Statistics and Study Design-Statistics and Study Design Overview

Statistics and Study Design Overview

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The presence of bias is a main concern in scientific reports, and it can enter into a study at different levels such as sample selection, intervention or observation methods, measurement, statistical analysis, and interpretation of findings. Sample selection is an important source of bias and convenience samples may not be representative of the general population. <br /><br />There are different clinical research designs, including randomized control trials (RCTs), cohort studies, cross-sectional studies, and case-control studies. RCTs control for biases by assigning eligible patients randomly to treatment groups and are considered the gold standard for evidence. Cohort studies follow samples with and without exposure to observe potential outcome differences, but there is a high potential for confounding. Cross-sectional studies make measurements at once and can make conclusions about associations but not cause and effect. Case-control studies start with the outcome and work backward to identify exposures of interest but have the potential for bias as variables are measured retrospectively. <br /><br />Statistical analysis depends on having a clearly defined primary objective and appropriate measurement. Different tests are used based on whether the variable is continuous and normally distributed, non-normally distributed, ordinal, or nominal. Power analysis must be performed to ensure the sample size is sufficient for potential differences. <br /><br />It is important to note that although a study may reach statistical significance, it does not necessarily correlate with clinical significance. Proper reporting of statistical analysis is important for medical journals.<br /><br />Recommended reading includes guidelines for statistical reporting in medical journals by Bailar and Mosteller.

Keywords

bias

scientific reports

sample selection

intervention methods

observation methods

measurement

statistical analysis

interpretation of findings

randomized control trials

cohort studies