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Biomedical subjects

Vance W Berger

Publications and source records attributed to Vance W Berger.

At least 19 recordsLinked to original sources
PubMed2006

Evaluating research training outcomes: experience from the cancer prevention fellowship program at the National Cancer Institute.

PURPOSE: The authors describe an evaluation approach to assess research training that is easy to implement, takes into account individual experience and diversity in research disciplines, and can be adapted to measure various outcomes, depending upon program goals. METHOD: Using publications as the outcome measure, the authors analyzed data from 66 trainees in the National Cancer Institute's Cancer Prevention Fellowship Program (CPFP) to illustrate this evaluation strategy. For postdoctoral fellows entering the CPFP between 1987 and 1997, the authors considered the three-year period prior to entry in the CPFP (pre-CPFP), the period during training, and the three-year period after completion of the CPFP (post-CPFP). Summary measures for individuals' publications during each of the three time periods were calculated, and the probability of change in total, peer-reviewed, and first-authored publications post-CPFP compared to pre-CPFP was assessed. RESULTS: Compared to pre-CPFP, the CPFP fellows published significantly more total, peer-reviewed, and first-authored publications post-CPFP. Post-CPFP younger individuals published more than older fellows. MDs had a greater increase in publications over time than did PhDs, but both groups had similar overall numbers of publications post-CPFP. Individuals pursuing a master of public health degree during training published more post-CPFP than did those who did not pursue this training in the program. CONCLUSIONS: Training programs facing the challenge of evaluating research outcomes will require new evaluation methods that take into account program goals. This easily adaptable, longitudinal evaluation strategy allows for diversity in research disciplines and research experience and can inform programmatic needs and individual progress.

Adult↗
PubMed2005

The reverse propensity score to detect selection bias and correct for baseline imbalances.

The propensity score has been proposed, and for the most part accepted, as a tool to allow for the evaluation of medical interventions in the presence of baseline imbalances arising in the context of observational studies. The lack of an analogous tool to allow for the evaluation of medical interventions in the presence of potentially systematic baseline imbalances in randomized trials has required the use of ad hoc methods. This, in turn, leads to challenges to the conclusions. For example, much of the controversy surrounding recommendations for or against mammography for some age groups stems from the fact that all the randomized trials to study mammography had baseline imbalances, to some extent, in important prognostic covariates. While some of these trials used cluster randomization, baseline imbalances are prevalent also in individually randomized trials. We provide a systematic approach for evaluating medical interventions in the presence of potentially systematic baseline imbalances in individually randomized trials with allocation concealment. Specifically, we define the reverse propensity score as the probability, conditional on all previous allocations and the allocation procedure (restrictions on the randomization), that a given patient will receive a given treatment. We demonstrate how the reverse propensity score allows for both detection of and correction for selection bias, or systematic baseline imbalances.

Biometry↗
PubMed2005

Adjusting for observable selection bias in block randomized trials.

In this paper, we propose a model-based approach to detect and adjust for observable selection bias in a randomized clinical trial with two treatments and binary outcomes. The proposed method was evaluated using simulations of a randomized block design in which the investigator favoured the experimental treatment by attempting to enroll stronger patients (with greater probability of treatment success) if the probability of the next treatment being experimental was high, and enroll weak patients (with less probability of treatment success) if the probability of the next treatment being experimental was low. The method allows not only testing for the presence of observable selection bias, but also testing for a difference in treatment effects, adjusting for possible selection bias.

Humans↗
PubMed2005

Nonparametric adjustment techniques for binary covariates.

Though a variety of reasons are often articulated for adjusting analyses for covariates, these reasons often fall into one of two general objectives, specifically to increase precision or to decrease bias. In practice, one does not generally choose between these objectives, because the methods that address one tend to address the other, as well. Because of this, no distinction is made in the methods used to correct for a baseline imbalance with respect to a prognostic covariate versus to ensure a fair comparison across treatment groups by making the comparisons within the levels of a prognostic covariate. Yet the literal translation of these two uses of covariate adjustment will lead to two distinct adjustment methods. We illustrate this divergence in the simplest case of a single binary covariate, a binary outcome, and two treatments, and we note that it is possible to combine the two approaches to derive yet a third approach. Each of these approaches is nonparametric and exact, and so it is the precise reason for adjusting that should dictate which would be used in any given situation.

Analysis of Variance↗
PubMed2005

Quantifying the magnitude of baseline covariate imbalances resulting from selection bias in randomized clinical trials.

Selection bias is most common in observational studies, when patients select their own treatments or treatments are assigned based on patient characteristics, such as disease severity. This first-order selection bias, as we call it, is eliminated by randomization, but there is residual selection bias that may occur even in randomized trials which occurs when, subconsciously or otherwise, an investigator uses advance knowledge of upcoming treatment allocations as the basis for deciding whom to enroll. For example, patients more likely to respond may be preferentially enrolled when the active treatment is due to be allocated, and patients less likely to respond may be enrolled when the control group is due to be allocated. If the upcoming allocations can be observed in their entirety, then we will call the resulting selection bias second-order selection bias. Allocation concealment minimizes the ability to observe upcoming allocations, yet upcoming allocations may still be predicted (imperfectly), or even determined with certainty, if at least some of the previous allocations are known, and if restrictions (such as randomized blocks) were placed on the randomization. This mechanism, based on prediction but not observation of upcoming allocations, is the third-order selection bias that is controlled by perfectly successful masking, but without perfect masking is not controlled even by the combination of advance randomization and allocation concealment. Our purpose is to quantify the magnitude of baseline imbalance that can result from third-order selection bias when the randomized block procedure is used. The smaller the block sizes, the more accurately one can predict future treatment assignments in the same block as known previous assignments, so this magnitude will depend on the block size, as well as on the level of certainty about upcoming allocations required to bias the patient selection. We find that a binary covariate can, on average, be up to 50% unbalanced by third-order selection bias.

Analysis of Variance↗
PubMed2005

Analysis of trichotomous pharmaceutical endpoints.

Many important clinical trial endpoints are measured on an ordered categorical scale. These include objective tumor response in oncology trials, the Thombolysis in Myocardial Infarction (TIMI) flow in cardiovascular trials, and the American College of Radiology (A CR) criterion in rheumatology trials. A common tendency among researchers is to simplify the ordered outcomes and collapse the data into a 2 x 2 contingency table in order to perform simpler statistical tests, such as the chi-square test or Fisher's exact test. Recently, more appropriate approaches, such as adaptive tests, have been developed for the analysis of ordered categorical endpoints. Each test in the adaptive class of tests is exact and balances good global power with nearly optimal power to detect a specific alternative of most interest. Prior knowledge of the direction of the treatment effect and the level of confidence in this prior information can be used to select a specific test from this class. However, little guidance has been offered regarding the selection of adaptive parameters when prior information is available. The purpose of this paper is to fill this gap by offering an objective approach for parameter selection, and to provide real data examples to illustrate the use of this objective approach.

Algorithms↗
PubMed2004

Does the Prentice criterion validate surrogate endpoints?

Randomized Phase II or Phase III clinical trials that are powered based on clinical endpoints, such as survival time, may be prohibitively expensive, in terms of both the time required for their completion and the number of patients required. As such, surrogate endpoints, such as objective tumour response or markers including prostate specific antigen or CA-125, have gained widespread popularity in clinical trials. If an improvement in a surrogate endpoint does not itself confer patient benefit, then consideration must be given to the extent to which improvement in a surrogate endpoint implies improvement in the true clinical endpoint of interest. That this is not a trivial issue is demonstrated by the results of an NIH-sponsored trial of anti-arrhythmic drugs, in which the ability to correct an irregular heart beat not only did not correspond to a survival benefit but in fact led to excess mortality. One approach to the validation of surrogate endpoints involves ensuring that a valid between-group analysis of the surrogate endpoint constitutes also a valid analysis of the true clinical endpoint. The Prentice criterion is a set of conditions that essentially specify the conditional independence of the impact of treatment on the true endpoint, given the surrogate endpoint. It is shown that this criterion alone ensures that an observed effect of the treatment on the true endpoint implies a treatment effect also on the surrogate endpoint, but contrary to popular belief, it does not ensure the converse, specifically that the observation of a significant treatment effect on the surrogate endpoint can be used to infer a treatment effect on the true endpoint.

Antineoplastic Agents↗
PubMed2004

Ensuring the comparability of comparison groups: is randomization enough?

BACKGROUND: It is widely believed that baseline imbalances in randomized trials must necessarily be random. In fact, there is a type of selection bias that can cause substantial, systematic and reproducible baseline imbalances of prognostic covariates even in properly randomized trials. It is possible, given complete data, to quantify both the susceptibility of a given trial to this type of selection bias and the extent to which selection bias appears to have caused either observable or unobservable baseline imbalances. Yet, in articles reporting on randomized trials, it is uncommon to find either these assessments or the information that would enable a reader to conduct them. Nevertheless, there have been a few published reports that contain descriptions of either this type of selection bias or indicators that it may have occurred. OBJECTIVE: To document that the same type of selection bias has been described in numerous randomized trials and therefore that it represents a problem deserving of greater attention. STUDY SELECTION: Computerized searches were not useful in locating trials with one or more elements that contribute to or are indicative of selection bias in randomized trials. We limit our treatment to trials that were previously questioned for susceptibility to selection bias or for large baseline imbalances. RESULTS: We found 14 randomized trials that appear to be suspicious for selection bias. This may represent only the tip of the iceberg, because the status of other trials is inconclusive. CONCLUSIONS: Authors of clinical trial reports should be required to disclose sufficient details to allow for an assessment of both allocation concealment and selection bias. The extent to which a randomized study was susceptible to selection bias should be considered in determining the relative contribution it makes to any subsequent meta-analysis, policy or decision.

Bias↗
PubMed2004

On the generation and ownership of alpha in medical studies.

Much is known about how to split alpha between or among several comparisons, or how to preserve the nominal alpha level with an exact analysis, but the issue of how alpha is generated, or where it comes from, has not received a commensurate degree of attention. It would seem that there is little point in working out methods to allocate or conserve alpha if it is unlimited in supply. Moreover, there seems to be a logical inconsistency in requiring that a given amount of alpha, generally 0.05, be split among the primary comparisons performed by a given set of researchers, yet allowing other researchers to analyze the same data with a new 0.05 to work with. We will address these inconsistencies, and ask more generally where alpha comes from, how it can be generated, and under what conditions it should be one-tailed or two-tailed.

Bias↗