·P-56 A model for comparing the efficacy of randomization procedures in controlled clinical trials

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Abstracts P-54 TRAINING LEVELS COMPARISON (TLC) TRIAL: DESIGN ISSUES Jeannette Lee, Albert Oberman, Gerald Fletcher, Julie Sulentic, and Barbara FJetcher Umverslty of Alabama at Birmingham B~rm~ngham,Alabama

The Tra~mng Level Companson (TLC) Thai =s a randomized collaboraWe study to determine the opttmal exercise levels for men with coronary heart d~sease enrolled ~n exercise rehabd~tat~onprograms Men ehg=ble for the trial must have sustained a documented heart event -- myocardial ~nfarct=on, coronary artery bypass surgery, percutaneous translum~nal coronary ang~oplasty, or ang~ograph=cewdence of at least 70% or more blockage m one major coronary vessel w~th~n the preceding 24 months Two hundred patients will be randomized into either a h~ghqntens~ty(80%-85% VO2 Max) or low-intensity (50%-60% VO2 Max) 2-year training program Key design features relate to characteristics of CHD, comphance to exercise regimen, and safety monltonng The primary outcome wdl be the change =n peak exercise left ventncular ejection fraction from basehne to one year Other indices of efficacy are changes ~n hemodynam~c measures, functional capacity, and nsk factor modification Results w~ll prowde a bas~s for prescnb=ng exercise =n CHD patients

P'55 DOSAGE ADJUSTMENTS IN RESPONSE TO MONITORED PLASMA CONCENTRATIONS: CAN UNBLINDED STAFF ADHERE TO OBJECTIVE CRITERIA? J. Todd Sahlroot and G.W. Pledger Nabonal Instttutes of Health Bethesda, Maryland The Epilepsy Branch, NIH, conducted a chn=cal thai of felbamate ~n the treatment of partial seizures The thai was performed at two centers according to a randomized, double-bhnd, two-penod crossover design comparing felbamate to placebo =npatients rece=wngconcomitant treatment w~thtwo standard ant=epileptic drugs, phenyto~n and carbamazep~ne Dunng the pilot study, an interact=on was seen, felbamate administration was associated with an approximate 20% increase =n plasma phenyto=n levels Consequently, the controlled trial was designed to account for th~s interact=on by reducing the phenyto~n dosage by approximately 20% dunng the felbamate treatment period and using matching placebo capsules to maintain the bhnd Unbhnded staff were allowed to make additional phenyto=n dosage adjustments to keep the phenyto=n levels w~th=n20% of the patient's basehne mean Data from the trial ~nd=cate that, at one center, a significantly greater number of phenyto=n dosage adjustments were made dunng the felbamate treatment penod than were made durtng the placebo treatment penod, despite the rough comparab~hty =n the number of phenyto~n levels outside the basehne range Thus, knowledge of treatment assignment may have influenced decisions regarding dosage adjustments even though the protocol specified objective rules for those adjustments Because inequities =n the number of dosage changes can comphcate the analysis of treatment effect, =tCsadvisable to have plasma concentrations monitored by bhnded staff

P-56 A MODEL FOR COMPARING THE EFFICACY OF RANDOMIZATION PROCEDURES IN CONTROLLED CLINICAL TRIALS Franck Chauvin and Jean-Pierre Boissel Centre Leon Berard Lyon, France The ma~n methodolog~c step ~n designing controlled chnlcal tnals ~s the randomization procedure used to obtain comparable treatment groups Although many procedures were described to fit different chn~cals~tuat~ons, there ~s no tool to test the best procedure to be used ~n a g~ven scheme Kahsh and Begg gave a classification according to three criteria (randomization, balance of treatments, efficiency of treatment comparison) and

Abstracts

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=nd~catedthree propert=es (vahd~ty,efhc=ency and complexity of the method). Nevertheless no decision procedure =s available Cons~denng the need of such a tool, we developed a computenzed model slmulat=ng chn=cal tnals to rule the we=ght of the d=fferent factors on the chmcal tr=al final results The factors accounted for the model are sample s~ze, number of part=c=pat=ngunits, patient charactenst=cs, and center charactenst=cs Bas=c pnnc=ples of th~s s~mulat~on are (1) the inclus=on of pat=ents ~n a chmcal trial and the charactenst~cs of each patient are random events s~mulated by the d=rect Monte-Carlo method, (2) each factor ~ss~mulated w~th a random multdevel d~screte variable, the allocated treatment being a b~nary variable, and (3) the response =s calculated for each s~mulated patient as the result of a hnear equat=on of covanables we=ghted w=th parameters Results: One hundred populat=ons of 100 s=mulated pat=ents were generated us=ng the SAS software random function. Four d~fferent allocation procedures (d~rect randomization w=th or w~thout blocks, strat=ficat~on w~th or w~thout blocks) were apphed to these populations One hundred chn~cal trials were s~mulated w=th each procedures Comparisons between procedures used the d=stance between the est~mat=onof the treatment effect obta=ned from s=mulated thai results and the theoret=cal one cons=denng the d~fferent values of the parameters Th~s model allows to evaluate the "error" generated by us=ng an =nappropr=ate random=zat~on procedure

P-57 SAMPLE SIZE FOR TRIALS WHERE SURVIVAL DEPENDS ON FIRST RESPONSE RATE Benny Zee and Joseph Pater

Nabonal Cancer Institute of Canada Chmca/ Tna/s Group K~ngston, Ontano, Canada A chmcal thai has been designed to compare ~nterferon versus no additional treatment ~n multiple myeloma patients who have responded to Melphalan and Predn~sone (MP) All patients w~ll be treated with MP once they are registered ~n the trial Random allocation of e~ther ~nterferon or no additional treatment wdl be made only ~f patients have responded to MP, which ~s characterized by a 50% drop ~n serum M-prote~n and 10% drop ~n urine M-prote~n on at least two separate measurements taken at least 4 weeks apart The objective of th~s paper ~s to present an approach to estimate the necessary number of registered patients to detect a g~ven d~fference ~n survival We assumed that patient's hfetime after randomization follows an exponential d~stnbut~onand ~talso depends on the t~me to response to MP, the latter ~s assumed to be exponentially d~stnbuted Th~s model allows adm~mstrat~ve censoring to occur ~n both t~me to response to MP and patient's I~fet~meafter randomization The parameters for estimating sample s~ze using th~s method are s~mdar to other methods except that th~s method requires the knowledge of the median time to response to MP, which can be obtained from prewous trials

• P-58 POWER AND SAMPLE SIZE CALCULATIONS: A REVIEW AND COMPUTER PROGRAM William D. Dupont and Walton D. Plummer, Jr.

Vanderbllt Umverslty School of Medlclne Nashville, Tennessee Methods of sample s~ze and power calculations are rewewed for the most common study designs. The sample s~ze and power equations for these designs are shown to be special cases of two generic formulae for sample s~ze and power calculations. An interactive computer program is available that can be used for studies w~th d~chotomous, continuous, or survival response measures The alternative hypotheses of ~nterest may be specified either m terms of dlffenng response rates, means, or survival times; or In terms of relative nsks or odds ratios. Studies w~th d~chotomous or continuous outcomes may ~nvolve e~ther a matched or ~ndependent study design The program can determine the sample s~ze needed to detect a specified alternative hypothes~s with the required power, the power with which a specific alternative hypothes~s can be detected w~th a g~ven sample s~ze, or the specific alternative hypotheses that can be detected w~th a g~ven power and sample s~ze. The program can generate help messages on request that facd~tate the use of th~s software It wntes a log file of all calculated estimates and can produce an output file for plotting power curves It ~s written ~n FORTRAN-77 and ~s ~n the public domain