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BCOP > Stats, Trial Design, Interpretation > Flashcards

Stats, Trial Design, Interpretation Flashcards

1
Q

Internal validity

A

 How is the study structured?

 Is it a “good” study?

 Study design issues

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2
Q

External Validity

A

 Does the study apply to my situation?

 Is it applicable to the patients I see?

 Is it practical?

 Generalizability

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3
Q

Types of Study Design -5

A

 Descriptive

 Observational
 Case control
 Follow-up
 Cross-sectional

 Experimental

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4
Q

Types of Study Design - descriptive -3

A

No comparative group– no intervention

ex. case study, case series, survey, education intervention with no comparator

can be large (ie. 30,000 high dose theophylline)

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5
Q

Types of Study Design - observational (epidemiological) (think watchful scientist)

A

Comparative group; no intervention

  1. case control: based on outcome
  2. follow-up: based on risk factors
  3. cross-sectional: hybrid of the two
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6
Q

Types of Study Design - experimental -7

A

Comparative group

 Patients selected

 Consent

 Investigator allocates

 Intervention

 Measurements

 Assess outcome

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7
Q

Types of Study Design - experimental - Parallel vs Crossover - PARALLEL DEF-4

A

 Each patient receives one therapy

 Two concurrent groups

 Interpatient variability

NEED MORE PTS

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8
Q

Types of Study Design - experimental - Parallel vs Crossover - CROSSOVER DEF-5

A

 Each patient receives one therapy then another

 Randomized to sequence (everyone gets both drugs)

 Tx A -> outcome -> Washout (5 half-lifes) ->Tx B -> outcome

 Position effect (statistics)

FEWER PATIENTS NEEDED

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9
Q

Types of Study Design - experimental - ADVANTAGES -4

A

 Control more variables, can blind

 Decrease sources of bias

 Ascertain cause and effect (can not say that in other trials)

 “Cadillac” of study designs

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10
Q

Analyzing Methods Section - FLUFF

A

 Types of bias

 Often use flowcharting to follow a patient through the study

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11
Q

Selection Bias -5

use Table 1

A

 Was bias introduced in how the patients were selected?

 Is the study population adequately defined?

 Inclusion and exclusion criteria

 Treatment groups comparable

 See “Table 1” of study

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12
Q

Classification Bias def -2

A

Refers to how classifications made (bias can be made in recruiting pts, often defs in supplementary material, definitions can be extensive)

 ex. Postmenopausal, Receptor positive, Outcomes—disease-free—survival event

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13
Q

Preventing Classification Bias -3

A

 Use structured definitions

 Use “reliable,” “complete” sources of information

-is EHR good source of info

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14
Q

Allocation Bias def -2

use Table 1

A

 Was bias introduced when patients assigned to their groups? - very hard to assess because studies often just say “pts were randomized”

 Was it truly random? use Table 1 to see if equal

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15
Q

Randomization method -1

A

 Permutated blocks (for every 4 pts, assign 2 to a group -> this allows study to stop in middle if needed)
 Keeping even numbers of patients in the groups throughout the conduct of the study (to allow better stats)
 Stratified according to participating center
 Chemotherapy planned to be given before, during or not at all

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16
Q

Compliance Bias def -3

A

 How was compliance assessed?

 Not ALWAYS specifically addressed in study - this makes it HARD to assess

 ie. Semiannual visits for first 5 years

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17
Q

Attrition Bias def -3

A

 Drop-outs and why (acct for all pts)

  • ie. may just drop out (withdraw consent) or be ineligible following medical review

 If more patients drop out of one group vs another, does this introduce bias or influence the results?

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18
Q

Interventions def -3

A

 Comparable

 Blinding
Double-blind: Neither investigator nor patient knows patient allocation
 Single-blind: Either patient or investigator does not know

 Competing interventions (that would influence results)

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19
Q

Observer and Measurement bias -5

prevent with blinding

A

 How are outcomes measured?

 Is it appropriate?

 Patient or observer influences

 Sufficient observation (challenging, need many yrs for oncology)

 Is it clinically meaningful?

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20
Q

Confounding Bias -4

all studies susceptible

A

 Attributing the outcome to a risk factor not related to the outcome (wrongly attributing outcome)

 Can control for many variables in the analysis

 Often difficult to prevent

 Look at exclusion criteria

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21
Q

Preventing Other Problems - Is the study powered to be meaningful? -3

A

 Study enough patients

 Discuss with statistical power

 Usually discussed when sample size calculations presented in methods

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22
Q

Analyzing Results

A

 Add numbers to flow chart (assess attrition)

 Follow the numbers

 Attrition

 Present results for EVERYTHING mentioned in methods

 Statistics

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23
Q

What Data To Include - Intention to treat

A

All patients randomized included in analysis

Considered the most conservative analysis

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24
Q

What Data To Include - Modified intention to treat

A

all patients randomized AND received at least one dose of therapy

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25
Q

What Data To Include - Per protocol

A

Only those patients who completed the study per protocol (ie, pt’s dropped if ADR and stopped tx)

For many studies useful to have both an intention to treat method and a per protocol

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26
Q

Statistical Analysis -Descriptive -3

A

 measure of central tendency

mean, median, mode, etc.

 spread of the data

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27
Q

Statistical Analysis -Inferential 1

A

Null hypothesis =No difference exists

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28
Q

Non-inferiority Trials -3 &&&

new in last 10 years

hard

A

 Use a different hypothesis: The two treatments are not non-inferior to each other (tough because double negatives) (think the treatments are same)

 P values mean different things—if less than 0.05, means they are non-inferior

 Can’t claim superiority with these trials but can do a non-inferiority analysis then a superiority analysis

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29
Q

Non-inferiority Trials -Why? -4

A

 Unethical to do a placebo controlled trial

 Treatment expected to be similar to standard treatment (Therapeutic non-inferiority to active control)

 Treatment assumed to be better than placebo

 Treatment likely to have other advantages (safety, cost, convenience . . .)

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30
Q

Non-inferiority Trials -set up -3

no clue here

A

 Set a “marginal difference”

 Uses alternative hypothesis

 Set confidence interval threshold

-actually need more pts for this type of trial

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31
Q

study types - superiority vs equivalence vs non-inferior def -3

A

 Superiority trials (Is new therapy significantly better or worse?)

 Equivalence trials “neither any better or any worse” (Establish equivalence range. Is it in the range to be similar?) (to see if 2 drugs are pharmaceutically equivalent)

 Non-inferiority “not much worse than the active comparator” (Is new therapy no worse than control?)

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32
Q

Statistical Tests - Nominal: yes or no - def and examples

A

 Categorical

 Response rate (patients responded or not)

 Adverse events or not

 Alive or dead

 Pregnant or not

 Race

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33
Q

Statistical Tests - Nominal Data “Traps” -2

A

 Percentages
 Seem like on a continuous scale
 Think of the data origin
 Did the patient have a response or not?
 Response is yes-no
 Presented as % patients with response

 Multiple groups or categories
 Still assess if yes or no they belong to each group
 No ranking

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34
Q

Statistical Tests - Nominal Data Tests -4

A

 Chi-Square (lots of rules)
 N>40
 20-40 use if expected frequency of cells >5

 Fishers exact (if N<30 use but can use for all nominal data)

 Related samples: McNemar (cross-over)

 3 or more independent groups: Chi-Square (also called Chi-Squared for independent groups)

35
Q

Statistical Tests - Ordinal Data def -6

A

 Ranked

 Likert scales (strongly agree to strongly disagree)

 Hierarchy

 Responses not mathematically equal

 ie. Years of HRT (none, 0-5 years, 5-10 years, greater than 10 years), Age of diagnosis (less than 50, 50-55, 55-65, older)

36
Q

Statistical Tests - Ordinal Data “Traps” -4

A

 Likert scale (1-5, strongly disagree-strongly agree)

 Calculate means

 Behaves like continuous data and presented as continuous—need to remember still ordinal data!!

 Useful to present median, mode, “top box” = positive responses like Likert 4 and 5 only

-USE MEDIAN NOT MEAN

37
Q

Statistical Tests - Ordinal Data Tests -4

A

 Mann Whitney U test (based on MEDIAN)

 Wilcoxon rank sum test

 Related samples (cross-over)
 Sign test
 Wilcoxon signed rank test

 Kruskal-Wallis ANOVA (for multiple groups)

38
Q

Statistical Tests - Continuous Data -Continuous “Traps” -2

A

 Data presented as % probably not continuous

 Are composite scales, etc, really continuous?— many times yes!
 Battery of ordinal scales
 Total (when all batteries combined together) behaves as continuous

39
Q

Statistical Tests - Continuous Data -def and examples

A

 Interval, ratio data

 Time to disease progression

 WBC, platelet count

 Serum creatinine

 age, weight

40
Q

Statistical Tests - Continuous Data: VAS -2

A 
 Visual analog scales (VAS)
   Scale of 0-10, 0 being no pain, 10 being the worst pain imaginable (DO NOT DEFINE THE POINTS IN BETWEEN)
   Only anchors the ends 
   Administered verbally or in writing 
   Handled as continuous data

 Other pain scales: 0=no pain, 1=mild, 2=moderate, 3=severe
 Defines all points
 Handled as ordinal data

41
Q

Statistical Tests - Continuous Data Tests -5

A

 Parametric vs Non-parametric

 Mann-Whitney U (median)

 Student’s t-test (2 groups) (mean)
 Normal distribution (are both mean and median similar), equal variance (are std deviations the same)

 Related Data: paired t-test (cross-over)

 ANOVA (3 or more groups)

42
Q

Hypothesis Testing -4

A

 Start with null hypothesis

 Superiority trial: There is no difference

 Equivalence: The groups are not equivalent

 Non-inferority: The therapy is not non-inferior to the other therapy

43
Q

Types of Error—Superiority

columns across are truths

rows are experiment

A
  1. Type I or alpha error (alpha, p value) TOP RIGHT OF BOX = experiment shows “difference exists” WHEN IN FACT truth is “no difference”. alpha is set up front as 0.05, P VALUE DETERMINED AFTER STUDY AND IS NEW ALPHA AND TELLS IF SIG
  2. Type II or beta error (beta) BOTTOM LEFT OF BOX= experiment shows “no difference exists” WHEN IN FACT truth is “difference”. beta is set up front. 0.2 is good, some do lower UNFORTUNATELY NO EQUIVALENT P VALUE TO FIGURE WHERE WE REALLY FELL
44
Q

Types of Error—Superiority

columns across are truths

rows are experiment

A
  1. Type I or alpha error (alpha, p value) TOP RIGHT OF BOX = experiment shows “difference exists” WHEN IN FACT truth is “no difference”. alpha is set up front as 0.05, P VALUE DETERMINED AFTER STUDY AND IS NEW ALPHA AND TELLS IF SIG
  2. Type II or beta error (beta) BOTTOM LEFT OF BOX= experiment shows “no difference exists” WHEN IN FACT truth is “difference”. beta is set up front when doing sample size. 0.2 is good, some do lower UNFORTUNATELY NO EQUIVALENT P VALUE TO FIGURE WHERE WE REALLY FELL
45
Q

Power and Sample Size -5

picked at the begining

A

 Power = 1 - beta

 Determined by alpha (p value) and beta values desired

 Estimated response rate

 Difference believed to be valuable

 Front-end concept!!

46
Q

Sample Size Calculations using power

A

slide 80 &&&

47
Q

Expressing Risk - three types and data used -3

A

 Expressed as odds ratio, relative risk or hazard ratio

 Used for nominal data ONLY!!!

 Use a 2x2 table—helpful for organizing data in
study

48
Q

Odds Ratio def and trial use -3

ESTIMATE OF RISK

A

Based on prevalence

No denominator, making assumptions

Case Control, cross-sectional

49
Q

Relative Risk def and trial use -4

A

Based on incidence

Denominator

Association between exposure and disease over time

Follow-up, experimental

50
Q

Incidence -2

PREVALENCE IS WITHOUT UNIT OF TIME

A

 (Number of persons developing dx/ total at risk) per unit of time

 Direct estimate of probability or risk

51
Q

Relative Risk rationale and calculation -5

A

 Expression of risk for follow-up studies (also experimental trials)

 Accounts for denominator information

 Calculation: RR = (a/a+b) / (c/c+d)

 The proportion between the two!!

 Usually presented with a confidence interval

52
Q

Interpreting Risk (by outcome) -4

A

 1 = no difference between the groups

 2-5 = mild association

 5-10 = moderate association

 > 10 = strong association

53
Q

Relative Risk Reduction -3

RELATIVE BENEFIT INCREASE

A

 The most “optimistic” way to present risk

ie.
 Calculated 1-RR = 1-0.82= 0.18= 18%
 Letrozole decreased the risk of a disease free survival event by 18%

54
Q

Absolute Risk Reduction -3

ABSOLUTE BENEFIT INCREASE

A

 Takes into account the actual values of the numbers rather than just the proportion

 Are we talking events that occur 1 in 10 or 1 in 1000?!!

ie.
 Calculated (A/A+B)- (C/C+D) =8.8%-10.7% = 1.9% (absolute value) -NOTE INCIDENCE DIFF

55
Q

Number Needed to Treat (NNT) calculation and use-3

VERY IMPT -KNOW

A

 Inverse of ARR
 Be sure to convert percentages to decimals

 Way to make numbers more practical and meaningful

 Concept is “Number Needed to Harm” (NNH) for adverse events

-ALWAYS ROUND TO NEAREST WHOLE PERSON

56
Q

Survival Analysis -5 &&&

BASICALLY RELATIVE RISK AMPED UP

EX. BREAST CANCER AND HORMONE EVENTS JUMPED AFTER 5 YRS - THINGS CHANGE OVER TIME

A

 Takes into account the timing of events

 Weighted relative risk over the entire study

 Result is Hazard Ratio (HR)

 Data presented in Kaplan—Meier curves

 Cox proportional hazards regression the most common for multivariate analyses; log rank test for differences in survival

57
Q

example need for survival analysis - 2

A

 Takes into account that you had many patients for the first two years, and not as many in the last 3 years

 Same number of events in end, but different denominators over time

58
Q

Censoring def -1 and examples -4 -slide 101&&&when is arm favored??

A

 Accounting for missing or incomplete data
 Study ends before patient has an event
 Patient is lost to follow-up
 Patient withdraws due to an adverse event
 Patients voluntarily crossed over to other tx (ie. letrazole)

59
Q

IPCW: Inverse Probability of Censoring Weighted analysis DEF -1

A

Modeling technique to account for bias introduced in censoring

60
Q

Censoring vs ITT issues -3

A

 ITT: Tamoxifen looks better than it may be (patients that crossed over to letrozole included =pt likely had better outcomes)

 Censored: Only disease free patients allowed to cross-over. High risk patients left in tamoxifen group.=pts likely had worse outcomes

-USE IPCW analysis to account for adjust for these biases

61
Q

RR vs HR &&&slide 103 clarify

A

 Relative risk can easily be calculated from numbers presented in the study

 Hazard ratio is the same concept but is the weighted relative risk over time
 Adjusts for change over time
 Adjusts for “repeated measures”
 Adjusts for different “slopes” of the line

62
Q

P-Value def -3

A

 Probability results due to chance alone

 Determine level of significance (alpha value)
prior to conducting the study

 By custom, p < 0.05 is considered “statistically significant”

63
Q

Statistical Pearls as related to p value -4

A

 The size of the p value has nothing to do with the importance of the result (ONLY YOU DETERMINE THIS, ie. p=0.001 for bp med which measured 2mmHG diff)

 Do not confuse statistical significance with clinical significance

 Results that are not statistically significant MAY still be important

Statistics do not determine what is important, statistics determine how certain we are.

64
Q

Confidence Interval -5

A

 95% CI (If study was repeated 100 times, 95% of the time the result would likely fall in this range)

 Provides a “range” to result (Inferences on the population) (DO NOT CONFUSE CI WITH STD DEV, which only tell you about variation in study)

 Calculation based on Standard Error of Mean (SEM

 CI can be applied to any type data
 IMPT TO Determine value that represents no difference

 When used with OR, RR or HR
 No difference value = 1 (If CI doesn’t include 1, then statistically significant)

65
Q

Other Statistical Issues: Repeated Measures - Cox model - 3

A

 If made multiple measurements over time, then need to correct for it using a statistical test that takes into account repeated measures

 ie. Evaluated every 6 months

 Cox model accounts for this

66
Q

Other Statistical Issues: Bonferroni Effect -3 &&&clarify slide 113

NOT CLEAR

A

 If look at enough things, something will be statistically significant just by chance alone

 If didn’t make correction and should have, multiply the p value by number of comparisons.

 2009 states adverse drug reaction (ADR) analysis not adjusted for multiple comparisons

67
Q

WHEN IS IT POSSIBLE TO MAKE A BETA ERROR?

A

When p value is >0.5 because you are saying there is NO DIFFERENCE BETWEEN THE GROUPS.

68
Q

Duration of Studies

A

 Were patients studied for sufficient duration?

 Do the results change over time?

 Are the same things being compared at each time point? &&&

69
Q

Subgroup Analysis key points -4

A

 Allocation no longer applies (NOT RANDOM)

 Sample size calculations don’t hold for
subgroups (POWER NOT APPLIED)

 As more subgroups evaluated, more opportunity for finding a significant result when one does not exist

 Results can be overstated and misleading

70
Q 
Interpreting Forest Plots 
when used? -2
what does bar mean? -1
what does box mean? -2
do shorter bars usually have larger boxes?
A

 Used with subgroup analysis and meta-analyses

 Bar = confidence interval

 Box
 Location = HR
 Size = number of people in analyses

 Usually shorter bars have larger boxes = smaller
confidence interval as increase sample = more confident of result

71
Q

Meta-Analysis

A

 Combine results from many studies

 Reanalyze

 Decrease beta

 Specific criteria for selection and classification of studies (selection bias refers to how studies selected!!)

 Studies should have similar methodologies

 Compounds problems observed in the individual studies

72
Q

Effect Size slide128-130&&&never heard of this

NOT COVERED IN VIDEO

A

 Way to standardize the effect

 Used for continuous data with normal
distribution

 Calculated by dividing the difference of means by standard deviation

 Use table from website to interpret and make more practical

73
Q

Reporting Data key concepts &&&clarify

A

 How reported affects significance placed on data

 Watch graphs!! (CAN BE MISLEADING)

 Changing numbers to %

 Collapsing data in categories

 % change from baseline

74
Q

Case-control Studies def -3

A

 Identify cases with the disease of interest (outcome)

 Identify controls without the outcome

 Look back in time (from present to past) to assess the risk factors

75
Q

Retrospective study. . . .key concepts -3

A

 Often confusing terminology

 Study design: another name for case control

 Refer to the time frame of the study

76
Q

Application of Case- Control -4
what to apply to?
“rare” yes or no?
expensive?

A

 Applied to new diseases or outbreaks

 Can study “rare” diseases

 Evaluate multiple risk factors

 Relatively easy and less expensive

77
Q

Weakness of Case - Control, aka types of bias -4

A

 Selection bias

 Classification bias

 Information bias&&&

 Confounding bias&&&

78
Q

Cross - Sectional study def -4

A

 Identify a study population (from present going forward)

 First Classify based on outcome

 Second separate outcomes to Classify based on risk factor

 Predict prevalence

79
Q

Cross - Sectional Problems / weaknesses -4

A

 Chicken and the egg

 Confounding bias &&&

 Selection bias

 Classification bias

80
Q

Follow-up Studies def -5

A

 Identify a study population (from present to future)

 Exclude individuals with the outcome of interest

 THEN those without outcome of interest -> Classify based on risk factor

 Follow over time

 Assess outcome

81
Q

Follow-up study features -4

why best?

A

 Strongest study design

 Strongest causal link

 Denominator; predict incidence

 Can usually address information bias &&&

82
Q

Follow-up bias / weakness -4

A

 Hawthorne effect &&&

 Surveillance bias

 Change over time

 Attrition bias &&&

83
Q

Issues in Oncology Studies -6

A

 Duration of therapy and evaluation

 Results represented

 Endpoints selected

 Combination therapy

 Doses, regimens, routes

 Balancing cost and clinical outcomes

84
Q

basics of statistical tests -3

A

type of data (nominal, ordinal, continuous)

number of groups

independent (parallel) OR related (cross-over) groups

BCOP (32 decks)

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