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POPH192 > Module 5: Critical Thinking > Flashcards

Module 5: Critical Thinking Flashcards

1
Q

What is internal validity

A

Is our study estimate an accurate estimate of the actual value in the source population. I.e are there other explanations for the study findings, other than them being right?

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2
Q

What are the three factors to consider in internal validity

A

Chance, bias, confounding

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3
Q

What is external validity

A

The extent to which the study findings are applicable to a broader or different population (also known as generalisability) Judgement depending on what is being studied and who it is being applied to

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4
Q

What is sampling error

A

If you continuously sampled from the same source population, most of the time you would get a sample with a similar composition to the population you sampled from but some samples would be quite different just due to chance

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5
Q

How can sampling error be mitigated (can’t eliminate but can reduce)

A

Increase sample size: less sampling variability, increases likelihood of getting a representative sample and precision of parameter estimate

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6
Q

What is the statistical definition of a 95% confidence interval

A

If you repeated a study 100 times with a random sample each time and got 100 confidence intervals, in 95 of the 100 studies the parameter would lie within that study’s 95% confidence interval

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7
Q

What is the interpretation we use of the 95% confidence interval (CI can be applied to any numerical measure)

A

We are 95% confident that the true population value lies between the limits of the confidence interval

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8
Q

What effect does increasing the sample size have on the confidence interval

A

Makes it narrower

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9
Q

When is a study clinically important

A

When the confidence interval is entirely below the clinical importance threshold (a different value to null)

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10
Q

What are p values

A

Probability of getting study estimate (or one further from the null) when there is really no association, just because of sampling error. If probability very low, unlikely that estimate is due to sampling error. Probability of finding an association when there actually isn’t one.

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11
Q

What is the null hypothesis

A

That there really is no association in the population (parameter = null)

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12
Q

What is the alternative hypothesis

A

That there really is an association in the population (parameter does not equal null value)

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13
Q

What is the threshold for determining how unlikely is acceptable for a p value

A

<0.05

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14
Q

How is a p value of <0.05 interpreted

A

Reject null hypothesis, accept alternative hypothesis, association is statistically significant

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15
Q

How is a p value of >0.05 interpreted

A

Fail to reject null hypothesis, reject alternative hypothesis, association is not statistically significant

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16
Q

What is a type 1 error

A

Finding an association when there truly is no association

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17
Q

What is a type 2 error

A

Finding no association when there truly is an association. Incorrectly fail to reject the null hypothesis when should’ve

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18
Q

Why do type 2 errors occur

A

Typically due to having too few people in the study (bigger sample size = more likely to get small p)

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19
Q

How can statisticians work out how to minimise type 2 errors

A

Calculate power to work out how many study participants are needed to minimise chance of a type 2 error

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20
Q

If the confidence interval includes the null value what is the p value

A

p>0.05, not statistically significant

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21
Q

If the confidence interval does not include the null value what is the p value

A

p<0.05, statistically significant

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22
Q

Why are p values problematic

A

Arbitrary threshold, only about the null hypothesis, nothing about importance

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23
Q

At the 5% threshold when will a statistically significant association be found when there really isn’t one

A

At least one time in twenty (wrong 5% of the time)

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24
Q

What is the problem with p values regarding importance

A

If you include enough people in your study you’ll find a statistically significant difference, even if people were randomly assigned. Statistical significance is not clinical significance- don’t say anything about whether the results are useful, valid, or correct. Absence of a statistically significant association is not evidence of absence of a real association

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25
Q

What is bias

A

Any systematic error in a study that results in an incorrect estimate of the association between exposure and risk of disease

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26
Q

What is systematic error

A

Error due to things other than sampling (opposite of random error)

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27
Q

When can selection and information bias be controlled

A

Only during the design and data collection phases of a study. So investigators must identify potential sources of bias and identify ways to minimise these

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28
Q

What is selection bias

A

When there is a systematic difference between the people included in a study and those who are not, or when study and comparison groups are selected inappropriately or using different criteria

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29
Q

What can affect who is part of a study (selection bias)

A

How people are recruited, whether people agree to participate, whether everyone remains in the study

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30
Q

How can loss to follow up be reduced

A

Alternative contact details obtained at start of study, maintaining regular contact throughout study, making several attempts to contact people

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31
Q

What must be considered regarding selection bias in cross sectional studies

A

Who entered the study, is the sample representative of the source population, what is the response rate

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32
Q

What must be considered regarding selection bias in case control studies

A

Participants are selected based on their outcome status. If this is in some way dependent on their exposure status, then bias can occur. Must ensure high participation, clearly define population of interest, reliable way of ascertaining all cases or a representative sample of cases

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33
Q

What are the potential biases in selection of controls

A

Ensure controls are from the same defined population as the cases over the same time period, same inclusion and exclusion criteria for cases and controls, ensure high participation

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34
Q

What would happen to the odds ratio if cases who are exposed are more likely to be identified or to participate

A

Overestimation of harmful effect of exposure, OR is biased away from the null, numerically upward

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35
Q

What is a harmful factor when MOA is underestimated

A

Biased numerically downward toward the null

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36
Q

What is a protective factor when MOA is underestimated

A

Biased numerically upward toward the null

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37
Q

What would happen to the odds ratio if cases who are exposed are less likely to be identified or to participate

A

Underestimation of harmful effect of exposure, OR is biased toward the null, numerically downward

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38
Q

What are the common types of selection bias in cohort studies

A

Loss to follow up (if related to exposure and outcome this can lead to bias), comparison group selected separately from exposed group can lead to bias

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39
Q

What does a loss to follow up in the exposed group result in

A

Underestimate incidence proportion in exposed group, resulting in underestimated relative risk, RR biased numerically downward toward the null

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40
Q

What is a source of selection bias in RCTs

A

Loss to follow up

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41
Q

When is it important to consider systematic error

A

When critically appraising scientific literature, in evidence based practice, considering studies reported in the media, undertaking research

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42
Q

What is information bias

A

Observation or information bias results from systematic differences in the way data on exposure or outcome are obtained from the various study groups

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43
Q

How is data collected in a study

A

By participants or collected/measured by someone else

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44
Q

How can measurement error occur

A

Participants provide inaccurate responses, data is collected incorrectly/inaccurately

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45
Q

What is measurement error

A

Can be random: lack of precision, or systematic: lack of accuracy.

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46
Q

What effect can measurement error have in descriptive and analytic studies

A

Descriptive: inaccurate measurement of prevalence
Analytic: misclassification

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47
Q

What is non-differential misclassification

A

Not different between the study groups. Measurement error and misclassification don’t occur equally in all groups being compared. Normally RR moves toward null

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48
Q

What is differential misclassification

A

Different between study groups. Estimate can move toward or away from the null

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49
Q

What are some examples of differential misclassification

A

Cross sectional: people with outcome might report exposure differently to those without outcome.
Case control: cases might more accurately recall past exposures compared to controls, interviewers may probe cases more (or exposed in cohort studies)

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50
Q

What is a source of information bias in case control studies

A

Recall bias

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51
Q

What is recall bias

A

Systematic error due to differences in accuracy or completeness of recall to memory of past events or experiences

52
Q

How can recall bias be minimised

A

Objective measures (instead of self reported subjective measures), validate self reported measures with other information, memory aids

53
Q

What is interviewer/observer bias

A

E.g interviewer/observer knows exposure status and examined outcome differently for those in exposed group compared to comparison group

54
Q

How can interviewer/observer bias be minimised

A

Clearly defined study protocol and measures, standardised structured questionnaire and prompts, training of interviewers, blinding

55
Q

How could bias occur in RCTs

A

If knowledge of the treatment/exposure category influences the assessment of the outcome (i.e ensure blinding), if measurements are undertaken differently for different treatment groups

56
Q

How can information bias be minimised

A

Validated survey instruments, objective measures. Use standardised equipment, calibrated equipment, ensure blinding, structured interviews, trained interviewers, well defined exposures/outcomes, etc

57
Q

What is publication bias

A

Positive, new findings more likely to be published and available. “The result of the tendency of authors to submit, organisations to encourage, reviewers to approve and editors to publish articles containing positive findings”

58
Q

What is confounding

A

A mixing or muddling of effects when the relationship we are interested in is confused by the effect of something else - the confounder

59
Q

What does the saying risk factors party together mean

A

People with one risk factor tend to have multiple others

60
Q

What are the three properties of a potential confounder

A

Independently associated with the outcome, independently associated with the exposure, not on the causal pathway

61
Q

What does independently associated with the outcome mean

A

A risk/protective factor for the outcome by itself

62
Q

What does independently associated with the exposure mean

A

Different proportions of people with potential confounder across exposure groups

63
Q

What does not on the causal pathway mean

A

Not the mechanism by which the exposure affects the risk of the outcome

64
Q

What can confounding do?

A

Over/under estimation of true association, give appearance of association when there isn’t one, change direction of true association (e.g risk factor becomes protective)

65
Q

How can potential confounders be identified?

A

Collect information on all potential confounders: use literature to identify known and suspected risk factors for outcome, collect information on factors strongly associated with exposure regardless if known risk factor. If you don’t measure it, difficult to do anything about it later. Look for imbalance in potential confounder between groups

66
Q

How can confounding be controlled in the study design

A

Randomisation, restriction, matching

67
Q

What is randomisation and when can it be done

A

Design study to minimise confounding by selection and allocation of participants. Can only be done in RCTs

68
Q

What is restriction

A

All attempt to make groups being compared alike with regard to potential confounders. Restrict sample to one stratum of a potential confounder (e.g one age group)

69
Q

What is necessary for randomisation

A

Large sample size, equipoise, intention to treat analysis

70
Q

What are the cons of restriction

A

Can reduce generalisability, number of potential participants. Potential for residual confounding with imprecisely measured (or broadly defined) confounders, usually only one potential confounder

71
Q

What is matching and when does it occur

A

Choose people to make the control/comparison group have the same composition as the case/exposed group regarding the potential confounder. Usually in case control studies

72
Q

What are the two types of matching

A

Individual (each case matched with one or more controls) and frequency (matching at an aggregated level: same proportions between groups)

73
Q

What are the positives of matching

A

Useful for difficult to measure/complex potential confounders, can improve efficiency of case control studies with small numbers

74
Q

What are the cons of matching

A

Individual matching can be difficult and limit number of participants. Need special matched analysis for individual matching, otherwise will underestimate measure of association

75
Q

What is the main problem with randomisation, restriction and matching

A

Can’t assess whether truly a confounder.

76
Q

How can confounding be controlled in the study analyses

A

Stratification, multivariable analysis, standardisation

77
Q

What is stratification

A

Calculating measure of association for each stratum of potential confounder and comparing them.

78
Q

What is the crude MoA

A

Overall, not stratum specific

79
Q

If crude MoA does not equal stratum specific MoA,

A

Confounding is present

80
Q

What are the pros of stratification

A

Easy for small number of potential confounders with limited strata. Can evaluate impact of confounding. Can identify effect modification

81
Q

What are the cons of stratification

A

Can leave residual confounding, not feasible when dealing with lots of potential confounders with many strata

82
Q

What is multivariable analysis

A

Statistical method for estimating MoA whilst controlling for multiple potential confounders. Can work in situations where stratification won’t. Variety of different techniques recognisable by the term “regression”

83
Q

What is standardisation

A

E.g age standardisation: age structures differ and disease risk varies by age

84
Q

What are the cons of standardisation

A

Similar issues as stratification with multiple potential confounders/number of strata. Multivariable analysis often more efficient in analytic studies

85
Q

What are the main issues in controlling for confounding in study analyses

A

Residual confounding, can only control what you’ve measured (still need to think about potential confounders and what you could do about them)

86
Q

How much change (between crude MoA and stratum specific/adjusted MoA) indicates confounding

A

If controlling for confounding changes the measure of association by 10% or more

87
Q

What is effect modification

A

The association between exposure and outcome differs across strata of the effect modifier: an important finding (i.e after adjusting for confounding)

88
Q

What is the difference between confounding and effect modification

A

Confounding is a third factor distorting the association, effect modification is difference between the exposure and outcome across strata of the effect modifier (an important finding)

89
Q

What is association

A

Does the exposure increase or decrease occurrence of the outcome. Need to consider internal validity of apparent associations

90
Q

What is a cause

A

An event, condition or characteristic that plays an essential role in producing an occurrence of the disease

91
Q

What is the causal pie model

A

Whole pie = sufficient cause for an outcome. Each exposure is a component of the sufficient cause, so each exposure is a component cause. Exposures can be part of more than one sufficient cause

92
Q

What is a component cause

A

An exposure required to have an outcome (size of component cause in causal pie is not relevant)

93
Q

What is a necessary cause

A

A component cause which is necessary for the disease to occur (must be part of every sufficient cause). If you eliminate a necessary cause you entirely eliminate the outcome

94
Q

How do we determine causation

A
  1. Is the association internally valid
  2. Consider each of the guidelines and then make a judgement based on the totality of evidence (biological plausibility, experimental evidence, specificity, temporal sequence, consistency, dose response relationship, strength of association)
95
Q

What are the guidelines to determining causation (BEST CDS)

A

Biological plausibility, experimental evidence, specificity, temporal sequence, consistency, dose response relationship, strength of association

96
Q

What is biological plausibility

A

Is there a plausible mechanism for the association

97
Q

What is experimental evidence

A

Is there evidence from human RCTs or animal experiments (will only be applicable in RCTs)

98
Q

What is specificity

A

Is the exposure specifically associated with a particular outcome but not others? If it is this adds weight to causal likelihood

99
Q

What is temporal sequencing

A

Is there evidence of temporal sequence between exposure and outcome

100
Q

What is consistency

A

Are the findings consistent with findings from other studies (can be a number reasons why they may not be)

101
Q

What is the dose response relationship

A

Does the risk of the outcome change with increasing or decreasing amounts of the exposure (not all relationships are linear)

102
Q

What is strength of association

A

The stronger the association the less likely it is to be due to confounding or bias

103
Q

How are the guidelines used to make a judgement determining causality

A

Consider them all, then make a judgement based on the totality of evidence (is there another explanation for the findings more likely than cause and effect?)

104
Q

What are the two main types of review

A

Narrative and systematic

105
Q

What is a narrative review

A

Often broad in scope, not usually specified, potentially biased, variable, often a qualitative summary, sometimes evidence based. May be heavily influenced by opinion

106
Q

What is a systematic review

A

Often a focused clinical question, comprehensive sources and explicit search strategy, criterion-based selection, uniformly applied, rigorous critical appraisal, quantitative summary, usually evidence based. Replicable, transparent and systematic

107
Q

Why are systematic reviews done

A

Collate evidence, synthesise results. Done well, reduce bias that may otherwise be encountered with narrative reviews

108
Q

How are systematic reviews conducted

A
  1. Formulation of a clear question
  2. Write protocol for review
  3. Search for relevant studies
  4. Collect data from studies
  5. Assessment of included studies
  6. Synthesis of findings
  7. Interpretation of results
109
Q

What are the protocol methods for conducting a systematic review

A
  1. Question (PECOT)
  2. Relevance (importance)
  3. Objectives
  4. Search strategy (specific and thorough)
  5. Selection criteria
  6. Eligibility screen
  7. Risk of bias
  8. Data extraction
  9. Data synthesis (may involve meta-analysis)
110
Q

What is meta-analysis

A

The results of individual studies are combined to produce an overall statistic. Aims to provide a more precise estimate of the effects of an intervention and reduce uncertainty

111
Q

What are the limitations of meta-analyses

A

Designs of studies are too different, outcomes measured are not sufficiently similar, concerns about quality of studies

112
Q

What do forest plots show

A

Compare many studies, diamond overall meta-analysis

113
Q

When is it unethical to continue a cumulative meta-analysis

A

When subsequent trials continue to narrow CI and move toward null

114
Q

What are the challenges of meta-analyses

A

Doing all steps well, publication bias, poor quality trials/studies, heterogeneity can lead to conflicting reviews and inconclusive results

115
Q

What’s the difference between a systematic review and meta-analysis

A

A systematic review answers a specific research question by evaluating and summarising all the studies on the topic. A meta-analysis goes on to use statistical methods to combine the data from the studies.

116
Q

What are the pros of meta-analyses

A

Reproducibility, rigour lead to increased confidence. Comprehensive, transparent limits, gaps in knowledge, basis for decisions

117
Q

What is good about Cochrane

A

Global, independent, international and interdisciplinary network, not for profit, no commercial sponsorship, freely available to everyone in NZ

118
Q

What is critical appraisal

A

The process of carefully and systematically examining research to judge its trustworthiness, and its value and relevance in a particular context

119
Q

What does the abstract include

A

Summary of paper contents, main findings

120
Q

What does the introduction include

A

Background to the research, what was already known on the subject, what they wanted to investigate with the study, aims and objectives of the study

121
Q

What does the methods include

A

Selection of participants, structure of the study, definition of exposures and outcomes measured, how demographics, exposures and outcomes were measured/classified, methods used to control for confounding and statistical analysis

122
Q

What does the results include

A

Reporting of all results in text, tables and figures. Assessment of chance, bias, confounding

123
Q

What does the discussion include

A

Strengths and challenges experienced during the study, evidence for causation, researchers’ assessment of the implications of the results, importance of this information

124
Q

What does the conclusion include

A

Outlines what the study adds to current knowledge, where to from here

125
Q

What does critical appraisal require

A

Epidemiological knowledge, systematic and structured approach, note taking, reasoning and logical thought, use of frameworks to aid you in extracting information from papers

126
Q

What does table 1 show

A

Participants’ baseline characteristics

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