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Sol Kaufman

Publications and source records attributed to Sol Kaufman.

6 recordsLinked to original sources
PubMed2005

Improved estimation of controlled direct effects in the presence of unmeasured confounding of intermediate variables.

Adjusting for a causal intermediate is a common analytic strategy for estimating an average causal direct effect (ACDE). The ACDE is the component of the total exposure effect that is not relayed through the specified intermediate. Even if the total effect is unconfounded, the usual ACDE estimate may be biased when an unmeasured variable affects the intermediate and outcome variables. Using linear programming optimization to compute non-parametric bounds, we develop new ACDE estimators for binary measured variables in this causal structure, and use root mean square confounding bias (RMSB) to compare their performance with the usual stratified estimator in simulated distributions of target populations comprised of the 64 possible potential response types as well as distributions of target populations restricted to subsets of 18 or 12 potential response types defined by monotonicity or no-interactions assumptions of unit-level causal effects. We also consider target population distributions conditioned on fixed outcome risk among the unexposed, or fixed true ACDE in one stratum of the intermediate. Results show that a midpoint estimator constructed from the optimization bounds has consistently lower RMSB than the usual stratified estimator both unconditionally and conditioned on any risk in the unexposed. When conditioning on true ACDE, this midpoint estimator performs more poorly only when conditioned on an extreme true ACDE in one stratum of the intermediate, yet outperforms the stratified estimator in the other stratum when interaction is permitted. An alternate 'limit-modified crude' estimator can never perform less favourably than the stratified estimator, and often has lower RMSB.

Bias↗
PubMed2005

Bounding causal effects under uncontrolled confounding using counterfactuals.

Common sensitivity analysis methods for unmeasured confounders provide a corrected point estimate of causal effect for each specified set of unknown parameter values. This article reviews alternative methods for generating deterministic nonparametric bounds on the magnitude of the causal effect using linear programming methods and potential outcomes models. The bounds are generated using only the observed table. We then demonstrate how these bound widths may be reduced through assumptions regarding the potential outcomes under various exposure regimens. We illustrate this linear programming approach using data from the Cooperative Cardiovascular Project. These bounds on causal effect under uncontrolled confounding complement standard sensitivity analyses by providing a range within which the causal effect must lie given the validity of the assumptions.

Adrenergic beta-Antagonists↗
PubMed2004

A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation.

BACKGROUND: Epidemiologic research is often devoted to etiologic investigation, and so techniques that may facilitate mechanistic inferences are attractive. Some of these techniques rely on rigid and/or unrealistic assumptions, making the biologic inferences tenuous. The methodology investigated here is effect decomposition: the contrast between effect measures estimated with and without adjustment for one or more variables hypothesized to lie on the pathway through which the exposure exerts its effect. This contrast is typically used to distinguish the exposure's indirect effect, through the specified intermediate variables, from its direct effect, transmitted via pathways that do not involve the specified intermediates. METHODS: We apply a causal framework based on latent potential response types to describe the limitations inherent in effect decomposition analysis. For simplicity, we assume three measured binary variables with monotonic effects and randomized exposure, and use difference contrasts as measures of causal effect. Previous authors showed that confounding between intermediate and the outcome threatens the validity of the decomposition strategy, even if exposure is randomized. We define exchangeability conditions for absence of confounding of causal effects of exposure and intermediate, and generate two example populations in which the no-confounding conditions are satisfied. In one population we impose an additional prohibition against unit-level interaction (synergism). We evaluate the performance of the decomposition strategy against true values of the causal effects, as defined by the proportions of latent potential response types in the two populations. RESULTS: We demonstrate that even when there is no confounding, partition of the total effect into direct and indirect effects is not reliably valid. Decomposition is valid only with the additional restriction that the population contain no units in which exposure and intermediate interact to cause the outcome. This restriction implies homogeneity of causal effects across strata of the intermediate. CONCLUSIONS: Reliable effect decomposition requires not only absence of confounding, but also absence of unit-level interaction and use of linear contrasts as measures of causal effect. Epidemiologists should be wary of etiologic inference based on adjusting for intermediates, especially when using ratio effect measures or when absence of interacting potential response types cannot be confidently asserted.

Journal Article↗
PubMed2003

Causal inference from randomized trials in social epidemiology.

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.

Asthma↗
PubMed2003

Carcinoma of the head and neck: a 5- to 20-year experience with preoperative chemotherapy, uncompromised surgery, and selective radiotherapy.

BACKGROUND: A 5- to 20-year evaluation of preoperative chemotherapy uncompromised surgery and selective radiotherapy in stage III/IV head and neck squamous cell carcinoma. METHODS: Eighty-two consecutive patients, single surgeon previously untreated, operable, and resectable for cure. Sites included the oral cavity, oropharynx, larynx, and hypopharynx. Two chemotherapeutic regimens were used: initial regimen (A), cisplatin/bleomycin (n = 45 patients); revised regimen (B), cisplatin/5-fluorouracil (n = 37 patients). The extent of surgery was carefully documented before chemotherapy-tattoo when feasible. This forms a strict guide for uncompromised surgery. Selective postoperative radiotherapy was based on specific criteria. RESULTS: Minimum follow-up was 5 years. Absolute survival: total group, 60%; regimen A, 46%; and regimen B, 77% (P =.004). Relative survival (correcting for life table mortality): total group, 66%; regimen A, 50%; and regimen B, 83% (P =.003). Recurrences: primary site, n = 9 (regimen A, n = 7 [16%]; regimen B, n =2 [5%]) and neck, n = 6 (13%, all in regimen A). Distant metastasis occurred in 12 patients (10 [22%] in regimen A and 2 [5%] in regimen B). CONCLUSIONS: This study suggests treatment of advanced head and neck squamous cell carcinoma (resectable for cure) with preoperative chemotherapy (regimen B); resection of original tumor volume, regardless of response to chemotherapy; and selective (rather than routine) postoperative radiotherapy results in improved survival. More controlled studies are recommended.

Adult↗