Improve your sample size calculations with nQuery clinical trial design templates and data
Two sample binomial blinded sample size re-estimation is a statistical method used in clinical trials to adjust the sample size of a study based on the results of an interim analysis.
Binary Data is data where subjects are categorized into one of two categories (e.g. disease/no disease) as the outcome of interest. This data is extremely common across various clinical areas with common statistical methods for binary data including chi-squared tests and logistic regression.
Ordered categorical (also known as ordinal) data is where subjects are categorized into one of the available categories and in which each category is ordered from best to worst. Scales for areas such as pain or disability are a common example in clinical trials with common statistical methods including the proportional odds model and the Wilcoxon test.
Linear regression is used to evaluate the relationship between two continuous variables. The goal of linear regression is often to model the relationship between an independent variable (such as a treatment or a risk factor) and a dependent variable (such as a patient outcome or a biomarker.
Cox regression (proportional hazards model) is used to compare the survival or time-to-event outcomes between two or more treatment groups. The Cox regression model estimates the hazard ratio, which is a measure of the relative risk of an event occurring in one treatment group compared to another.
This type of design allows for more efficient and flexible study conduct, as it enables the trial to be modified in response to emerging results or changing circumstances. The primary goal of adaptive trial design is to improve the efficiency and accuracy of clinical trials, by enabling researchers to modify the trial design in real-time. This approach can help to minimize the number of participants needed, reduce the overall cost of the study, and speed up the time to reach the study endpoint.
A clinical prediction model is used to estimate the probability of a particular health outcome or disease in an individual patient. It combines various patient characteristics (such as age, gender, medical history, and test results), to provide an individualized estimate of the likelihood of a particular outcome.
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