Data entry formats in CMA

The earlier article (Introduction to CMA) gave a brief overview of the various functionalities offered by the CMA software. This article acquaints the user with the procedure of selecting appropriate data entry formats. Entering the data into one of the pre-defined formats is essential to calculate suitable effect size.

Selecting the data entry format

The present article will take the procedure up from the quick options toolbar. The toolbar offered the Insert column for effect size data option. This option takes the user to the Insert columns for effect size data dialog box. This dialog box further takes the user to different data entry formats directories, as shown below. As the entered data is used to calculate effect size, hence the designation, column for effect size data.

Types of data entry formats

Image 1: Types of data entry formats

The user can either select “Insert column for effect size data” icon from the toolbar or follow “Insert” -> “Effect Size data” to enter desired data entry format. After selecting “Effect Size data” the software offers Welcome dialog box, as shown above.  The software offers a second dialog box named Types of studies included, after selecting “Next”Shown below are the different categories of data entry formats.

Different categories of data entry formats

Flowchart 1: Different categories of data entry formats

The selection of suitable data entry format follows the display of suitable spreadsheet. Following the display of spreadsheet, the data is entered into it. The different clinical studies often follow different study designs such as randomized control trials, cohort studies, cross-sectional studies and others.

Data entry formats for comparisons and single group

As indicated in the previous section, there are different categories of data entry formats as discussed below:

Studies reporting comparisons:

Shown below are the different type of data entry formats for comparison of two different groups, time points or exposures. These formats pertain to the experimental design involving the comparison between two groups, time points or exposure conditions. The different types of data such as continuous, binary or correlations, serve as indices or measures for computing effect size.

For example, the standardized mean difference is computed using the continuous data involving the mean and standard deviation from different treatment groups. The dichotomous data involves discreet data pertaining to two distinct categories, such as responders and non-responders of a treatment. The continuous data includes the data measured on a continuum, such as data for means, standard deviation, and others.

Data entry format for comparison of two groups, time points, or exposures (includes correlation)

Image 2: Comparison of two groups, time points, or exposures (includes correlation)

Studies reporting estimates for one group or a single time point:

Besides comparison between 2 datasets, CMA also provides options for performing the meta-analysis of the studies involving a single group. Image below shows the formats related to the experimental designs reporting mean, proportion or the rates within a single group or a single time point.

For example: the user can enter dichotomous data for the number of events and samples size or the continuous data such as mean values.

Data entry format for estimate of means, proportions, or rates in one group at one time-point

Image 3: Estimate of means, proportions, or rates in one group at one time-point

Data entry formats for generic data

Lastly, studies can also be analysed by reporting data as generic point estimates, in either raw units or log scales. Point estimates in clinical trials refer to the single number data summarizing the results from the trial, these include odds ratio, risk ratio, or risk difference.

Data entry format for generic point estimates raw data and log scale

Image 4: Generic point estimates raw data and log scale

Yashika Kapoor

Yashika Kapoor

Research analyst at Project Guru
Yashika has completed her bachelors in life sciences and holds a masters in forensic sciences. Being a major in forensic biology, she is trained in techniques of DNA extraction and sequencing. She also has hands on experience of dealing with sensitive evidences and case files. She aims at developing her knowledge base through fact based learning. With a penchant for reading, and writing, she likes to keep her facts concrete. She is a confident person and aims at achieving perfection in every task assigned to her. She aims at securing a place in her professional life which allows her to explore different areas relevant to her field of work. Along with academics, she is a creative soul. Food, art and craft are some of her other passions.
Yashika Kapoor

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