Biomedical subjects

Susan Griffin

Publications and source records attributed to Susan Griffin.

3 recordsLinked to original sources

PubMed2002

Occupational therapy practice in acute care neurology and orthopaedics.

The purpose of this research was to develop a consensus description of occupational therapy practice in acute care orthopaedics and neurology by Australian therapists. The Delphi technique was used to develop consensus concerning therapists' aims for their patients, the interventions they used, and the factors affecting their practice in acute care. Therapists' primary aim was to conduct a thorough assessment for referral and further treatment purposes. Therapists in acute hospital neurology focus on developing maximal independence in self-care activities and preventing deformities, followed by preparations for discharge or transfer to rehabilitation. Therapists with an orthopaedic caseload state independence in self-care as their second most important aim, but the ranking of intervention methods indicates preparation for discharge in various ways takes precedence over direct self-care retraining. This difference may indicate a disparity between practice ideals and the realities of the acute care setting. The most important factor affecting practice in acute care is early referral for occupational therapy services. Recommendations are made for managers and educators to ensure the most effective practice in acute care are made.

Acute Disease↗

PubMedDate not supplied

Incorporating direct and indirect evidence using bayesian methods: an applied case study in ovarian cancer.

OBJECTIVE: To demonstrate the application of a Bayesian mixed treatment comparison (MTC) model to synthesize data from clinical trials to inform decisions based on all relevant evidence. METHODS: The value of an MTC model is demonstrated using a probabilistic decision-analytic model developed to assess the cost-effectiveness of second-line chemotherapy in ovarian cancer. Three clinical trials were found that each made a different pair-wise comparison of three treatments of interest in the overall patient population. As no common comparator existed between the three trials, an MTC model was used to assess the combined weight of evidence on survival from all three trials simultaneously. This analysis was compared to an alternative approach that combined two of the trials to make the same comparison of all three treatments using a common comparator, and an informal approach that did not synthesize the available evidence. RESULTS: By including all three trials using an MTC model, the credible intervals around estimated overall survival were reduced compared with making the same comparison using only two trials and a common comparator. Nevertheless, the survival estimates from the MTC model result in greater uncertainty around the optimal treatment strategy at a cost-effectiveness threshold of 30,000 pounds per quality-adjusted life-year. CONCLUSIONS: MTC models can be used to combine more data than would typically be included in a traditional meta-analysis that relies on a common comparator. They can formally quantify the combined uncertainty from all available evidence, and can be conducted using the same analytical approaches as standard meta-analyses.

Antineoplastic Agents↗

PubMedDate not supplied

Probabilistic analysis and computationally expensive models: Necessary and required?

OBJECTIVE: To assess the importance of considering decision uncertainty, the appropriateness of probabilistic sensitivity analysis (PSA), and the use of patient-level simulation (PLS) in appraisals for the National Institute for Health and Clinical Excellence (NICE). METHODS: Decision-makers require estimates of decision uncertainty alongside expected net benefits (NB) of interventions. This requirement may be difficult in computationally expensive models, for example, those employing PLS. NICE appraisals published up until January 2005 were reviewed to identify those where the assessment group utilized a PLS model structure to estimate NB. After identifying PLS models, all appraisals published in the same year were reviewed. RESULTS: Among models using PLS, one out of six conducted PSA, compared with 16 out of 24 cohort models. Justification for omitting PSA was absent in most cases. Reasons for choosing PLS included treatment switching, sampling patient characteristics and dependence on patient history. Alternative modeling approaches exist to handle these, including semi-Markov models and emulators that eliminate the need for two-level simulation. Stochastic treatment switching and sampling baseline characteristics do not inform adoption decisions. Modeling patient history does not necessitate PLS, and can depend on the software used. PLS addresses nonlinear relationships between patient variability and model outputs, but other options exist. Increased computing power, emulators or closed-form approximations can facilitate PSA in computationally expensive models. CONCLUSIONS: In developing models analysts should consider the dual requirement of estimating expected NB and characterizing decision uncertainty. It is possible to develop models that meet these requirements within the constraints set by decision-makers.

Computer Simulation↗