Need Help With – Brief descriptions and critique of two assigned studies Custom Paper Help

In your assigned group area:

Post a brief description of the two studies you were assigned, with a particular focus on the study design and methods. Then:

• Describe at least one strength and one limitation of each study’s design.
• Identify the population, data sources, and epidemiologic measures of association that the authors used.
• Finally, share your insights about the appropriateness of the design for the study. Do you agree with the researchers’ choice of design?
• Do you agree with the researchers’ conclusions? Justify your reasoning.

Study 1 — Example (Prospective cohort study)

Study description (design & methods)
This study used a prospective cohort design to examine whether daily cigarette smoking increases the 10-year incidence of myocardial infarction (MI) among adults aged 40–70. At baseline, non-MI participants were recruited from primary care clinics, completed questionnaires on smoking and covariates (age, sex, BMI, hypertension, cholesterol, physical activity), and received baseline clinical measures. Researchers followed participants for 10 years using medical records and hospital discharge data to identify incident MIs. Time-to-event analyses used Cox proportional hazards models.

At least one strength and one limitation

• Strength: Prospective design establishes temporality (exposure measured before outcome) and allows calculation of incidence and hazard ratios.

• Limitation: Potential for loss to follow-up and residual confounding if important confounders (e.g., socioeconomic status, secondhand smoke) were incompletely measured.

Population, data sources, and measures of association

• Population: Adults aged 40–70 attending primary care clinics in the study region (n ≈ 8,000).

• Data sources: Baseline questionnaires, clinical exam records, hospital discharge registries, death registry.

• Epidemiologic measures: Incidence rate of MI (per 1,000 person-years), hazard ratios (HR) from Cox regression, adjusted HRs for covariates.

Appropriateness of the design; do I agree with the choice & conclusions?

• Appropriateness: A cohort design is appropriate for studying exposure (smoking) → outcome (incident MI) because it supports temporality and incidence estimation.

• Do I agree with the researchers’ choice? Yes — a prospective cohort is the right choice here.

• Do I agree with their conclusions? Provisionally yes if their analyses adjusted well for confounders, handled missing data appropriately, and checked model assumptions (e.g., proportional hazards). I would be cautious about any causal language beyond “associated with” unless sensitivity analyses (e.g., E-values, negative control exposures) supported a causal interpretation. If they failed to adjust for key confounders or lost many participants to follow-up differentially by smoking status, I would question the strength of causal claims.

Study 2 — Example (Case–control study)

Study description (design & methods)
This study used a case–control design to investigate whether prior antibiotic exposure increases odds of Clostridioides difficile infection (CDI) in hospitalized patients. Cases were patients with laboratory-confirmed CDI; controls were hospitalized patients from the same wards without CDI, matched on age and admission date. Antibiotic exposure in the prior 30 days was ascertained from pharmacy dispensing records. Conditional logistic regression estimated matched odds ratios (ORs) adjusting for comorbidities and proton pump inhibitor use.

At least one strength and one limitation

• Strength: Efficient for a relatively uncommon outcome (CDI) and allows study of multiple antibiotic exposures. Matching on ward/date reduces confounding by temporal or ward-level factors.

• Limitation: Susceptible to selection bias (how controls were selected) and exposure misclassification if dispensing records miss antibiotic use outside the hospital. Also temporality can be less clear if some exposures occurred very near outcome onset.

Population, data sources, and measures of association

• Population: Hospitalized patients in the study hospital system during the study period.

• Data sources: Lab-confirmed CDI records, hospital electronic health records, pharmacy dispensing logs.

• Epidemiologic measures: Matched odds ratios (OR) for CDI associated with recent antibiotic use from conditional logistic regression; 95% confidence intervals.

Appropriateness of the design; do I agree with the choice & conclusions?

• Appropriateness: A case–control design is appropriate for studying CDI (relatively rare among all hospitalized patients) and examining prior exposures.

• Do I agree with the researchers’ choice? Yes, provided they selected controls representatively (controls should be at risk of becoming a case) and matched/adjusted properly.

• Do I agree with their conclusions? I would cautiously accept their conclusion that certain antibiotics are associated with higher odds of CDI. I would not claim causality without stronger evidence (e.g., prospective cohort or experimental data) and would check whether they addressed reverse causation (e.g., antibiotics started because of early, undiagnosed GI symptoms) and residual confounding.

Copy-paste Template for YOUR two assigned studies

(Replace bracketed text with specific details from each study.)

Study A — [Insert short title of Study A]

1. Brief description (design & methods)
Design used: [e.g., prospective cohort / case–control / cross-sectional / randomized trial / ecological / mixed methods].
Methods summary: [Describe sample selection, exposure measurement, outcome ascertainment, follow-up period (if any), matching or randomization (if applicable), main analytic technique].

2. One strength and one limitation of the design

• Strength: [state at least one strong point — e.g., temporality, control of confounding, efficiency, internal validity].

• Limitation: [state at least one limitation — e.g., bias, confounding, misclassification, inability to infer causality].

3. Population, data sources, and epidemiologic measures

• Population: [who was studied — inclusion/exclusion, geography, sample size]

• Data sources: [e.g., survey, electronic health records, registry, laboratory tests, administrative claims, direct measurement]

• Measures of association used: [e.g., RR, HR, OR, incidence rate, prevalence ratio; statistical models used]

4. Appropriateness of the design and agreement with researchers’ choices/conclusions

• Was the design appropriate? [Yes/No — with brief reasoning.]

• Do you agree with the researchers’ conclusions? [Yes/No — justify: consider bias, confounding, measurement validity, statistical power, and whether conclusions match the data (associations vs. causation).]

Study B — [Insert short title of Study B]

1. Brief description (design & methods)
Design used: [e.g., case–control / cohort / RCT / cross-sectional].
Methods summary: [sample selection, exposure/outcome measures, matching/randomization, analysis method].

2. One strength and one limitation of the design

• Strength: [e.g., efficient for rare outcome, precise exposure measurement, randomization reduces confounding].

• Limitation: [e.g., recall bias, selection bias, limited external validity, short follow-up].

3. Population, data sources, and epidemiologic measures

• Population: [who, where, n]

• Data sources: [records, surveys, labs]

• Measures of association used: [OR, RR, HR, prevalence ratio, etc.]

4. Appropriateness of the design and agreement with researchers’ choices/conclusions

• Was the design appropriate? [Yes/No + brief reasoning]

• Do you agree with the researchers’ conclusions? [Yes/No + justification: consider whether conclusions are supported and limitations adequately discussed.]

Quick tips for writing your post (keep it clear & concise)

1. Identify the design first. Clues:

   • Prospective cohort = exposure measured before outcome; follow-up time; incidence.

   • Case–control = starts with outcome (cases) and compares exposures in controls; uses OR.

   • Cross-sectional = exposure and outcome measured at the same time; prevalence reported.

   • Randomized trial = participants randomized to exposure/intervention; causal claims stronger.

2. Focus on the “why” when you evaluate design appropriateness: Does design match the research question (causal vs. descriptive)? Are there better feasible alternatives?

3. Be specific when naming biases (selection bias, information bias/misclassification, confounding, reverse causation). Show how they could change the study’s findings.

4. When evaluating conclusions, separate what the data actually show (associations) from what the authors claim (causal statements). Note whether authors acknowledged limitations and did sensitivity analyses.

5. Keep your post short (200–400 words per study) — professors and classmates read many posts. Use bullet points for clarity.

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