On effect‐measure modification: Relationships among changes in the relative risk, odds ratio, and risk difference

2008 ◽  
Vol 27 (18) ◽  
pp. 3453-3465 ◽  
Author(s):  
Babette Brumback ◽  
Arthur Berg
Keyword(s):  
Relative Risk ◽  
Odds Ratio ◽  
Risk Difference ◽  
Effect Measure ◽  
Effect Measure Modification

Risky Business: Making Sense of Estimates of Risk

1998 ◽  
Vol 43 (4) ◽  
pp. 411-415 ◽  
Author(s):  
David L Streiner
Keyword(s):  
Relative Risk ◽  
Odds Ratio ◽  
Risk Difference ◽  
Attributable Risk ◽  
Absolute Difference ◽  
Number Needed To Treat ◽  
Making Sense ◽  
Specific Outcome ◽  
Relative Differences ◽  
Risky Business

This article describes various indices of risk, which is the probability that a person will develop a specific outcome. The risk difference is the absolute difference in risks between 2 groups and can be used either to compare the outcome of 2 groups, one of which was exposed to some genetic or environmental factor, or to see how much of an effect a treatment may have. The reciprocal of the risk difference, the number needed to treat, expresses how many patients must receive the intervention in order for 1 person to derive some benefit. Attributable risk reflects the proportion of cases due to some putative cause and indicates the number of cases that can be averted if the cause were removed. Finally, the relative risk and odds ratio reflect the relative differences between groups in achieving some outcome, either good (a cure) or bad (development of a disorder).

scholarly journals When Is a Complete-Case Approach to Missing Data Valid? The Importance of Effect-Measure Modification

2020 ◽  
Vol 189 (12) ◽  
pp. 1583-1589
Author(s):  
Rachael K Ross ◽  
Alexander Breskin ◽  
Daniel Westreich
Keyword(s):  
Missing Data ◽  
Case Analysis ◽  
Risk Difference ◽  
Complete Case Analysis ◽  
Unbiased Estimation ◽  
Complete Case ◽  
Marginal Effects ◽  
Effect Measure ◽  
Effect Measure Modification ◽  
Marginal Risk

Abstract When estimating causal effects, careful handling of missing data is needed to avoid bias. Complete-case analysis is commonly used in epidemiologic analyses. Previous work has shown that covariate-stratified effect estimates from complete-case analysis are unbiased when missingness is independent of the outcome conditional on the exposure and covariates. Here, we assess the bias of complete-case analysis for adjusted marginal effects when confounding is present under various causal structures of missing data. We show that estimation of the marginal risk difference requires an unbiased estimate of the unconditional joint distribution of confounders and any other covariates required for conditional independence of missingness and outcome. The dependence of missing data on these covariates must be considered to obtain a valid estimate of the covariate distribution. If none of these covariates are effect-measure modifiers on the absolute scale, however, the marginal risk difference will equal the stratified risk differences and the complete-case analysis will be unbiased when the stratified effect estimates are unbiased. Estimation of unbiased marginal effects in complete-case analysis therefore requires close consideration of causal structure and effect-measure modification.

70 Choosing an appropriate dichotomous effect measure for meta-analysis: Empirical evidence of the appropriateness of the odds ratio and relative risk

1997 ◽  
Vol 18 (3) ◽  
pp. S84-S85 ◽  
Author(s):  
J.J. Deeks ◽  
D.G. Altman ◽  
G. Dooley ◽  
D.L.S. Sackett
Keyword(s):  
Relative Risk ◽  
Empirical Evidence ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Effect Measure

scholarly journals Formulas, Algorithms and Examples for Binomial Distributed Data Confidence Interval Calculation: Excess Risk, Relative Risk and Odds Ratio

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2506
Author(s):  
Lorentz Jäntschi
Keyword(s):  
Relative Risk ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Risk Difference ◽  
Excess Risk ◽  
Distributed Data ◽  
Medical Studies ◽  
Measures Of Association ◽  
Discrete Population ◽  
Current Guidelines

Medical studies often involve a comparison between two outcomes, each collected from a sample. The probability associated with, and confidence in the result of the study is of most importance, since one may argue that having been wrong with a percent could be what killed a patient. Sampling is usually done from a finite and discrete population and it follows a Bernoulli trial, leading to a contingency of two binomially distributed samples (better known as 2×2 contingency table). Current guidelines recommend reporting relative measures of association (such as the relative risk and odds ratio) in conjunction with absolute measures of association (which include risk difference or excess risk). Because the distribution is discrete, the evaluation of the exact confidence interval for either of those measures of association is a mathematical challenge. Some alternate scenarios were analyzed (continuous vs. discrete; hypergeometric vs. binomial), and in the main case—bivariate binomial experiment—a strategy for providing exact p-values and confidence intervals is proposed. Algorithms implementing the strategy are given.

scholarly journals Personalised high tibial osteotomy has mechanical safety equivalent to generic device in a case–control in silico clinical trial

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alisdair R. MacLeod ◽  
Nicholas Peckham ◽  
Gil Serrancolí ◽  
Ines Rombach ◽  
Patrick Hourigan ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Relative Risk ◽  
Knee Osteoarthritis ◽  
High Tibial Osteotomy ◽  
Fatigue Failure ◽  
In Silico ◽  
Odds Ratio ◽  
Case Control ◽  
Tibial Osteotomy ◽  
3D Printed

Abstract Background Despite favourable outcomes relatively few surgeons offer high tibial osteotomy (HTO) as a treatment option for early knee osteoarthritis, mainly due to the difficulty of achieving planned correction and reported soft tissue irritation around the plate used to stablise the osteotomy. To compare the mechanical safety of a new personalised 3D printed high tibial osteotomy (HTO) device, created to overcome these issues, with an existing generic device, a case-control in silico virtual clinical trial was conducted. Methods Twenty-eight knee osteoarthritis patients underwent computed tomography (CT) scanning to create a virtual cohort; the cohort was duplicated to form two arms, Generic and Personalised, on which virtual HTO was performed. Finite element analysis was performed to calculate the stresses in the plates arising from simulated physiological activities at three healing stages. The odds ratio indicative of the relative risk of fatigue failure of the HTO plates between the personalised and generic arms was obtained from a multi-level logistic model. Results Here we show, at 12 weeks post-surgery, the odds ratio indicative of the relative risk of fatigue failure was 0.14 (95%CI 0.01 to 2.73, p = 0.20). Conclusions This novel (to the best of our knowledge) in silico trial, comparing the mechanical safety of a new personalised 3D printed high tibial osteotomy device with an existing generic device, shows that there is no increased risk of failure for the new personalised design compared to the existing generic commonly used device. Personalised high tibial osteotomy can overcome the main technical barriers for this type of surgery, our findings support the case for using this technology for treating early knee osteoarthritis.

Effect Measure Modification and Confounding of Severe Head Injury Mortality by Age and Multiple Organ Injury Severity

2007 ◽  
Vol 17 (2) ◽  
pp. 142-147 ◽  
Author(s):  
Shai Linn ◽  
Leon Levi ◽  
Peter D. Grunau ◽  
Itzhak Zaidise ◽  
Salman Zarka
Keyword(s):  
Head Injury ◽  
Severe Head Injury ◽  
Injury Severity ◽  
Multiple Organ ◽  
Organ Injury ◽  
Injury Mortality ◽  
Effect Measure ◽  
Effect Measure Modification ◽  
Multiple Organ Injury

scholarly journals Meta-Analysis of the Relationship Between Ethnicity, Obesity, and Type 2 Diabetes of Adults in Urban Populations of Central America

2016 ◽  
Vol 5 (3) ◽  
pp. 274
Author(s):  
William G Wuenstel ◽  
James A. Johnson ◽  
James Humphries ◽  
Cheryl Samuel
Keyword(s):  
Type 2 Diabetes ◽  
Relative Risk ◽  
Odds Ratio ◽  
Meta Analysis ◽  
Central American ◽  
Significance Level ◽  
Study Participants ◽  
The Impact ◽  
The Relationship

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td rowspan="2" valign="top" width="387">The purpose of this meta-analysis was to examine the impact of ethnicity and obesity as it relates to Type-2 Diabetes (T2D) in specific Central American countries. A meta-analysis was conducted to determine the association of ethnicity, obesity, and T2D.  Four studies that qualified for inclusion were identified by searching MEDLINE and PubMed databases. The studies on the association of ethnicity and T2D had a combined population resulted in 265,858 study participants. Two studies on the association of obesity and T2D had 197,899 participants. An analysis of the data was conducted utilizing the relative risk ration, odds ratio, and forest plots. The comparison of the relative risk of T2D across ethnic categories by studies range for Blacks was 1.59 to 2.74, Asians was 1.43 to 2.08, and Hispanics .92 to 2.91.  The ethnic difference in the prevalence of diabetes was almost two-fold higher in all ethnic groups than among the Caucasians with a significance level of 95%. A comparison of relative risk of T2D across weight categories was significantly higher among those with a diagnosed of diabetes in all reported areas. The odds ratio was very close to the risk ratio in both ethnicity and obesity to the development of T2D. The meta-analysis findings documented that an association does exist between ethnicity and obesity to the development of type 2 diabetes.</td><td width="0" height="85"> </td></tr><tr><td width="0" height="82"> </td></tr></tbody></table>

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