A Method for Calculating the Total Effect of Clinical Interventions?

There is no single, universally accepted equation or method for calculating the cumulative effect of various clinical interventions such as lifestyle modifications. However, researchers often use several statistical and modeling approaches to estimate the overall effect of interventions, taking into account both individual and combined effects of different factors.

Common Methods:

Multivariate Regression Models: In clinical research, multivariate regression models are commonly used to analyze the effects of multiple interventions on an outcome, such as insulin resistance or polycystic ovary syndrome (PCOS). These models help to isolate the effect of each intervention (e.g., diet, exercise, sleep) while controlling for confounding factors. When multiple interventions are involved, the model can assess synergistic or additive effects, if present (Field, 2013).

Meta-Analysis: For a broader understanding of the cumulative effects, meta-analyses of existing studies can be used. By aggregating data from multiple studies on different interventions, researchers can calculate an overall effect size using methods like Cohen’s d or odds ratios, depending on the type of data involved. This is especially useful when evaluating interventions like lifestyle changes across various studies (Higgins & Green, 2011).

Clinical Decision Support Tools and Algorithms: In practice, some clinical tools use scoring systems or algorithms to estimate the combined impact of multiple lifestyle factors. For example, lifestyle interventions are scored based on their impact on specific conditions, and their cumulative score provides a measure of overall health improvement. These approaches, though, are generally more qualitative than quantitative and often tailored to individual clinical settings.

System Dynamics or Simulation Models: More complex models, such as system dynamics or agent-based modeling, simulate the impact of lifestyle interventions over time. These models account for the interactions between different factors (e.g., diet, exercise, sleep) and how they influence health outcomes like insulin resistance. Such models are particularly useful in understanding long-term effects and nonlinear interactions between interventions (Homer & Hirsch, 2006).

References:

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
Higgins, J. P. T., & Green, S. (2011). Cochrane Handbook for Systematic Reviews of Interventions. Cochrane Collaboration.
Homer, J. B., & Hirsch, G. B. (2006). System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96(3), 452-458.

Mohammad al-Attar

Thanks for the answer.. and I will try to read about simulation methods.

Actually, I meant by the question a way to collect the results of several studies.

For example: the result of study (A) is that chewing food well reduces insulin resistance by 20%

And study (B) indicates that exercising for two hours a week reduces insulin resistance by 10%

And study (C) indicates that sleeping regularly reduces insulin resistance by 10%.

Is there a way to collect these results? Like the methods used in meta-analysis (meaning taking into account the sample size and the effect size)

For example, the result of all previous studies if the patient worked on them would be a 40% reduction in insulin resistance..

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