Power Calculation in 2k Factorial Optimization Trials: An Interactive Web Application, with Investigation of
Jillian C Strayhorn1, Phuc Vu2, Ruoxiang Zheng3
1School of Global Public Health, New York University, New York, NY, 10003, USA. jcs9972@nyu.edu.
Summary
The two-arm approximation for power calculations in 2k factorial optimization trials with binary outcomes is generally sufficient, especially with independent randomization. However, accuracy decreases with increasing intraclass correlation (ICC) under cluster randomization.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Statistical Power Analysis
Background:
- Traditional power calculations for 2k factorial optimization trials with continuous outcomes use a two-arm approximation, ignoring other effects.
- This study investigates the applicability of this approximation for trials with binary outcomes.
Purpose of the Study:
- To assess the validity of the two-arm approximation for power calculations in 2k factorial optimization trials with binary outcomes.
- To compare empirical power with approximated power across various trial designs and randomization strategies.
Main Methods:
- Monte Carlo simulations of 2k factorial optimization trials with binary outcomes.
- Varied design elements including sample sizes, randomization strategies, and intraclass correlations (ICCs).
- Compared observed power against power calculated using the two-arm approximation.
Main Results:
- The two-arm approximation performed well for binary outcomes with independent randomization.
- Under within-cluster randomization, accuracy decreased as ICC increased above 10%.
- Discrepancies were larger under between-cluster randomization, especially with high ICC and low cluster counts.
Conclusions:
- The two-arm approximation is often sufficient for sizing 2k factorial optimization trials with binary or continuous outcomes.
- Researchers should be cautious with the approximation under cluster randomization, particularly with high ICC.
- An interactive web application is available to aid in trial sizing, with warnings for specific clustering and ICC conditions.
Related Concept Videos
01:24Testing a Claim about Population Proportion
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
01:28Methods of Medium Optimization
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
02:01Factorial Design
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
01:24One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...