Estimating and testing blip effects of treatments in sequence via standardized point effects of treatments

Suppose that a sequence of treatments are assigned to influence an outcome of interest that occurs after the last treatment. Between treatments, there are time-dependent covariates that may be post-treatment variables of the earlier treatments and confounders of the subsequent treatments. In this article, we study identification and estimation of the net effect of each treatment in the treatment sequence. We construct a point parametrization for the joint distribution of treatments, time-dependent covariates and the outcome, in which the point parameters of interest are the point effects of treatments considered as single-point treatments. We identify net effects of treatments by their expressions in terms of point effects of treatments and express patterns of net effects of treatments by constraints on point effects of treatments. We estimate net effects of treatments through their point effects under the constraint by maximum likelihood and reduce the number of point parameters in the estimation by the treatment assignment condition. As a result, we obtain an unbiased consistent maximum-likelihood estimate for the net effect of treatment even in a long treatment sequence. We also show by simulation that the interval estimation of the net effect of treatment achieves the nominal coverage probability.

Methodology for clinical practice guidelines for the management of arteriovenous access

Randomized trials, observational studies, and the illusive search for the source of truth

PROTOCOL: Nutrition interventions and programs for reducing mortality and morbidity in pregnant and lactating women and women of reproductive age: a systematic review

Issues regarding ‘immortal time’ in the analysis of the treatment effects in observational studies

Understanding Propensity Score Analyses

Propensity Scores Do Not Necessarily Lie!

THE RANDOMIZED CLINICAL TRIAL (RCT) IS THE ideal method for measuring treatment effects. Participants in clinical trials are randomly assigned to a treatment or control group. Randomization reduces biases by making treatment and control groups “equal with respect to all features,” except the treatment assignment. When randomization is performed correctly, differences in efficacy found by statistical comparisons can be attributed to the difference between the treatment and control. However, the RCT does not necessarily provide the final answer to treatment effectiveness, as there are many restrictions that limit generalizability. For example, RCTs are often restricted to patients with limited disease, comorbidity, and concomitant medications. Thus, RCTs generally demonstrate efficacy rather than effectiveness, where efficacy is the treatment effect under the restricted conditions of the RCT and effectiveness is the treatment effect under the conditions of usual practice. Observational, nonrandomized studies have a role when RCTs are not available, and, even when RCTs are available, to quantify effectiveness and other real world experiences. A contemporary example of this is the evaluation of drugeluting stents, for which RCTs have demonstrated shortterm efficacy for relatively healthy patients and observational studies are beginning to address long-term effectiveness and safety problems and use of clopidogrel in a broader array of patients. There are many approaches for making statistical inferences from observational data. Some approaches focus on study design, others on statistical techniques. However, even with the best of designs, observational studies, unlike the RCTs, do not automatically control for selection biases. Therefore, statistical methods involving matching, stratification, and/or covariance adjustment are needed. Lack of randomization in observational studies may result in large differences on the observed (and unobserved) participant characteristics between the treatment and control groups. These differences can lead to biased estimates of treatment effects. The goal of the statistical techniques that focus on observational data is to create an analysis that resembles what would occur had the treatment been randomly assigned. In RCTs, the balance is achieved on participant characteristics that occur before the treatment is administered. The success of randomization in creating balance can be assessed before any outcome measurements are taken. Therefore, in observational studies, the first goal of a statistical technique is to create balance between treatments on characteristics that are assessed before the actual treatment is administered. Once this balance has been achieved, outcome measurements can be ascertained and compared between groups. In practice, this goal is often difficult to achieve because the data available for observational studies usually contain measured patient characteristics that are obtained before, during, and after treatment administration, and it is often difficult to determine exactly which patient characteristics are pretreatment or not. Furthermore, there frequently are unmeasured characteristics that are not available, inadequately measured, or unknown. Thus, the statistical methods in observational studies need first to be judged based on their performance in creating a balance on background characteristics between treated and control groups and the impact of outcome data should play no role in this assessment. To adjust for pretreatment imbalances, 2 statistical approaches often used are analysis of covariance methods and propensity score methods. These 2 methods complement each other and generally should be used together rather than choosing between one or the other. Analysis of covariance refers here to standard statistical analyses that produce estimates of treatment effects adjusted for background characteristics (covariates), which are included explicitly in a statistical (regression) model. With observational studies, this technique can produce biased estimates of treatment effects if there is extreme imbalance of the background characteristics and/or the treatment effect

How Good Are Observational Studies in Assessing Psychiatric Treatment Outcomes?

Aim: One of the aims of the Observation Health Data Sciences and Informatics (OHDSI) initiative is population-level treatment effect estimation in large observational databases. Since treatment effects are well-known to vary across groups of patients with different baseline risk, we aimed to extend the OHDSI methods library with a framework for risk-based assessment of treatment effect heterogeneity. Materials and Methods: The proposed framework consists of five steps: 1) definition of the problem, i.e. the population, the treatment, the comparator and the outcome(s) of interest; 2) identification of relevant databases; 3) development of a prediction model for the outcome(s) of interest; 4) estimation of propensity scores within strata of predicted risk and estimation of relative and absolute treatment effect within strata of predicted risk; 5) evaluation and presentation of results. Results: We demonstrate our framework by evaluating heterogeneity of the effect of angiotensin-converting enzyme (ACE) inhibitors versus beta blockers on a set of 9 outcomes of interest across three observational databases. With increasing risk of acute myocardial infarction we observed increasing absolute benefits, i.e. from -0.03% to 0.54% in the lowest to highest risk groups. Cough-related absolute harms decreased from 4.1% to 2.6%. Conclusions: The proposed framework may be useful for the evaluation of heterogeneity of treatment effect on observational data that are mapped to the OMOP Common Data Model. The proof of concept study demonstrates its feasibility in large observational data. Further insights may arise by application to safety and effectiveness questions across the global data network.

Applied ecologists are often interested in understanding the effects of management on ecological systems. If management (treatment) is applied nonrandomly, as in observational studies, then analysis must account for the potential confounding caused by variables that could have influenced both treatment assignment and the outcome of interest. Methods that do not adjust for all confounding variables can only estimate associations between treatment and outcome, not treatment effects. Data collected in observational studies are usually analysed with linear or generalized linear models, which can estimate treatment effects by adjusting for confounding variables. However, if there is little overlap in the distributions of confounding variables among the treatment groups then conventional regression extrapolates to areas of the covariate space where at least one of the treatment groups was unlikely to be observed. An alternative procedure for assessing treatment effects is to use the propensity score, which is the probability of treatment assignment given potential confounding variables. The propensity score can be used to reduce systematic differences in confounding variables among the treatment groups, ensuring that data more closely resemble that expected under a randomized experiment. The propensity score also identifies situations where treatment inferences must rely on strong assumptions. We used Monte Carlo simulation to examine the properties of commonly used propensity score methods for estimating treatment effects in the presence of nonrandom allocation of treatments. We then illustrated their application in a case study estimating the effects of invasive herbivore management on tree condition. Our results indicate that propensity score methods can be robust to model misspecification, allowing the estimation of average causal effects and resulting in more reliable inferences. We discuss key considerations for using propensity score methods for analysing ecological data.

Because of their well-documented advantages, randomized controlled trials (RCTs) represent the gold standard for testing hypotheses in medical research. However, RCTs are not ideal for addressing certain research questions ( e.g. , the risk for renal insufficiency associated with alcohol intake). In addition, conducting a properly designed RCT often requires substantial time and resources. For these reasons, including barriers of cost and need for rapid answers, the majority of clinical research studies in the renal literature use an observational design. Although there clearly is a great need for more RCTs that are conducted in populations with kidney disease, rigorous observational studies are extremely valuable and in many circumstances yield similar results as rigorous RCTs (1). Furthermore, although RCTs are considered the gold standard for examining the efficacy of a therapy, studies of prognosis often are addressed best by cohort studies. In this first of a multipart series dedicated to reviewing clinical research methods in nephrology, after providing a brief overview of observational studies, we focus on one of the major subtypes of observational designs: The cohort study. A glossary of common terms that are used in this and subsequent sections of this series is included in Table 1. Details on other methods ( e.g. , case-control studies, RCTs) will be the subject of future reviews. View this table: Table 1. Glossary The simplest form of observational study is the case report or case series, which describes the clinical course of individuals with a particular condition or diagnosis. Such studies often highlight a single clinical condition and at times even suggest a potential biologic mechanism. In a case series, the clinician gains an appreciation of the breadth of abnormalities ( e.g. , range of proteinuria in patients with HIV-associated nephropathy) that may characterize a single disease process. Although these studies may attempt to identify factors or treatments that …

Impact of follow-up on generalized pairwise comparisons for estimating the irinotecan benefit in advanced/metastatic gastric cancer

Pseudorandomization using an instrumental variable: a strong tool to break through selection bias

ystematic reviews provide the best estimates ofthe true effects (both beneficial and adverse) ofmedical interventions (1). In this era of evidence-based medicine, clinicians are increasingly using sys-tematic reviews to keep up with new evidence and toguide their clinical decision-making. Yet the mainchallenges for clinicians are to translate the results ofsystematic reviews into clinical practice and to pro-vide optimal patient care. This concept is known as“applicability.” Applicability addresses whether aparticular treatment that showed an overall benefit ina study or systematic review can be expected to con-vey the same benefit to an individual patient (2).This paper outlines a framework for how quantita-tive systematic reviews (meta-analyses) should be re-ported and how they may be used to identify thoseindividuals in whom the treatment is likely to do moregood than harm. We illustrate the concepts by usingdata from systematic reviews of ondansetron for thetreatment and prevention of postoperative nausea andvomiting (PONV). Throughout this paper, we use theterms “baseline” and “underlying risk” interchange-ably. Underlying risk is defined as the risk of event fora patient under the control condition; it indicates theaverage risk of a patient if not treated (3).