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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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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
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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Distributions to Estimate Population Parameter01:26

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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An Example to Illustrate Randomized Trial Estimands and Estimators.

Linda J Harrison1, Sean S Brummel1

  • 1Center for Biostatistics in AIDS Research, Department of Biostatistics, Harvard T.H. Chan School of Public Health.

The American Statistician
|August 13, 2025
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Summary
This summary is machine-generated.

This study clarifies five estimand strategies for handling post-randomization events in clinical trials, including treatment discontinuation. It provides practical methods for estimating treatment effects under different scenarios, aiding regulatory adoption.

Keywords:
causal inferenceclinical trialestimandintercurrent eventobjectivetarget population

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Causal Inference

Background:

  • The International Council for Harmonisation (ICH) has established a new estimand framework for randomized trials.
  • Regulatory bodies globally have adopted this framework to standardize trial analysis.
  • Handling post-randomization events, such as treatment discontinuation, is crucial for accurate treatment effect estimation.

Purpose of the Study:

  • To elucidate the differences between the five estimand strategies proposed by the ICH framework.
  • To provide estimation techniques for each of the five estimands.
  • To illustrate the application of these estimands using treatment discontinuation as an intercurrent event.

Main Methods:

  • Utilized potential outcome notation to define five distinct estimands.
  • Described corresponding estimators for each estimand, including intention-to-treat, per-protocol, g-computation, and principal stratum approaches.
  • Analyzed specific scenarios where estimands may be equivalent and explored the 'while on treatment' strategy with repeated measures.

Main Results:

  • Presented five estimands: treatment policy, composite, while on treatment, hypothetical, and principal stratum.
  • Demonstrated intention-to-treat estimators for total effect and a composite outcome.
  • Illustrated per-protocol, g-computation, and principal stratum estimators for specific treatment adherence or hypothetical scenarios.

Conclusions:

  • The study provides a clear framework and practical methods for applying ICH estimand strategies.
  • Understanding these estimands is vital for robust causal inference in randomized trials.
  • Facilitates the adoption of standardized methods for handling intercurrent events in clinical research and regulatory submissions.