How to Prepare a Presentation to Compare Statistical Approaches ?

Abstract :

Step 1: Provide an Overview of Each Statistical Approach

Briefly describe the purpose, key steps, and assumptions of each method. Include a reference to a foundational or widely cited paper that supports or introduces the approach.

Step 2: Summarize Results Using a Comparative Table
Apply all methods to the same dataset and present the key results side-by-side (e.g., treatment effect estimates, standard errors, p-values). This allows for a clear and direct comparison across approaches.

Step 3: Discuss Strengths and Limitations
Highlight the pros and cons of each method. Consider how each handles issues like missing data, differences in statistical power, and the impact on p-values. Discuss whether the conclusions are consistent across methods or if notable differences arise.

Step 4: Include Code and Detailed Outputs in Backup Slides
Provide supporting SAS or R code along with detailed output results in the appendix or backup section. This allows technical audiences to validate and reproduce the analysis if needed.

More details and examples : 

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In clinical research and statistical practice, we’re often faced with choosing between multiple statistical methods to answer the same question. Whether it’s evaluating treatment effect across multiple endpoints or handling missing data, it’s critical to compare methods transparently, objectively, and reproducibly.

Here’s a practical guide to prepare a presentation that compares statistical approaches clearly, complete with suggested structure, code inclusion, and example discussion points.

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✅ Step 1: Summarize the Statistical Approaches

Start with a concise overview of each method you’re comparing. This should include:

• Purpose: When and why it is used

• Method Summary: Core assumptions, statistical model, outcome

• Reference: Cite original or key validation papers

Example Approaches:

Approach	Summary	Reference
MMRM (Mixed Model for Repeated Measures)	Accounts for correlation in repeated measures; flexible handling of missing-at-random data.	Mallinckrodt et al. (2003)
ANCOVA	Adjusts for baseline covariates; assumes data are missing completely at random or MAR.	Vickers & Altman (2001)
O’Brien’s OLS	Combines multiple correlated endpoints into one composite t-statistic.	O’Brien (1984); Lehmacher et al. (1991)

Keep this step short and visual – 1–2 slides per method, using diagrams or model formulas as needed.

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📊 Step 2: Show Results Using the Same Dataset

Apply each method to the same dataset and summarize the results side-by-side. This gives the audience a tangible sense of how each method performs in practice.

Example Table:

Method	Treatment Effect Estimate	Standard Error	p-value
MMRM	0.24	0.0556	0.0021
ANCOVA	0.22	0.0568	0.0040
O'Brien OLS	–	–	0.0013

If applicable, include effect sizes or confidence intervals. Highlight when conclusions are consistent and when they diverge.

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🧠 Step 3: Discussion – Pros and Cons

Now explain the practical implications of choosing one approach over another. Guide the audience through tradeoffs:

Method	Missing Data Handling	Power	Interpretability	Limitations
MMRM	MAR, flexible with repeated data	High if assumptions hold	Clear marginal means	Sensitive to covariance structure
ANCOVA	Assumes MCAR or MAR	Moderate	Widely understood	Less flexible with missing data
O’Brien OLS	Uses all endpoints jointly	Higher power under correlation	Composite interpretation	Requires correlation matrix

You can also include simulation insights if applicable — e.g., show that power improves under O’Brien OLS when endpoints are highly correlated.

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🧾 Step 4: Back-Up Slides — Code and Details

Reserve backup slides for SAS or R code so interested viewers can reproduce your results.

Example: O’Brien’s OLS in SAS

sas
data pval_calc;
  t1=0.24; t2=0.36;
  se1=0.0556; se2=0.05995;
  v1=213; v2=209;
  r=0.80636;

  var1 = se1**2;
  var2 = se2**2;

  numerator = (var1 + var2)**2;
  denominator = (var1**2 / v1) + (var2**2 / v2);
  v_eff = numerator / denominator;

  t_ols = (t1+t2)/sqrt(1*2 + r*2);
  df = v_eff;
  pv = 2 * (1 - probt(t_ols, df));
  put t_ols= df= pv=;
run;

Tip: Provide GitHub repo or supplementary R scripts if your audience includes statisticians.

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💡 Final Thoughts

This four-step structure allows you to build a rigorous, reproducible, and easy-to-follow comparison presentation. It empowers stakeholders to make evidence-based choices about statistical methods — grounded in theory, demonstrated with data, and supported by code.