability of assigning subjects from same target population to a group. Subjects differ from population with same condition

34
Q

Performance bias

A

Difference in care provided

35
Q

Detection bias

A

Difference in how the outcome was assessed

36
Q

Attrition bias

A

Difference in withdrawal rates from the study

37
Q

Attention/observational bias

A

Subject change in action because aware of being observed

38
Q

Compliance/adherence bias

A

More subjects in one group fail to follow protocol

39
Q

Recall/information bias

A

Subject in one group more likely to accurately remember facts of interest

40
Q

Odds Ratio

A

Prevalence of EXPOSURE in group with outcome compared with group without outcome

Use in case control study

41
Q

Intervention = Y
Control = X

A

Positive outcome
Intervention (Y) = a
Control (X) = c

Negative outcome
(Y) = b
(X) = d

Y = a /(a+b)
X = c/(c+d)

ARR = X - Y
RR = Y/X

RRR = (1 - RR) * 100

42
Q

Dependent Stats Test for
Interval/Ratio data

A

2 groups: Paired t-test

Multiple measures in >=2 groups: repeated measure ANOVA or ANCOVA

43
Q

Dependent Stats Test for
Ordinal Data

A

2 groups: Wilcoxon signed rank

Multiple measure in >=2 groups: Friedman

44
Q

Dependent Stats Test for
Nominal Data

A

2 groups: McNemar

Multiple measure in >= 2 groups: Cochrane Q

45
Q

Independent Stats Test for
Interval/Ratio Data

A

2 groups: t-test

> 2 groups: one -way & two-way ANOVA or ANCOVA

46
Q

Independent Stats Test for
Ordinal Data

A

2 groups: Mann-Whitney U (Wilcoxon rank sum)

> 2 groups: Kruskal-Wallis

47
Q

Independent States Test for
Nominal Data

A

2 groups: Fischer’s exact

> 2 groups: Chi-square

48
Q
A

BCPS (9 decks)

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