Sentence examples for minimize confounding variables from inspiring English sources

In each case bias must be avoided either when choosing the particular samples and their number that make up the data set, or in analysing the data and methods to minimize confounding variables.

In addition, methods for data assessment and analysis should aim to minimize confounding variables (see below).

For example, the short duration of the intervention coupled with utilizing a single post-testing point one week after the intervention will minimize confounding variables.

Patient/control samples were assayed in parallel and randomized/anonymized to the researchers processing them in order to minimize confounding variables.

In an attempt to minimize confounding variables with regard to the analysis of SF from patients with subclinical and preradiographic OA, the control group for this study was chosen from volunteers that were younger than 30 years old.

In an attempt to apply a study population as homogenous as possible, only patients with ventilator-associated pneumonia and sepsis were enrolled in order to minimize confounding variables occurring when patients with different underlying infections offering a great variety of antigenic stimuli for the innate immune system were encountered together.

Meta-analyses should be performed with high methodological quality homogenous studies (Level I or II) or evidence randomized studies, to minimize confounding variable bias.

7 Many reasons are provided to justify the exclusion of elderly individuals from clinical trials, such as avoiding attrition (mortality, relocation, health decompensation), minimizing confounding variables associated with comorbidities, avoiding lengthier study processes, and so on.

Strengths of the present study included the design with cases identified in a large population based clinical register and the use of matched controls in order to minimize confounding of the socioeconomic variables, and also the baseline determination of outcome variables at the age of one year before breast cancer diagnosis for the cases.

When a list of variables becomes sufficient enough to minimize confounding and selection bias under a causal assumption [ 25], DAG presents a model described as 'minimal sufficient adjustment set' of covariates.

We also stratified socio-demographic variables into sub-groups so to minimize confounding bias during the analysis.