Balanced Treatment Groups Research Articles

Growth performance and behaviour of finishing beef cattle illegally treated with growth promoters

The study aimed at evaluating growth performance, feeding and social behaviour of finishing beef cattle treated with Dexamethasone per os alone or in association with Estradiol. Twenty-four French cross-bred beef bulls were allotted to 3 balanced treatment groups: Control (C); Dexamethasone (D) and Dexamethasone + Estradiol (D+E). All the bulls received the same diet and the experimental period lasted 43 days. All the productive traits and eating behaviour were similar among treatments. Bulls provided with D+E showed a prolonged rumination during the 8 h following diet distribution but this behaviour was partially performed in standing position, since lying was significantly reduced in both treated groups as compared to Control. However, difference in behaviour were too limited to be considered a reliable indicator of these illegal treatments.

Emergent Suicidality in a Clinical Psychotherapy Trial for Adolescent Depression

The authors' goal was to examine the incidence and predictors of emergent suicidality that occurred during a clinical trial of psychotherapy for adolescent depression. The rates and predictors of emergent suicidality in 88 medication-free depressed adolescent outpatients who reported no current suicidality during an intake interview were assessed over 12 to 16 weeks of psychotherapy treatment. The incidence of emergent suicidality was 12.5% (11 of 88 subjects). Self-reported suicidal thoughts at intake were a significant predictor of emergent suicidality, even when suicidality was denied at intake interview. Emergent suicidality is a common occurrence in psychosocial treatment of adolescent depression, with rates similar to those reported recently in antidepressant trials. To evaluate accurately the role of treatment in emergent suicidality, it is important to assess self-reported suicidality at intake and to balance treatment groups on this key predictor of emergent suicidality.

Methods to assess intended effects of drug treatment in observational studies are reviewed

Background and objective To review methods that seek to adjust for confounding in observational studies when assessing intended drug effects. Methods We reviewed the statistical, economical and medical literature on the development, comparison and use of methods adjusting for confounding. Results In addition to standard statistical techniques of (logistic) regression and Cox proportional hazards regression, alternative methods have been proposed to adjust for confounding in observational studies. A first group of methods focus on the main problem of nonrandomization by balancing treatment groups on observed covariates: selection, matching, stratification, multivariate confounder score, and propensity score methods, of which the latter can be combined with stratification or various matching methods. Another group of methods look for variables to be used like randomization in order to adjust also for unobserved covariates: instrumental variable methods, two-stage least squares, and grouped-treatment approach. Identifying these variables is difficult, however, and assumptions are strong. Sensitivity analyses are useful tools in assessing the robustness and plausibility of the estimated treatment effects to variations in assumptions about unmeasured confounders. Conclusion In most studies regression-like techniques are routinely used for adjustment for confounding, although alternative methods are available. More complete empirical evaluations comparing these methods in different situations are needed.

The method of minimization for allocation to clinical trials: a review

Minimization is a largely nonrandom method of treatment allocation for clinical trials. We conducted a systematic literature search to determine its advantages and disadvantages compared with other allocation methods. Minimization was originally proposed by Taves and by Pocock and Simon. The latter paper introduces a family of allocation methods of which Taves' method is the simplest example. Minimization aims to ensure treatment arms are balanced with respect to predefined patient factors as well as for the number of patients in each group. Further extensions of the method have also been proposed by other authors. Simulation studies show that minimization provides better balanced treatment groups when compared with restricted or unrestricted randomization and that it can incorporate more prognostic factors than stratified randomization methods such as permuted blocks within strata. Some more computationally complex methods may give an even better performance. Concerns over the use of minimization have centered on the fact that treatment assignments may be predicted with certainty in some situations and on the implications for the analysis methods used. It has been suggested that adjustment should always be made for minimization factors when analyzing trials where minimization is the allocation method used. The use of minimization may sometimes result in added organizational complexity compared with other methods. Minimization has been recommended by many commentators for use in clinical trials. Despite this it is still rarely used in practice. From the evidence presented in this review, we believe minimization to be a highly effective allocation method and recommend its wider adoption in the conduct of randomized controlled trials.

Minimization in crossover trials with non-prognostic strata: theory and practical application.

To obtain meaningful results in any clinical trial, patients need to be allocated to treatments in such a way that valid analysis can be carried out. Balancing treatment groups before analysis is carried out is more desirable than trying to compensate for incomparability at a later date. Therefore, the development of allocation procedures to produce comparable groups in which prognostic factors are equally represented is important. Minimization, a deterministic allocation method, aims to ensure balance on such factors, particularly in small trials when traditional randomization methods are likely to fail. However, views on the use of conventional analysis following minimization are divided. The use of minimization in two randomised crossover trials is described where, in addition to the comparisons between randomised treatments, it was desired to have balance between groups based on differential trial procedures. Theoretical concerns about the use of minimization are not applicable in this setting, and therefore minimization is shown to be a useful technique for obtaining balance.

An Extension of the Propensity Score Adjustment Method for the Analysis of Group Differences in MIMIC Models

This article proposes an extension of the propensity score adjustment method to latent variable models. The propensity score was proposed by Rosenbaum and Rubin (1983) as a means of balancing treatment and control groups with respect to observed covariates in non-randomized studies. The propensity score is defined as the conditional probability of assignment to a treatment group given a set of observed covariates. In a typical application of this approach, each observation is associated with a propensity to be assigned to the treatment group. The distribution of propensity scores is then divided into strata and analyses of treatment group differences are conducted within strata. Comparisons of treatment group differences within and across strata provide evidence for whether or not the bias due to non-random selection into treatment groups has been accounted for by the propensity score adjustment. This article extends the application of the propensity score approach to the analysis of group differences on latent variables. In particular, multi-sample MIMIC modeling is utilized to test hypotheses about treatment group differences on latent variables across strata. The role of factorial invariance as it relates to the approach advocated in this article is also discussed. An application to the problem of academic tracking differences in self-concept and locus-of-control, using data from the National Educational Longitudinal Study of 1988 (National Center for Education Statistics, 1988), illustrates the procedure.

Erratum: Cage Allocation Designs for Rodent Carcinogenicity Experiments

Cage allocation designs for rodent carcinogenicity experiments are discussed and presented with the goal of avoiding dosage group biases related to cage location. Considerations in selecting a cage design are first discussed in general terms. Specific designs are presented for use in experiments involving three, four, and five dose groups and with one, four, and five rodents per cage. Priorities for balancing treatment groups include horizontal position on shelf and shelf of rack, nearest neighbor balance, and mate-feale alance. It is proposed thatthese balance criteria be considered together with practical issues, such as the ability to accurately conform to a design and to determine a sensible and efficient design for each experiment.

Cage allocation designs for rodent carcinogenicity experiments

Cage allocation designs for rodent carcinogenicity experiments are discussed and presented with the goal of avoiding dosage group biases related to cage location. Considerations in selecting a cage design are first discussed in general terms. Specific designs are presented for use in experiments involving three, four, and five dose groups and with one, four, and five rodents per cage. Priorities for balancing treatment groups include horizontal position on shelf and shelf of rack, nearest neighbor balance, and male-female balance. It is proposed that these balance criteria be considered together with practical issues, such as the ability to accurately conform to a design and to determine a sensible and efficient design for each experiment.

Cage allocation designs for rodent carcinogenicity experiments.

Cage allocation designs for rodent carcinogenicity experiments are discussed and presented with the goal of avoiding dosage group biases related to cage location. Considerations in selecting a cage design are first discussed in general terms. Specific designs are presented for use in experiments involving three, four, and five dose groups and with one, four, and five rodents per cage. Priorities for balancing treatment groups include horizontal position on shelf and shelf of rack, nearest neighbor balance, and male-female balance. It is proposed that these balance criteria be considered together with practical issues, such as the ability to accurately conform to a design and to determine a sensible and efficient design for each experiment.

Cage Allocation Designs for Rodent Carcinogenicity Experiments

Cage allocation designs for rodent carcinogenicity experiments are discussed and presented with the goal of avoiding dosage group biases related to cage location. Considerations in selecting a cage design are first discussed in general terms. Specific designs are presented for use in experiments involving three, four, and five dose groups and with one, four, and five rodents per cage. Priorities for balancing treatment groups include horizontal position on shelf and shelf of rack, nearest neighbor balance, and male–female balance. It is proposed that these balance criteria be considered together with practical issues, such as the ability to accurately conform to a design and to determine a sensible and efficient design for each experiment.