As a real-world study

interventional cohort study has good external authenticity

whose evidence level is lower only than randomized controlled trials（RCTs）

but its greatest controversy is bias. This paper discussed how to control common biases from the perspective of research design

implementation and statistical analysis. In controlling selective bias

the author emphasizes that the researcher is an observer and should not interfere with the intervention assignment. Exposure factors and intensity should be clearly defined to help researchers to determine the final exposure attribution of subjects. In some special cases

the randomization principle can reduce the occurrence of selective bias. Matching method can increase the balance between groups. In controlling information bias

it is suggested to use objective and quantitative indicators to collect data. If this is difficult to achieve

then "blinded"data collection method shall be used. For wide collection of information

the content should be clear and easy to understand

in order to avoid too cumbersome to create non-response bias. In controlling compounding bias

it is necessary to properly and clearly set the inclusion and exclusion criteria. The clear confounding factors can be excluded to reduce the occurrence of confounding bias. In the analysis phase

the correction analysis

subgroup analysis

multivariate regression analysis

and propensity scores are commonly used for controlling bias. In addition

the interventional cohort study of traditional Chinese medicine has its own particularity. When choosing sub-centers

researchers should focus on the match of different regions

as well as traditional Chinese medicine hospitals

western medicine hospitals and general hospitals. The author expects to improve the internal authenticity and evidence level of interventional cohort studies by controlling bias and optimizing design. Researchers can change research ideas in case of such individualized and complicated intervention systems like traditional Chinese medicine treatment

which does not allow randomization

and often with ethical issues

and let more patients benefit from clinical trials.