Literature DB >> 7992143

Statistical methods for measuring outcomes.

G Dunn1.   

Abstract

This issue of the journal concerns promotion of the routine use of outcome measures in clinical practice; the purpose of this particular article, however, is to warn care providers to think very very carefully before routinely using such measures. Just what are the benefits of their use? What are the outcome measures intended to demonstrate? In order to try to convince the reader that there might be real difficulties in the interpretation of the results, the main body of the paper concentrates on the difficulties in the interpretation of data from a structured research project that has been specifically designed to evaluate an innovation in mental health care provision. The difficulties of interpreting haphazardly collected data as part of routine clinical or administrative practice will be far greater. One of the main purposes of an evaluative exercise is comparison: which approach to service provision is the better? If care providers really want to be involved in mental health service evaluation then their time would be much better spent in taking part in a large multicentre trial.

Mesh:

Year:  1994        PMID: 7992143     DOI: 10.1007/BF00796377

Source DB:  PubMed          Journal:  Soc Psychiatry Psychiatr Epidemiol        ISSN: 0933-7954            Impact factor:   4.328


  13 in total

1.  What is a health care trial?

Authors:  W O Spitzer; A R Feinstein; D L Sackett
Journal:  JAMA       Date:  1975-07-14       Impact factor: 56.272

Review 2.  Design and analysis of reliability studies.

Authors:  G Dunn
Journal:  Stat Methods Med Res       Date:  1992       Impact factor: 3.021

Review 3.  A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979-1989.

Authors:  A Donner; K S Brown; P Brasher
Journal:  Int J Epidemiol       Date:  1990-12       Impact factor: 7.196

4.  Sample size estimation for comparing two or more treatment groups in clinical trials.

Authors:  S J Day; D F Graham
Journal:  Stat Med       Date:  1991-01       Impact factor: 2.373

5.  Cluster randomization in large public health trials: the importance of antecedent data.

Authors:  S W Duffy; M C South; N E Day
Journal:  Stat Med       Date:  1992-02-15       Impact factor: 2.373

6.  Sample size formulae for intervention studies with the cluster as unit of randomization.

Authors:  F Y Hsieh
Journal:  Stat Med       Date:  1988-11       Impact factor: 2.373

7.  Confidence interval construction for effect measures arising from cluster randomization trials.

Authors:  A Donner; N Klar
Journal:  J Clin Epidemiol       Date:  1993-02       Impact factor: 6.437

8.  The design of controlled experiments in the evaluation of non-therapeutic interventions.

Authors:  C Buck; A Donner
Journal:  J Chronic Dis       Date:  1982

9.  Randomization by group: a formal analysis.

Authors:  J Cornfield
Journal:  Am J Epidemiol       Date:  1978-08       Impact factor: 4.897

10.  Introduction to sample size determination and power analysis for clinical trials.

Authors:  J M Lachin
Journal:  Control Clin Trials       Date:  1981-06
View more
  2 in total

1.  The evaluation of a mental health facilitator in general practice: effects on recognition, management, and outcome of mental illness.

Authors:  K Bashir; B Blizard; A Bosanquet; N Bosanquet; A Mann; R Jenkins
Journal:  Br J Gen Pract       Date:  2000-08       Impact factor: 5.386

2.  Effectiveness of teaching general practitioners skills in brief cognitive behaviour therapy to treat patients with depression: randomised controlled trial.

Authors:  Michael King; Oliver Davidson; Fiona Taylor; Andrew Haines; Deborah Sharp; Rebecca Turner
Journal:  BMJ       Date:  2002-04-20
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.