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Related Concept Videos

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
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.
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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An R-Based Landscape Validation of a Competing Risk Model
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A note on Using regression models to analyze randomized trials: asymptotically valid hypothesis tests despite

Jane Paik Kim1

  • 1Department of Medicine, School of Medicine, Stanford University, Stanford, California, USA.

Biometrics
|October 2, 2012
PubMed
Summary
This summary is machine-generated.

Hypothesis tests for treatment effects on survival data are robust to model misspecification. This ensures accurate Type I error rates in randomized trials, even with proportional hazards and Cox models.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Rosenblum and van der Laan (2009) established asymptotic Type I error control for linear and generalized linear models in randomized trials.
  • Assessing treatment effects on time-to-event outcomes requires robust statistical methods.

Purpose of the Study:

  • To investigate the robustness of hypothesis tests for treatment effects on the survivor function in randomized trials.
  • To determine if models for time-to-event data maintain correct Type I error rates under misspecification.

Main Methods:

  • Direct application of arguments from Rosenblum and van der Laan (2009) for certain multiplicative hazards models.
  • Utilizing an approach from Struthers and Kalbfleisch (1986) for Cox and linear link models with unspecified baseline hazards.

Main Results:

  • Hypothesis tests using proportional hazards models and parameterized baseline hazard models demonstrate asymptotically correct Type I error.
  • Tests based on Cox models and linear link models with unspecified baseline hazards also maintain correct Type I error rates.

Conclusions:

  • Hypothesis tests for treatment effects on survival outcomes are asymptotically valid under model misspecification.
  • These findings provide assurance for the use of various survival models in randomized clinical trials, even when model assumptions are not perfectly met.