## What is an outcome measure in a study?

An outcome measure, endpoint, effect measure or measure of effect is a measure within medical practice or research, (primarily clinical trials) which is used to assess the effect, both positive and negative, of an intervention or treatment. Measures can often be quantified using effect sizes.

## What are examples of outcome measures?

Outcome measures reflect the impact of the health care service or intervention on the health status of patients. For example: The percentage of patients who died as a result of surgery (surgical mortality rates). The rate of surgical complications or hospital-acquired infections.

**What are primary and secondary outcome measures?**

Secondary Outcome Measure: defined by ClinicalTrials.gov as an outcome measure that is of lesser importance than a primary outcome measure but is part of a pre-specified analysis plan for evaluating the effects of the intervention or interventions under investigation in a clinical study and is not specified as an …

### What is the secondary outcome of a study?

SECONDARY OUTCOME MEASURE. A planned outcome measure in the protocol that is not as important as the primary outcome measure, but is still of interest in evaluating the effect of an intervention. Most clinical studies have more than one secondary outcome measure.

### Can you have two primary outcomes?

The FWER needs to be considered in trials involving multiple primary outcomes when ‘success of intervention’ is defined as showing an effect on at least one outcome. In this scenario the primary outcomes are referred to as multiple primary outcomes [9] and the p-values must be adjusted for multiplicity.

**What is the difference between primary and secondary endpoints?**

The primary endpoint of a clinical trial is the endpoint for which the trial is powered. Secondary endpoints are additional endpoints, preferably also pre-specified, for which the trial may not be powered.

## Why are secondary endpoints studied?

A Secondary endpoint has secondary objectives. For example, a drug designed to prevent allergy-related deaths might also have a measure of whether quality of life is improved. A Secondary endpoint is therefore always paired with a primary one.

## What is a primary outcome measure?

The primary outcome measure is the outcome that an investigator considers to be the most important among the many outcomes that are to be examined in the study. The primary outcome needs to be defined at the time the study is designed.

**What is a multiplicity adjustment?**

There is a consensus in the literature that multiplicity adjustments are required if the different treatment arms are related. 4. For instance, if a trial evaluates different dosages or regimens of a treatment compared with the same control arm, then adequate multiple testing adjustments should be performed.

### What is statistical multiplicity?

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

### What are Type 1 errors in statistics?

A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance. For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error.

**Do multiple outcome measures require P value adjustment?**

Readers should balance a study’s statistical significance with the magnitude of effect, the quality of the study and with findings from other studies. Researchers facing multiple outcome measures might want to either select a primary outcome measure or use a global assessment measure, rather than adjusting the p-value.

## How do you adjust for multiple tests?

General procedures for multiple test adjustments Adjusted P values are calculated by k × Pi, where Pi for i = 1, … , k are the individual unadjusted P values. In the same manner Bonferroni adjusted confidence intervals can be constructed by dividing the multiple confidence level with the number of confidence intervals.

## What does a Bonferroni test do?

The Bonferroni test is a statistical test used to reduce the instance of a false positive. In particular, Bonferroni designed an adjustment to prevent data from incorrectly appearing to be statistically significant.

**How do you change the p value?**

The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values). Using the p.

### Why is p value adjusted?

The adjustment limits the family error rate to the alpha level you choose. If you use a regular p-value for multiple comparisons, then the family error rate grows with each additional comparison. The adjusted p-value also represents the smallest family error rate at which a particular null hypothesis will be rejected.

### What affects p value?

A P value is also affected by sample size and the magnitude of effect. Generally the larger the sample size, the more likely a study will find a significant relationship if one exists. As the sample size increases the impact of random error is reduced.

**What is FDR p value?**

The FDR is the rate that features called significant are truly null. An FDR of 5% means that, among all features called significant, 5% of these are truly null. Just as we set alpha as a threshold for the p-value to control the FPR, we can also set a threshold for the q-value, which is the FDR analog of the p-value.

## How does deseq2 calculate P value?

Bonferroni: The adjusted p-value is calculated by: p-value * m (m = total number of tests). This is a very conservative approach with a high probability of false negatives, so is generally not recommended.

## What is corrected P value?

The adjusted P value is the smallest familywise significance level at which a particular comparison will be declared statistically significant as part of the multiple comparison testing.