3 Ways to Control Confounding Factors. #biostatistics #shorts

First is randomization, which is mostly done with RCS, so participants are randomly allocated to different study groups. Clear randomization or distributing known and unknown co-founders equally among the groups is known here. The unknown co-founders are distributed equally among the groups. The known co-founders are considered the most effective method in randomized control trials. An example is the randomization in MET of patients to CBT SRI and combining therapy, so you've taken Toakkira 30. 35:46 Then the restriction now inclusion criteria are restricted so that all the participants are at the same level of potential as the founders, so in my study the age was restricted. In fact, the gender was also restricted in my study. I didn't cover females, maybe as a male thing, or bakiro co-mobilities; he likes smoking nicotine. Another substance use BOB he especially excluded not being there in my inclusion right area. So much confounding by SATI basically reduces the variability due to that factor. I cannot comment on the females because it hasn't taken females' intonation, and then they are talking about matching. Now this is for the case-controlled people. All of you who are going to be sitting or are designing a study that is of case-control until matching are very important. So participants in the comparison RUB matched on important confounding variables. #ConfoundingFactors #Biostatistics #ResearchMethods #DataAnalysis #StatisticalControl #Epidemiology #StudyDesign #QuantitativeResearch #HealthStatistics #scientificresearch