clinical science steven a. del chiaro, psyd. san josé state university [psyc 160]
TRANSCRIPT
Clinical Science
Steven A. Del Chiaro, PsyD.San José State University
[PSYC 160]
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Review of the homework Trull: Chapter 4 Kalal: Critical thinking in clinical practice
(article) Questions? Callaghan: Demonstrating effectiveness
(article) Richards et al.: Single subject research
(chapter) Questions? Comments? How do these fit in?
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Scientists, Clinicians, and Clinical Science Questions posed by clinical researchers
and practicing clinicians What are the predictors for suicide
attempters and completers? Who benefits most from therapy? How does therapy work? Is one treatment approach (theory) better
than another? How do you assess for clinical change? How do you assess for psychopathology?
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Epistemological assumptions of science Assumption of Science #1:
Determinism There is cause related to effect Need to be able to show: If I manipulate
this variable (independent variable) then I should be able to show a change in that variable (dependent measure)
We are eliminating the assumption of a variable having free will
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Epistemological assumptions of science Assumption of Science #2: Empiricism
Attempt to restrict attention to observables or measurable variables
Can also have non-observables or hypothetical constructs
There must be theoretically available observables, though, that are related to actual observable variables
Examples: black holes, freedom, and self-esteem (how about cognitive schema)
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Epistemological assumptions of science Assumption of Science #3: Order
Need lawful patterns Goal of science is prediction and control Can we do this with a science of
behavior? Some have backed off into prediction
and influence Argue that prediction is implausible
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Clinical Psychology Research Methodology The Case Study
Records in detail and in narrative form what happens during therapy
Useful for documenting rare cases Used to generate hypotheses we
might want to test Can generate research questions
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Clinical Psychology Research Methodology Case Study
Not convincing data for causal modeling because it does not control for outside factors (can't even know all of the factors)
Does not generalize Information is subject to interviewer's bias
for soliciting selective info and to patient's bias for recollecting selective information
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Clinical Psychology Research Methodology Epidemiological research
Incidence New case rate per unit of time
Prevalence Total number of cases in certain time
frame Retrospective studies Prospective studies
Longitudinal study
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Prevalence Rates Example of Kessler et al. (1994)
National Comorbidity Study NCS 8000 people interviewed Lifetime (and 12-month) prevalence rates
for all disorders Have any disorder at any point in life 48% life time prevalence for all disorders
Depression 12-20% lifetime prevalence
Anxiety disorders 17-25% lifetime prevalence
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Lifetime and 12-mo Prevalence RatesNCS (1994) data
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Critical Thinking Moment Making sense of prevalence data
What do these data say to us? What do these data mean? Are these numbers changing?
Consider the numbers on the next slide using 1985 ECA data and 1994 NCS data for 12-month prevalence rates
What does this mean? Why else might this be occurring?
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1985 ECA Prevalence
(% )
1994 NCS Prevalence
(% )Any Anxiety Disorder 13.1 18.7
Simple Phobia 8.3 8.6Social Phobia 2 7.4Agoraphobia 4.9 3.7
GAD (1.5)* 3.4Panic Disorder 1.6 2.2
OCD 2.4 (0.9)*PTSD (1.9)* 3.6
Any Mood Disorder 7.1 11.1MD Episode 6.5 10.1Unipolar MD 5.3 8.9
Dysthymia 1.6 2.5Bipolar I 1.1 1.3
Bipolar II 0.6 0.2Schizophrenia 1.3 —Somatization 0.2 —
ASP 2.1 —Anorexia Nervosa 0.1 —
Severe Cog. Impairment 1.2 —Any Disorder 19.5 23.4
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Clinical Psychology Research Methodology Correlational Research
Example of abuse history and psychological disorders or later abuse
Causation and direction of causality Height and weight Education and IQ number SES and prevalence of schizophrenia Serotonin levels and depressive
symptoms
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Group level experimental designs Psychotherapy outcome research
Do therapy and evaluate people pre-therapy, during therapy, and post-therapy to see if the therapy has an effect
Also do follow-up research to see whether there is a lasting effect
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How do you know if therapy works? Depends on how you define “works” Depends also on your variables
(assessment and measurement are key here)
Scientifically Reliability Internal validity External validity
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Efficacy and Effectiveness Clinical Efficacy Trails
Concerned primarily with Internal Validity
why and how does the treatment work?
Clinical Effectiveness Trials Concerned more with External Validity
Where and with whom does the treatment work, given that it seems to work
See as stage 1 and stage 2 process
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Clinical Psychology Research Methodology Group level experimental designs Group methodology
Larger design with manipulated IV and DV
Compare with group not receiving treat’t.
Statistical significance testing Results (differences between groups)
cannot be explained given that chance was operating
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Depression S
core
40
30
20
10
Pre-treatment Post-treatment
Does CBT make people less depressed?
CBT treatment
16 weeks of therapy
Control condition
No therapy
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Depression S
core
40
30
20
10
Pre-treatment Post-treatment
Difference between two averages
F(1,98) = 3.68, p <.05
A statistical result showing a significant difference
Post = 38
Pre = 40
Post = 22
Does CBT make people less depressed?
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Depression S
core
40
30
20
10
Pre-treatment Post-treatment
Post = 38
Pre = 40
Post = 22
THIS IS NON-DEPRESSED
THIS is where we are aiming our treatments
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Depression S
core
40
30
20
10
Pre-treatment Post-treatment
Post = 38
Pre = 40
Post = 22
THIS IS NON-DEPRESSED
This is clinically significant change
Post = 8
Does CBT make people less depressed?
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Clinical Psychology Research Methodology Group methodology
Clinical significance testing Move from one population to another in a
meaningful way
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Effect Size
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Dysfunctional Functional
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Best Available Treatment
Functional
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The Placebo Effect in psychotherapy Benefit from just coming in to
therapy Been an issue since the advent of
psychotherapy research What is the placebo for therapy?
To whom do we compare our group who receives treatment?
Many solutions Waiting list Innocuous "treatment"
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The Placebo Effect Still unresolved Appears that therapy is more effective
than just a placebo effect There is an active mechanism in therapy
that makes a difference across therapies Types of research designs to address
the placebo problem Double blind procedure Problems with this approach
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Meta-analysis Studying Therapy Research that has
Already Been Done: Meta-analysis Therapy and research
Early Example: Eysenck - examined 24 studies Most were psychodynamically oriented Results - you couldn't tell that therapy was effective Thoughtful analysis BUT it has been criticized
Study occurred at a primitive stage in the development of research
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Meta-analysis Meta-analysis
technique of analyzing multiple studies already existing in the literature
collapse several studies to come up with one finding
Need empirical research for this to work there must be a measurable test of whether
and how much the therapy worked Go to literature and find every article that
relates to the question you are interested in
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Meta-analysis You must have set criteria for
whether you will use the articles Conduct statistical analysis to
collapse findings and get an Effect Size Tells you how useful or effective the
treatment was
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Meta-analytic findings Research now indicates that the
average person who is treated is better off than 80% of the untreated sample and effects of therapy seem to last for a while Smith, Glass, & Miller (1980)
Hundreds of clinical evaluation studies compared meta-analytically
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Meta-analytic findings What is not found, however, are
important differences between different kinds of therapy One study reports that all therapies are the
same in effectiveness if we control for experimenter or therapist bias
Caveat: with meta-analytic studies, there is an issue with sensitivity of actually finding a difference which is not yet resolved
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Typical outcomes How much do therapies typically
effect client outcome? Most therapies report an effect size of
0.5 Considered a moderate effect size Change did occur statistically
Remember clinical significance?
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Effect size (d = .5)
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Comparison
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Is this meaningfulchange?
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Goal is informative science for clinical practice Reality is science does not get
consumed by practitioners Why don’t clinicians consume
Science? Morrow-Bradley and Elliot, 1986 Cohen, Sargent, and Sechrest, 1986
Research conducted to answer these questions
Still same reasons today Here are the top six reasons…
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Why don’t clinicians consume Science?
Questions addressed are not clinically relevant
Variables contained in reported studies are not representative of actual practice
Methods and populations used are not adequately described or selected
Group statistics and statistical significance are overemphasized in data analyses
Researchers do not attempt to convey results in ways clinicians can use them
Limitations are imposed on clinicians themselves which prevent them from utilizing research, such as limited time and inertia
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Critical Thinking Moment So how do we make the scientist
practitioner model really work How do we solve this dilemma Do we go after scientists? Do we go after clinicians? What do we do, specifically???
Rethinking Science
At least a little bit…
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Alternative approaches to science There are better ways to answer
some of these questions There are other ways to conduct
science that allow us to answer these questions
These other methods may be much better for clinical psychology
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Alternative approaches to clinical science Observational coding
methodologies Access the mechanism of change
Single case experimental designs Research at the level of primary
interest
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Observational Research We Are All Observational Researchers To some extent we are all interested in
observational research You have to observe something to engage
in research Not all of us are using observation to
our advantage in the social sciences
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Chaos and Psychology Much of what we are interested in
occurs in an open system It is uncontrollable It is unpredictable at the level of specific
instance This drives us crazy as “scientists”
We try to create situations that will help us exert control and make better predictions
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Advantages to Observation Naturalistic (cf. analog experiments or
post hoc analysis) Not tied to verbal self-report or other
report (cf. questionnaires) Good when we cannot exert control
over an open system e.g., weather (NASA research) e.g., human interactions (therapy, crisis
situations, etc.)
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Differences in Science Observational research is not the
same as classical experimentation Philosophical issues with
understanding of what “science” is e.g. ethology and not exerting
experimental influence (i.e. prediction and control)
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What is “Science?” Is this what science looks like?
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What is “Science?” Need to rethink our version of
science Consider the compromise between
internal validity and external validity
Many are questioning the constraints of classic “science” Example: clinical psychology
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Limitations of Experimentation May not be able to answer our questions
of interest “Why does this happen?” is not as easily
answered as “Whether this happens” The “Why” questions get us closer to
hypothesized mechanisms of action in psychology research Hypothesized mechanisms help us answer
our deeper research questions
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Consider a Clinical Question Does Therapy X reduce levels of
depression? Answered with a straightforward research
design with high internal validity AKA clinical efficacy trial
Can anyone else do Therapy X after or outside of that study? Look at external validity AKA clinical effectiveness trial
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What do We Want to Know? Why does Therapy X make clients
better? This can’t be as easily answered
Does the therapist (doing Therapy X) have anything to do with making clients better?
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Mechanisms of Change These questions help address our
underlying hypothesis of change in settings we cannot easily change most of research on human behavior
occurs in an open system need to be careful about what we are
we are controlling and limiting get at this with process research
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Process Vs Outcome Research Most of us are interested in outcome
variables What changes? How much does it change?
Process Research Why is change occurring? Why is change not occurring? What is going on in this situation or
interaction?
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Process Research Process refers to what occurs during a
situation or interaction Examining process can get us closer to what
is really going on Look at how process relates to outcome
This requires us to rethink how we answer our research questions
Can you think “outside the box” about your research?
What do you really want to know?
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Observational coding No longer tied to what the
experiment can tell us Now we can answer questions that
get us closer to our research questions of “why” not “whether”
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Types of Observation Qualitative
Ethnography (e.g., field notes) Case studies (narratives)
Quantitative Observational coding methodologies
e.g., marital interactions, families, parent-child interactions, psychotherapy research
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Observational Coding Research Want to be able to approach the task of
observation in empirical manner
Can answer important research questions with data This is quantitative research Almost any qualitative research can be
made quantitative with coding methodology
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Issues with Observational Coding Attempting to become more
“scientific” or consistent with observations
Coding refers to consistent documentation of observed variables using a formal system Need to specify what you are trying to
code
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Types of Coding Approaches Time based or Time series
Look at occurrence of codes at specified time intervals
Violence on a playground
Event based coding Look at occurrence of events that are
not dependent on time intervals “Floor changes” or “turns” in psychotherapy
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Coding Research Coding requires us to answer our
questions in stages What is our question?
What is occurring at the level of process? What is occurring at the level of outcome? How do we tie these together?
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Problems with Observational Coding Time consuming
Creating coding systems Training coding of observations
Observer Effects Observer Bias
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Advantages to Observational Coding Gets us closer to some of our real
hypotheses Does not constrain our research
models Do not have to create analogs Do not have to restrict external
validity
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Advantages to Observational Coding You can relate process to outcome
You can’t do this without either the process or the outcome component
You can’t do the process component without observational coding You can test these hypothesized
mechanisms of action Notice: You must be very explicit about your variables Notice: These variables must be specifiable and
observable
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Advantages to Observational Coding Innovation!
Asking hard questions and coming up with unique solutions to help answer them
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Single subject designs In depth, scientific study of one person
at a time Tend to emphasize measurable,
observable, meaningful change Not a case study! Key is repeated measurement and
evaluation of variability, level, and trend Can show true experimental control OR
true accountability of treatment effect By the clinician!
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Single subject designs ABAB design
A = baseline measure Record behavior without intervening No treatment time Want to observe regular pattern
B = Treatment Record changes in behavior
2nd A = remove treatment See if you have a return to baseline Naturally done with termination of treatment in
psychotherapy 2nd B = reinstate treatment
See if behavior returns to level of 1st B or maintains!
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Single subject designs Some terminology
Baseline Need sufficient data to determine: Variability Level (2?) Trend / stability (3?) Some data will be easier to hold at baseline
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Single subject designs Also use Multiple Baseline Approach look at baseline across
Situations Behaviors Participants and implement treatments staggeringly and look at
the effects Shows experimental control at n = 1 level of
analysis
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MB across situations
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Example of AB design for a treatment for social skills
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Strengths of MB design Can’t always to do a baseline
reversal Want to be able to show effects
across situations or even subjects This helps convey generalization
Actual generalization is across variables that really shown change
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Using Single Subject Research in Clinical Practice Callaghan (2001) article
This is the science that clinicians can do in clinical practice
This shows accountability Can combine these studies across
variables to demonstrate clinical effectiveness
Across cases Across clinicians
Issues Affecting All of Science
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Problems to look for in experimental research Experimental Confounds
another variable can influence results, that when not identified and controlled will lead to inaccurate statements of causality
Direction of causality does Variable X cause Y or does Y cause X?
(Depression and loneliness) Post Hoc explanation
not prediction, just after the fact explanation
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Problems The Quine-Duhem Problem and letting
go of bad theories Auxiliary Hypotheses and testing Core
Hypotheses Measurement issues are central Tend to measure illness, not
psychological health – this may impose a limit to how we analyze how therapy works
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Problems Kalal’s position on baloney-
detection tools Parsimony Falsifiability Multiple hypotheses Explaining mechanisms What happens when we let fraudulent
therapies exist?
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Human Subjects Committee Ruling board which analyzes the
components of an experiment and decides whether the experiment can be conducted ethically and legally with the minimum of risk to the subject
Very formal protocol involved
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Human Subjects Committee One of main features
Participant gives informed consent to participate and is debriefed as to the purpose of the study after it is over
Deception is frowned upon and is not used in experiments very often
Want to ensure confidentiality of participant data
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Fraudulent data The contingencies of fraudulent
data This is not frequently done, but Research and the bell curve
The ethics of fraudulent data