Analytical Problem Solving Based on Causal, Correlational and Deductive Models

Mast, Jeroen de; Steiner, Stefan H.; Nuijten, Wim; Kapitan, D.

doi:10.1080/00031305.2021.2023633

Cited by 15 publications

(15 citation statements)

References 49 publications

(3 reference statements)

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“…Additional studies are therefore required to establish causal pathways and to identify possible modifiable risk factors. 52 …”

Section: Discussionmentioning

confidence: 99%

Prediction of Early Periprosthetic Joint Infection After Total Hip Arthroplasty

Bülow¹,

Hahn²,

Andersen³

et al. 2022

CLEP

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Purpose To develop a parsimonious risk prediction model for periprosthetic joint infection (PJI) within 90 days after total hip arthroplasty (THA). Patients and Methods We used logistic LASSO regression with bootstrap ranking to develop a risk prediction model for PJI within 90 days based on a Swedish cohort of 88,830 patients with elective THA 2008–2015. The model was externally validated on a Danish cohort with 18,854 patients. Results Incidence of PJI was 2.45% in Sweden and 2.17% in Denmark. A model with the underlying diagnosis for THA, body mass index (BMI), American Society for Anesthesiologists (ASA) class, sex, age, and the presence of five defined comorbidities had an area under the curve (AUC) of 0.68 (95% CI: 0.66 to 0.69) in Sweden and 0.66 (95% CI: 0.64 to 0.69) in Denmark. This was superior to traditional models based on ASA class, Charlson, Elixhauser, or the Rx Risk V comorbidity indices. Internal calibration was good for predicted probabilities up to 10%. Conclusion A new PJI prediction model based on easily accessible data available before THA was developed and externally validated. The model had superior discriminatory ability compared to ASA class alone or more complex comorbidity indices and had good calibration. We provide a web-based calculator ( https://erikbulow.shinyapps.io/thamortpred/ ) to facilitate shared decision making by patients and surgeons.

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“…Additional studies are therefore required to establish causal pathways and to identify possible modifiable risk factors. 52 …”

Section: Discussionmentioning

confidence: 99%

Prediction of Early Periprosthetic Joint Infection After Total Hip Arthroplasty

Bülow¹,

Hahn²,

Andersen³

et al. 2022

CLEP

View full text Add to dashboard Cite

show abstract

“…When data are observational, it is possible that relationship and patterns are not the direct result of one factor causing changes in the other. This can often cause problems with solutions that assume a causal connection, but when implemented won't necessarily yield the intended results 1 . While there are methods for establishing causal relationships from observational data, 2 they are less direct than a well‐designed experiment and may be subject to misinterpretation.…”

Section: Introductionmentioning

confidence: 99%

“…This can often cause problems with solutions that assume a causal connection, but when implemented won't necessarily yield the intended results. 1 While there are methods for establishing causal relationships from observational data, 2 they are less direct than a well-designed experiment and may be subject to misinterpretation. With a controlled experiment, if the inputs are carefully manipulated and the responses change, there is much more evidence of a causal connection between the inputs and outputs, which can be more directly leveraged to improve understanding and the performance of the system.…”

Section: Introductionmentioning

confidence: 99%

Is designed data collection still relevant in the big data era?

Anderson‐Cook

2023

Quality & Reliability Eng

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The excitement and buzz around big data (BD) has been so prevalent that it seems BD has become thought of as a panacea for solving all challenges. But there are in fact limitations to what BD can do, which has led to missed opportunities and potentially suboptimal problem-solving and decision-making. Hence the answer to the question posed in the title is an emphatic 'Yes!' The intentional strategic use of designed data collection (DDC) represents an opportunity to elevate how studies involving BD are planned, executed, and interpreted to deliver more satisfying solutions. In this paper, we outline eleven opportunities for integrating DDC strategies, including statistical design of experiments (DoE) and sampling techniques, with BD at all stages of the study. There are opportunities for building background understanding of the problem and improving the quality of the data via strategic planning before acquiring BD. In addition, incorporating DDC into the collection and processing of the actual BD, and strengthening conclusions or augmenting with supplementary designed data after BD also represent key opportunities to increase the impact of BD in the era of Industry 4.0 and Quality 4.0.

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“…Indeed, deriving actionable and novel insights from Big Data requires a multifaceted framework involving descriptive, diagnostic, predictive, and prescriptive analytics (de Mast et al, 2022) and necessitates expertise beyond traditional technological and statistical tools and skills. Specifically, this encompasses "computational, algorithmic, statistical and mathematical techniques" (Leonelli, 2020, para.…”

Section: Introductionmentioning

confidence: 99%

Perspectives on Curricular Priorities for Big Data Analytics: A Qualitative Study of Educators and Practitioners

Hassad¹

2022

Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. Proceedings of the Eleventh International Conference

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Big Data is a universal concept that has become a metaphor for innovation and discovery; however, deriving this benefit requires going beyond traditional tools and expertise. The aim of this qualitative study (N=38) was to explore perspectives on curricular priorities for Big Data analytics from educators and practitioners. Nine themes were derived: computational and programming literacy, integrated and multidisciplinary curriculum, data quality and methodological standards, collaboration and domain-specific expertise, statistical literacy, basic statistics (probability and inference), ethico-legal considerations, critical analysis and effective communication skills, and meaning and definition of Big Data. Additionally, reflective learning was identified as an overarching theme, which can serve as a unifying pedagogical approach. These results provide a framework that appears to have content validity for informing curricular reform and development.

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Analytical Problem Solving Based on Causal, Correlational and Deductive Models

Cited by 15 publications

References 49 publications

Prediction of Early Periprosthetic Joint Infection After Total Hip Arthroplasty

Prediction of Early Periprosthetic Joint Infection After Total Hip Arthroplasty

Is designed data collection still relevant in the big data era?

Perspectives on Curricular Priorities for Big Data Analytics: A Qualitative Study of Educators and Practitioners

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