Flashcards: MCAT Physics and Math 11.3: Human Subjects Research

lower than

ethics

weaker than

In research, the process by which participants in clinical trials are assigned by chance to separate groups that are given different treatments or other interventions.

In a blind or blinded experiment, information which may influence the participants of the experiment is withheld until after the experiment is complete.

In single-blind experiments, only the patient or the assessor (the person who makes measurements on the patient or performs subjective evaluations) is blinded.

In double-blind experiments, the investigator, subject, and assessor all do not know the subject’s group.

The person who makes measurements on the patient or performs subjective evaluations

A beneficial effect produced by a placebo drug or treatment, which cannot be attributed to the properties of the placebo itself, and must therefore be due to the patient's belief in that treatment.

-Age

-Gender

-BMI

-Smoking status

-Diabetic status

And many others

A binary variable is a variable that has two possible outcomes.

If a variable can take on two particular real values such that it can also take on all real values between them (even values that are arbitrarily close together), the variable is continuous in that interval.

If it can take on a value such that there is a non-infinitesimal gap on each side of it containing no values that the variable can take on, then it is discrete around that value.

In statistics, a categorical variable (also called qualitative variable) is a variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.

quantitative

qualitative

mixed-method

-Cohort studies

-Cross-sectional studies

-Case-control studies

demonstrate causality

Cohort studies are those in which subjects are sorted into 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.

For example, 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 would be an example of a cohort study.

A longitudinal study is an observational research method that follows the same subjects over time. Therefore, a cohort study is a form of longitudinal study.

Cross-sectional studies attempt to categorize patients into different groups at a single point in time.

For example, a study to determine the prevalence of lung cancer in smokers and nonsmokers at a given point in time would be an example of a cross-sectional study.

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.

For example, a study in which 100 patients with lung cancer and 100 patients without lung cancer are assessed for their smoking history would be an example of a case–control study.

Hill’s criteria describe the components of an observed relationship that increase the likelihood of causality in the relationship. The more criteria that are satisfied by a relationship, the likelier it is that the relationship is causal.

Assesses the likelihood that the results of an observational study is due to a causal relationship.

Temporality

Strength

Dose–response relationship

Consistency

Plausibility

Analogy

Experiment

Specificity

Coherence

Temporality: The exposure (independent variable) must occur before the outcome (dependent variable).

This criteria is absolutely necessary. If this is not satisfied, all of the other criteria are null.

Strength: As more variability in the outcome variable is explained by variability in the study variable, the relationship is more likely to be causal.

Dose–response relationship: As the study or independent variable increases, there is a proportional increase in the response. The more consistent this relationship, the more likely it is to be causal.

Consistency: The relationship is found to be similar in multiple settings.

Plausibility: There is a reasonable mechanism for the independent variable to impact the dependent variable supported by existing literature.

Analogy: When there is strong evidence of a causal relationship between a particular agent and a specific disease, researchers should be more accepting of weaker evidence that a similar agent may cause a similar disease.

Experiment: Evidence drawn from experimental manipulation—particularly epidemiologic studies in disease risk declines following an intervention or cessation of exposure—may lead to the strongest support for causal inference.

Specificity: The change in the outcome variable is only produced by an associated change in the independent variable.

Coherence: The new data and hypothesis are consistent with the current state of scientific knowledge.

Bias is a result of flaws in the data collection phase of an experimental or observational study.

Confounding is an error during analysis.

The most prevalent type of bias is selection bias, in which the subjects used for the study are not representative of the target population. This is usually due to improper randomization.

Detection bias refers to systematic differences between groups in how outcomes are determined.

For example, high blood pressure (hypertension) and diabetes mellitus are more common in the obese population; thus, a physician may screen obese patients for hypertension and diabetes at a higher rate than healthy-weight patients, inflating the true value of the secondary measurement

The Hawthorne effect, or observation bias, posits that the behavior of study participants is altered simply because they recognize that they are being studied.

Confounding is a data analysis error. The data may or may not be flawed, but an incorrect relationship is characterized.

Temporality