Robert L. Obenchain

Factors associated with women's decisions to seek treatment for urinary incontinence

BACKGROUND Urinary incontinence is a highly prevalent and burdensome condition among women. However, fewer than half of women with symptoms talk to a physician about incontinence, and the determinants of treatment seeking are not well understood. DESIGN A two-stage cross-sectional survey of adult U.S. women; 45,000 households participating in NFO Worldgroup survey research received a questionnaire to identify adults with incontinence. Based on stratified random sampling of identified incontinent women, 2310 women received a detailed questionnaire. RESULTS Among 1970 women with urinary incontinence symptoms, 38% had initiated a conversation with a physician about incontinence. In multivariate logistic regression analysis, some of the factors associated significantly with treatment seeking were symptom duration >3 years (OR 2.33, 95% CI 1.57-3.45), having a history of a noticeable accident (OR 1.41, 95% CI 1.06-1.87), worse disease-specific quality of life scores (OR 1.89, 95% CI 1.32-2.70), not being embarrassed to talk with a physician about urinary symptoms (OR 1.65, 95% CI 1.28-2.14), talking with others about urinary incontinence (OR 3.34, 95% CI 2.49-4.49), and keeping regular appointments for routine/preventive care (OR 2.25, 95% CI 1.54-3.29). CONCLUSIONS Less than half of community-dwelling adult U.S. women with symptoms of urinary incontinence have talked with a physician about urinary incontinence. In addition to duration of symptoms, factors associated with treatment seeking included the impact of incontinence on quality of life, lack of embarrassment about talking to a physician about urinary symptoms, and attitudes toward healthcare use. Concerns about the meaning of incontinence for overall and future health were important reasons for women choosing to seek treatment.

Ridge Analysis Following a Preliminary Test of the Shrunken Hypothesis

RIDGE ANALYSIS is of interest when some generalized Ridge regression coefficient vectors are significantly more likely to have Mean Squared Error (MSE) optimality properties than is any uniformly SHRUNKEN version of the ordinary least squares estimator. The normal distribution theory likelihood ratio statistic for the corresponding composite hypothesis is derived, and a small sample F-test is shown to be conservative. The asymptotic distribution of the likelihood ratio provides a large sample test of the RISE optimality of any restricted Ridge FAMILY of solutions. The likelihood approach for solution selection WITHIN a given family is then compared and contrasted with some snggestions of Mallows [8] and Allen [1] and with a new, non-stochastic criterion, SSCBC.

The application of sample selection models to outcomes research: the case of evaluating the effects of antidepressant therapy on resource utilization

Non-randomized studies of treatment effects have come under criticism because of their failure to control for potential biases introduced by unobserved variables correlated with treatment selection and outcomes. This paper describes the basic concepts of sample selection models--a technique used widely in the economics evaluation literature for nearly two decades--and discusses the potential role of these models in outcomes research. In addition, it presents a case study of the application of the sample selection modelling approach to evaluation of the effects of antidepressant therapies on medical expenditures for physician services. This case study presents empirical comparisons of alternative model specifications and discusses practical issues in evaluation of sample selection models. We demonstrate that, in this particular case, sample selection models yield very different conclusions regarding treatment effects than traditional ordinary least squares regression.

Bootstrap Analyses of Cost Effectiveness in Antidepressant Pharmacotherapy

SummaryIn this study, we describe ‘bootstrap’ methodology for placing statistical confidence limits around an incremental cost effectiveness ratio (ICER). This approach was applied to a retrospective study of annual charges for patients undergoing pharmacotherapy for depression.We used MarketScanSM(service mark) data from 1990 to 1992, which includes medical and pharmacy claims for a privately insured group of employed individuals and their families in the US. Our primary effectiveness measure was the proportion of patients who remained stable on their initial antidepressant medication for at least 6 consecutive months. Our primary cost measure was the total annual charge incurred by patients taking the selective serotonin reuptake inhibitor fluoxetine, a tricyclic antidepressant or a heterocyclic antidepressant.On average, fluoxetine pharmacotherapy tended to decrease annual charges by

Classical F-Tests and Confidence Regions for Ridge Regression

US16.48 per patient for each percentage increase in depressed patients remaining stable on initial pharmacotherapy for 6 months, resulting in a negative ICER point-estimate. However, the upper ICER confidence limit is positive, which means that fluoxetine treatment may possibly increase annual per patient charges. With 95% confidence, any such increase was no more than

Determinants of Antidepressant Treatment Compliance: Implications for Policy:

US130 per patient for each percentage increase in patients remaining stable on initial pharmacotherapy for at least 6 months.One advantage of using a bootstrap approach to ICER analysis is that it does not require restrictive distributional assumptions about cost and outcome measures. Bootstrapping also yields a dramatic graphical display of the variability in cost and effectiveness outcomes that result when a study is literally ‘redone’ hundreds of times. This graphic also displays the ICER confidence interval as a ‘wedge-shaped’ region on the cost-effectiveness plane. In fact, bootstrapping is easier to explain and appreciate than the elaborate calculations and approximations otherwise involved in ICER estimation.Our discussion addresses key technical questions, such as the role of logarithmic transformation in symmetrising highly skewed cost distributions. We hope that our discussion contributes to a dialogue, leading ultimately to a consensus on analysis of ICERs.

RESAMPLING AND MULTIPLICITY IN COST-EFFECTIVENESS INFERENCE

For testing general linear hypotheses in multiple regression models. it is shown that non-stochastically shrunken ridge estimators yield the same central F-ratios and t-statistics as does the least squares estimator. Thus although ridge regression does produce biased point estimates which deviate from the least squares solution, ridge techniques do not generally yield “new” normal theory statistical inferences: in particular, ridging does not necessarily produce “shifted” confidence regions. A concept, the ASSOCIATFD PROBABILITY of a ridge estimate, is defined using the usual, hyperellipsoidal confidence region centered at the least squares estimator, and it is argued that ridge estimates are of relatively little interest when they are so “extreme” that they lie outside of the least squares region of say 90 percent confidence.

Mixed-Model Imputation of Cost Data for Early Discontinuers from a Randomized Clinical Trial

Depression is among the most prevalent, devastating, and undertreated disorders in our society. Treatment with antidepressant medications is effective in controlling symptoms, but treatment beyond the point of symptom resolution is necessary to restore functional status and prevent recurrent episodes. An important step in improving compliance is to identify the determinants of antidepressant treatment compliance. A broader motivation for our study is to examine compliance by patients with a chronic but treatable disease. With claims data between 1990 and 1993, this study uses logistic regression analysis to examine the determinants of compliance among 2,012 antidepressant recipients. The results show that initiating treatment with a tricyclic antidepressant reduces the probability of antidepressant treatment compliance. Initiating treatment with a selective serotonin reuptake inhibitor and undergoing family, group, or individual psychotherapy treatments increase the probability of compliance. Case management does not meaningfully affect compliance. Implications for policy and clinical practice are discussed.

Local control for identifying subgroups of interest in observational research: persistence of treatment for major depressive disorder

We compare published methods for placing statistical confidence limits around incremental cost-effectiveness ratio statistics and show that only a nonparametric, bootstrap approach using polar angles gives completely reasonable results when neither treatment has significant advantages in cost or effectiveness. We also discuss alternative ways to report analytical results using plots, confidence or tolerance limits, and quadrant acceptability fractions. Finally, we use simulation to study the multiplicity bias that can be introduced into ICER confidence limits when only the most favorable results are reported over several possible choices of numerator cost measure and denominator effectiveness measure.

Cost-Effectiveness Inferences from Bootstrap Quadrant Confidence Levels: Three Degrees of Dominance

For studies with appreciable attrition in which one expects not only differences between individual patients but also time trends in repeated measures, a “sophisticatedly simple” approach to imputation of missing values is illustrated. A linear model having random patient intercept and slope terms as well as fixed effects for treatment, investigator, time, and interactions between both treatment-investigator and treatment-time is used. In contrast to a purely fixed effects approach, mixed-model estimation then optimally shrinks patient-specific differences toward zero. This shrinkage moves the predictions for each patient toward the average time line for the corresponding investigator and treatment. Using a variety of sensitivity analyses, it is established that imputation of missing values using these mixed-model predictions provides a “benchmark” lower limit for cost differences between treatments. To illustrate concepts, supplementary analyses of selected cost and effectiveness outcomes from a randomized, double-blind trial of olanzapine versus haloperidol for the treatment of schizophrenia are presented.