Predicting the early therapeutic alliance in the treatment of drug misuse

Aims - To predict the early therapeutic alliance from a range of potentially relevant factors, including clients' social relationships, motivation and psychological resources, and counsellors' professional experience and ex-user status. Design - The study recruited 187 clients starting residential rehabilitation treatment for drug misuse in three UK services. Counsellor and client information was assessed at intake, and client and counsellor ratings of the alliance were obtained during weeks 1, 2 and 3. Measurements - The intake assessment battery included scales on psychological wellbeing, treatment motivation, coping strategies and attachment style. Client and counsellor versions of the Working Alliance Inventory (WAI-S) were used for weekly alliance measurement. Hierarchical linear models were used to examine the relationship between alliance and predictor variables. Findings - Clients who had better motivation, coping strategies, social support and a secure attachment style were more likely to develop good alliances. Findings with regard to counsellor characteristics were not clear cut: clients rated their relationships with ex-user counsellors, experienced counsellors and male counsellors as better, but more experienced counsellors rated their alliances as worse. Conclusions - The findings offer important leads as to what interventions might improve the therapeutic alliance. Further work will need to establish whether the therapeutic alliance and ultimately treatment outcomes can be enhanced by working on improving clients' motivation and psychosocial resources

A design study for an optimal non-linear receiver/demodulator Final report

Design study for optimal nonlinear receiver demodulato

Analysis of ionospheric refraction error corrections for GRARR systems

A determination is presented of the ionospheric refraction correction requirements for the Goddard range and range rate (GRARR) S-band, modified S-band, very high frequency (VHF), and modified VHF systems. The relation ships within these four systems are analyzed to show that the refraction corrections are the same for all four systems and to clarify the group and phase nature of these corrections. The analysis is simplified by recognizing that the range rate is equivalent to a carrier phase range change measurement. The equation for the range errors are given

01/07/1948 Letter from Baker Ice Machine Co.

Letter from A. K. Mallinckrodt, Chief Application Engineer of Baker Ice Machine Co., Inc., to Louis-Philippe Gagné.https://digitalcommons.usm.maine.edu/fac-lpg-letters-1948-01-06/1002/thumbnail.jp

GEOS-2 refraction program summary document

Data from an extensive array of collocated instrumentation at the Wallops Island test facility were intercompared in order to (1) determine the practical achievable accuracy limitations of various tropospheric and ionospheric correction techniques; (2) examine the theoretical bases and derivation of improved refraction correction techniques; and (3) estimate internal systematic and random error levels of the various tracking stations. The GEOS 2 satellite was used as the target vehicle. Data were obtained regarding the ionospheric and tropospheric propagation errors, the theoretical and data analysis of which was documented in some 30 separate reports over the last 6 years. An overview of project results is presented

A Multicomponent Stress Management Program for College Students

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89560/1/j.1556-6676.1986.tb01191.x.pd

A multiple-imputation-based approach to sensitivity analyses and effectiveness assessments in longitudinal clinical trials.

It is important to understand the effects of a drug as actually taken (effectiveness) and when taken as directed (efficacy). The primary objective of this investigation was to assess the statistical performance of a method referred to as placebo multiple imputation (pMI) as an estimator of effectiveness and as a worst reasonable case sensitivity analysis in assessing efficacy. The pMI method assumes the statistical behavior of placebo- and drug-treated patients after dropout is the statistical behavior of placebo-treated patients. Thus, in the effectiveness context, pMI assumes no pharmacological benefit of the drug after dropout. In the efficacy context, pMI is a specific form of a missing not at random analysis expected to yield a conservative estimate of efficacy. In a simulation study with 18 scenarios, the pMI approach generally provided unbiased estimates of effectiveness and conservative estimates of efficacy. However, the confidence interval coverage was consistently greater than the nominal coverage rate. In contrast, last and baseline observation carried forward (LOCF and BOCF) were conservative in some scenarios and anti-conservative in others with respect to efficacy and effectiveness. As expected, direct likelihood (DL) and standard multiple imputation (MI) yielded unbiased estimates of efficacy and tended to overestimate effectiveness in those scenarios where a drug effect existed. However, in scenarios with no drug effect, and therefore where the true values for both efficacy and effectiveness were zero, DL and MI yielded unbiased estimates of efficacy and effectiveness

Using principal stratification in analysis of clinical trials

The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in supplemental materials