Treatment Decision Analysis Model for Prostate Cancer: A Randomized Trial
Treatment Decision Analysis Model for Prostate Cancer: A Randomized Trial
This is a psychosocial/behavioral study and does not involve administration of any treatment
 or diagnostic procedures. We will use a randomized trial to test the hypothesis that a
 decision analysis model that provides individualized estimates of quality-adjusted
 disease-free survival for each of the treatment options for clinically localized prostate
 cancer will lead to higher quality treatment decisions congruent with a patient's values
 leading to improved decisional regret and treatment satisfaction. In this trial, all patients
 would be evaluated at baseline for their utilities for various clinically important health
 states. The control arm will receive counseling regarding treatment options using standard
 patient-physician interactions and nomogram-predicted probabilities of treatment outcome for
 the various treatment options and they will be unaware of the decision analysis
 recommendation. The treatment arm would be counseled using standard patient-physician
 interactions and they would also be provided with a personalized treatment recommendations
 based on the decision analysis model prior to treatment selection. The primary endpoint of
 this study will be regret-free survival at 2 years after treatment. There will be a 1:1
 randomization. A random permuted design will be used to assure approximate balanced number of
 patients in the two groups over time.
Following institutional review board approval for this study and after informed consent from
 patients is obtained, men referred to our institution for treatment of clinically localized
 prostate cancer will be interviewed at baseline following diagnosis but prior to making a
 treatment decision, by a research study coordinator whom we will train in utility assessment.
 At that initial interview, the patient's utilities will be assessed, in a private
 consultation room, for each important post-treatment health state that he potentially may
 enter. The states we would assess include living with untreated prostate cancer, impotence,
 urinary dysfunction, bowel dysfunction, rising PSA following definitive local therapy, and
 metastatic prostate cancer. We will also record characteristics of the patient's disease
 (pretreatment serum PSA, clinical stage, biopsy Gleason grade), demographics (age, race,
 level of education, employment, marital status) and comorbidity. Patient-reported urinary,
 bowel, and sexual function/bother will be assessed at baseline using a short form version of
 a 48-item validated HRQOL instrument for localized prostate cancer. All patient information
 will be entered into a secure, HIPAA-compliant database accessible only to the principal
 investigator, research study coordinator(s), data manager, and biostatistician. All patient
 identifying information will be removed prior to data analysis and will not be included in
 any publication or presentation that results from this study. For all patients, the
 quality-adjusted disease-free survival for each treatment modality will be calculated using
 the decision analysis model. For the decision analysis arm of the trial, this information
 will be conveyed to the patient and the physician prior to treatment decision. These patients
 will also receive standard treatment counseling. For the control arm, both patients and
 physicians will be unaware of the decision analysis recommendation and the patients will
 receive standard treatment counseling alone. After the patient has made a treatment decision
 he will be given a short questionnaire to assess decisional conflict. If after one month no
 treatment decision information is available, a letter and form requesting treatment
 information along with the decisional conflict questionnaire will be sent to patients. At 6,
 12, and 24 months following the treatment decision, the patient will be sent a brief
 validated questionnaire which will measure his regret of treatment choice, treatment
 satisfaction, decisional conflict and current health state utility using validated
 instruments. A short form version of a 48-item validated questionnaire will also assess his
 HRQOL for urinary, bowel and sexual function/bother domains at these time intervals.
 Approximately two weeks after receiving each follow-up questionnaire and/or letter, a member
 of the study group will contact the patient by telephone if we have not received the
 completed questionnaire. S/he will inquire about any difficulties with the questionnaire and
 will remind the patient about sending it back.

 The primary endpoint of this study (decisional regret) and secondary endpoints (treatment
 satisfaction, decisional conflict, and current health state utility) will be assessed by
 means of a patient-reported questionnaire using validated instruments assessed at 6, 12 and
 24 months after randomization. A decision analysis model will be developed using a Markov
 modeling approach. Using Treeage Data® pro suite, the model will incorporate cancer outcomes,
 treatment-related morbidity probabilities derived from nomograms, and patient-specific
 utilities to estimate the quality-adjusted disease-free survival for each treatment option.
 In the model, all patients will begin with localized prostate cancer with no evidence of
 metastases. A 6-month transition cycle will be used for the Markov model. Bootstrap
 simulation with replacement will be utilized to derive mean utilities and 95% confidence
 intervals. Sensitivity analyses will be performed by varying the disease progression rates
 and utilities within their 95% confidence intervals. To investigate if the decision analytic
 model, compared to standard interactions alone, will influence treatment choice and lead to
 higher quality treatment decisions that are congruent with a patient's values, an
 intent-to-treat analysis will be conducted on patients who did and did not receive the
 decision analysis recommendation with decisional regret as the primary endpoint. In a planned
 secondary analysis, we will analyze both arms of our trial and test the hypothesis that a
 patients who chooses a treatment strategy that did not appear to maximize his
 quality-adjusted disease-free survival (i.e. not the one the decision analysis would have
 yielded) will be at increased risk of regret. This will be conducted by identifying patients
 in both arms who choose the treatment that is also recommended by the decision model. The
 probability of selecting the decision analytic treatment strategy (which is also the
 probability of selecting the highest quality treatment decision congruent with a patient's
 values) will be investigated using a logistic regression model with covariates that include
 treatment arm, an indicator variable to define patients whose baseline treatment choice
 matched the decision analysis model, an interaction between the treatment arm and this
 indicator variable, as well as other clinically relevant baseline covariates and two way
 interactions. Testing for an interaction between treatment arm and the indicator variable
 will allow us to test if the effect that knowledge of the decision analytic model has on the
 probability of selecting the highest quality treatment decision is different for patients
 whose baseline treatment choice matched the decision analysis model compared to patients
 whose baseline treatment choice did not match the decision analysis model. Each patient will
 also have the following outcomes measured at 6 months, 12 months, and 24 months following
 their treatment: decisional regret, decisional conflict, treatment satisfaction, health state
 utility score, and HRQOL for urinary, bowel, and sexual function domain scores. All scores
 will be standardized such that each patient's score is between 0 and 100. Baseline decisional
 regret scores, informed decision scores, and treatment satisfaction scores will be set at
 100, indicating no decisional regret, a high degree of feeling as though an informed decision
 was made, and high treatment satisfaction. Scores for health state utility and HRQOL domains
 will also be obtained at baseline. Based on each patient's final treatment choice, we will
 also calculate the difference between the number of quality adjusted life years based on the
 decision analysis model and the final treatment choice.

 For each response, separate linear mixed models that include both fixed and random effects
 will be applied. To meet the distributional assumptions of this model, responses may be
 transformed prior to model fitting. Fixed effects that will be included in the model are
 clinically relevant baseline and time dependent covariates and two way interactions as well
 as linear or quadratic time trends. In particular, a covariate for treatment arm, an
 indicator variable to define patients who chose the decision analytic model based treatment
 choice, and an interaction between these two variables will be included. In this case, the
 interaction will allow us to test if the effect that knowledge of the decision analytic model
 has on various responses is different for patients who chose the decision analytic model
 treatment choice compared to patients who did not chose the decision analytic model treatment
 choice. A covariate for the difference between the number of quality adjusted life years
 based on the decision analysis model and the final treatment choice and an interaction
 between this variable and the treatment arm indicator variable will also be included in the
 model to examine if the effect that knowledge of the decision analytic model has on various
 responses is effected by the theoretical degree of mistake. To fully account for the
 variation in subject specific trajectories over time, a random intercept will be included in
 this model although linear and quadratic random effects will also be considered if
 appropriate. In addition, the residual covariance structure for each model will also be
 investigated to avoid misspecification and invalid inferences. In particular, correct
 specification of the covariance structure will be investigated using different ways to model
 the measurement error (due to variation in the measurement of the response) and serial
 correlation (due to decreasing correlation between measurements over time). For instance,
 structures for serial correlation will be investigated using a semi-variogram. Although the
 problem of a misspecified covariance structure can and will be avoided by using a robust
 variance estimator when making inferences about the fixed effects of interest based on the
 marginal model, misspecification also leads to an inability to properly account for the
 effect of missing data, a common occurrence in longitudinal studies. Note that once an
 appropriate model is fit to each response of interest, inference regarding the fixed
 treatment and covariate effects on the response will be based on marginal models using the
 robust variance estimator to protect against model misspecification of the covariance
 structure. Statistical significance of fixed effects will be based on a 0.05 significance
 level. As this study is exploratory, no attempt will be made to account for multiple
 comparisons.

 As discussed previously, missing data is a common problem in longitudinal studies. Typically,
 patients who drop out of the study are not comparable to patients who remain in the study at
 all times. Consequently, the likelihood of dropout is correlated with the underlying
 unobserved data. To account for this problem, pattern mixture models will also be fit in
 addition to the previous models that ignore the effect of missing data on resulting
 estimates. Pattern mixture models are based on fitting separate linear mixed model to each of
 the seven dropout patterns that can occur in the data such that a separate time trend is
 estimated for each pattern. The marginal treatment effect is then calculated by a weighted
 average of the pattern-specific treatment effects, with weights given by the probability of
 occurrence of the various patterns. Because the estimated marginal treatment effects rely on
 the extrapolation of fitted average profiles to time points where data is not observed,
 sensitivity analyses will also be performed to examine the robustness of the results to
 alternative assumptions. For instance, rather than estimating a separate time trend for each
 pattern, the model could be simplified so that drop-out pattern is used as a covariate in the
 model. In this case, the time trend within a pattern is unstructured but parallel across
 patterns. If estimated marginal treatment effects agree among the different models that are
 investigated, this will suggest that the results are not artifacts of the particular model
 used to account for dropout.
Inclusion Criteria:

 -  Patients referred to the Cleveland Clinic Foundation for the treatment of clinically
 localized prostate adenocarcinoma

 -  Ability to read English at a grade eight level

 Exclusion Criteria:

 -  No other cancer diagnosis for the past 3 years, except for non-melanoma skin cancer

 -  Prior chemotherapy within the last 3 years

 -  Previous pelvic radiation therapy

 -  Previous pelvic surgery

 -  Neoadjuvant therapy prior to referral, such as hormones