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Related Concept Videos

Randomized Experiments01:13

Randomized 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
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Testing a Claim about Population Proportion01:24

Testing 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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types 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...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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Estimating adjusted NNTs in randomised controlled trials with binary outcomes: a simulation study.

Ralf Bender1, Volker Vervölgyi

  • 1Department of Medical Biometry, Institute for Quality and Efficiency in Health Care (IQWiG), D-51105 Cologne, Germany. Ralf.Bender@iqwig.de

Contemporary Clinical Trials
|July 14, 2010
PubMed
Summary
This summary is machine-generated.

Adjusting for balanced covariates in randomized controlled trials (RCTs) improves the precision of the number needed to treat (NNT) estimates. This is particularly true when covariates are strong predictors with large variance, enhancing risk difference calculations.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Medical Research Methodology

Background:

  • The number needed to treat (NNT) is a key metric for assessing treatment efficacy in randomized controlled trials (RCTs) with binary outcomes.
  • The average risk difference (ARD) approach using logistic regression has been proposed for estimating NNTs, particularly in epidemiological contexts with covariate adjustment.
  • Existing methods for covariate adjustment in logistic regression have known trade-offs concerning precision and efficiency.

Purpose of the Study:

  • To evaluate the impact of adjusting for balanced covariates on the precision of NNT estimates in RCTs using the ARD approach.
  • To investigate whether adjusting for balanced covariates enhances precision when estimating risk differences and NNTs.

Main Methods:

  • Application of the average risk difference (ARD) approach to estimate adjusted NNTs in simulated RCT settings with balanced covariates.
  • Utilizing logistic regression for covariate adjustment within the ARD framework.
  • Conducting simulations to assess the precision of NNT and risk difference estimations with and without covariate adjustment.

Main Results:

  • Contrary to expectations based on regression coefficients, adjusting for balanced covariates in RCTs leads to a gain in precision for estimating risk differences and NNTs.
  • A significant gain in precision is observed when covariate predictors are strong and possess large variance.
  • The ARD approach with balanced covariate adjustment proves beneficial for NNT estimation in RCTs.

Conclusions:

  • Adjusting for balanced covariates is preferable in RCTs when treatment effects are quantified using risk differences and NNTs, especially when covariates are strong predictors.
  • The ARD approach with balanced covariate adjustment offers enhanced precision for NNT estimation in RCTs.
  • This method improves the reliability of treatment effect measures in clinical research.