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Health Care Management > Advanced Research Methods > Flashcards

Advanced Research Methods Flashcards

1
Q

Definition DAG

A

Directed Acyclic Graphs are graphical representations of the causal structure underlying a research question.

DAGs help to visualize the causal structure underlying a research question

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2
Q

What do you need for a DAG?

A
  • Prior knowledge of the subject
  • Data on all relevant variables
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3
Q

Path

A

Any route between exposure X and outcome Y
connection between exposure and outcome

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4
Q

Causal path

A

Follows the direction of the arrows

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5
Q

Backdoor Path

A

Does not follow the direction of the arrows

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6
Q

Open paths

A

All paths are open, unless they collide somewhere on a path

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7
Q

Closed paths

A

A path is closed if arrows collide in one variable on that path

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8
Q

When is an open path blocked?

A

When adjusting for a variable

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9
Q

What do we want to know from an causal inference?

A

We are not interested in the outcome per se, we are interested in the role of the treatment in achieving this outcome

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10
Q

Definition causal effect

A

In an individual, a treatment has a causal effect if the outcome under treatment 1 would be different from the outcome under treatment 2

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11
Q

Counterfactual outcome

A

Potential outcome that is not observed because the subject did not experience the treatment

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12
Q

Individual causal effect cannot be observed unless..

A

Except under extremely strong and generally unreasonable assumptions

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13
Q

When can a causal inference be determined?

A

Only when three identifiability conditions are met in a study

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14
Q

The three identifiability conditions

A

Positivity
Consistency
Exchangeability

If all conditions are met the association between exposure and outcome is an unbiased estimate of a causal effect

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15
Q

Positivity

A

Each individual has to have a positive probability of being assigned to each treatment arms

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16
Q

Consistency

A

The treatment has to be well defined

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17
Q

Exchangeability

A
  • The individuals assigned to the different treatment arms have to be similar
  • It does not matter who gets treatment A and who gets treatment B
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18
Q

How to meet the exchangeability condition

A
  1. randomized rct
    Individuals are randomly assigned to one of each treatment
  2. Matching
    For each individual who gets treatment A, there is an individual who gets treatment B
  3. Stratification
    Randomly select individuals from different subsets of the larger population. Almost impossible
  4. Adjustment
    Control for factors that influence the association between the treatment and outcome
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19
Q

Confounder

A

An variable that effects X and Y

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20
Q

Ethnography

A

The task is to document the culture, the perspectives and practices, of the people in the settings. The aim is to get inside the way each group of people sees the world

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21
Q

Correlation

A

A statistical relationship between the treatment and outcome

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22
Q

relative risk or risk ratio

A

the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group

RR = 1 exposure does not affect outcome
RR < 1 the risk of the outcome is decreased by the exposure
RR > 1 the risk of the outcome is increased by the exposure

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23
Q

Risk difference

A

The difference between the risk of an outcome in the exposed group and the unexposed group.

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24
Q

Absolute risk increase

A

When the risk of an outcome is increased by the exposure

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25
Q

odds ratio

A

is a statistic that quantifies the strengts of the association between two events.

OR = 1 A and B are independent
OR > 1 A and B are associated correlation
OR < 1 A and B are negatively correlated

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26
Q

confounding

A

bias caused by common cause of exposure and outcome
You have to include and control/adjust the variable

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27
Q

collider

A

Variable where two arrows collide. The variable has to be excluded

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28
Q

Blocking

A

adjusting for a variable amongst a path. Blocking can be done by adjusting for any variable along a path.

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29
Q

Unblocking

A

Adjusting for a collider, unblocking a path by adjusting for an already blocked path

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30
Q

selection bias

A

if there is no equal chance of person a or person b becoming part of the sample

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31
Q

publication bias

A

positive findings are more likely to be published, which can skew the results that we see

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32
Q

mediator

A

explains the relation between the independent and the dependent variable. It explains how or why there is a relation

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33
Q

moderator

A

is a variable that effects the strength of the relation between the predictor and the criterion variable

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34
Q

self selection bias

A

when individuals volunteer to be in a treatment group. The sample in not random

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35
Q

recall bias

A

systematic error that occurs when participants do not remember previous events omit details

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36
Q

survival ship bias

A

when some of many of the observations are falling out of the sample which changes the composition of observations that are left

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37
Q

healthy user bias

A

people who take vitamins regularly are likely to be healthy

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38
Q

omitted variables bias

A

variables are neglected that may be important in the relationship

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39
Q

regression equation

A

example:

Weight = B0 + B1 x heigh

B0= constant

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40
Q

p <0,05

A

difference is statistically significant

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41
Q

the chance of finding a statistically significant result depends on

A
  • sample size
  • variation in population
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42
Q

testing

A

gives dichotomous result yes/no

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43
Q

estimating

A

size/strength of estimated effect

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44
Q

interpretation 95% CI

A

if the study was repeated, 95% of intervals would contain correct value

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45
Q

data ministry

A

adding too many variables without any theoretical justitification

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46
Q

multicollinearity

A

highly correlated explanatory variables

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47
Q

extrapolating beyond the data

A

regression results are only valid for populations similar to that of the study sample

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48
Q

non linearity

A

the assumption in regression analysis is that the association between the exposure and outcome is linear, but the association may be logarithmic

49
Q

Unadjusted analysis

A

a researcher only focuses on bivariate association of two variables, for instance the outcome and exposure

50
Q

adjusted analysis

A

more variables are included in the analysis

51
Q

Logistic

A

observed outcome is dichotomous yes/no

X is linearly associated with the log of the odds of the outcome

Ln(p/(1-p)) = constant + X1 x something

52
Q

odds formula

A

odds = (p : (1-p))

53
Q

OLS

A

Observed outcome is continuous

X is linearly associated with the outcome

Y = constant + x X1 + x X2

54
Q

continuous outcome example

A

weight or height

55
Q

dichotomous outcome example

A

weight > 70kg yes or no

56
Q

OLS can be used to

A

predict the outcome
direction and size of effect

57
Q

Logistic regression can be used to

A

predict the probability of an outcome
direction of effect

58
Q

Qualitative research

A
  • Holism
  • Open, how, what and why
  • Flexible, naturalistic setting
  • observation, interviews, documents
  • words, description, interpretation
  • researcher as instrument; involved
59
Q

Quantitative research

A
  • Reductionism
  • To test hypothesis, to prove an assumption or causality, predict
  • Closed question; associations
  • Controlled or structured experiment
  • structured observation, surveys, measurements, data
  • tables, measure, calculation, statistical test
  • detached, external instruments, tests, surveys
60
Q

discourse analysis

A
  • language use is important and should be the object of study
  • language can be strategically used for all kinds of purposes
  • the use of language can have consequences: it can shape how we think and how we behave
61
Q

Hodges et al gives three forms of discourse analysis

A

Formal linguistic discourse
Empirical / conversation analysis
Critical discourse analysis

62
Q

formal linguistic discourse

A

studying text to discover grammatical and linguistic rules
Sentences, structure and grammar

63
Q

Empirical/conversation analysis

A
  • studying talk in interaction to understand social practice
  • also non verbal language
  • for example non verbal language
64
Q

critical discourse analysis

A
  • studying macro discourses to understand the reproduction of power
  • society level competition health care providers
  • solidarity
65
Q

Alvesson and Karreman levels of discourse analysis

A

micro
meso
grand
mega

66
Q

Micro

A
  • Detailed study of text itself without wanting to make broader claims beyond
  • just text
  • textual details
67
Q

Meso

A
  • studying language use to understand broader social practices
  • overlaps with empirical/conversation
  • Daily talk and meaning for social practice
68
Q

Grand

A
  • studying discourses that structure organizational reality
  • level of university, ministry
69
Q

Mega

A
  • Studying universal discourses that structure human reality/ the way we view the world
  • capitalism
70
Q

Immersion

A

Immerse in the environment you study, become part of the group and try to understand them

71
Q

Insider/emic perspective

A

you need to become part of that part of the society, feel what they feel and what they think

72
Q

ethnography details:

A

ethnographic studies zoom in on daily practices, in order to understand these in context

73
Q

Organizational Ethnography

A
  • Understanding organizations as cultural entities
  • Understanding the micro, going in depth through participant observations
74
Q

Organizational Ethnography in healthcare

A
  • care as organized practices
  • bottom up / critical perspectives on care
  • empowerment of minority voices
75
Q

Ethnography in practice

A

Abduction
Sensitizing concepts
Theory field theory

76
Q

Abduction

A

theory driven

77
Q

Sensitizing concepts

A

Are concepts that you keep in mind while doing research. It helps to get closer and zoom in to a theoretical perspective.

78
Q

Theory-field-theory

A

New insights from the field

79
Q

Subtle realism Mays and Pope
(Criteria for qualitative research)

A
  • epistemic position
  • there is a reality that can be studied
  • reliability, validity, generalizability
  • triangulation, fair dealing, respondent validation, attention to negative cases, clear exposition of data collection
  • importance of neutrality
80
Q

Relativism Rolfe
(Criteria for qualitative research)

A
  • Reality is multiple and socially constructed
  • Open to challenge and depends on purpose
  • No predetermined criteria, appraisal resides in the eyers of the beholder
  • not neutral
81
Q

Three different reasons for examining associations through quantative research

A
  • description
  • prediction
  • causal inference
82
Q

exchangeability through..

A
  • DAGs
  • Design Study
  • Interpret results
  • Draw conclusions
83
Q

Goal of description

A
  • to identify patterns in data
  • obtain factual information
  • not explaining patterns
  • not drawing causal conclusions
84
Q

Description Statistical Methods

A

Bivariate analysis
- Continuous outcome
- Mean, median, interquartile range (boxplot)
- OLS with one exposure variable

Dichotomous outcome variable
- Proportions, percentages, frequency
- Mean, median per category
- Logistic regression with one exposure

No adjustment, full associations

85
Q

Description: Design and interpretation

A

Population data: eg election results
- Observations
- No uncertainty

Sample data:
- observations
- no uncertainty
- testing

86
Q

Description: evaluation

A

Are the results interesting?
Starting point for further research?

87
Q

Prediction Goal

A
  • Predict the future
  • If you know ABC what can you say about D
  • Not to draw causal conclusion
88
Q

Prediction Examples

A

If you have these symptoms, you will probably have this disease

If you have watched these films, you will probably like this film

89
Q

Prediction Statistical Methods

A

Multivariate Regression Analysis
- Theory or data driven
- Difference between line and observations is ideally 0
- expand equation as far as possible so it explains ad much variation as possible

90
Q

Prediction Interpretation

A
  • Predicting outcome variable as accurate as possible
  • Reducing uncertainty (error ideally 0)
  • Interpretation of individual coefficients usually irrelevant
91
Q

Prediction Evaluation

A
  • how good is the data
  • how well does the regression model fit the data
  • how well does the regression model predict the outcome of interest
92
Q

Causal Inference goal

A

Estimating causal effects

93
Q

Causal Inference Design

A

RCT, 3 identifiability conditions

94
Q

Causal inference Statistical Methods

A

DAG
bivariate regression
multivariate regression
depending on research question, adjustment

95
Q

Causal inference interpretation

A

Individual associations relevant, focus on X

96
Q

Causal inference Evaluation

A

Assumptions transparent; to what extent was bias avoided

97
Q

Strengths of qualitative research

A
  • Rich description of processes and experiences
  • Knowledge construction and power relations
  • Moving targets and phenomena in formation
98
Q

two perspectives on observational studies

A
  1. avoid causal language
  2. emphasize causal language
99
Q

avoid causal language

A
  • bias cannot be avoided with certainty
  • describe association
  • emphasize that causality cannot be inferred
100
Q

emphasize causal language

A
  • be transparent about the real objective of the study
  • design the study carefully, be transparent about assumptions
  • acknowledge that bias cannot be ruled out
101
Q

observational dimensions

A
  • Space
  • Actor
  • Activity
  • Object
  • Act
  • Event
  • Time
  • Goal
  • Feeling
102
Q

Space

A

where the researcher volunteered
layout of the place

103
Q

Actor

A

people involved
Doctors, nurses, speech therapists etc

104
Q

Activity

A

A set of related acts by several individuals. eg medical consult

105
Q

Objects

A

physical things present
for example: protocols, MRI, medical journals, shampoo, braces

106
Q

Act

A

single action by one individual

107
Q

Event

A

Something out of the ordinary

108
Q

Time

A

A sequence of events

109
Q

Goal

A

The goal of the actors that are being observed

110
Q

feeling

A

emotions felt and expressed

111
Q

gap spotting

A

Researchers reviewed existing literature with the aim of spotting gaps in the literature and, based on that, formulated specific research questions
- Conservative way to think about science, building further on previous research
- takes a long time

112
Q

P

A

The chance of something happening
The chance to roll 6 is, 1 in 6

113
Q

Relative risk interpretation

A

twee groepen delen door elkaar
1- dat getal

“Women are 7% less likely to be referred than men”

114
Q

Risk difference interpretation

A

group - group = for example 5.9

Women were 5,9% POINT less likely to be referred than men

115
Q

Problematization

A

It means taking something that is commonly seen as good or natural, and turning it into something problematic

116
Q

Confusion spotting

A

The main focus in this way of constructing research questions is to spot some kind of confusion in existing literature. Previous research on the topic exists, but available evidence is contradictory

117
Q

Neglect spotting

A

Spotting something neglected in existing literature is the most common mode of constructing research questions in our sample. It tries to identify a topic or an area where no (good) research has been carried out.
o Overlooked, under researched, lack empirical support

118
Q

Application spotting

A

It searches mainly for a shortage of a particular theory or perspective in a specific area of research.

Health Care Management (4 decks)

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