TOWARD THE IDENTIFICATION OF VARIABLES FOR EVALUATING FAMILY THERAPY WORKSHOPS*

If the field of family therapy is to improve the quality of its continuing education, a useful measurement of workshop quality must be developed. In a preliminary step towards this goal, 14 expert subjects from the field of family therapy were interviewed to identify variables and relationships that can be hypothesized to influence the quality–as assessed by participants–of family therapy workshops. This paper reports the opinions of the subjects, using counts and quotations to convey as much of the raw information as space allows, and presents the methods through which the information was organized by the authors into four categories and 58 (assumed to be interdependent) variables.     

ADJUSTMENT TO PRODUCTION UNCERTAINTY AND THE THEORY OF THE FIRM: REPLY

This reply responds to two comments on our 1986 article in this journal under the same title. The comments discuss a special case of the model which gives a certainty result, and also propose several alternative formulations of the model. The reply argues that the special certainty result is not an interesting or reasonable case, and discusses the relationship between the proposed alternative formulations and the original model. The influence of risk preferences on optimal output is analyzed through comparative statics for changes in risk in the context of the original model.     

Inference for current leukemia free survival

Donor lymphocyte infusion (DLI) for patients who relapse following an allogeneic stem cell transplant has proved remarkably durable. Because of the potential for second remissions with DLI, the current leukemia free survival (CLFS), which is the probability that a patient has not failed the entire course of the treatment, is becoming of interest to clinical investigators. Based on either a multistate Markov model or a linear combination of Kaplan–Meier estimators, we explore regression models for the CLFS. We focus on the two sample problem and we develop confidence bands for the CLFS or for differences in CLFS as well as a Kolmogorov type hypothesis test using a re-sampling technique. We also examine the use of pseudo-values to make inference on the direct effects of covariates on the CLFS function and we develop a score test for the equality of two CLFS. We illustrate these inference methods on a bone marrow transplant dataset.     

Analyzing center specific outcomes in hematopoietic cell transplantation

Reporting transplant center-specific survival rates after hematopoietic cell transplantation is required in the United States. We describe a method to report 1-year survival outcomes by center, as well as to quantify center performance relative to the transplant center network average, which can be reliably used with censored data and for small center sizes. Each center's observed 1-year survival outcome is compared to a predicted survival outcome adjusted for patient characteristics using a pseudovalue regression technique. A 95% prediction interval for 1-year survival assuming no center effect is computed for each center by bootstrapping the scaled residuals from the regression model, and the observed 1-year survival is compared to this prediction interval to determine center performance. We illustrate the technique using a recent center specific analysis performed by the Center for International Blood and Marrow Transplant Research, and study the performance of this method using simulation.     

Structured measurement error in nutritional epidemiology: applications in the Pregnancy, Infection, and Nutrition (PIN) Study

Preterm birth, defined as delivery before 37 completed weeks' gestation, is a leading cause of infant morbidity and mortality. Identifying factors related to preterm delivery is an important goal of public health professionals who wish to identify etiologic pathways to target for prevention. Validation studies are often conducted in nutritional epidemiology in order to study measurement error in instruments that are generally less invasive or less expensive than "gold standard" instruments. Data from such studies are then used in adjusting estimates based on the full study sample. However, measurement error in nutritional epidemiology has recently been shown to be complicated by correlated error structures in the study-wide and validation instruments. Investigators of a study of preterm birth and dietary intake designed a validation study to assess measurement error in a food frequency questionnaire (FFQ) administered during pregnancy and with the secondary goal of assessing whether a single administration of the FFQ could be used to describe intake over the relatively short pregnancy period, in which energy intake typically increases. Here, we describe a likelihood-based method via Markov Chain Monte Carlo to estimate the regression coefficients in a generalized linear model relating preterm birth to covariates, where one of the covariates is measured with error and the multivariate measurement error model has correlated errors among contemporaneous instruments (i.e. FFQs, 24-hour recalls, and/or biomarkers). Because of constraints on the covariance parameters in our likelihood, identifiability for all the variance and covariance parameters is not guaranteed and, therefore, we derive the necessary and suficient conditions to identify the variance and covariance parameters under our measurement error model and assumptions. We investigate the sensitivity of our likelihood-based model to distributional assumptions placed on the true folate intake by employing semi-parametric Bayesian methods through the mixture of Dirichlet process priors framework. We exemplify our methods in a recent prospective cohort study of risk factors for preterm birth. We use long-term folate as our error-prone predictor of interest, the food-frequency questionnaire (FFQ) and 24-hour recall as two biased instruments, and serum folate biomarker as the unbiased instrument. We found that folate intake, as measured by the FFQ, led to a conservative estimate of the estimated odds ratio of preterm birth (0.76) when compared to the odds ratio estimate from our likelihood-based approach, which adjusts for the measurement error (0.63). We found that our parametric model led to similar conclusions to the semi-parametric Bayesian model.     

Using Mixture Regression to Identify Varying Effects: A Demonstration With Paternal Incarceration

The most widely used techniques for identifying the varying effects of stressors involve testing moderator effects via interaction terms in regression or multiple‐group analysis in structural equation modeling. The authors present mixture regression as an alternative approach. In contrast to more widely used approaches, mixture regression identifies varying effects without reliance on tests of moderator variables, such as using interaction terms or multiple group analyses. In many instances, the use of mixture regression also more effectively tests higher order and multiple interactions. A mixture regression example is presented using 214 families from the Fragile Families and Child Wellbeing study, half of whom had experienced paternal incarceration. Whereas typical regression and moderator analyses fail to find an effect or varying effects, mixture regression identified 4 classes uniquely influenced by the incarceration.     

Evaluation of a personal device in reducing the risk of low back disorders during stooped work

Adoption of the stooped posture in the workplace is widespread throughout the world in agriculture, construction, and mining. This type of posture has been established as a risk factor for developing low back disorders (LBDs). The purpose of this study is to evaluate a personal weight transfer device as a possible intervention to reduce the load on the lumbar spine, thereby reducing the risk of developing LBDs. Eighteen healthy subjects performed stooped posture tasks in a laboratory study designed to simulate harvesting of low-growing crops. Results showed that when wearing the device in the static stooped posture, biceps femoris activity was reduced by 17%, lumbar flexion was reduced by 12%, ankle plantar-flexion increased by 5%, and the lumbar erector spinae of those subjects who did not experience flexion-relaxation of the erector spinae was reduced by 26%. Hip and knee flexion were not significantly altered. Therefore, the device may be beneficial for those with existing LBDs, and who use the stooped posture routinely.     

Trajectories of Conflict Over Raising Adolescent Children and Marital Satisfaction

The present study examined trajectories of marital satisfaction among couples with adolescent children and evaluated how changes in parents' conflict over raising adolescent children were associated with changes in marital satisfaction over 4 years. Using a prospective, longitudinal research design and controlling for family socioeconomic status, dyadic growth curve analysis from a sample of 431 couples with adolescent children indicated that marital satisfaction decreased over time for parents with adolescent children and that the trajectories for mothers and fathers were substantially linked. More importantly, the study demonstrated that increases or decreases in parents' marital conflict over raising adolescent children were associated with corresponding decreases or increases in marital satisfaction for both mothers and fathers.     

Nonparametric Two‐Sample Tests of the Marginal Mark Distribution with Censored Marks

Occasionally, investigators collect auxiliary marks at the time of failure in a clinical study. Because the failure event may be censored at the end of the follow‐up period, these marked endpoints are subject to induced censoring. We propose two new families of two‐sample tests for the null hypothesis of no difference in mark‐scale distribution that allows for arbitrary associations between mark and time. One family of proposed tests is a nonparametric extension of an existing semi‐parametric linear test of the same null hypothesis while a second family of tests is based on novel marked rank processes. Simulation studies indicate that the proposed tests have the desired size and possess adequate statistical power to reject the null hypothesis under a simple change of location in the marginal mark distribution. When the marginal mark distribution has heavy tails, the proposed rank‐based tests can be nearly twice as powerful as linear tests.