Critical Assessment of Observational Studies

True (A)

Research question and study design

<p>cohort</p> Signup and view all the answers

<p>The research question should be included in the conclusion section. (C)</p> Signup and view all the answers

<p>Research Question = Clarity of hypothesis and background Study Design = Type of study conducted Study Population = Characteristics of the participants Data Analysis = Statistical methods and confounding Policy Implications = Relevance to real world practice</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>cardiovascular disease or CVD</p> Signup and view all the answers

<p>They allow the examination of rare events and long-term developments. (D)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Measurement bias</p> Signup and view all the answers

<p>confounding factor</p> Signup and view all the answers

<p>Errors in the estimate of effect due to procedures used to select subjects or other factors that influence the study. (B)</p> Signup and view all the answers

<p>Instrumental variable methods (C)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Stratification and statistical adjustment</p> Signup and view all the answers

<p>external</p> Signup and view all the answers

<p>Selection Bias = Systematic error in how subjects are chosen for a study. Measurement Bias = Systematic error on how exposures or outcomes are measured Confounding Factor = A variable associated with both the treatment and the outcome but is not a consequence of the treatment. Internal Validity = Whether the study results are valid</p> Signup and view all the answers

<p>Increases LDL cholesterol and lowers HDL cholesterol (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>To infer causal relationship between hormone replacement therapy and cardiovascular disease.</p> Signup and view all the answers

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<p>To reduce the chance of confounding (A)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Baseline characteristics</p> Signup and view all the answers

<p>30, 55</p> Signup and view all the answers

<p>Nurses may be more adherent to medications (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Intention-to-treat analysis</p> Signup and view all the answers

<p>Case-control study = Has controls that did not experience the outcome Cohort study = Includes a comparison group representing common changes over time Intent-to-treat analysis = Analyzes subjects in their originally assigned groups Attrition = Rates that should include reasons related to the outcome</p> Signup and view all the answers

<p>It increases during the first year. (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>Patient reports, medical records, and pharmacy claims data.</p> Signup and view all the answers

<p>short</p> Signup and view all the answers

<p>Current estrogen-progestin combination use = Use of a combination of estrogen and progestin at present Current estrogen alone use = Use of estrogen without progestin at present Past use = Any previous use of hormone therapy before the current time Never use = No history of hormone therapy at any time</p> Signup and view all the answers

<p>How long it takes the event to develop (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>0.3mg, 0.625mg, 1.25mg, and ≥1.25mg.</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>Experimental Study (A)</p> Signup and view all the answers

<p>prospective</p> Signup and view all the answers

<p>To infer a causal relationship between hormone replacement therapy (HRT) and CVD.</p> Signup and view all the answers

<p>Cohort Study = Follows groups with different exposures over time to see who develops the outcome Case-Control Study = Compares people with a disease to people without the disease to identify risk factors Cross-sectional Study = Looks at a snapshot of a population at a single point in time to identify associations</p> Signup and view all the answers

<p>Better at establishing causality (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>How well it represents the target population for the research question.</p> Signup and view all the answers

<p>55% (A)</p> Signup and view all the answers

<p>True (A)</p> Signup and view all the answers

<p>1,183</p> Signup and view all the answers

<p>heart disease</p> Signup and view all the answers

<p>Nominal Risk Reduction = Difference of absolute risks Relative Risk = Risk in treatment group vs. risk in control group Absolute Risk = Actual risk reduction observed Number Needed to Treat (NNT) = Number of patients needed to treat to prevent one event</p> Signup and view all the answers

<p>Utilizing findings from observational studies (B)</p> Signup and view all the answers

<p>False (B)</p> Signup and view all the answers

<p>It does not appear to attenuate the cardioprotective effects.</p> Signup and view all the answers

A set of guidelines that helps evaluate the quality of observational studies in epidemiology.

The extent to which the results of a study can be generalized to other populations and settings.

A well-defined question that guides the research, including any specific predictions.

The method used to collect data, such as cohort studies, case-control studies, or cross-sectional studies.

The group of individuals who are included in the study.

The variable or factor being investigated, such as exposure to a drug or a specific lifestyle factor.

The health outcome being measured, such as disease development or disease progression.

The process of analyzing data to identify associations between exposure and outcome while controlling for other factors that could influence the results.

Systematic errors in the way subjects are selected, measured, or analyzed, leading to inaccurate study results.

A type of bias that occurs when subjects selected for the study are not representative of the population of interest.

Bias that happens during data collection, where there are systematic differences in how exposures and outcomes are measured or reported between study groups.

A factor that is associated with both the outcome and the treatment being studied, but is not a consequence of the treatment.

A technique used to control for observable confounding factors by dividing the study population into subgroups based on the confounding variable.

A statistical method used to control confounding factors by adjusting for their effect on the outcome.

Methods used to address unobservable confounding factors by using a variable that is related to the treatment but not to the outcome.

A statistical model used to address unobservable confounding factors by taking into account the selection process of the study participants.

A research design that observes participants over a period of time to investigate the relationship between an exposure and an outcome of interest.

A research design that compares cases with a specific outcome to controls without that outcome, looking for differences in exposures in the past.

In epidemiology, the group of individuals from which a sample is drawn for a study.

The group of individuals to whom the results of a study are intended to be generalized.

Criteria used to determine who is eligible to participate in a study.

Criteria used to exclude individuals from participating in a study.

Observational study design used to investigate the association between exposure and outcome in a population at a single point in time.

Observational study design involving detailed descriptions of a single case or a small group of cases with a specific outcome or condition.

The process of selecting participants for a study. This involves understanding the source of the population and applying inclusion and exclusion criteria.

A group of individuals who did not experience the outcome being investigated. This helps compare the treatment group to a similar group without the intervention.

Characteristics of study participants that are similar between treatment and control groups. This helps ensure a fair comparison and minimize the influence of other factors.

When participants drop out of a study before completion. It's important to understand why attrition happens as it can affect study results.

Analyzing all subjects as originally assigned, even if they dropped out of the study. This helps ensure that the results are not biased by attrition.

How well the study findings can be generalized to other populations beyond those included in the study. This depends on the representativeness of the sample to the broader population.

Factors other than the intervention being studied that can influence the outcome. It is important to minimize confounding variables to ensure the results are accurate.

Strict criteria used to select participants for a study. They help reduce confounding variables, but may limit the generalizability of results.

Exposure is how the researchers measure what they are studying (e.g. hormone therapy use) and outcome is how they measure the result (e.g. cardiovascular disease).

Using medical records to accurately measure hormone therapy use is an example of how exposure is measured.

Collecting data on heart attacks or strokes is an example of how outcomes are measured.

In a study looking at long-term effects, ensuring follow-up is long enough to observe the outcome is crucial.

When studying past exposure, researchers need to look back far enough in time to capture the relevant exposure.

Objectively measured outcomes like lab tests or medical records are preferable to subjective reports.

The outcome measure must be relevant to the study's purpose and provide sufficient details.

Adjusting for confounding variables during analysis reduces bias, making the results more reliable.

The impact of a treatment or intervention on the overall risk of a disease, taking into account the baseline risk in the population.

A measure of the strength of association between an exposure and an outcome, expressed as the ratio of the risk of the outcome in the exposed group to the risk in the unexposed group.

The number of patients who need to be treated with a particular intervention to prevent one adverse event or achieve one beneficial outcome.

A study design in which researchers observe and collect data on a group of individuals over a period of time without attempting to manipulate the outcome.

Warnings issued by the FDA based on potential risks associated with a drug or medical device, often derived from observational studies.

Studies designed to compare the effects of different interventions or treatments on a clearly defined outcome.