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PHASE 3 lai liao
- Thread starter nirvarq
- Start date
They need tests that are more specific less sensitive.
https://en.wikipedia.org/wiki/Sensi...test sensitivity,disease (true negative rate).
Medical examples[edit]
In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate). If 100 patients known to have a disease were tested, and 43 test positive, then the test has 43% sensitivity. If 100 with no disease are tested and 96 return a completely negative result, then the test has 96% specificity. Sensitivity and specificity are prevalence-independent test characteristics, as their values are intrinsic to the test and do not depend on the disease prevalence in the population of interest.[10] Positive and negative predictive values, but not sensitivity or specificity, are values influenced by the prevalence of disease in the population that is being tested. These concepts are illustrated graphically in this applet Bayesian clinical diagnostic model which show the positive and negative predictive values as a function of the prevalence, the sensitivity and specificity.
Prevalence threshold[edit]
The relationship between a screening tests' positive predictive value, and its target prevalence, is proportional - though not linear in all but a special case. In consequence, there is a point of local extrema and maximum curvature defined only as a function of the sensitivity and specificity beyond which the rate of change of a test's positive predictive value drops at a differential pace relative to the disease prevalence. Using differential equations, this point was first defined by Balayla et al. [11] and is termed the prevalence threshold ({\displaystyle \phi _{e}}
). The equation for the prevalence threshold is given by the following formula, where a = sensitivity and b = specificity:
{\displaystyle \phi _{e}={\frac {{\sqrt {a(-b+1)}}+b-1}{(a+b-1)}}}
Where this point lies in the screening curve has critical implications for clinicians and the interpretation of positive screening tests in real time.
Misconceptions[edit]
It is often claimed that a highly specific test is effective at ruling in a disease when positive, while a highly sensitive test is deemed effective at ruling out a disease when negative.[12][13] This has led to the widely used mnemonics SPPIN and SNNOUT, according to which a highly specific test, when positive, rules in disease (SP-P-IN), and a highly 'sensitive' test, when negative rules out disease (SN-N-OUT). Both rules of thumb are, however, inferentially misleading, as the diagnostic power of any test is determined by both its sensitivity and its specificity.[14][15][16]
The tradeoff between specificity and sensitivity is explored in ROC analysis as a trade off between TPR and FPR (that is, recall and fallout).[17] Giving them equal weight optimizes informedness = specificity+sensitivity-1 = TPR-FPR, the magnitude of which gives the probability of an informed decision between the two classes (>0 represents appropriate use of information, 0 represents chance-level performance, <0 represents perverse use of information).[18]
Sensitivity index[edit]
The sensitivity index or d' (pronounced 'dee-prime') is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, compared against the standard deviation of the noise distribution. For normally distributed signal and noise with mean and standard deviations {\displaystyle \mu _{S}}
and {\displaystyle \sigma _{S}}
, and {\displaystyle \mu _{N}}
and {\displaystyle \sigma _{N}}
, respectively, d' is defined as:
{\displaystyle d'={\frac {\mu _{S}-\mu _{N}}{\sqrt {{\frac {1}{2}}(\sigma _{S}^{2}+\sigma _{N}^{2})}}}}
[19]
An estimate of d' can be also found from measurements of the hit rate and false-alarm rate. It is calculated as:
d' = Z(hit rate) – Z(false alarm rate),[20]
where function Z(p), p ∈ [0,1], is the inverse of the cumulative Gaussian distribution.
d' is a dimensionless statistic. A higher d' indicates that the signal can be more readily detected.
Worked example[edit]
A worked exampleA diagnostic test with sensitivity 67% and specificity 91% is applied to 2030 people to look for a disorder with a population prevalence of 1.48%
Related calculations
On the other hand, this hypothetical test demonstrates very accurate detection of cancer-free individuals (NPV = 99.5%). Therefore, when used for routine colorectal cancer screening with asymptomatic adults, a negative result supplies important data for the patient and doctor, such as ruling out cancer as the cause of gastrointestinal symptoms or reassuring patients worried about developing colorectal cancer.
Estimation of errors in quoted sensitivity or specificity[edit]
Sensitivity and specificity values alone may be highly misleading. The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of only 80%. A common way to do this is to state the binomial proportion confidence interval, often calculated using a Wilson score interval.
Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at a given confidence level (e.g., 95%).[23]
Terminology in information retrieval[edit]
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents. This assumption of very large numbers of true negatives versus positives is rare in other applications.[18]
The F-score can be used as a single measure of performance of the test for the positive class. The F-score is the harmonic mean of precision and recall:
{\displaystyle F=2\times {\frac {{\text{precision}}\times {\text{recall}}}{{\text{precision}}+{\text{recall}}}}}
In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.
https://en.wikipedia.org/wiki/Sensi...test sensitivity,disease (true negative rate).
Medical examples[edit]
In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate). If 100 patients known to have a disease were tested, and 43 test positive, then the test has 43% sensitivity. If 100 with no disease are tested and 96 return a completely negative result, then the test has 96% specificity. Sensitivity and specificity are prevalence-independent test characteristics, as their values are intrinsic to the test and do not depend on the disease prevalence in the population of interest.[10] Positive and negative predictive values, but not sensitivity or specificity, are values influenced by the prevalence of disease in the population that is being tested. These concepts are illustrated graphically in this applet Bayesian clinical diagnostic model which show the positive and negative predictive values as a function of the prevalence, the sensitivity and specificity.
Prevalence threshold[edit]
The relationship between a screening tests' positive predictive value, and its target prevalence, is proportional - though not linear in all but a special case. In consequence, there is a point of local extrema and maximum curvature defined only as a function of the sensitivity and specificity beyond which the rate of change of a test's positive predictive value drops at a differential pace relative to the disease prevalence. Using differential equations, this point was first defined by Balayla et al. [11] and is termed the prevalence threshold ({\displaystyle \phi _{e}}
{\displaystyle \phi _{e}={\frac {{\sqrt {a(-b+1)}}+b-1}{(a+b-1)}}}
Where this point lies in the screening curve has critical implications for clinicians and the interpretation of positive screening tests in real time.
Misconceptions[edit]
It is often claimed that a highly specific test is effective at ruling in a disease when positive, while a highly sensitive test is deemed effective at ruling out a disease when negative.[12][13] This has led to the widely used mnemonics SPPIN and SNNOUT, according to which a highly specific test, when positive, rules in disease (SP-P-IN), and a highly 'sensitive' test, when negative rules out disease (SN-N-OUT). Both rules of thumb are, however, inferentially misleading, as the diagnostic power of any test is determined by both its sensitivity and its specificity.[14][15][16]
The tradeoff between specificity and sensitivity is explored in ROC analysis as a trade off between TPR and FPR (that is, recall and fallout).[17] Giving them equal weight optimizes informedness = specificity+sensitivity-1 = TPR-FPR, the magnitude of which gives the probability of an informed decision between the two classes (>0 represents appropriate use of information, 0 represents chance-level performance, <0 represents perverse use of information).[18]
Sensitivity index[edit]
The sensitivity index or d' (pronounced 'dee-prime') is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, compared against the standard deviation of the noise distribution. For normally distributed signal and noise with mean and standard deviations {\displaystyle \mu _{S}}
{\displaystyle d'={\frac {\mu _{S}-\mu _{N}}{\sqrt {{\frac {1}{2}}(\sigma _{S}^{2}+\sigma _{N}^{2})}}}}
An estimate of d' can be also found from measurements of the hit rate and false-alarm rate. It is calculated as:
d' = Z(hit rate) – Z(false alarm rate),[20]
where function Z(p), p ∈ [0,1], is the inverse of the cumulative Gaussian distribution.
d' is a dimensionless statistic. A higher d' indicates that the signal can be more readily detected.
Worked example[edit]
A worked exampleA diagnostic test with sensitivity 67% and specificity 91% is applied to 2030 people to look for a disorder with a population prevalence of 1.48%
| Patients with bowel cancer (as confirmed on endoscopy) | |||||
| Condition positive | Condition negative | Prevalence = (TP+FN)/Total_Population = (20+10)/2030 ≈1.48% | Accuracy(ACC) = (TP+TN)/Total_Population = (20+1820)/2030 ≈90.64% | ||
| Fecal occult blood screen test outcome | Test outcome positive | True positive (TP) = 20 (2030 x 1.48% x 67%) | False positive (FP) = 180 (2030 x (100 - 1.48%) x (100 - 91%)) | Positive predictive value (PPV), Precision = TP / (TP + FP) = 20 / (20 + 180) = 10% | False discovery rate(FDR) = FP/(TP+FP) = 180/(20+180) = 90.0% |
| Test outcome negative | False negative (FN) = 10 (2030 x 1.48% x (100 - 67%)) | True negative (TN) = 1820 (2030 x (100 -1.48%) x 91%) | False omission rate(FOR) = FN / (FN + TN) = 10 / (10 + 1820) ≈ 0.55% | Negative predictive value(NPV) = TN / (FN + TN) = 1820 / (10 + 1820) ≈ 99.45% | |
| TPR, Recall, Sensitivity = TP / (TP + FN) = 20 / (20 + 10) ≈ 66.7% | False positive rate (FPR),Fall-out, probability of false alarm = FP/(FP+TN) = 180/(180+1820) =9.0% | Positive likelihood ratio(LR+) = TPR/FPR = (20/30)/(180/2000) ≈7.41 | Diagnostic odds ratio (DOR) = LR+/LR− ≈20.2 | F1 score = 2 · Precision · Recall/Precision + Recall ≈0.174 | |
| False negative rate (FNR), Miss rate = FN/(TP+FN) = 10/(20+10) ≈ 33.3% | Specificity, Selectivity, True negative rate(TNR) = TN / (FP + TN) = 1820 / (180 + 1820) = 91% | Negative likelihood ratio(LR−) = FNR/TNR = (10/30)/(1820/2000) ≈0.366 |
- False positive rate (α) = type I error = 1 − specificity = FP / (FP + TN) = 180 / (180 + 1820) = 9%
- False negative rate (β) = type II error = 1 − sensitivity = FN / (TP + FN) = 10 / (20 + 10) = 33%
- Power = sensitivity = 1 − β
- Likelihood ratio positive = sensitivity / (1 − specificity) = 0.67 / (1 − 0.91) = 7.4
- Likelihood ratio negative = (1 − sensitivity) / specificity = (1 − 0.67) / 0.91 = 0.37
- Prevalence threshold = {\displaystyle PT={\frac {{\sqrt {TPR(-TNR+1)}}+TNR-1}{(TPR+TNR-1)}}}
= 0.19 => 19.1%
On the other hand, this hypothetical test demonstrates very accurate detection of cancer-free individuals (NPV = 99.5%). Therefore, when used for routine colorectal cancer screening with asymptomatic adults, a negative result supplies important data for the patient and doctor, such as ruling out cancer as the cause of gastrointestinal symptoms or reassuring patients worried about developing colorectal cancer.
Estimation of errors in quoted sensitivity or specificity[edit]
Sensitivity and specificity values alone may be highly misleading. The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of only 80%. A common way to do this is to state the binomial proportion confidence interval, often calculated using a Wilson score interval.
Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at a given confidence level (e.g., 95%).[23]
Terminology in information retrieval[edit]
In information retrieval, the positive predictive value is called precision, and sensitivity is called recall. Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents. This assumption of very large numbers of true negatives versus positives is rare in other applications.[18]
The F-score can be used as a single measure of performance of the test for the positive class. The F-score is the harmonic mean of precision and recall:
{\displaystyle F=2\times {\frac {{\text{precision}}\times {\text{recall}}}{{\text{precision}}+{\text{recall}}}}}
In the traditional language of statistical hypothesis testing, the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that is not applicable in the present context. A sensitive test will have fewer Type II errors.
Means before call chicken take test n if negative can call chicken ? I ok with that. I volunteer to swipe pussies.
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