| Title: | Clinical Significance Measures |
|---|---|
| Description: | Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index. |
| Authors: | Mike Malek-Ahmadi <[email protected]>, Kjera Schack <[email protected]> |
| Maintainer: | Mike Malek-Ahmadi <[email protected]> |
| License: | GPL-3 |
| Version: | 1.2 |
| Built: | 2025-03-03 04:47:53 UTC |
| Source: | https://github.com/cran/ClinSigMeasures |
Calculates a Cohen's d effect size using the means and standard deviations of two independent groups
cohens_d(Group1_Mean, Group1_SD, Group2_Mean, Group2_SD)cohens_d(Group1_Mean, Group1_SD, Group2_Mean, Group2_SD)
Group1_Mean |
Mean for Group 1 |
Group1_SD |
Standard Deviation for Group 1 |
Group2_Mean |
Mean for Group 2 |
Group2_SD |
Standard Deviation for Group 2 |
A single value representing the Cohen's d effect size
Mike Malek-Ahmadi
1. Cohen, Jacob (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge. ISBN 978-1-134-74270-7.
2. Malek-Ahmadi M, Perez SE, Chen K, Mufson EJ. Neuritic and diffuse plaque associations with memory in non-cognitively impaired elderly. J Alzheimers Dis 2016;53(4):1641-1652.
#From Table 2 in Malek-Ahmadi et al (2016) #comparing groups with (0.75+/-0.35) and without (0.49+/-0.29) neuritic plaques #on a global cognitive score (z-score). cohens_d(0.75, 0.35, 0.49, 0.29)#From Table 2 in Malek-Ahmadi et al (2016) #comparing groups with (0.75+/-0.35) and without (0.49+/-0.29) neuritic plaques #on a global cognitive score (z-score). cohens_d(0.75, 0.35, 0.49, 0.29)
Calculates Cohen's q for the effect size of the difference between two correlation values
cohens_q(corr1, corr2)cohens_q(corr1, corr2)
corr1 |
Correlation for First Group |
corr2 |
Correlation for Second Group |
A single value representing Cohen's q
Mike Malek-Ahmadi
1. Cohen, Jacob (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge. ISBN 978-1-134-74270-7.
2. Yang G, Li D, Rao Y, Lu F. The relationship between cortical thickness and language comprehension varies with sex in healthy young adults: a large sample analysis. Neuroreport 2020;31(2):184-188.
#From Yang et al (2020), Cohen's q for the difference between female and male correlation #values for vocabulary comprehension and cortical thickness. cohens_q (0.318, 0.174)#From Yang et al (2020), Cohen's q for the difference between female and male correlation #values for vocabulary comprehension and cortical thickness. cohens_q (0.318, 0.174)
Calculates the coefficient of variation for a mean and standard deviation
cv(Mean, SD)cv(Mean, SD)
Mean |
Mean for a dataset |
SD |
Standard Deviation for a dataset |
A single value representing the Coefficient of Variation
Mike Malek-Ahmadi
1. Everitt B (1998). The Cambridge Dictionary of Statistics. Cambridge, UK New York: Cambridge University Press. ISBN 978-0521593465.
2. Bedeian AG, Mossholder KW. On the use of the coefficient of variation as a measure of diversity. Organizational Research Methods 2000;3(3):285-297.
#From Bedeian & Mossholder (2000), Table 2 Group A data. cv(28, 7)#From Bedeian & Mossholder (2000), Table 2 Group A data. cv(28, 7)
Calculates diagnostic test likelihood ratio negative and 95 percent confidence intervals for data from a 2x2 table
lr_neg(Cell1, Cell2, Cell3, Cell4)lr_neg(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Likelihood Ratio Negative and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Grimes DA, Schultz KF. Refining clinical diagnosis with likelihood ratios. Lancet 2005;365:1500-1505.
2. Dujardin B, Van den Ende J, Van Gompel A, Unger JP, Van der Stuyft P. Likelihood ratios: a real improvement for clinical decision making? European Journal of Epidemiology 1994 Feb;10(1):29-36.
#From Table 1 in Dujardin et al (1994) lr_neg(72, 9, 25, 137)#From Table 1 in Dujardin et al (1994) lr_neg(72, 9, 25, 137)
Calculates diagnostic test likelihood ratio positive and 95 percent confidence intervals for data from a 2x2 table
lr_pos(Cell1, Cell2, Cell3, Cell4)lr_pos(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Likelihood Ratio Positive and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Grimes DA, Schultz KF. Refining clinical diagnosis with likelihood ratios. Lancet 2005;365:1500-1505.
2. Dujardin B, Van den Ende J, Van Gompel A, Unger JP, Van der Stuyft P. Likelihood ratios: a real improvement for clinical decision making? European Journal of Epidemiology 1994 Feb;10(1):29-36.
#From Table 1 in Dujardin et al (1994) lr_pos(72, 9, 25, 137)#From Table 1 in Dujardin et al (1994) lr_pos(72, 9, 25, 137)
Calculates the Number Needed to Diagnose for data from a 2x2 table
nnd(Cell1, Cell2, Cell3, Cell4)nnd(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Number Needed to Diagnose
Mike Malek-Ahmadi
1. Larner AJ. Number Needed to Diagnose, Predict, or Misdiagnose: Useful Metrics for Non-Canonical Signs of Cognitive Status? Dement Geriatr Cogn Disord Extra 2018;8:321–327
#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" #NND is the inverse of the Youden Index (1 / Youden Index) nnd(105, 171, 15, 87)#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" #NND is the inverse of the Youden Index (1 / Youden Index) nnd(105, 171, 15, 87)
Calculates number needed to treat and 95 percent confidence intervals for data from a 2x2 table
nnt(Cell1, Cell2, Cell3, Cell4)nnt(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive outcome |
Cell2 |
Value for cases with a negative outcome |
Cell3 |
Value for controls with a positive outcome |
Cell4 |
Value for controls with a negative outcome |
Number Needed to Treat and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Cook RJ, Sackett DL. The number needed to treat: a clinically useful measure of treatment effect [published correction appears in BMJ 1995 Apr 22;310(6986):1056]. BMJ. 1995;310(6977):452-454.
2. Zar HJ, Cotton MF, Strauss S et al Effect of isoniazid prophylaxi on mortality of tuberculosis in children with HIV: randomised controlled trial. BMJ 2007; 136-9.
#Mortality data from Zar et al (2007) nnt(121, 11, 110, 21)#Mortality data from Zar et al (2007) nnt(121, 11, 110, 21)
Calculates diagnostic test negative predictive value and 95 percent confidence intervals for data from a 2x2 table
npv(Cell1, Cell2, Cell3, Cell4)npv(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Negative Predictive Value and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Trevethan R. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Frontiers in Public Health 2017;5:307.
2. Safari S, Baratloo A, Elfil M, Negida A. Evidence Based Emergency Medicine Part 2: Positive and negative predictive values of diagnostic tests. Emerg (Tehran) 2015;3(3):87-88.
#From Figure 2 in Safari et al (2015) npv(15, 6, 25, 34)#From Figure 2 in Safari et al (2015) npv(15, 6, 25, 34)
Calculates an odds ratio and 95 percent confidence intervals for data from a 2x2 table
odds_ratio(Cell1, Cell2, Cell3, Cell4)odds_ratio(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with the factor/exposure of interest |
Cell2 |
Value for cases without the factor/exposure of interest |
Cell3 |
Value for controls with the factor/exposure of interest |
Cell4 |
Value for controls without the factor/exposure of interest |
Odds ratio and 95 percent confidence intervals
Mike Malek-Ahmadi
1.Mufson EJ, Malek-Ahmadi M, Perez SE, Chen K. Braak staging, plaque pathology, and APOE status in elderly persons without cognitive impairment. Neurobiol Aging 2016;37:147-153.
# From Table 1 in Mufson et al (2016), using data for sex (Male/Female) #and Braak stage group classification (I-II/III-V). #Female/Braak III-V = 46, Female/Braak I-II = 14, Male/Braak III-V = 32, #Male/Braak I-II = 31. odds_ratio(46, 14, 32, 31)# From Table 1 in Mufson et al (2016), using data for sex (Male/Female) #and Braak stage group classification (I-II/III-V). #Female/Braak III-V = 46, Female/Braak I-II = 14, Male/Braak III-V = 32, #Male/Braak I-II = 31. odds_ratio(46, 14, 32, 31)
Calculates partial eta squared effect size for ANOVAs
partial_eta_sq(SS.Between, SS.Error)partial_eta_sq(SS.Between, SS.Error)
SS.Between |
Sum of Squares Between from ANOVA Output |
SS.Error |
Sum of Squares Error from ANOVA Output |
A single value representing partial eta squared
Mike Malek-Ahmadi
1. Levine TR, Hullett CR. Eta squared, partial eta squared, and misreporting of effect size in communication research. Human Communication Research 2002;28:612-625.
#From Levine & Hullett (2002), Example 1 in Table 1 partial_eta_sq(2500, 800)#From Levine & Hullett (2002), Example 1 in Table 1 partial_eta_sq(2500, 800)
Calculates diagnostic test positive predictive value and 95 percent confidence intervals for data from a 2x2 table
ppv(Cell1, Cell2, Cell3, Cell4)ppv(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Positive Predictive Value and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Trevethan R. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Frontiers in Public Health 2017;5:307.
2. Safari S, Baratloo A, Elfil M, Negida A. Evidence Based Emergency Medicine Part 2: Positive and negative predictive values of diagnostic tests. Emerg (Tehran) 2015;3(3):87-88.
#From Figure 2 in Safari et al (2015) ppv(15, 6, 25, 34)#From Figure 2 in Safari et al (2015) ppv(15, 6, 25, 34)
Calculates the Predictive Summary Index for data from a 2x2 table
psi(Cell1, Cell2, Cell3, Cell4)psi(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Predictive Summary Index
Mike Malek-Ahmadi
1. Linn S, Grunau PD. New patient-oriented summary measure of net total gain in certainty for dichotomous diagnostic tests. Epidemiol Perspect Innov 2006;3:11.
2. Shaikh SA. Measures Derived from a 2 x 2 Table for an Accuracy of a Diagnostic Test. J Biomet Biostat 2011, 2:5
#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" psi(105, 171, 15, 87)#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" psi(105, 171, 15, 87)
Calculates the robust effect size for a two-group comparison using the means, standard deviations, and sample sizes for each group
robust_effect_size(M1, M2, SD1, SD2, N1, N2)robust_effect_size(M1, M2, SD1, SD2, N1, N2)
M1 |
Mean for Group 1 |
M2 |
Mean for Group 2 |
SD1 |
Standard deviation for Group 1 |
SD2 |
Standard deviation for Group 2 |
N1 |
Sample Size for Group 1 |
N2 |
Sample Size for Group 2 |
Robust Effect Size
Kjera Schack
Vandekar S, Tao R, Blume J. A Robust Effect Size Index [published correction appears in Psychometrika. 2020 Dec;85(4):946]. Psychometrika. 2020;85(1):232-246. doi:10.1007/s11336-020-09698-2
#From Table 2 in Malek-Ahmadi et al (2016) #comparing groups with (0.75+/-0.35, n=45) and without (0.49+/-0.29, n=78) neuritic plaques #on a global cognitive score (z-score). robust_effect_size(0.75, 0.49, 0.35, 0.29, 45, 78)#From Table 2 in Malek-Ahmadi et al (2016) #comparing groups with (0.75+/-0.35, n=45) and without (0.49+/-0.29, n=78) neuritic plaques #on a global cognitive score (z-score). robust_effect_size(0.75, 0.49, 0.35, 0.29, 45, 78)
Calculates diagnostic test sensitivity and 95 percent confidence intervals for data from a 2x2 table
sensitivity(Cell1, Cell2, Cell3, Cell4)sensitivity(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Sensitivity and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Trevethan R. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Frontiers in Public Health 2017;5:307.
2. Weissberger GH, Strong JV, Stefanidis KB, Summers MJ, Bondi MW, Stricker NH. Diagnostic accuracy of memory measures in Alzheimer's dementia and mild Cognitive Impairment: a Systematic Review and Meta-Analysis. Neuropsychol Rev. 2017;27(4):354-388.
#Sensitivity calculation from Figure 11, Line 22 of Weissberger et al sensitivity (121, 50, 13, 199)#Sensitivity calculation from Figure 11, Line 22 of Weissberger et al sensitivity (121, 50, 13, 199)
Calculates diagnostic test specificity and 95 percent confidence intervals for data from a 2x2 table
specificity(Cell1, Cell2, Cell3, Cell4)specificity(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Specificity and 95 percent confidence intervals
Mike Malek-Ahmadi
1. Trevethan R. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Frontiers in Public Health 2017;5:307.
2. Weissberger GH, Strong JV, Stefanidis KB, Summers MJ, Bondi MW, Stricker NH. Diagnostic accuracy of memory measures in Alzheimer's dementia and mild Cognitive Impairment: a Systematic Review and Meta-Analysis. Neuropsychol Rev. 2017;27(4):354-388.
#Specificity calculation from Figure 11, Line 22 of Weissberger et al specificity (121, 50, 13, 199)#Specificity calculation from Figure 11, Line 22 of Weissberger et al specificity (121, 50, 13, 199)
Calculates the Youden Index for data from a 2x2 table
youden_index(Cell1, Cell2, Cell3, Cell4)youden_index(Cell1, Cell2, Cell3, Cell4)
Cell1 |
Value for cases with a positive test |
Cell2 |
Value for controls with a positive test |
Cell3 |
Value for cases with a negative test |
Cell4 |
Value for controls with a negative test |
Youden Index
Mike Malek-Ahmadi
1. Ruopp MD, Perkins NJ, Whitcomb BW, Schisterman EF. Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection. Biom J 2008;50(3):419-430.
2. Shaikh SA (2011) Measures derived from a 2 x 2 table for an accuracy of a diagnostic test. J Biomet Biostat 2:128
#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" youden_index(105, 171, 15, 87)#From Shaikh (2011), page 3, 2x2 table for "Diagnostic Test Evaluation" youden_index(105, 171, 15, 87)