1,362 research outputs found
The use of prognostic scores for causal inference with general treatment regimes
In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients
Propensity score analysis in the Genetic Analysis Workshop 17 simulated data set on independent individuals
Genetic Analysis Workshop 17 provided simulated phenotypes and exome sequence data for 697 independent individuals (209 case subjects and 488 control subjects). The disease liability in these data was influenced by multiple quantitative traits. We addressed the lack of statistical power in this small data set by limiting the genomic variants included in the study to those with potential disease-causing effect, thereby reducing the problem of multiple testing. After this adjustment, we could readily detect two common variants that were strongly associated with the quantitative trait Q1 (C13S523 and C13S522). However, we found no significant associations with the affected status or with any of the other quantitative traits, and the relationship between disease status and genomic variants remained obscure. To address the challenge of the multivariate phenotype, we used propensity scores to combine covariates with genetic risk factors into a single risk factor and created a new phenotype variable, the probability of being affected given the covariates. Using the propensity score as a quantitative trait in the case-control analysis, we again could identify the two common single-nucleotide polymorphisms (C13S523 and C13S522). In addition, this analysis captured the correlation between Q1 and the affected status and reduced the problem of multiple testing. Although the propensity score was useful for capturing and clarifying the genetic contributions of common variants to the disease phenotype and the mediating role of the quantitative trait Q1, the analysis did not increase power to detect rare variants
The Wikipedia Gender Gap Revisited: Characterizing Survey Response Bias with Propensity Score Estimation
Opt-in surveys are the most widespread method used to study participation in online communities, but produce biased results in the absence of adjustments for non-response. A 2008 survey conducted by the Wikimedia Foundation and United Nations University at Maastricht is the source of a frequently cited statistic that less than 13% of Wikipedia contributors are female. However, the same study suggested that only 39.9% of Wikipedia readers in the US were female – a finding contradicted by a representative survey of American adults by the Pew Research Center conducted less than two months later. Combining these two datasets through an application and extension of a propensity score estimation technique used to model survey non-response bias, we construct revised estimates, contingent on explicit assumptions, for several of the Wikimedia Foundation and United Nations University at Maastricht claims about Wikipedia editors. We estimate that the proportion of female US adult editors was 27.5% higher than the original study reported (22.7%, versus 17.8%), and that the total proportion of female editors was 26.8% higher (16.1%, versus 12.7%).Sloan School of ManagementHarvard University. Berkman Center for Internet & SocietyFord Visionary Leadership FundNorthwestern University (Evanston, Ill.
Propensity score matching in estimating the effect of managerial education on academic planning behavior. Study design: a cross-sectional study
<p>Abstract</p> <p>Background</p> <p>In many academic settings teaching a particular topic is applied to every student enrolled in the same academic year, it is a difficult task for researchers to design a randomized control group study. This research aimed to estimate the effect of teaching management and planning on increasing academic planning behavior (APB), using propensity score matching (PSM).</p> <p>Methods</p> <p>In a cross-sectional survey utilizing a self-reported structured questionnaire on a systematic random sample of 421 students in Hanoi Medical University, one of the eight medical schools in Vietnam, this evaluation study adopted regression procedures to assess model fit, then PSM to create a matched control group in order to allow for evaluating the effect of management education.</p> <p>Results</p> <p>The study showed both direct and indirect effects of the education on behavior. After PSM to adjust for the possible confounders to balance statistically two groups - with and without management education, there is statistically a significant difference in APB between these two groups, making a net difference of 18.60% (p < .05). The estimated 18.6 percentage point increase can be translated into the practice of APB by 670 students in the population. This number of academic planners can be attributed to a high recall of important management and planning education.</p> <p>Conclusions</p> <p>The study provided theoretical as well as practical implications to guide the design of the education and evaluation of teaching.</p
Comparison of resource use by COPD patients on inhaled therapies with long-acting bronchodilators: a database study
<p>Abstract</p> <p>Background</p> <p>The purpose of this analysis was to compare health care costs and utilization among COPD patients who had long-acting beta-2 agonist (LABA) OR long-acting muscarinic antagonist (LAMA); LABA AND LAMA; or LABA, LAMA, AND inhaled corticosteroid (ICS) prescription claims.</p> <p>Methods</p> <p>This was a 12 month pre-post, retrospective analysis using COPD patients in a national administrative insurance database. Propensity score and exact matching were used to match patients 1:1:1 between the LABA or LAMA (formoterol, salmeterol, or tiotropium), LABA and LAMA (tiotropium/formoterol or tiotropium/salmeterol), and LABA, LAMA and ICS (bronchodilators plus steroid) groups. Post-period comparisons were evaluated with analysis of covariance. Costs were evaluated from a commercial payer perspective.</p> <p>Results</p> <p>A total of 523 patients were matched using 29 pre-period variables (e.g., demographics, medication exposure). Post-match assessments indicated balance among the cohorts. COPD-related costs differed among groups (LABA or LAMA 2,823 SE = 62; LABA, LAMA and ICS 911 SE = 91) compared to the LABA and LAMA therapy group (100 greater for the LABA or LAMA therapy group relative to both LABA and LAMA (p = .0018) and LABA, LAMA, and ICS (p = .0071) therapy groups. While there was no observed difference in outpatient costs, there was a slightly higher number of outpatient visits per patient in the LABA and LAMA (25.5 SE = 0.9, p = 0.0070) relative to the LABA or LAMA therapy group (22.3 SE = 0.8) and higher utilization (89.7% of patients) with COPD visits in the LABA and LAMA therapy group relative to both the LABA or LAMA (73.8%; p < .0001) and LABA, LAMA and ICS therapy groups (85.3; p = 0.0305).</p> <p>Conclusions</p> <p>Significant cost differences driven mainly by pharmaceuticals were observed among LABA or LAMA, LABA and LAMA and LABA, LAMA and ICS therapies. A COPD-related cost offset was observed from single bronchodilator to two bronchodilators. Addition of an ICS with two bronchodilators resulted in higher treatment costs without reduction in other COPD-related costs compared with two bronchodilators.</p
Multilevel latent class casemix modelling: a novel approach to accommodate patient casemix
<p>Abstract</p> <p>Background</p> <p>Using routinely collected patient data we explore the utility of multilevel latent class (MLLC) models to adjust for patient casemix and rank Trust performance. We contrast this with ranks derived from Trust standardised mortality ratios (SMRs).</p> <p>Methods</p> <p>Patients with colorectal cancer diagnosed between 1998 and 2004 and resident in Northern and Yorkshire regions were identified from the cancer registry database (n = 24,640). Patient age, sex, stage-at-diagnosis (Dukes), and Trust of diagnosis/treatment were extracted. Socioeconomic background was derived using the Townsend Index. Outcome was survival at 3 years after diagnosis. MLLC-modelled and SMR-generated Trust ranks were compared.</p> <p>Results</p> <p>Patients were assigned to two classes of similar size: one with reasonable prognosis (63.0% died within 3 years), and one with better prognosis (39.3% died within 3 years). In patient class one, all patients diagnosed at stage B or C died within 3 years; in patient class two, all patients diagnosed at stage A, B or C survived. Trusts were assigned two classes with 51.3% and 53.2% of patients respectively dying within 3 years. Differences in the ranked Trust performance between the MLLC model and SMRs were all within estimated 95% CIs.</p> <p>Conclusions</p> <p>A novel approach to casemix adjustment is illustrated, ranking Trust performance whilst facilitating the evaluation of factors associated with the patient journey (e.g. treatments) and factors associated with the processes of healthcare delivery (e.g. delays). Further research can demonstrate the value of modelling patient pathways and evaluating healthcare processes across provider institutions.</p
Mortality in Peripheral Arterial Disease: A Comparison of Patients Managed by Vascular Specialists and General Practitioners
BACKGROUND: Peripheral arterial disease (PAD) is undertreated by general practitioners (GPs). However, the impact of the suboptimal clinical management is unknown. OBJECTIVE: To assess the mortality rate of PAD patients in relation to the type of physician who provides their care (GP or vascular specialist). DESIGN: Prospective study. SETTING: Primary care practice and academic vascular laboratory. PARTICIPANTS: GP patients (n = 60) were those of the Peripheral Arteriopathy and Cardiovascular Events study (PACE). Patients managed by specialists (n = 82) were consecutive subjects with established PAD who were referred to our vascular laboratory during the enrolment period of the PACE study. MEASUREMENTS: All-cause and cardiovascular mortality. RESULTS: After 32 months of follow-up, specialist management was associated with a lower rate of all-cause mortality (RR = 0.04; 95% CI 0.01–0.34; p = .003) and cardiovascular mortality (RR = 0.07; 95% CI 0.01–0.65; p = .020), after adjustment for patients’ characteristics. Specialists were more likely to use antiplatelet agents (93% vs 73%, p < .001), statins (62% vs 25%, p < .001) and beta blockers (28% vs 3%, p < .001). Survival differences between specialists and GPs disappeared once the use of pharmacotherapies was added to the proportional hazard model. The fully adjusted model showed that the use of statins was significantly associated with a reduced risk of all-cause mortality (RR = 0.02; 95% CI 0.01–0.73, p = .034) and cardiovascular mortality (RR = 0.02; 95% CI 0.01–0.71, p = .033). CONCLUSIONS: Specialist management of patients with symptomatic PAD resulted in better survival than generalist management. This effect appears to be mainly caused by the more frequent use of effective medicines by specialists
Identification and estimation of causal effects of multiple treatments under the conditional independence assumption
Assessment and Implication of Prognostic Imbalance in Randomized Controlled Trials with a Binary Outcome – A Simulation Study
Chance imbalance in baseline prognosis of a randomized controlled trial can lead to over or underestimation of treatment effects, particularly in trials with small sample sizes. Our study aimed to (1) evaluate the probability of imbalance in a binary prognostic factor (PF) between two treatment arms, (2) investigate the impact of prognostic imbalance on the estimation of a treatment effect, and (3) examine the effect of sample size (n) in relation to the first two objectives.We simulated data from parallel-group trials evaluating a binary outcome by varying the risk of the outcome, effect of the treatment, power and prevalence of the PF, and n. Logistic regression models with and without adjustment for the PF were compared in terms of bias, standard error, coverage of confidence interval and statistical power.For a PF with a prevalence of 0.5, the probability of a difference in the frequency of the PF≥5% reaches 0.42 with 125/arm. Ignoring a strong PF (relative risk = 5) leads to underestimating the strength of a moderate treatment effect, and the underestimate is independent of n when n is >50/arm. Adjusting for such PF increases statistical power. If the PF is weak (RR = 2), adjustment makes little difference in statistical inference. Conditional on a 5% imbalance of a powerful PF, adjustment reduces the likelihood of large bias. If an absolute measure of imbalance ≥5% is deemed important, including 1000 patients/arm provides sufficient protection against such an imbalance. Two thousand patients/arm may provide an adequate control against large random deviations in treatment effect estimation in the presence of a powerful PF.The probability of prognostic imbalance in small trials can be substantial. Covariate adjustment improves estimation accuracy and statistical power, and hence should be performed when strong PFs are observed
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