Everything to Know About Sample Size Determination
A step-by-step interactive guide including common pitfalls
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- Blinded Sample Size Re-estimation.nqt
- Blinded SiteAndSubject.nqt
- Center Covariate Reducing Sample Size.nqt
- Cluster Randomized Extension.nqt
- External Pilot Study Sample Size Example.nqt
- Log-Rank Test Everolimus.nqt
- Log-Rank Test with Dropout.nqt
- MaxCombo Model Selection with Delayed Effect.nqt
- Responder Analysis Higher Sample Size Chi-Squared.nqt
- Two Means Group Sequential Replication.nqt
- Two Proportions Inequality Difference Scale.nqt
- Two Proportions Inequality Ratio Scale.nqt
- Two Proportions Non-inferiority Difference Scale.nqt
- Two Proportions Non-inferiority Ratio Scale.nqt
- Two Sample t-test Simvastin.nqt
- Win Ratio for Composite Endpoint.nqt
Webinar Playback:
Everything to Know About Sample Size Determination
In this interactive tutorial, Ronan Fitzpatrick, Lead Statistician at nQuery, has provided a comprehensive overview of sample size determination, the key steps to successfully finding the appropriate sample size and cover several common pitfalls researchers fall into when finding the sample size for their study.
Learning objectives of this webinar:
1. Understanding Sample Size Determination
- Define sample size determination and its importance in study design.
- Recognize the balance between statistical and practical considerations in determining sample size.
2. Step-by-Step Guide to Sample Size Determination
- Identify the key steps involved in calculating the appropriate sample size for a study.
- Apply these steps to various research scenarios to ensure robust study design.
3. Identifying and Addressing Common Pitfalls
- Recognize frequent mistakes researchers make in sample size calculation.
- Implement strategies to avoid these pitfalls and enhance the efficiency of clinical trials.
Everything to Know About Sample Size Determination: A quick guide for biostatisticians
A step-by-step interactive guide including common pitfalls
Sample size determination is a critical aspect of study design, directly influencing the validity and reliability of research findings. This guide provides a concise overview tailored for biostatisticians, outlining essential considerations and steps in the sample size determination process.
Importance of Sample Size Determination
Ensuring an adequate sample size is vital for achieving sufficient statistical power, thereby reducing the risk of Type II errors. Balancing the need for statistical significance with ethical concerns involves enrolling an appropriate number of participants without exposing them to unnecessary risk. Optimal sample size contributes to efficient use of resources, including time, funding, and manpower.
Key Steps in Sample Size Calculation
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Define the Objective: Clearly articulate the primary aim of the study and the hypotheses to be tested.
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Select the Statistical Method: Choose the appropriate statistical test or model that aligns with the study design and data type.
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Determine Effect Size: Estimate the expected difference or association in the population, which is crucial for calculating the necessary sample size.
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Set Significance Level (α) and Power (1-β): Commonly, a significance level of 0.05 and a power of 80% or 90% are used, but these should be tailored to the specific context of the study.
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Account for Variability: Incorporate estimates of variability, such as standard deviation, to refine the sample size calculation.
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Adjust for Potential Dropouts: Anticipate and adjust for possible participant attrition to maintain the study's integrity.
Common Pitfalls and Solutions
Overestimation or underestimation of effect size can lead to underpowered studies or unnecessary resource expenditure. Mitigate this by conducting thorough literature reviews and, if feasible, performing pilot studies to obtain accurate estimates. Neglecting variability measures can result in miscalculations; ensure these are based on reliable data sources. Ignoring multiple comparisons increases the risk of false positives; adjust sample size calculations to account for multiple testing to control the family-wise error rate. Inadequate handling of dropouts can bias results; implement strategies to monitor and minimize dropouts, and adjust the sample size accordingly during the planning phase.
Practical Application Example
Consider a clinical trial aiming to compare the efficacy of a new drug to a standard treatment.
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Objective: Demonstrate that the new drug has a superior effect on reducing blood pressure compared to the standard treatment.
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Statistical Method: Two-sample t-test for comparing means.
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Effect Size: Assume a clinically significant difference of 5 mmHg between groups.
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Significance Level and Power: Set α = 0.05 and power = 90%.
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Variability: Estimated standard deviation of 10 mmHg based on previous studies.
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Sample Size Calculation: Using these parameters, calculate the required sample size per group, adjusting for an estimated 10% dropout rate.
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