14.3 Sample size re-estimation methods
3 min read•august 7, 2024
methods are crucial in . They allow researchers to adjust participant numbers mid-study based on interim data, ensuring adequate while maintaining trial integrity.
These methods come in two flavors: blinded and unblinded. Blinded reviews use pooled data to estimate parameters, while unblinded reviews analyze treatment groups separately. Each approach has its pros and cons, impacting trial design and execution.
Blinded and Unblinded Sample Size Review
Sample Size Re-estimation Methods
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- Sample size re-estimation involves adjusting the sample size during an ongoing clinical trial based on interim data analysis
- Can be performed in a blinded or unblinded manner depending on whether treatment group information is used
- Aims to ensure the study has adequate power to detect a clinically meaningful treatment effect
- Helps to address uncertainties in initial sample size calculations and adapt to changes in variability or estimates
Blinded Sample Size Review
- is conducted without knowledge of treatment group assignments
- Utilizes pooled data from all treatment groups to estimate (variability, response rates)
- Preserves the integrity of the trial by maintaining blinding and minimizing operational bias
- Requires careful consideration of the timing and frequency of interim analyses to avoid inflating
Unblinded Sample Size Review
- involves analyzing data by treatment group at an
- Provides more accurate estimates of treatment effect size and variability compared to blinded methods
- Requires strict control of Type I error rate through appropriate statistical methods (, )
- May introduce operational bias and impact trial integrity if not properly managed
Internal Pilot Study Approach
Internal Pilot Study Design
- approach involves using a portion of the total sample size as a "pilot" phase
- Data from the internal pilot is used to re-estimate the sample size for the remainder of the trial
- Allows for a more accurate assessment of nuisance parameters and effect size estimates
- Requires pre-specification of the internal pilot study design, including the timing and criteria for sample size adjustment
Effect Size Estimation in Internal Pilot Studies
- Effect size estimation in internal pilot studies is based on the observed treatment difference and variability
- Utilizes statistical methods to account for the uncertainty in effect size estimates from the pilot phase (, )
- Helps to ensure the final sample size provides adequate power to detect the true treatment effect
- Requires careful consideration of the potential impact on Type I and rates
Conditional and Predictive Power
Conditional Power Calculations
- is the probability of rejecting the null hypothesis at the end of the trial, given the observed data at an interim analysis
- Calculated based on the observed treatment effect, variability, and the remaining sample size
- Helps to assess the futility or promising nature of the trial and inform decisions on early stopping or sample size adjustment
- Requires specification of the true treatment effect and variability for the remainder of the trial (usually assumed to be the same as observed)
Predictive Power Calculations
- is the average conditional power over the posterior distribution of the true treatment effect, given the observed data at an interim analysis
- Accounts for the uncertainty in the true treatment effect by integrating over its posterior distribution (based on prior information and observed data)
- Provides a more comprehensive assessment of the trial's prospects compared to conditional power
- Can be used to guide decisions on early stopping, sample size adjustment, or trial continuation based on the probability of success
Key Terms to Review (18)
Adaptive clinical trials: Adaptive clinical trials are a type of study design that allows for modifications to the trial procedures based on interim results. This flexibility helps researchers make timely adjustments to optimize the trial's effectiveness, such as changing the sample size or altering treatment regimens while ensuring participant safety and scientific integrity.
Adjusted Confidence Intervals: Adjusted confidence intervals are modified statistical ranges that account for factors such as sample size changes, variability in data, or other adjustments to provide a more accurate estimate of the true parameter in a population. They help in reflecting the uncertainty of an estimate more precisely, especially when sample sizes are re-evaluated or re-estimated during a study.
Alpha Spending Functions: Alpha spending functions are statistical tools used to control the Type I error rate in adaptive clinical trials by adjusting the significance level over time as data is collected. These functions help determine how much of the overall alpha level can be spent at each interim analysis, balancing the need to make decisions based on accumulating data while maintaining the integrity of the study results. The concept is crucial in sample size re-estimation methods as it directly impacts how researchers manage and interpret ongoing trial results.
Blinded sample size review: A blinded sample size review is a process in clinical trials where an independent review committee evaluates the sample size while remaining unaware of the treatment allocation. This method aims to minimize bias in decision-making related to whether to continue, modify, or terminate a study based on interim data. By keeping the reviewers blinded, the integrity of the trial is preserved, ensuring that the evaluation is based solely on statistical evidence rather than influenced by knowledge of which participants received which treatment.
Conditional power: Conditional power is the probability of achieving statistically significant results at the end of a study, given the data collected so far and any changes in the sample size or effect size. It helps researchers assess the likelihood that a study will meet its objectives after interim analysis, particularly when considering sample size re-estimation methods. This concept is crucial for understanding how adjustments in the study's design can impact the final outcomes.
Conditional Power Calculations: Conditional power calculations are statistical assessments used to estimate the likelihood of achieving a significant result in a study, given the data collected up to a certain point. These calculations help researchers determine if the current sample size is sufficient to meet their study objectives, allowing for adjustments in future sample size if necessary. This technique is particularly useful in adaptive trial designs where interim analyses can guide decision-making.
Effect Size: Effect size is a quantitative measure that reflects the magnitude of a treatment effect or the strength of a relationship between variables in a study. It helps in understanding the practical significance of research findings beyond just statistical significance, offering insights into the size of differences or relationships observed.
Group sequential designs: Group sequential designs are a type of clinical trial design that allows for interim analysis of data at predetermined points during the trial, enabling researchers to make decisions about the continuation or termination of the study based on accumulated results. This approach is beneficial as it can provide early insights into treatment efficacy or safety, ultimately enhancing the ethical conduct of trials and improving resource allocation.
Interim analysis: Interim analysis refers to the evaluation of data collected during a clinical trial at predetermined points before the trial is completed. This practice is crucial as it helps researchers determine whether the study should continue, be modified, or be stopped based on the results observed thus far. By integrating interim analyses, researchers can adapt their experimental designs, making informed decisions that can enhance the efficacy and safety of clinical interventions.
Internal pilot study: An internal pilot study is a preliminary investigation conducted within the main study to assess feasibility, refine procedures, and gather data for sample size estimation. This approach allows researchers to identify any necessary adjustments before full-scale implementation, ensuring that the main study is appropriately powered to detect the desired effects.
Nuisance parameters: Nuisance parameters are variables that are not of primary interest in a statistical analysis but can affect the outcome of the study and need to be accounted for. These parameters can complicate the estimation of the main parameters and often require specific methods to manage their influence, especially when determining the sample size needed for reliable results.
Predictive Power: Predictive power refers to the ability of a statistical model or test to accurately forecast outcomes based on input data. It is an important measure of how well a model can generalize its findings to new, unseen data, indicating its effectiveness in making reliable predictions and guiding decision-making.
Predictive Power Calculations: Predictive power calculations are statistical methods used to determine the ability of a study's design to detect an effect if it exists. This concept is crucial for researchers when planning experiments, as it helps in estimating the sample size needed to achieve reliable results. By understanding predictive power, researchers can ensure their studies are adequately powered to avoid false negatives and effectively assess the impact of interventions or treatments.
Sample size re-estimation: Sample size re-estimation is a statistical method used in adaptive experimental designs that allows researchers to adjust the number of participants in a study based on interim results. This technique helps ensure that the study maintains sufficient power to detect an effect if one exists, by reassessing the sample size requirements as data is collected. By integrating this method, researchers can make informed decisions about resource allocation and improve the validity of their findings.
Statistical Power: Statistical power is the probability that a statistical test will correctly reject a false null hypothesis, which means detecting an effect if there is one. Understanding statistical power is crucial for designing experiments as it helps researchers determine the likelihood of finding significant results, influences the choice of sample sizes, and informs about the effectiveness of different experimental designs.
Type I Error: A Type I error occurs when a null hypothesis is incorrectly rejected, leading to the conclusion that there is an effect or difference when none actually exists. This mistake can have serious implications in various statistical contexts, affecting the reliability of results and decision-making processes.
Type II Error: A Type II error occurs when a statistical test fails to reject a false null hypothesis, leading to the incorrect conclusion that there is no effect or difference when one actually exists. This concept is crucial as it relates to the sensitivity of tests, impacting the reliability of experimental results and interpretations.
Unblinded Sample Size Review: An unblinded sample size review is a process in clinical trials where the sample size is reassessed based on interim results without masking the treatment assignments. This allows researchers to adjust the number of participants needed to achieve sufficient statistical power and ensures that the study remains valid and ethical as it progresses.