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Bounds on functionals of the distribution treatment effects

Firpo, Sergio Pinheiro; Ridder, Geert
Fonte: Fundação Getúlio Vargas Publicador: Fundação Getúlio Vargas
Tipo: Trabalho em Andamento
Português
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45.74%
Bounds on the distribution function of the sum of two random variables with known marginal distributions obtained by Makarov (1981) can be used to bound the cumulative distribution function (c.d.f.) of individual treatment effects. Identification of the distribution of individual treatment effects is important for policy purposes if we are interested in functionals of that distribution, such as the proportion of individuals who gain from the treatment and the expected gain from the treatment for these individuals. Makarov bounds on the c.d.f. of the individual treatment effect distribution are pointwise sharp, i.e. they cannot be improved in any single point of the distribution. We show that the Makarov bounds are not uniformly sharp. Specifically, we show that the Makarov bounds on the region that contains the c.d.f. of the treatment effect distribution in two (or more) points can be improved, and we derive the smallest set for the c.d.f. of the treatment effect distribution in two (or more) points. An implication is that the Makarov bounds on a functional of the c.d.f. of the individual treatment effect distribution are not best possible.

Power and Sample Size Calculation for Log-rank Test with a Time Lag in Treatment Effect

Zhang, Daowen; Quan, Hui
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 28/02/2009 Português
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45.71%
The log-rank test is the most powerful nonparametric test for detecting a proportional hazards alternative and thus is the most commonly used testing procedure for comparing time-to-event distributions between different treatments in clinical trials. When the log-rank test is used for the primary data analysis, the sample size calculation should also be based on the test to ensure the desired power for the study. In some clinical trials, the treatment effect may not manifest itself right after patients receive the treatment. Therefore, the proportional hazards assumption may not hold. Furthermore, patients may discontinue the study treatment prematurely and thus may have diluted treatment effect after treatment discontinuation. If a patient’s treatment termination time is independent of his/her time-to-event of interest, the termination time can be treated as a censoring time in the final data analysis. Alternatively, we may keep collecting time-to-event data until study termination from those patients who discontinued the treatment and conduct an intent-to-treat (ITT) analysis by including them in the original treatment groups. We derive formulas necessary to calculate the asymptotic power of the log-rank test under this non-proportional hazards alternative for the two data analysis strategies. Simulation studies indicate that the formulas provide accurate power for a variety of trial settings. A clinical trial example is used to illustrate the application of the proposed methods.

Evaluation of Treatment-Effect Heterogeneity Using Biomarkers Measured on a Continuous Scale: Subpopulation Treatment Effect Pattern Plot

Lazar, Ann A.; Cole, Bernard F.; Bonetti, Marco; Gelber, Richard D.
Fonte: American Society of Clinical Oncology Publicador: American Society of Clinical Oncology
Tipo: Artigo de Revista Científica
Português
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45.76%
The discovery of biomarkers that predict treatment effectiveness has great potential for improving medical care, particularly in oncology. These biomarkers are increasingly reported on a continuous scale, allowing investigators to explore how treatment efficacy varies as the biomarker values continuously increase, as opposed to using arbitrary categories of expression levels resulting in a loss of information. In the age of biomarkers as continuous predictors (eg, expression level percentage rather than positive v negative), alternatives to such dichotomized analyses are needed. The purpose of this article is to provide an overview of an intuitive statistical approach—the subpopulation treatment effect pattern plot (STEPP)—for evaluating treatment-effect heterogeneity when a biomarker is measured on a continuous scale. STEPP graphically explores the patterns of treatment effect across overlapping intervals of the biomarker values. As an example, STEPP methodology is used to explore patterns of treatment effect for varying levels of the biomarker Ki-67 in the BIG (Breast International Group) 1-98 randomized clinical trial comparing letrozole with tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor–positive breast cancer. STEPP analyses showed patients with higher Ki-67 values who were assigned to receive tamoxifen had the poorest prognosis and may benefit most from letrozole.

A Shrinkage Approach for Estimating a Treatment Effect Using Intermediate Biomarker Data in Clinical Trials

Li, Yun; Taylor, Jeremy M.G.; Little, Roderick J.A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
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45.72%
In clinical trials, a biomarker (S) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z) on T. A useful biomarker can be measured earlier than T and cost less than T. In this paper we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed. In an ideal but often unrealistic setting, when S satisfies Prentice’s definition for perfect surrogacy, there is the potential for substantial gain in precision by using data from S to estimate the treatment effect on T. When S is not close to a perfect surrogate, it can provide substantial information only under particular circumstances. We propose to use a targeted shrinkage regression approach that data-adaptively takes advantage of the potential efficiency gain yet avoids the need to make a strong surrogacy assumption. Simulations show that this approach strikes a balance between bias and efficiency gain. Compared with competing methods, it has better mean squared error properties and can achieve substantial efficiency gain, particularly in a common practical setting when S captures much but not all of the treatment effect and the sample size is relatively small. We apply the proposed method to a glaucoma data example.

Systematic Review and Meta-Analysis of Antimicrobial Treatment Effect Estimation in Complicated Urinary Tract Infection

Singh, Krishan P.; Li, Gang; Mitrani-Gold, Fanny S.; Kurtinecz, Milena; Wetherington, Jeffrey; Tomayko, John F.; Mundy, Linda M.
Fonte: American Society for Microbiology Publicador: American Society for Microbiology
Tipo: Artigo de Revista Científica
Publicado em /11/2013 Português
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45.71%
Noninferiority trial design and analyses are commonly used to establish the effectiveness of a new antimicrobial drug for treatment of serious infections such as complicated urinary tract infection (cUTI). A systematic review and meta-analysis were conducted to estimate the treatment effects of three potential active comparator drugs for the design of a noninferiority trial. The systematic review identified no placebo trials of cUTI, four clinical trials of cUTI with uncomplicated urinary tract infection as a proxy for placebo, and nine trials with reports of treatment effect estimates for doripenem, levofloxacin, or imipenem-cilastatin. In the meta-analysis, the primary efficacy endpoint of interest was the microbiological eradication rate at the test-of-cure visit in the microbiological intent-to-treat population. The estimated eradication rates and corresponding 95% confidence intervals (CI) were 31.8% (26.5% to 37.2%) for placebo, 81% (77.7% to 84.2%) for doripenem, 79% (75.9% to 82.2%) for levofloxacin, and 80.5% (71.9% to 89.1%) for imipenem-cilastatin. The treatment effect estimates were 40.5% for doripenem, 38.7% for levofloxacin, 34.7% for imipenem-cilastatin, and 40.8% overall. These treatment effect estimates can be used to inform the design and analysis of future noninferiority trials in cUTI study populations.

Estimation of treatment effect under nonproportional hazards and conditionally independent censoring

Boyd, Adam P.; Kittelson, John M.; Gillen, Daniel L.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.71%
In clinical trials with time-to-event outcomes, it is common to estimate the marginal hazard ratio from the proportional hazards model, even when the proportional hazards assumption is not valid. This is unavoidable from the perspective that the estimator must be specified a priori if probability statements about treatment effect estimates are desired. Marginal hazard ratio estimates under non-proportional hazards (NPH) are still useful, as they can be considered to be average treatment effect estimates over the support of the data. However, as many have shown, under NPH the “usual” unweighted marginal hazard ratio estimate is a function of the censoring distribution, which is not normally considered to be scientifically relevant when describing the treatment effect. In addition, in many practical settings, the censoring distribution is only conditionally independent (e.g., differing across treatment arms), which further complicates the interpretation. In this paper, we investigate an estimator of the hazard ratio that removes the influence of censoring and propose a consistent robust variance estimator. We compare the coverage probability of the estimator to both the usual Cox model estimator and an estimator proposed by Xu and O’Quigley (2000) when censoring is independent of the covariate. The new estimator should be used for inference that does not depend on the censoring distribution. It is particularly relevant to adaptive clinical trials where...

Estimation of Treatment Effect for the Sequential Parallel Design

Tamura, Roy N.; Huang, Xiaohong; Boos, Dennis D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
45.71%
The sequential parallel clinical trial is a novel clinical trial design being used in psychiatric diseases which are known to have potentially high placebo response rates. The design consists of an initial parallel trial of placebo versus drug augmented by a second parallel trial of placebo versus drug in the placebo non-responders from the initial trial. Statistical research in the design has focused on hypothesis tests. However, an equally important output from any clinical trial is the estimate of treatment effect and variability around that estimate. In the sequential parallel trial, the most important treatment effect is the effect in the overall population. This effect can be estimated by considering only the first phase of the trial but this ignores useful information from the second phase of the trial. We develop estimates of treatment effect which incorporate data from both phases of the trial. Our simulations and a real data example suggest that there can be substantial gains in precision by incorporating data from both phases. The potential gains appear to be greatest in moderate sized trials which would typically be the case in Phase II trials.

Treatment effect of the method of Tai Chi exercise in combination with inhalation of air negative oxygen ions on hyperlipidemia

Ma, Ming; Song, Qing-Hua; Xu, Rong-Mei; Zhang, Quan-Hai; Shen, Guo-Qing; Guo, Yan-Hua; Wang, Yi
Fonte: e-Century Publishing Corporation Publicador: e-Century Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 15/08/2014 Português
Relevância na Pesquisa
45.74%
Objective: To observe the improvement effect of the treatment method of Tai Chi exercise in combination with inhalation of the air negative oxygen ions on the blood lipid indicator of the patient suffering from the hyperlipidemia. Methods: 56 patients, who are diagnosed with hyperlipidemia, are the study objects and divided into an observation group and a control group by the random number method. Each group consists of 28 patients. The patients in the control group do Tai Chi exercise for about 60 min once a day; the patients in the observation group, in addition to Tai Chi exercise, are treated by inhalation of the air negative oxygen ions. Before the treatment and after 6 months’ treatment, respectively test and compare body fat content, blood lipid, blood rheology and psychological adaptation as well as other indicators for these two groups of patients. Results: In comparison with the ordinary materials of the patients in two groups before the treatment, it shows no significant difference, P>0.05; after they are respectively treated for 6 months, it is found that the testing indicators of the patients in two groups are improved to some extent, but those of the observation group are better. Compared with the improvement effect of the control group...

Dealing with Limited Overlap in Estimation of Average Treatment Effects

Hotz, V. Joseph; Crump, Richard K.; Imbens, Guido; Mitnik, Oscar A.
Fonte: Oxford University Press Publicador: Oxford University Press
Português
Relevância na Pesquisa
55.7%
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely. Under some conditions, the optimal selection rules depend solely on the propensity score. For a wide range of distributions, a good approximation to the optimal rule is provided by the simple rule of thumb to discard all units with estimated propensity scores outside the range [0.1,0.9].; Economics

Essays in partial identification and applications to treatment effects and policy evaluation.

Mourifié, Ismael Yacoub
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
Português
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55.7%
Dans cette thèse, je me suis interessé à l’identification partielle des effets de traitements dans différents modèles de choix discrets avec traitements endogènes. Les modèles d’effets de traitement ont pour but de mesurer l’impact de certaines interventions sur certaines variables d’intérêt. Le type de traitement et la variable d’intérêt peuvent être défini de manière générale afin de pouvoir être appliqué à plusieurs différents contextes. Il y a plusieurs exemples de traitement en économie du travail, de la santé, de l’éducation, ou en organisation industrielle telle que les programmes de formation à l’emploi, les techniques médicales, l’investissement en recherche et développement, ou l’appartenance à un syndicat. La décision d’être traité ou pas n’est généralement pas aléatoire mais est basée sur des choix et des préférences individuelles. Dans un tel contexte, mesurer l’effet du traitement devient problématique car il faut tenir compte du biais de sélection. Plusieurs versions paramétriques de ces modèles ont été largement étudiées dans la littérature, cependant dans les modèles à variation discrète, la paramétrisation est une source importante d’identification. Dans un tel contexte...

Essays on Treatment Effects Evaluation

Guo, Ronghua
Português
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55.77%
The first chapter uses the propensity score matching method to measure the average impact of insurance on health service utilization in terms of office-based physician visits, total number of reported visits to hospital outpatient departments, and emergency room visits. Four matching algorithms are employed to match propensity scores. The results show that insurance significantly increases office-based physician visits, and its impacts on reported visits to hospital outpatient departments and emergency room visits are positive, but not significant. This implies that physician offices will receive a substantial increase in demand if universal insurance is imposed. Government will need to allocate more resources to physician offices relative to outpatient or emergency room services in the case of universal insurance in order to accommodate the increased demand. The second chapter studies the sensitivity of propensity score matching methods to different estimation methods. Traditionally, parametric models, such as logit and probit, are used to estimate propensity score. Current technology allows us to use computationally intensive methods, either semiparametric or nonparametric, to estimate it. We use the Monte Carlo experimental method to investigate the sensitivity of the treatment effect to different propensity score estimation models under the unconfoundedness assumption. The results show that the average treatment effect on the treated (ATT) estimates are insensitive to the estimation methods when index function for treatment is linear...

Treatment effect identification using alternative parallel assumptions

Mora, Ricardo; Reggio, Iliana
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: application/pdf; text/plain
Publicado em /12/2012 Português
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65.76%
The core assumption to identify the treatment effect in difference-in-differences estimators is the so-called Parallel Paths assumption, namely that the average change in outcome for the treated in the absence of treatment equals the average change in outcome for the non-treated. We define a family of alternative Parallel assumptions and show for a number of frequently used empirical specifications which parameters of the model identify the treatment effect under the alternative Parallel assumptions. We further propose a fully flexible model which has two desirable features not present in the usual econometric specifications implemented in applied research. First, it allows for flexible dynamics and for testing restrictions on these dynamics. Second, it does not impose equivalence between alternative Parallel assumptions. We illustrate the usefulness of our approach by revising the results of several recent papers in which the difference-in-differences technique has been applied.The core assumption to identify the treatment effect in difference-in-differences estimators is the so-called Parallel Paths assumption, namely that the average change in outcome for the treated in the absence of treatment equals the average change in outcome for the non-treated. We define a family of alternative Parallel assumptions and show for a number of frequently used empirical specifications which parameters of the model identify the treatment effect under the alternative Parallel assumptions. We further propose a fully flexible model which has two desirable features not present in the usual econometric specifications implemented in applied research. First...

dqd: A command for treatment effect estimation under alternative assumptions

Mora, Ricardo; Reggio, Iliana
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper
Publicado em 01/04/2014 Português
Relevância na Pesquisa
65.75%
Conventional difference-in-differences (DID) methods that are used to estimate the effect of a treatment rely on important identifying assumptions. Identification of the treatment effect in a DID framework requires some assumption relating trends for controls and treated in absence of treatment, the most common being the assumption of Parallel Paths. When several pre-treatment periods are available, Mora and Reggio (2012) show that treatment effect identification does not uniquely depend on the Parallel Path assumption, but also on the trend modeling strategy. They further define a family of alternative Parallel assumptions and propose a more flexible model which can be a helpful starting tool to study robustness to alternative Parallel assumptions and trend dynamics. In this paper we introduce a Stata command that implements the fully flexible model presented in Mora and Reggio (2012), producing tests for the equivalence of alternative parallel assumptions and for the dynamic effects of the treatment. The standard DID in model with or without polynomial trends can also be obtained.; We acknowledge financial help from the Spanish government through grant ECO2012-31358.

Statistical Inference for the Treatment Effect in Cancer Clinical Trials

JIANG, Shan
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
Português
Relevância na Pesquisa
65.76%
Randomized clinical trials provide the best evidence on the effect of treatment studied. There are different types of measures on the treatment effect, depending on the endpoints of the trials. For a given measure, based on the data from clinical trials, various statistical procedures are available for the inference of the treatment effect in terms of this measure. In a cancer clinical trial with a time to an event as the endpoint, hazard ratio is a popular measure for the relative difference between treatment groups. Most current statistical inference procedures for hazard ratio rely on the proportional hazard assumption, which may not be applicable to practice when it does not hold. Nonparametric confidence intervals for the hazard ratio have been proposed based on the asymptotic normality of the kernel estimate for the hazard ratio, but they were found not very satisfactory in the simulation studies. In the first part of this thesis, the empirical likelihood method is used to construct the confidence interval for the time-dependent hazard ratio. The asymptotic distribution of the empirical likelihood ratio is derived and simulation studies are conducted to evaluate the proposed method. It was also argued that the measure of the relative treatment effect based on the hazard ratio may be difficult to understand by clinicians. An alternative measure called probabilistic index was suggested and the C-index was proposed to estimate this index. However...

Estimating treatment effect heterogeneity in randomized program evaluation

Imai, Kosuke; Ratkovic, Marc
Tipo: Artigo de Revista Científica
Publicado em 24/05/2013 Português
Relevância na Pesquisa
45.73%
When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers must investigate when the treatments do and do not work. Indeed, the estimation of treatment effect heterogeneity plays an essential role in (1) selecting the most effective treatment from a large number of available treatments, (2) ascertaining subpopulations for which a treatment is effective or harmful, (3) designing individualized optimal treatment regimes, (4) testing for the existence or lack of heterogeneous treatment effects, and (5) generalizing causal effect estimates obtained from an experimental sample to a target population. In this paper, we formulate the estimation of heterogeneous treatment effects as a variable selection problem. We propose a method that adapts the Support Vector Machine classifier by placing separate sparsity constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. The proposed method is motivated by and applied to two well-known randomized evaluation studies in the social sciences. Our method selects the most effective voter mobilization strategies from a large number of alternative strategies...

Generalized Quantile Treatment Effect: A Flexible Bayesian Approach Using Quantile Ratio Smoothing

Venturini, Sergio; Dominici, Francesca; Parmigiani, Giovanni
Tipo: Artigo de Revista Científica
Publicado em 03/09/2015 Português
Relevância na Pesquisa
45.75%
We propose a new general approach for estimating the effect of a binary treatment on a continuous and potentially highly skewed response variable, the generalized quantile treatment effect (GQTE). The GQTE is defined as the difference between a function of the quantiles under the two treatment conditions. As such, it represents a generalization over the standard approaches typically used for estimating a treatment effect (i.e., the average treatment effect and the quantile treatment effect) because it allows the comparison of any arbitrary characteristic of the outcome's distribution under the two treatments. Following Dominici et al. (2005), we assume that a pre-specified transformation of the two quantiles is modeled as a smooth function of the percentiles. This assumption allows us to link the two quantile functions and thus to borrow information from one distribution to the other. The main theoretical contribution we provide is the analytical derivation of a closed form expression for the likelihood of the model. Exploiting this result we propose a novel Bayesian inferential methodology for the GQTE. We show some finite sample properties of our approach through a simulation study which confirms that in some cases it performs better than other nonparametric methods. As an illustration we finally apply our methodology to the 1987 National Medicare Expenditure Survey data to estimate the difference in the single hospitalization medical cost distributions between cases (i.e....

Average treatment effect estimation via random recursive partitioning

Iacus, Stefano; Porro, Giuseppe
Tipo: Artigo de Revista Científica
Publicado em 09/11/2004 Português
Relevância na Pesquisa
45.75%
A new matching method is proposed for the estimation of the average treatment effect of social policy interventions (e.g., training programs or health care measures). Given an outcome variable, a treatment and a set of pre-treatment covariates, the method is based on the examination of random recursive partitions of the space of covariates using regression trees. A regression tree is grown either on the treated or on the untreated individuals {\it only} using as response variable a random permutation of the indexes 1...$n$ ($n$ being the number of units involved), while the indexes for the other group are predicted using this tree. The procedure is replicated in order to rule out the effect of specific permutations. The average treatment effect is estimated in each tree by matching treated and untreated in the same terminal nodes. The final estimator of the average treatment effect is obtained by averaging on all the trees grown. The method does not require any specific model assumption apart from the tree's complexity, which does not affect the estimator though. We show that this method is either an instrument to check whether two samples can be matched (by any method) and, when this is feasible, to obtain reliable estimates of the average treatment effect. We further propose a graphical tool to inspect the quality of the match. The method has been applied to the National Supported Work Demonstration data...

Estimating confidence regions of common measures of (baseline, treatment effect) on dichotomous outcome of a population

Yin, Li; Wang, Xiaoqin
Tipo: Artigo de Revista Científica
Publicado em 21/01/2015 Português
Relevância na Pesquisa
45.72%
In this article we estimate confidence regions of the common measures of (baseline, treatment effect) in observational studies, where the measure of baseline is baseline risk or baseline odds while the measure of treatment effect is odds ratio, risk difference, risk ratio or attributable fraction, and where confounding is controlled in estimation of both baseline and treatment effect. To avoid high complexity of the normal approximation method and the parametric or non-parametric bootstrap method, we obtain confidence regions for measures of (baseline, treatment effect) by generating approximate distributions of the ML estimates of these measures based on one logistic model.

Nonparametric Tests for Treatment Effect Heterogeneity

Hotz, V.J.; Crump, Richard; Imbens, Guido; Mitnik, Oscar
Fonte: Review of Economics and Statistics Publicador: Review of Economics and Statistics
Tipo: Artigo de Revista Científica Formato: 233881 bytes; application/pdf
Publicado em //2008 Português
Relevância na Pesquisa
55.78%
A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are interested in the effects of programs beyond estimates of the overall average or the average for the subpopulation of treated individuals. It may be of substantive interest to investigate whether there is any subpopulation for which a program or treatment has a nonzero average effect, or whether there is heterogeneity in the effect of the treatment. The hypothesis that the average effect of the treatment is zero for all subpopulations is also important for researchers interested in assessing assumptions concerning the selection mechanism. In this paper we develop two nonparametric tests. The first test is for the null hypothesis that the treatment has a zero average effect for any subpopulation defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, in other words, that there is no heterogeneity in average treatment effects by covariates. Sacrificing some generality by focusing on these two specific null hypotheses we derive tests that are straightforward to implement.

Staff training and ambulatory tuberculosis treatment outcomes: a cluster randomized controlled trial in South Africa

Lewin,Simon; Dick,Judy; Zwarenstein,Merrick; Lombard,Carl J.
Fonte: World Health Organization Publicador: World Health Organization
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2005 Português
Relevância na Pesquisa
45.72%
OBJECTIVE: To assess whether adding a training intervention for clinic staff to the usual DOTS strategy (the internationally recommended control strategy for tuberculosis (TB)) would affect the outcomes of TB treatment in primary care clinics with treatment success rates below 70%. METHODS: A cluster randomized controlled trial was conducted from July 1996 to July 2000 in nurse-managed ambulatory primary care clinics in Cape Town, South Africa. Clinics with successful TB treatment completion rates of less than 70% and annual adult pulmonary TB loads of more than 40 patients per year were randomly assigned to either the intervention (n = 12) or control (n = 12) groups. All clinics completed follow-up. Treatment outcomes were measured in cohorts of adult, pulmonary TB patients before the intervention (n = 1200) and 9 months following the training (n = 1177). The intervention comprised an 18-hour experiential, participatory in-service training programme for clinic staff delivered by nurse facilitators and focusing on patient centredness, critical reflection on practice, and quality improvement. The main outcome measure was successful treatment, defined as patients who were cured and those who had completed tuberculosis treatment. FINDINGS: The estimated effect of the intervention was an increase in successful treatment rates of 4.8% (95% confidence interval (CI): -5.5% to 15.2%) and in bacteriological cure rates of 10.4% (CI: -1.2% to 22%). A treatment effect of 10% was envisaged...