Design case-control study on e-cigarette use and lung injury. Help me: 1) Define cases (diagnostic criteria) and control selection, 2) Calculate required sample size (OR=2.5, 80% power), 3) Design data collection instrument (exposure assessment), 4) Plan statistical analysis (conditional logistic regression if matched), 5) Control for confounding (smoking history, demographics), 6) Calculate adjusted odds ratios with 95% CIs, 7) Consider recall bias and mitigation

Prompt

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Designing a case-control study on e-cigarette use and lung injury involves several steps. Here’s a detailed plan based on the components you provided:

1) Define Cases and Control Selection

Cases:

Diagnostic Criteria:

Participants must have a clinically confirmed diagnosis of e-cigarette or vaping product use-associated lung injury (EVALI) as defined by the Centers for Disease Control and Prevention (CDC). This includes a history of e-cigarette use in the month prior to hospitalization, evidence of lung injury (such as chest imaging), and absence of an alternative diagnosis (e.g., pneumonia, infection).

Controls:

Control Selection:

Controls should be matched based on age, gender, and ethnicity with the cases to minimize confounding. Potential controls can be recruited from the same geographic area but must not have any history of lung injury. They may be selected from outpatient clinics or social media/community outreach.

Random sampling can also be considered, ensuring controls have similar smoking/vaping history to cases but diagnosed with conditions unrelated to lung injury.

2) Calculate Required Sample Size

To calculate the required sample size, we will assume:

Odds Ratio (OR) = 2.5

Power = 80%

Significance level (alpha) = 0.05

Using an online sample size calculator or the standard formula can yield results. However, a rough estimation can be made using \( n = \frac{{(Z{1-\alpha/2} + Z{1-\beta})^2 \cdot (p1(1 - p1) + p0(1 - p0))}}{{(p1 - p0)^2}} \) where:

\( p_0 \) is the exposure prevalence in controls

\( p1 = OR \cdot p0 / (1 + (OR - 1) \cdot p_0) \)

Assuming \( p_0 \) (the probability of e-cigarette use among controls) is 0.2 (20%), we can compute the required \( n \).

With the numbers inserted:

\( n \) will approximate to ensure enough cases for the desired power and OR. Let’s assume after calculations you would need, for example, a total of 100 cases and 200 controls.

3) Design Data Collection Instrument

Exposure Assessment:

A structured questionnaire/interview guide should cover:

Demographics: Age, gender, ethnicity

Vaping history: Types and brands of e-cigarettes used, frequency of use, duration of use, recent changes in use patterns

Cigarette smoking history: Duration, quantity, when smoking started/stopped

Other relevant exposures: Occupational exposures, environmental factors, pre-existing lung conditions, and recent infections.

4) Plan Statistical Analysis

Statistical Analysis:

Use conditional logistic regression if cases and controls are matched.

Assess the association between e-cigarette use and lung injury while controlling for confounding variables such as age, gender, smoking history, and other relevant covariates.

5) Control for Confounding

Control for Confounding:

Include confounders such as demographic variables (age, gender, ethnicity) and smoking history (current smoker, former smoker, never smoker).

Include potential mediating variables (e.g. other substance use) to see if the association remains after adjusting for these.

6) Calculate Adjusted Odds Ratios with 95% CIs

Run the conditional logistic regression analysis.

Report adjusted odds ratios (AOR) for e-cigarette use with 95% confidence intervals (CIs).

For instance, output might show something like:

AOR = 2.2 (95% CI: 1.5-3.5) indicating that those who use e-cigarettes have more than double the odds of developing lung injury compared to non-users, after adjusting for confounders.

7) Consider Recall Bias and Mitigation

Recall Bias:

Since this study relies on self-reported data regarding e-cigarette use, there is a risk for recall bias where cases may remember their e-cigarette use differently than the controls.

Mitigation Strategies:

Validate self-reported e-cigarette use with medical records or biological markers (e.g., cotinine levels).

Use standardized questionnaires with clear timeframes and definitions.

Blinding interviewers to case status can help reduce bias in data collection.

This framework outlines the basic steps necessary to conduct the study effectively.

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MistralMistral AI

Sure, let's break down each step for designing a case-control study on e-cigarette use and lung injury.

1. Define Cases (Diagnostic Criteria) and Control Selection

Cases:

Diagnostic Criteria: Patients diagnosed with lung injury based on clinical symptoms, radiological findings (e.g., chest X-ray or CT scan), and laboratory tests (e.g., blood tests, bronchoalveolar lavage).

Inclusion Criteria: Patients with a confirmed diagnosis of lung injury within the past 12 months.

Exclusion Criteria: Patients with lung injury due to known causes other than e-cigarette use (e.g., bacterial pneumonia, lung cancer).

Controls:

Selection Criteria: Individuals without lung injury, matched to cases by age (±5 years) and gender.

Source: General population or hospital patients without lung injury.

Inclusion Criteria: No history of lung injury or respiratory diseases.

Exclusion Criteria: Individuals with a history of lung injury or respiratory diseases.

2. Calculate Required Sample Size

To calculate the required sample size, we need the following parameters:

Odds Ratio (OR): 2.5

Power: 80%

Significance Level (α): 0.05

Proportion of Controls Exposed: Assume 20% (based on literature or pilot data)

Using a sample size calculator for case-control studies, we can determine the required number of cases and controls. For example, using the Epi Info software or an online calculator, we might find that approximately 100 cases and 100 controls are needed.

3. Design Data Collection Instrument (Exposure Assessment)

Data Collection Instrument:

Questionnaire: Structured questionnaire to assess e-cigarette use and other relevant exposures.

Sections:

Demographics: Age, gender, education, occupation.

E-cigarette Use: Frequency, duration, type of e-cigarette, flavors used.

Smoking History: Current and past smoking status, number of cigarettes per day, duration of smoking.

Medical History: History of respiratory diseases, other medical conditions.

Lifestyle Factors: Alcohol use, physical activity, diet.

4. Plan Statistical Analysis

Statistical Analysis:

Conditional Logistic Regression: If cases and controls are matched, use conditional logistic regression to account for the matching.

Univariate Analysis: Descriptive statistics for demographics and exposure variables.

Bivariate Analysis: Chi-square tests or t-tests to compare cases and controls.

Multivariate Analysis: Conditional logistic regression to estimate the odds ratio for e-cigarette use and lung injury, adjusting for confounders.

5. Control for Confounding

Confounding Variables:

Smoking History: Current and past smoking status.

Demographics: Age, gender, education, occupation.

Medical History: History of respiratory diseases, other medical conditions.

Adjustment:

Include these variables in the conditional logistic regression model to control for confounding.

6. Calculate Adjusted Odds Ratios with 95% CIs

Adjusted Odds Ratios:

Use the conditional logistic regression model to calculate adjusted odds ratios (aOR) for e-cigarette use and lung injury, with 95% confidence intervals (CIs).

7. Consider Recall Bias and Mitigation

Recall Bias:

Definition: Differential recall of e-cigarette use between cases and controls.

Mitigation Strategies:

Standardized Questionnaire: Use a standardized questionnaire to minimize variability in responses.

Blinding: Ensure that interviewers are blinded to the case-control status of participants.

Validation: Validate self-reported e-cigarette use with objective measures, such as biochemical verification (e.g., urine cotinine levels).

Proxy Respondents: Use proxy respondents (e.g., family members) to corroborate information, especially for cases who may be too ill to respond accurately.

By following these steps, you can design a robust case-control study to investigate the association between e-cigarette use and lung injury, while controlling for potential biases and confounding factors.