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Although this p-value objectified research outcome, using it as a rigid cut off point can have potentially serious consequences: (i) clinically important differences observed in studies might be statistically non-significant (a type II error, or false negative result) and therefore be unfairly ignored; this often is a result of having a small ...
Exclusion criteria concern properties of the study sample, defining reasons for which patients from the target population are to be excluded from the current study sample. Typical exclusion criteria are defined for either ethical reasons (e.g., children, pregnant women, patients with psychological illnesses, patients who are not able or willing ...
Beneficence is a concept in research ethics that states that researchers should have the welfare of the research participant as a goal of any clinical trial or other research study. The antonym of this term, maleficence , describes a practice that opposes the welfare of any research participant.
[7] The study investigated whether new, high-risk medical devices had been proven safe and effective for women, minorities or patients over 65 years of age. The paper concluded that most studies did not conduct subgroup analysis on all these major demographic groups, thus providing no information about safety or effectiveness for most patients.
The RAND Health Insurance Experiment (RAND HIE) was an experimental study from 1974 to 1982 of health care costs, utilization and outcomes in the United States, which assigned people randomly to different kinds of plans and followed their behavior.
Clinical research is different from clinical practice: in clinical practice, established treatments are used to improve the condition of a person, while in clinical research, evidence is collected under rigorous study conditions on groups of people to determine the efficacy and safety of a treatment.
Bradford Hill's criteria had been widely accepted as useful guidelines for investigating causality in epidemiological studies but their value has been questioned because they have become somewhat outdated. [5] In addition, their method of application is debated. [citation needed] Some proposed options how to apply them include:
Marginal structural models are a class of statistical models used for causal inference in epidemiology. [ 1 ] [ 2 ] Such models handle the issue of time-dependent confounding in evaluation of the efficacy of interventions by inverse probability weighting for receipt of treatment, they allow us to estimate the average causal effects.