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TG: Treatment group; 1: Random assignment; 1: the subjects were randomly assigned to the experimental conditions; 0.5: there was no random assignment but some method was applied to control confounding variables; 0: there was neither random assignment nor control of confounding variables.
The magnitude of the effect vary from study to study because of differences in the features of these studies (design, population, control of confounding variables etc).. From the various studies an estimator is formed by pooling the results found in each study in one summary measure.
The strengths of this study were mainly attributed to (1) the large population-based sample size to power the study, (2) the comprehensiveness of data within the NHIRD, (3) the use of specific ICD diagnostic codes to define comorbid diseases and (4) the control of confounding variables by matching the study groups according to age, sex and socioeconomic status.
Such a model would allow for control of confounding variables, such as multiple other infections, nutritional status, and ethical concerns, which could impact such studies in humans.
Control of confounding variables = region, HIV testing rate, and age.
Appropriate control of confounding variables is always important in the analysis of observational studies.
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Future studies would benefit from better experimental controls, ideally with more than one time point, verifications and reflections on qualitative work, robust statistical analysis, appropriate study controls, consideration of confounding variables, and transparent reporting of population samples, methodologies, and analyses used.
To control the effect of confounding variables, stepwise logistic regression was done.
To control the effect of confounding variables, stepwise logistics regression was done.
More advanced analytical techniques are needed to control the effects of confounding variables and to provide more accurate estimates.
In order to predict the likelihood of utilization of PMTCT/HIV-test in both study subjects (not utilized and utilized), multiple logistic regression was used by controlling the effect of confounding variables if any exist and enter method was used.
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