SMHS OR RR

Scientific Methods for Health Sciences - Odds Ratio and Relative Risk

Overview

The relative risk is measure of dependence which allows us to compare two probabilities in terms of their ratio $ \frac{p_1}{p_2} $ rather than their difference $(p_1 – p_2) $. The relative risk measure is widely used in many studies of public health. Another way to compare two probabilities is in terms of the odds. If an event takes place with probability p, then the odds in favor of the event are $ \frac{p}{1 - p} $. The odds ratio is the ratio of odds for two complementary probabilities.

Motivation

Suppose we study Brain Cancer in the context of cell phone use. The table below illustrates some (simulated) data. One clear healthcare question in this case-study could be: Is cell phone use associated with higher incidence of brain cancer? To address this question, we can look at the relative risk of cell-phone usage.

		Brain Cancer (BC)		Total
		Yes	No
Cell Phone (CP)	Yes	18	80	98 (B)
	No	7	95	102 (C)
Total		25	175	200

Computing the (conditional!) probabilities (P) of brain cancer (BC) given either cell-phone use, P1, no cell-phone use, P2, we can form their ratio to determine if the relative risk of brain cancer (BC) is higher in cell-phone users (CP), relative to non-users (NCP).

$$ P1 = P(BC|CP) = \dfrac {18}{98} = 0.184 $$

$$ P_2= P(BC|NCP) = \dfrac {7} {102} = 0.069 $$

So the relative risk is: $$ RR= \dfrac {0.184}{0.069} = 2.67. $$

The risk of having brain cancer is more than 2.5 times greater for cell-phone users when compared to no-cellphone owners.

For the same example, the odds ratio (OR) of brain cancer relative to cell-phone use is:

$$ OR = \frac{\frac{P \left( A \mid B \right)}{1 - P \left( A \mid B \right)}}{\frac{P \left( A \mid C \right)}{1 - P \left( A \mid C \right)}} = \frac{\frac{\frac{18}{98}}{1 - \frac{18}{98}}} {\frac{\frac{7}{102}}{1 - \frac{7}{102}}} =\frac{\frac{0.184}{0.816}}{\frac{0.069}{0.931}} = 3.04 $$

Thus, the odds of having brain cancer is about 3 times greater for cell phone owners when compared to non-cell phone owners. We could have compared the odds of owning a cell phone, given that a patient had brain cancer (i.e., the column-wise probabilities), $ P(CP|BC) = 18/25 = 0.72 $ versus $ P(CP|NBC) = 80/175 = 0.457 $. However this does not seem as important scientifically.

Theory

		Factor 1		Total
		Yes	No
Factor 2	Yes	$n_1,1$	$N_1,2$	$n_1,1+n_1,2$
	No	$n_2,1$	$n_2,2 $	$n_2,1+n_2,2$
Total		$n_1,1 + n_2,1$	$n_1,2 + n_1,2$	N

$$OR = \frac {n_{1,1}×n_{2,2}}{n_{1,2}×n_{2,1}}.$$

Interpretation

• RR: In general, relative risk (RR) measure is interpreted as follows
  • RR = 1 indicates that the probabilities of two events are the same.
  • RR > 1 implies that there is increased risk
  • RR < 1 implies that there is decreased risk

• OR
  • If event $A|B$ has probability $p = 1/2$, then the odds are $\frac{1/2}{1/2}=1$, or $1:1$, or 1 to 1 (the probability that event A|B occurs is equal to the probability that it does not occur).
  • If event $A|C$ has probability $p = 3/4$, then the odds are $\frac{3/4}{1/4}= 3$, or 3 to 1 (the probability that event A|C occurs is three times as large as the probability that it does not occur).
  • Similarly, if A|D has probability $p = 1/4$, then the odds are $\frac {1/4}{3/4}=\frac {1}{3}$, or 1 to 3 (the probability that event A|D occurs is three times smaller the probability that it does not occur).

• RR vs. OR
  • The formula and reasoning for the relative risk is a little bit easier to follow. In most cases the OR and RR measures are roughly equal to each other.
  • Odds ratios have an advantage over relative risk because they can be calculated no matter the row or column comparison
  • Relative risk runs into problems when the study design is a cohort study or a case-control design
  • Odds ratios are an approximation of relative risk: $ OR = RR \times \frac{1-P_2} {1-P_1}$.

Inference

• Inference about the Odds Ratio: In practice, we commonly report odds ratios along with their Confidence Intervals (CIs). It turns out that the distribution of OR’s is not normal, however, the log-transformed OR is approximately normally distributed, and the standard error of $ ln(OR)$ is:

$$ SE(ln(OR))= \sqrt{\dfrac {1} {n_{1,1}}+ \dfrac {1} {n_{1,2}} + \dfrac {1} {n_{2,1}} + \dfrac{1} {n_{2,2}}}$$.

Thus, if $\alpha$ is the false-positive (Type I) error rate, the $ (1-\alpha)100\% $ CI (of the log-transformed OR) can be computed by: $$ln(OR)±z\dfrac{_a}{2}SE(ln(OR))$$,

where odds-ratio point-estimate is $OR = \frac {n_{1,1}×n_{2,2}}{n_{1,2}×n_{2,1}}$ and the standard error of the log-transformed OR is listed above $ (SE(ln(OR))) $.

You can use the SOCR Student’s T-distribution calculator to compute the value of the standard-normal Z statistics (for a given false-positive error rate $\alpha$).

NOTE: Remember that once you find the lower $(L=ln(OR)-z\dfrac{\alpha}{2}SE(ln(OR))$ and upper $(U=ln(OR)+z\dfrac{\alpha}{2}SE(ln(OR))$ limits of the $ln(OR)$ confidence interval, these represent log-transformed data. To convert these confidence limits into real OR terms, you need to invert the log transform (using the exponential function). Thus, the $ CI(OR) $ would be: $(e^L,e^R)$.

Applications

• This article studies retrospectively the relationship between surveillance, staffing, and serious adverse events in children on general care postoperative units. The paper investigates these hypotheses: (1) the relationship between patient factors and surveillance would be moderated by staffing (i.e., registered nurse hours per patient per shift), and (2) the relationship between staffing and serious adverse events would be mediated by surveillance.

The study shows that one additional registered nurse full-time equivalent per day reduced the odds of in-hospital mortality, respiratory failure, pneumonia, and failure to rescue, with the greatest cost-benefit for adult surgical patients. Table 4 of the results show the OR and CI(OR). Interpret the findings. Predictors of adverse events as shown in final logistic regression analysis.

Factors	β (S.E.)	p-Value	Odds ratio [95% CI)
Staffing	−0.41 (0.33)	0.219	0.66 [0.35, 1.28]
American Society of Anesthesiologists Physical Status	0.94 (0.39)	0.017	2.57 [1.88, 5.55]
Comorbidity	0.57 (0.43)	0.189	1.76 [0.76, 4.12]
Perioperative complication	0.64 (0.22)	0.003	1.90 [1.24, 2.92]
Interaction staffing × surveillance	−1.04 (0.42)	0.012	0.354 [0.157, 0.798]

• This article investigates whether hospitals with a good organization of care (e.g., improved nurse staffing and work environments) can affect patient care and nurse workforce stability in European countries. It uses data from 488 clinics in 12 European countries; 617 in the United States) and is based on 33,659 nurses and 11,318 patients in Europe; 27,509 nurses and more than 120,000 patients in the US.

Some of the authors’ findings included (a) nurses in hospitals with better work environments were half as likely to report poor or fair care quality (Europe, adjusted odds ratio 0.56, 95% confidence interval 0.51 to 0.61; US, 0.54, 0.51 to 0.58) and (b) to give their hospitals poor or failing grades on patient safety (0.50, 0.44 to 0.56 EU; 0.55, 0.50 to 0.61 US).

Interpret the results in the Table below. Note that in this nurse outcomes study, the authors adjusted the regression estimates (odds ratios) at the hospital level for differences in the composition of nurses between hospitals and between countries (age, sex, full time employment status, and specialty) by a multilevel model structure in which nurses were nested within hospitals and countries. Effects of nurse staffing and practice environment on nurse outcomes in study countries.

Nurse Outcome	Europe			US
	Unadjusted odds ratio (95% CI)	Adjusted odds ratio (95% CI)		Unadjusted odds ratio (95% CI)	Adjusted odds ratio (95% CI)
Poor or fair quality of care in ward
Practice environment	0.58	0.56		0.52	0.54
	(0.53 to 0.63)	(0.51 to 0.61)		(0.49 to 0.56)	(0.51 to 0.58)
Staffing	1.11	1.11		1.2	1.06
	(1.08 to 1.13)	(1.07 to 1.15)		(1.16 to 1.25)	(1.03 to 1.1)
Poor or fair quality of care in ward
Practice environment	0.5	0.5		0.53	0.55
	(0.43 to 0.57)	(0.44 to 0.56)		(0.48 to 0.59)	(0.5 to 0.61)
Staffing	1.04	1.1		1.18	1.05
	(1.01 to 1.08)	(1.05 to 1.16)		(1.12 to 1.23)	(1 to 1.1)
Burnout
Practice environment	0.69	0.67		0.69	0.71
	(0.63 to 0.76)	(0.61 to 0.73)		(0.66 to 0.73)	(0.68 to 0.75)
Staffing	1.06	1.05		1.12	1.03
	(1.04 to 1.08)	(1.02 to 1.09)		(1.08 to 1.15)	(1 to 1.06)
Job dissatisfaction
Practice environment	0.63	0.52		0.58	0.6
	(0.57 to 0.69)	(0.47 to 0.57)		(0.55 to 0.61)	(0.57 to 0.64)
Staffing	1.1	1.07		1.17	1.06
	(1.08 to 1.12)	(1.04 to 1.11)		(1.13 to 1.21)	(1.03 to 1.09)
Intention to leave in the next year
Practice environment	0.72	0.61		0.7	0.69
	(0.66 to 0.79)	(0.56 to 0.67)		(0.65 to 0.76)	(0.64 to 0.75)
Staffing	1.04	1.05		1.1	1.03
	(1.01 to 1.06)	(1.02 to 1.09)		(1.05 to 1.15)	(0.98 to 1.08)
Not confident that patients can manage own care after hospital discharge
Practice environment	0.62	0.73		0.71	0.72
	(0.56 to 0.69)	(0.69 to 0.78)		(0.67 to 0.75)	(0.68 to 0.77)
Staffing	1.08	1.03		1.1	1.04
	(1.05 to 1.11)	(1 to 1.05)		(1.06 to 1.13)	(1.01 to 1.07)
Not confident that hospital management would resolve patients’ problems
Practice environment	0.5	0.53		0.56	0.56
	(0.46 to 0.54)	(0.48 to 0.58)		(0.53 to 0.59)	(0.54 to 0.59)
Staffing	1.04	1.02		1.12	1.01
	(1.01 to 1.07)	(0.98 to 1.06)		(1.09 to 1.17)	(0.98 to 1.03)

¹http://www.sciencedirect.com/science/article/pii/S0378375812001954

Software

• T Distribution Calculator

• Distribution Applets

Problems

Formulate some clinically relevant questions in terms of the OR and RR and try to answer them in the following situations. Interpret the results. E.g., the estimate of the relative risk of a heart attack is about <blank> as great for those who smoke versus who do not smoke. Compute the CI (OR).

		Heart Attack (HA)		Total
		Yes	No
Smoking (S)	Yes	33	18	51
	No	167	182	349
Total		200	200	400

References

• Nursing surveillance moderates the relationship between staffing levels and pediatric postoperative serious adverse events: A nested case–control study
• Patient safety, satisfaction, and quality of hospital care: cross sectional surveys of nurses and patients in 12 countries in Europe and the United States
• Reducing bias and mean squared error associated with regression-based odds ratio estimators

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