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Anabolic Steroids: What They Are, Uses, Side Effects & Risks
Below is a ready‑to‑use outline for a peer‑reviewable medical article (e.g., a research paper, systematic review, or guideline).
Feel free to replace the placeholders with your own data, results, and citations.
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1. Title
Concise, descriptive, and includes the study design when appropriate
Example: "Randomized Controlled Trial of Drug X vs. Placebo in Adults With Condition Y"
2. Running Head / Short Title (optional)
≤50 characters; appears on each page
3. Author List & Affiliations
|
| Name | Affiliation(s) | Email |
|---|------|-----------------|-------|
| 1 | First Author | Department, Institution | email@example.com |
| … | … | … | … |
Corresponding author designated; include contact details
4. Abstract (Structured)
Section Word Limit
Background / Objective ≤50 words
Methods ≤100 words
Results ≤150 words
Conclusions ≤50 words
Use concise, precise language
Avoid abbreviations unless standard
5. Keywords
3–6 terms reflecting study focus (e.g., "epidemiology", "case‑control", "disease X", "risk factor Y").
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6. Introduction / Background
Context – Brief epidemiological landscape of the disease/condition.
Gap – Why current knowledge is insufficient or conflicting.
Objective – Specific research question(s) or hypothesis.
Keep introduction succinct; aim for ~400–600 words.
7. Methods (Detailed)
Section Content
Study Design e.g., "a matched case‑control study" with justification.
Setting & Period Geographic location, timeframe of data collection.
Population Inclusion/exclusion criteria for cases and controls.
Sampling How participants were selected (consecutive admissions, random sampling).
Sample Size Calculation Formula used, assumptions (odds ratio to detect, power, significance level).
Data Collection Instruments Structured questionnaire, chart abstraction form; description of variables (exposures, outcomes, confounders).
Variable Definitions Primary outcome (e.g., "death within 30 days"), primary exposures (e.g., comorbidities), covariates.
Measurement Procedures Training of data collectors, inter-rater reliability checks.
Ethical Considerations IRB approval, informed consent procedures, confidentiality measures.
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2. Data Analysis
2.1 Preliminary Steps
Data Cleaning: Check for missing values, outliers; apply appropriate imputation or sensitivity analyses.
Descriptive Statistics: Summarize patient demographics and baseline characteristics.
2.2 Statistical Methods
Goal Test/Model Rationale
Compare categorical variables (e.g., gender distribution across groups) Chi‑square test / Fisher’s exact test Assess association between group membership and categorical outcomes.
Compare continuous variables (e.g., age, lab values) Independent t‑test or Mann–Whitney U (if non‑normal) Evaluate differences in means/medians between two groups.
Adjust for confounders when assessing the effect of a predictor on outcome Multivariable logistic regression (binary outcomes) / Cox proportional hazards model (time‑to‑event) Estimate adjusted odds ratios or hazard ratios, controlling for covariates such as age, sex, comorbidities.
Validate model assumptions Residual analysis, Hosmer–Lemeshow goodness‑of‑fit test, proportional hazards check Ensure reliability of inference.
Interpretation
Odds Ratio (OR) >1 indicates increased odds; <1 indicates decreased odds.
Hazard Ratio (HR) >1 indicates higher hazard (risk) over time; <1 indicates lower risk.
Confidence intervals that do not cross 1 and p‑values <0.05 signify statistical significance.
These tools allow researchers to quantify the impact of specific variables on outcomes while controlling for confounding factors, ensuring robust and reproducible conclusions.
4. Practical Take‑away Checklist
Step Action Best Practice
1. Study Design Choose prospective cohort or RCT with adequate sample size. Power calculations, inclusion/exclusion criteria clearly defined.
2. Data Collection Use standardized instruments; train staff on data entry. Pilot test forms; double‑entry for critical variables.
3. Handling Missing Data Document reasons; assess pattern (MCAR, MAR, MNAR). Consider multiple imputation or sensitivity analysis if missing not trivial.
4. Statistical Analysis Start with descriptive stats → inferential tests → multivariable modeling. Check assumptions; report effect sizes and confidence intervals.
5. Reporting Results Present tables/figures with clear legends; include p‑values, CI, η² or R² as appropriate. Discuss limitations (e.g., residual confounding, sample size).
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6. Practical Example: Comparing Two Groups on a Continuous Variable
Suppose you have two independent groups (Group A vs. Group B) and want to compare their mean scores on the "Cognitive Flexibility" subscale.
Statistic Value
Mean (A) 24.3
SD (A) 5.2
N (A) 30
Mean (B) 19.8
SD (B) 6.1
N (B) 28
Steps
Calculate the difference in means: Δ = 24.3 – 19.8 = 4.5.
Compute pooled standard error:
SE = √(SD_A² / N_A) + (SD_B² / N_B) ≈ √(27.04/30)+(37.21/28) ≈ √0.901+1.329 ≈ √2.230 ≈ 1.493.
t‑statistic: t = Δ / SE = 4.5 / 1.493 ≈ 3.01.
Degrees of freedom (Welch‑Satterthwaite):
ν ≈ (SD_A²/N_A + SD_B²/N_B)² / (SD_A⁴/(N_A²(N_A-1))) + (SD_B⁴/(N_B²(N_B-1)) ) ≈ ~70.
p‑value: For df≈70, t=3.01 yields p <0.005.
Interpretation: The difference in mean scores between Group A and Group B is statistically significant at α=0.05; there is strong evidence that the two groups differ in their performance on the measure. If we had a directional hypothesis (e.g., Group A expected to score higher), the one‑sided test would yield p≈0.0025, reinforcing the conclusion.
Assumptions & Caveats:
Normality and equal variances were assumed; if violated, consider Welch’s t‑test or non‑parametric alternatives (e.g., Mann–Whitney U).
The test only indicates that means differ; it does not quantify effect size. Computing Cohen’s d would provide additional insight.
Multiple comparisons could inflate Type I error; adjust significance levels accordingly.
Next Steps:
Report the results with 95 % confidence intervals for the mean difference.
Include an effect‑size estimate (Cohen’s d) and its confidence interval.
If sample sizes are unequal or variances differ, re‑run Welch’s t‑test to confirm robustness.
Final Note: The calculation of p‑values in hypothesis testing is a straightforward application of the sampling distribution corresponding to your test statistic. For a two–sided test with a normal approximation and standard errors derived from sample variance, you use the standard normal CDF; for discrete data (counts), you may need Poisson or binomial distributions. The key is to match the null hypothesis distribution with the observed statistic to determine how extreme your result would be under the assumption that the null holds. This procedure yields the p‑value, which quantifies evidence against the null.
Gender: Female