PODSE: A computer program for optimal design of trials with discrete-time survival endpoints.
ABSTRACT In experimental settings, one or more groups of subjects receive a treatment and they are compared to a group of subjects that receives a standard treatment or no treatment at all. These compared groups might have an equal number of subjects or some of the groups might have more participants relative to the other groups. Moreover, subjects in these groups can be followed over a short or a long period. To conduct experiments in a sufficient way, researchers should find a good design in the planning phase of the trial. The optimal design for experimental studies on event occurrence with discrete-time survival endpoints where two treatment groups are followed over time, is an optimal combination of the number of time periods, the total number of participants in the trial and the proportion of subjects in the experimental group. It is easy to find the best design for such studies using the PODSE program.
- Biometrics 04/1973; 29(1):101-8. · 1.41 Impact Factor
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ABSTRACT: The study of individual psychotherapeutic approaches to the treatment of schizophrenia has yielded equivocal findings, partly because of methodologic problems. Further, the ability of psychosocial treatments to prevent psychotic relapse appears to lessen over time. The authors' goal was to develop and test a demonstrably effective individual therapy for schizophrenia. Using a study design that addressed previous methodologic issues, the authors evaluated personal therapy specifically designed to forestall late relapse in patients with schizophrenia. They evaluated the effectiveness of personal therapy over a period of 3 years after hospital discharge among 151 patients with schizophrenia or schizoaffective disorder diagnosed according to Research Diagnostic Criteria. The patients were randomly assigned to receive either personal therapy or contrasting therapies in one of two concurrent trials. One trial studied patients who were living with family (N = 97); the other studied patients who were living independent of family (N = 54). All of the patients had extensive psychiatric histories, but only 44 (29%) experienced recurrent psychotic episodes over the 3-year study period, and only 27 (18%) prematurely terminated the study; most of those who left the study were in the no-personal-therapy conditions. Among patients living with family, personal therapy was more effective than family and supportive therapies in preventing psychotic and affective relapse as well as noncompliance. However, among patients living independent of family, those who received personal therapy had significantly more psychotic decompensations than did those who received supportive therapy. Personal therapy had a positive effect on adverse outcomes among patients who lived with family. However, personal therapy increased the rate of psychotic relapse for patients living independent of family. The application of personal therapy might best be delayed until patients have achieved symptom and residential stability.American Journal of Psychiatry 12/1997; 154(11):1504-13. · 14.72 Impact Factor
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ABSTRACT: Random-effects regression modelling is proposed for analysis of correlated grouped-time survival data. Two analysis approaches are considered. The first treats survival time as an ordinal outcome, which is either right-censored or not. The second approach treats survival time as a set of dichotomous indicators of whether the event occurred for time periods up to the period of the event or censor. For either approach both proportional hazards and proportional odds versions of the random-effects model are developed, while partial proportional hazards and odds generalizations are described for the latter approach. For estimation, a full-information maximum marginal likelihood solution is implemented using numerical quadrature to integrate over the distribution of multiple random effects. The quadrature solution allows some flexibility in the choice of distributions for the random effects; both normal and rectangular distributions are considered in this article. An analysis of a dataset where students are clustered within schools is used to illustrate features of random-effects analysis of clustered grouped-time survival data.Statistical Methods in Medical Research 05/2000; 9(2):161-79. · 2.36 Impact Factor