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. Author manuscript; available in PMC: 2013 Nov 18.
Published in final edited form as: JAMA. 2011 Sep 21;306(11):10.1001/jama.2011.1333. doi: 10.1001/jama.2011.1333

Table 1.

Multivariable Logistic Regression Models Predicting Functional Erections Suitable for Intercourse at 2 Years After Treatment, According to Planned Primary Prostate Cancer Treatment in the PROSTQA Cohorta

Treatment, Variable Parameter
Estimate
(SE)
OR (95% CI) Wald χ2
P Value
Bootstrap
Parameter
Estimate (SE)
Prostatectomy
Intercept −2.96 (1.38) −3.00 (1.42)

Pretreatment sexual HRQOL score (per 10 points) 0.45 (0.07) 1.6 (1.4–1.8) <.001 0.45 (0.07)

Age (per 10 y) −0.56 (0.16) 0.6 (0.4–0.8) <.001 −0.58 (0.16)

Nerve-sparing 1.29 (0.52) 3.6 (1.3–10.1) .01 1.39 (0.57)

PSA ≤10 ng/mL 0.85 (0.36) 2.3 (1.2–4.7) .02 0.88 (0.37)

External radiotherapy
Intercept −5.22 (0.76) −5.37 (0.79)

Pretreatment sexual HRQOL score (per 10 points) 0.54 (0.08) 1.7 (1.4–2.0) <.001 0.55 (0.09)

No neoadjuvant hormone therapy 1.18 (0.39) 3.3 (1.5–7.0) .003 1.24 (0.41)

PSA <4 ng/mL 1.17 (0.46) 3.2 (1.3–8.0) .01 1.24 (0.48)

Brachytherapy
Intercept −3.13 (2.21) −3.40 (2.34)

Pretreatment sexual HRQOL score (per 10 points) 0.72 (0.11) 2.1 (1.7–2.5) <.001 0.75 (0.11)

Age (per 10 y) −0.63 (0.28) 0.5 (0.3–0.9) .03 −0.64 (0.30)

African American race/ethnicity 1.13 (0.60) 3.1 (0.9–10.0) .06 1.18 (0.64)

BMIb

<25 2.22 (0.86) 9.2 (1.7–50.0) .01 2.30 (0.94)

25–34.9 1.40 (0.77) 4.0 (0.9–18.4) .07 1.45 (0.85)

≥35 0 1 [Reference]

Abbreviations: BMI, body mass index; CI, confidence interval; HRQOL, health-related quality of life; OR, odds ratio; PROSTQA, Prostate Cancer Outcomes and Satisfaction With Treatment Quality Assessment; PSA, prostate-specific antigen.

a

Model areas under the receiver operating characteristic curve were 0.77 (95% CI, 0.72–0.82) for prostatectomy, 0.83 (95% CI, 0.78–0.88) for external radiotherapy, and 0.89 (95% CI, 0.85–0.94) for brachytherapy. Individual predicted probabilities of functional erections suitable for intercourse at 2 years can be calculated using the inverse logistic function {exp[X′ β]/[1 + exp(X′ β)]}, where X′ β is the sum with X representing individual characteristics observed and β representing the associated model parameter estimates for the individual characteristics.

b

Calculated as weight in kilograms divided by height in meters squared.