Causal inference

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Causal inference is the process of determining a causal relationship based on the circumstances surrounding the occurrence of an effect (Yao et al., 2021). The article by Costa and Yakusheva (2016) discussed the causal inference of nursing staffing and patient outcomes using three study designs, namely cross-sectional, longitudinal, and randomized control trials framework. It is interesting to note that although the same topic of study of nursing staff and patient outcomes was analyzed, every methodology design brought different results. Using complex statistical methods and multi-variable regression modeling, one may eliminate bias from the system-level elements and patient characteristics that impact the outcome (Costa & Yakusheva, 2016). For instance, in a cross-sectional design, they examined the nurse staffing and patient outcomes at the hospital level. There are unobserved or unmeasured variables that are not confounding the results that may lead to understated results. Another variable for understated results is suppression, where researchers study various nursing characteristics in the same analysis model. In longitudinal studies, trends of patient outcomes across many organizations or units observed concurrently can be compared by researchers and analyzed over five years in 19 hospitals. Costa and Yakusheva (2016) found that, like cross-sectional design, longitudinal designs are susceptible to confounding and suppression effects from time-varying factors. The impact of changes in nurse staffing on patient outcomes, for instance, may be overstated or understated depending on changes in quality-improvement procedures or seasonal variations in patient outcomes. Additionally, the statistical power may be limited if nurse staffing stays largely stable during the study period. A randomized controlled trial (RCT) is the highest standard for analyzing data to demonstrate the connection between nurse staffing and patient outcomes. This study design would enable researchers to precisely measure the causal inference of a change in nurse staffing on patient outcomes. However, there are currently no randomized controlled trials (RCTs) of nurse staffing and patient outcomes in the literature because of financial, logistical, and ethical obstacles to randomly assigning patients to different nurse staffing levels (Costa & Yakusheva, 2016). Costa and Yakusheva (2016) further discussed that approximating RCTs and using predicted probabilities could result in the strongest evidence of the effect of nurse staffing on patient outcomes.

In my quality improvement (QI) project, I will use a non-random-drawn convenience sample where nurses deliberately select only patients qualified to be included in the text message intervention. Convenience sampling is the most popular technique used when busy clinicians conduct research utilizing retrospective data since the predictor, confounding, clinical, and outcome variables are already present and can be quickly mined and analyzed (Elliot & Valliant, 2017). Data gathered from convenience samples cannot be utilized to draw causal inferences because convenience sampling does not employ randomization (Elliot & Valliant, 2017). One bias that may result from a convenience sample in this QI project is sampling bias.

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