Chapter 11 - Reasoning About the Design and Execution of Research Flashcards
Scientific Method Steps:
Generate a testable question
Gather data and resources
Form a hypothesis
Collect new data
Analyze the data
Interpret the data and existing hypothesis
Publish
Verify results
Hypothesis:
proposed explanation or proposed answer to our testable question
In the form of an if-then statement, which will be tested in subsequent steps
Experimentation:
manipulating and controlling variables of interest
Observation:
involves no changes in the subject’s environment
Finer method:
method to determine whether the answer to one’s question will add to the body of scientific knowledge in a practical way and within a reasonable time period
Finer Method Questions:
Is the necessary research study going to be feasible?
Do other scientists find this question interesting?
Is this particular question novel?
Has someone asked this question before and published it?
Would the study obey ethical principles?
Is the question relevant outside the scientific community?
Basic science research:
research conducted in a lab, not on people
Easiest to design b/c experimenter has the most control
Control/standard:
acts as method of verifying results
Positive controls:
those that ensure a change in the dependent variable when it is expected
Negative controls:
ensure no change in the dependent variable when no change is expected
Placebo effect:
an observed or reported change when an individual is given a sugar pill or sham intervention
Independent variable vs, Dependent Variable:
Independent variable: variable that is manipulated
Dependent variable: variable that is measured or observed
What needs to happen in order for a relationship to be considered causal?
If the change in the independent variable always precedes the change in the dependent variable, and the change in the dependent variable does not occur in the absence of experimental intervention, the relationship is said to be causal
Accuracy (validity):
ability of an instrument to measure a true value
Ex: accurate scale should register a 170lb person as 170 pounds
Precision (reliability):
ability of an instrument to read consistently, or within a narrow range
Systematic error:
when bias is considered
How to overcome error from random chance?
Random chance can also introduce error into an experiment
Can be overcome by using a large sample size
Human Subjects Research:
- consists of what
- experimental approach
Consists of experimental and observational studies
Experimental Approach - b/c subjects are in a less-controlled conditions, the data analysis phase is more complicated than in laboratory studies
Randomization:
method used to control for differences between subject groups in biomedical research
Uses an algorithm to determine the placement of each subject into either a control group that receives no treatment, or one or more treatment groups
Blinded:
- definition
- what happens without blinding
- means they do not have information about which group the subject is in
- Without blinding, the placebo effect would be greatly reduced in the control group, but still present in the treatment group
Single-blind experiments:
only the patient or assessor (person who makes measurements on the patients or performs subjective evaluations) is blinded
Double-blind experiments:
the investigator, subject, and assessor all do not know the subject’s group
Confounding variables:
- types of Variables:
Binary: yes vs. no, better vs. worse
Continuous: amount of weight lost, percent improvement in cardiac output
Categorical: state of residence, socioeconomic status
Regression analysis:
may demonstrate linear, parabolic, exponential, logarithmic or other relationships
Observational studies:
- what they are
- three categories (just list)
- draw on available data and analyze it
- cohort studies, cross-sectional studies, case-control studies
Observational studies - Cohort studies:
those in which subjects are sorted into two groups based on differences in risk factors (exposures), and then assessed at various intervals to determine how many subjects in each group had a certain outcome
Ex: a study in which 100 smokers and 100 nonsmokers are followed for 20 years while counting the number of subjects who develop lung cancer in each group
Observational studies - Corss-sectional studies:
attempt to categorize patients into different groups at a single point in time
Ex: a study to determine the prevalence of lung cancer in smokers and nonsmokers at a given point in time
Observational studies - Case-control studies:
start by identifying the number of subjects with or without a particular outcome, and then look backwards to assess how many subjects in each group had exposure to a particular risk factor
Ex: a study in which 100 patients with lung cancer and 100 patients without lung cancer are assessed for their smoking history
Hill’s criteria:
- what it is
- criteria (just list)
- components of an observed relationship that increase the likelihood of causality in the relationship
- temporality
strength
dose-responsive relationship
consistency
Hill’s Criteria - Temporality:
the exposure (independent variable) must occur before the outcome (dependent variable)
Hill’s Criteria - Strength:
as more variability in the outcome variable is explained by variability in the study variable, the relationship is more likely to be causal
Hill’s Criteria - Dose-responsive relationship:
as the study or independent variable increases, there is a proportional increase in the response
Hill’s Criteria - Consistency:
the relationship is found in multiple settings
Bias:
result of flaws in the data collection phase of an experimental or observational study
Systematic error
Confounding:
- what it is
- third party variable
- an error during analysis
- Data may or may not be flawed, but an incorrect relationship is characterized
Third party variables called confounding variables or confounders
Third variable that could effect both variables
Selection bias:
- definition
- volunteers
the subjects used for the study are not representative of the target population
- People who volunteer may be vert different from people who do not volunteer*
- May also apply in cases where one gender is more prevalent in a study than another*
- Measurement and assessment of selection bias occurs before any intervention*
Detection Bias:
- results from what
- influence of prior studies
- results from educated professionals using their knowledge in an inconsistent way
- Because prior studies have indicated that there is a correlation between two variables, finding one of them increases the likelihood that the researcher will search for the second
* Ex: high blood pressure and diabetes mellitus are more common in the obese population; so a physician will screen obese population for these things more than a patient of healthy weight*
Hawthorne effect (observation bias):
posits that the behavior of study participants is altered simply because they recognize that they are being studied
Ex: patients in a study for a given weight loss drug may begin exercising more frequently or make dietary changes
Ethical Tenets - Beneficence:
obligation to act in the patient’s best interest
is must be our intent to cause a net positive charge for both the study population and general population, and we must do our best to minimize any potential harms
Research should be conducted in the least invasive, painful, or traumatic way possible
Four core ethical tenents: (just list)
- Beneficence
- Nonmaleficence
- Autonomy
- Justice
Ethical Tenets - Nonmaleficence:
obligation to avoid treatments or interventions in which the potential for harm outweighs the potential for benefit
Ethical Tenets - Autonomy:
responsibility to respect patients’ decisions and choices about their own healthcare
Ethical Tenets - Justice:
responsibility to treat similar patients with similar care, and to distribute healthcare resources fairly
Respect for persons:
includes the need for honesty between the subject and the researcher; prohibits deception
Informed consent:
a patient must be adequately counseled on the procedures, risks and benefits, and goals of a study to make knowledgeable decision about whether or not to participate in the study
Investigator cannot exert a coercive influence over the subjects
Also includes the need to respect the subjects’ wishes to continue with or cease participation in a study
Vulnerable persons:
(children, pregnant women, prisoners) require special protections above and beyond those taken with the general population
Justice - as it applies to selection and execution of research:
applies to both the selection of a research topic and the execution of the research
The only way to determine the selection of a research question to maintain justice is through random chance
When there is risk associated with a study, it must be fairly distributed so as to not unduly harm any group
Morally relevant differences:
- what they are
- examples
- what are not considered morally relevant differences
- differences between individuals that are considered an appropriate reason to treat them differently
- ex: age, population size
- Not considered: ethnicity, sexual orientation, financial status
Population:
complete group of every individual that satisfies the attributes of interest
Parameter:
information that is calculated using every person in a population
Sample:
- what they are
- must represent what
- what is the gold standard
- any group taken from a population that does not include all individuals from the population
- Samples will be representative of the population
- Random samples are considered the gold standard
Statistic:
information about a sample
Can be used to estimate population parameters
Internal validity:
support for causality
External validity (generalizability):
- studies with low generalizability
- studies with high generalizability
- example
- Studies with low generalizability have very narrow conditions for sample selection that do not reflect the target population
- High generalizability have samples that are representative of the target population
- ex: a psoriasis study with low generalizability will have people only diagnosed within the year, high generalizability would have participants with a distribution of time since diagnosis
Statistically significant:
not the result of random chance
Clinical significance:
notable or worthwhile change in health status as a result of our intervention
