46 lines
1.6 KiB
Text
46 lines
1.6 KiB
Text
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# Results
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```{r}
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#| include: false
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#| cache: true
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library(magrittr)
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load("test.Rda")
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```
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The final data set used for this analysis consisted of 11,340
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observations. All observations contained a TSH and Free T4 result and
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less than three missing results from all other analytes selected for the
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study. The dataset was then randomly split into a training set
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containing 9071 observations and a testing set containing 2269
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observations. The data was split using stratification of the Free T4
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laboratory diagnostic value. @tbl-strata shows the split percentages.
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```{r}
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#| label: tbl-strata
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#| tbl-cap: Data Stratification
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#| echo: false
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strata_table %>% knitr::kable()
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```
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First, the report shows the ability of classification algorithms to
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predict whether Free T4 will be diagnostic, with the prediction quality
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measured by Area Under Curve (AUC) and accuracy. Data regarding the
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univariate association between each predictor analyte and the Free T4
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Diagnostic value is then presented. Finally, data is presented with the
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extent to which FT4 can be predicted by examining the correlation
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statistics denoting the relationship between measured and predicted Free
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T4 values.
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## Predictability of Free T4 Classifications
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In clinical decision-making, a key consideration in interpreting
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numerical laboratory results is often just whether the results fall
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within the normal reference range [@luo2016]. In the case of Free T4
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reflex testing, the results will either fall within the normal range
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indicating the Free T4 is not diagnostic of Hyper or Hypo Throydism, or
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they will fall outside those ranges indicating they are diagnostic.
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