Zenecas DTC Advertising of Nolvadex Essay Example for Free

Zenecas DTC Advertising of Nolvadex Essay Zeneca is largest pharmaceutical and agricultural/industrial chemical company in United States. In 1999 breast cancer was the most frequent diseases were found in American women, about 175,000 cases of breast cancer were diagnosed and about 43,500 women were died due to the breast cancer. According to the breast cancer prevention trial (BCPT), 45% reduction in breast cancer cases came through the use of Nolvadex. In November 1998 due to the new indications from the Food and Drug Administration (FDA) for the use of Nolvadex, Zeneca had decided to start direct to consumer advertisement campaign for Nolvadex. Findings In finding report will find over all performance of Zenecas advertisement campaign. This report will also look at the ethical issues which were raised in the case study, issues which were raised by activist groups and recommendations for managing the activists protests. 1. 1-Uncertainties from medical o Lack of sufficient data form breast cancer preventing trial physicians were worried about the side effects of Nolvadex, because the women who have high risk of breast cancer who might die if they do not take drug but for healthy women Nolvadex were not suitable from their point of view. According to physicians past experiences, Nolvadex lowered the breast cancer risk in women age 35 or more but increase the risk of other diseases in healthy women like endometrial cancer and other minor side effects like blood clots in the legs and lungs, hot flashes flushing, irregular menstrual periods, dizziness, headaches, fatigue, loss of appetite, vomiting and irritation of skin around the vagina. o Same studies like breast cancer preventing trial came from British and Italian studies which failed to show that tamoxifne was associated with a cancer risk reduction benefit They give assurance that women can both learn and control their risk, when, in fact, the risk model that is being promoted by Zeneca has been rightfully criticized by biostatisticians. The drug that Zeneca is promoting is one whose short tern benefits in healthy women are in question, and whose long term risks among healthy women are still unknown. o Some doctors were argued that side effect was too serious to prescribe for women who did not have breast cancer. They were asking to add more advisability of using the drug to lower the risk of breast cancer. These physicians wanted to wait for more data regarding Nolvadex and comparing trial of Nolvadex with Eli Lillys Evista which some believed that safer then Zenecas Nolvadex. 1. 2-Uncertainties from regulatory o According to Food and Drug Administration (FDA) there were some problem with exact language that contained in the new labelling e. g. Zeneca wanted to use word Prevention for describing the Nolvadex but FDA was not agree because labelling was lack of enough data on what happened to the women in the study after five years of treatment FDA was disagreed with Zenecas ads. because they were lack of brief summary regarding the risk and other information about Nolvadex. They wanted to disclose those information in advertising campaign o Zeneca had made the video for unbranded advertisement which was included full information of breast cancer and about Nolvadex but FDA had argued that video was heavily branded and lack of fair balance between benefits and risks of Nolvadex o Zeneca was required to include a four-part disclaimer (including side effects) when ever the word Nolvadex comes in video FDAs Division of Drug Marketing, Advertising and Communication (DDMAC) reviewed the information material in advertising campaign and determine that they are false or misleading and lacking fair balance and they are full of product and in violation of the Act for the above reasons o Zeneca was failed to discuss the Gail Model Risk Assessment Tool in the print advertisement or brochure which damage the importance of an accurate risk assessment and side effects. Due to lack of mortality data FDA approved the Nolvadex for women at high risk of breast cancer but did not approved it for use as a breast cancer prevention agent and they refer Nolvadex as a risk reduction therapy 1. 3-Zenecas decisions on DTC campaign When Zeneca decided to do the DTC campaign for Nolvadex, they had done some research before advertising campaign but this research only focusing on womens thinking about breast cancer. They did not involve patients, physician, advocacy group and activist groups in ad campaign. Another draw back came from The Food and Drug Administrations regulation and guide lines which were keep changing during the development of direct to consumer advertising campaign. FDAs guide lines were influencing the Zenecas decisions on advertisement campaign. They were trying to follow FDAs regulations but other side they had ignored physicians and activist groups during the development of DTC advertisement campaign and later Zenecas campaign was concern by these independent groups. They had tried to make branded ad but due to the regulatory it was required full information regarding drug. They were feared that if they disclosed the information regarding drugs benefit and risk it would affect the advertisement effectiveness. Thats why they decided not do the branded advertisement. Zenecas unbranded advertisement was less concern by regulatory in terms of disclosing drugs information but more concern by activist groups because of incomplete information. Also assumptions in advertisement were not appropriately taken into consideration e. g. women care their body shape more then health or you can only get breast cancer if you have family history which were concerned by regulatory and activist groups. In brief Zeneca had taken inappropriate decision regarding DTC advertisement campaign. They did not involved patients, physicians, distributors and advocacy groups before developing DTC campaign. They had followed the rules and regulations of FDA but failed to take physicians, patients and activist groups in confidence.

Pneumatic Tyre Characteristics :: essays research papers

Introduction The purpose of this technical report is to communicate the results of the Pneumatic Tyre Characteristics laboratory by investigating the effect of cornering force on slip angle. The technical report is presented to the academic staff of the Engineering Systems Department at the Royal Military College of Science, Shrivenham. It is assumed that the reader is fully familiar with the experiment and with the equipment on which it is preformed. Experimental Conditions †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  For this particular experiment the gain of the output for the strain gauges are set to 1 as 0 to 8 degrees slip angle is been measured. If smaller angles are measured a larger gain may be required. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  The total displacement of the trolley that runs along the track is approximately 2.17 meters. However, this may slightly vary in the results, especially with higher slip angles due to the reaction force acting on the reversible hydraulic motor that drives the track. For consistency the corresponding point for each distance is taken in the results. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  The current details and condition of the tested tyre are given. - Manufacture: Avon. - Dimension: 710 / 22.0 – 13. - Condition: Generally worn all around with patches of the tyre (approximately 60mm in diameter) in worse condition on the inside of the wheel. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  The tyre was tested at 14, 16 and 18 psi pressure. For each test at each angle increment the air valve is pointing downwards purely for consistency as the tyres circumference may vary. The increments for each test are as follows. - 0 – 5 degrees in 0.25 degree increments. - 5 – 8 degrees in 0.5 degree increments. †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  The experimental equipment is set to output 1000 points of displacement and side force. The computer capture rate is set to 50Hz and a total run time of 20sec †¢Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Final experimental conditions that should be mentioned include: - Before the conduction of the experiment the camber of the wheel should be checked and adjusted to zero degrees. - The track is manufactured to have a sand paper surface. This is important to exert a sufficient side force on the tyre and is in good condition. - For each experiment, initially slip pads are used under the wheel as the mechanical mechanism used to apply the vertical force is applied at a radius and therefore would give an unwanted offset. - A constant vertical load of 1.5kN is applied to the tyre. This load is kept consistent by a mechanism which includes a beam and counter balance weights.

Sampling

SamplingSamplingis that portion ofstatisticalpractice concerned with the choice of an indifferent orrandomsubset of single observations within a population of persons intended to give some cognition about thepopulationof concern, particularly for the intents of doing anticipations based onstatistical illation. Sampling is an of import facet ofdata collection.AL The three chief advantages of trying are that the cost is lower, informations aggregation is faster, and since the information set is smaller it is possible to guarantee homogeneousness and to better the truth and quality of the informations. Eachobservationmeasures one or more belongingss ( such as weight, location, colour ) of discernible organic structures distinguished as independent objects or persons. Insurvey sampling, study weights can be applied to the informations to set for thesample design. Results fromprobability theoryandstatistical theoryare employed to steer pattern.ProcedureThe sampling procedure comprises several phases: * Specifying the population of concern * Stipulating asampling frame, asetof points or events possible to mensurate * Stipulating asampling methodfor choosing points or events from the frame * Determining the sample size * Implementing the sampling program * Sampling and informations roll uping * Reviewing the sampling procedurePopulation definitionSuccessful statistical pattern is based on focussed job definition. In trying, this includes specifying thepopulationfrom which our sample is drawn. A population can be defined as including all people or points with the characteristic one want to understand. Because there is really seldom adequate clip or money to garner information from everyone or everything in a population, the end becomes happening a representative sample ( or subset ) of that population. Although the population of involvement frequently consists of physical objects, sometimes we need to try over clip, infinite, or some combination of these dimensions. For case, an probe of supermarket staffing could analyze check-out procedure line length at assorted times, or a survey on endangered penguins might take to understand their use of assorted runing evidences over clip. For the clip dimension, the focal point may be on periods or distinct occasions.Sampling frameIn the most straightforward instance, such as the sentencing of a batch of stuff from production ( credence sampling by tonss ) , it is possible to place and mensurate every individual point in the population and to include any one of them in our sample. However, in the more general instance this is non possible. There is no manner to place all rats in the set of all rats. Not all frames explicitly list population elements. For illustration, a street map can be used as a frame for a door-to-door study ; although i t does n't demo single houses, we can choose streets from the map and so see all houses on those streets. The sampling frame must be representative of the population and this is a inquiry outside the range of statistical theory demanding the judgement of experts in the peculiar capable affair being studied. All the above frames omit some people who will vote at the following election and incorporate some people who will non ; some frames will incorporate multiple records for the same individual. Peoples non in the frame have no chance of being sampled. Statistical theory Tells us about the uncertainnesss in generalizing from a sample to the frame. In generalizing from frame to population, its function is motivational and implicative. A frame may besides supply extra ‘auxiliary information ‘ about its elements ; when this information is related to variables or groups of involvement, it may be used to better study design.Probability and non chance tryingAprobability samplingscheme is one in which every unit in the population has a opportunity ( greater than zero ) of being selected in the sample, and this chance can be accurately determined. The combination of these traits makes it possible to bring forth indifferent estimations of population sums, by burdening sampled units harmonizing to their chance of choice. Probability trying includes: Simple Random Sampling, Systematic Sampling, and Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. These assorted ways of chance trying have two things in common: 1. Every component has a known nonzero chance of being sampled and 2. Involves random choice at some point. Nonprobability samplingis any sampling method where some elements of the population havenochance of choice, or where the chance of choice ca n't be accurately determined. It involves the choice of elements based on premises sing the population of involvement, which forms the standard for choice. Hence, because the choice of elements is nonrandom, nonprobability sampling does non let the appraisal of trying mistakes. These conditions place bounds on how much information a sample can supply about the population. Information about the relationship between sample and population is limited, doing it hard to generalize from the sample to the population. Nonprobability Sampling includes: Accidental Sampling, Quota SamplingandPurposive Sampling. In add-on, nonresponse effects may turnanyprobability design into a nonprobability design if the features of nonresponse are non good understood, since nonresponse efficaciously modifies each component ‘s chance of being sampled.Sampling methodsWithin any of the types of frame identified above, a assortment of trying methods can be employed, separately or in combination. Factors normally act uponing the pick between these designs include: * Nature and quality of the frame * Availability of subsidiary information about units on the frame * Accuracy demands, and the demand to mensurate truth * Whether detailed analysis of the sample is expected * Cost/operational concernsSimple random tryingIn asimple random sample ( ‘SRS ‘ ) of a given size, all such subsets of the frame are given an equal chance. Each component of the frame therefore has an equal chance of choice: the frame is non subdivided or partitioned. Furthermore, any givenpairof elements has the same opportunity of choice as any other such brace ( and likewise for three-base hits, and so on ) . This minimises prejudice and simplifies analysis of consequences. In peculiar, the discrepancy between single consequences within the sample is a good index of discrepancy in the overall population, which makes it comparatively easy to gauge the truth of consequences. However, SRS can be vulnerable to trying mistake because the entropy of the choice may ensue in a sample that does n't reflect the make-up of the population. For case, a simple random sample of 10 people from a given state willon averageproduce five work forces and five adult females, but any given test is likely to overrepresent one sex and underrepresent the other. SRS may besides be cumbrous and boring when trying from an remarkably big mark population. In some instances, research workers are interested in research inquiries specific to subgroups of the population. For illustration, research workers might be interested in analyzing whether cognitive ability as a forecaster of occupation public presentation is every bit applicable across racial groups. SRS can non suit the demands of research workers in this state of affairs because it does non supply subsamples of the population.Systematic samplingSystematic samplingrelies on set uping the mark population harmonizing to some telling strategy and so choosing elements at regular intervals through that ordered list. Systematic trying involves a random start and so returns with the choice of everykth component from so onwards. In this instance, k= ( population size/sample size ) . It is of import that the starting point is non automatically the first in the list, but is alternatively indiscriminat ely chosen from within the first to thekth component in the list. Equally long as the get downing point israndomized, systematic sampling is a type ofprobability sampling. It is easy to implement and thestratificationinduced can do it efficient, ifthe variable by which the list is ordered is correlated with the variable of involvement. However, systematic sampling is particularly vulnerable to cyclicities in the list. If cyclicity is present and the period is a multiple or factor of the interval used, the sample is particularly likely to beunrepresentative of the overall population, doing the strategy less accurate than simple random sampling. Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical belongingss make it hard toquantifythat truth. Systematic sampling is an EPS method, because all elements have the same chance of choice.Stratified samplingWhere the population embraces a figure of distinguishable classs, the frame can be organized by these classs into separate â€Å" strata. † Each stratum is so sampled as an independent sub-population, out of which single elements can be indiscriminately selected. There are several possible benefits to stratified sampling. First, spliting the population into distinguishable, independent strata can enable research workers to pull illations about specific subgroups that may be lost in a more generalised random sample. Second, using a graded sampling method can take to more efficient statistical estimations ( provided that strata are selected based upon relevancy to the standard in inquiry, alternatively of handiness of the samples ) . Even if a graded sampling attack does non take to increased statistical efficiency, such a maneuver will non ensue in less efficiency than would simple random sampling, provided that each stratum is relative to the group ‘s size in the population. Third, it is sometimes the instance that informations are more readily available for single, preexistent strata within a population than for the overall population ; in such instances, utilizing a graded sampling attack may be more convenient than aggregating informations across groups ( though this may potentially be at odds with the antecedently noted importance of using criterion-relevant strata ) . Finally, since each stratum is treated as an independent population, different trying attacks can be applied to different strata, potentially enabling research workers to utilize the attack best suited ( or most cost-efficient ) for each identified subgroup within the population. A graded sampling attack is most effectual when three conditions are met 1. Variability within strata are minimized 2. Variability between strata are maximized 3. The variables upon which the population is stratified are strongly correlated with the coveted dependant variable.Advantages over other trying methods1. Focuss on of import subpopulations and ignores irrelevant 1s. 2. Allows usage of different trying techniques for different subpopulations. 3. Improves the accuracy/efficiency of appraisal. 4. Licenses greater reconciliation of statistical power of trials of differences between strata by trying equal Numberss from strata changing widely in size.Disadvantages1. Requires choice of relevant stratification variables which can be hard. 2. Is non utile when there are no homogenous subgroups. 3. Can be expensive to implement.Probability proportional to size samplingIn some instances the sample interior decorator has entree to an â€Å" subsidiary variable † or â€Å" size step † , believed to be correlated to the variable of involvement, for each component in the population. This information can be used to better truth in sample design. One option is to utilize the subsidiary variable as a footing for stratification, as discussed above. Another option is probability-proportional-to-size ( ‘PPS ‘ ) sampling, in which the choice chance for each component is set to be relative to its size step, up to a upper limit of 1. In a simple PPS design, these choice chances can so be used as the footing forPoisson trying. However, this has the drawbacks of variable sample size, and different parts of the population may still be over- or under-represented due to opportunity fluctuation in choices. To turn to this job, PPS may be combined with a systematic attack. The PPS attack can better truth for a given sample size by concentrating sample on big elements that have the greatest impact on population estimations. PPS sampling is normally used for studies of concerns, where component size varies greatly and subsidiary information is frequently available – for case, a study trying to mensurate the figure of guest-nights spent in hotels might utilize each hotel ‘s figure of suites as an subsidiary variable. In some instances, an older measuring of the variable of involvement can be used as an subsidiary variable when trying to bring forth more current estimations.Bunch tryingSometimes it is cheaper to ‘cluster ‘ the sample in some manner e.g. by choosing respondents from certain countries merely, or certain time-periods merely. ( About all samples are in some sense ‘clustered ‘ in clip – although this is seldom taken into history in the analysis. ) Cluster samplingis an illustration of ‘two-stage trying ‘ or ‘multistage trying ‘ : in the first phase a sample of countries is chosen ; in the 2nd phase a sample of respondentswithinthose countries is selected. This can cut down travel and other administrative costs. It besides means that one does non necessitate asampling framelisting all elements in the mark population. Alternatively, bunchs can be chosen from a cluster-level frame, with an element-level frame created merely for the selected bunchs. Cluster trying by and large increases the variableness of sample estimations above that of simple random sampling, depending on how the bunchs differ between themselves, as compared with the within-cluster fluctuation. However, some of the disadvantages of bunch trying are the trust of sample estimation preciseness on the existent bunchs chosen. If bunchs chosen are biased in a certain manner, illations drawn about population parametric quantities from these sample estimations will be far off from being accurate.Matched random tryingA method of delegating participants to groups in which brace of participants are foremost matched on some characteristic and so separately assigned indiscriminately to groups. The process for matched random sampling can be briefed with the following contexts, * Two samples in which the members are clearly paired, or are matched explicitly by the research worker. For illustration, IQ measurings or braces of indistinguishable twins. * Those samples in which the same property, or variable, is measured twice on each topic, under different fortunes. Normally called perennial steps. Examples include the times of a group of jocks for 1500m before and after a hebdomad of particular preparation ; the milk outputs of cattles before and after being fed a peculiar diet.Quota tryingInquota sampling, the population is foremost segmented intomutually exclusivesub-groups, merely as instratified sampling. Then judgement is used to choose the topics or units from each section based on a specified proportion. For illustration, an interviewer may be told to try 200 females and 300 males between the age of 45 and 60. It is this 2nd measure which makes the technique one of non-probability sampling. In quota trying the choice of the sample is non-random. For illustration interviewers might be tempted to interview those who look most helpful. The job is that these samples may be biased because non everyone gets a opportunity of choice. This random component is its greatest failing and quota versus chance has been a affair of contention for many old agesConvenience samplingConvenience samplingis a type of nonprobability trying which involves the sample being drawn from that portion of the population which is close to manus. That is, a sample population selected because it is readily available and convenient. The research worker utilizing such a sample can non scientifically do generalisations about the entire population from this sample because it would non be representative plenty. For illustration, if the interviewer was to carry on such a study at a shopping centre early in the forenoon on a given twenty-four hours, the people that he/she could interview would be limited to those given there at that given clip, which would non stand for the positions of other members of society in such an country, if the study was to be conducted at different times of twenty-four hours and several times per hebdomad. This type of trying is most utile for pilot proving. Several of import considerations for research workers utilizing convenience samples include: * Are at that place controls within the research design or experiment which can function to decrease the impact of a non-random, convenience sample whereby guaranting the consequences will be more representative of the population? * Is at that place good ground to believe that a peculiar convenience sample would or should react or act otherwise than a random sample from the same population? * Is the inquiry being asked by the research 1 that can adequately be answered utilizing a convenience sample?Panel samplingPanel samplingis the method of first choosing a group of participants through a random trying method and so inquiring that group for the same information once more several times over a period of clip. Therefore, each participant is given the same study or interview at two or more clip points ; each period of informations aggregation is called a â€Å" moving ridge † . This trying methodological analysis is frequently chosen for big graduated table or nation-wide surveies in order to estimate alterations in the population with respect to any figure of variables from chronic unwellness to occupation emphasis to weekly nutrient outgos. Panel sampling can besides be used to inform research workers about within-person wellness alterations due to age or aid explicate alterations in uninterrupted dependent variables such as bridal interaction. There have been se veral proposed methods of analysing panel sample informations, including MANOVA, growing curves, and structural equation patterning with lagged effects.Replacement of selected unitsSampling strategies may bewithout replacementorwith replacing. For illustration, if we catch fish, mensurate them, and instantly return them to the H2O before go oning with the sample, this is a WR design, because we might stop up catching and mensurating the same fish more than one time. However, if we do non return the fish to the H2O ( e.g. if we eat the fish ) , this becomes a WOR design.FormulasWhere the frame and population are indistinguishable, statistical theory outputs exact recommendations onsample size. However, where it is non straightforward to specify a frame representative of the population, it is more of import to understand thecause systemof which the population are results and to guarantee that all beginnings of fluctuation are embraced in the frame. Large Numberss of observations are o f no value if major beginnings of fluctuation are neglected in the survey. In other words, it is taking a sample group that matches the study class and is easy to study. Research Information Technology, Learning, and Performance Journalthat provides an account of Cochran ‘s expression. A treatment and illustration of sample size expressions, including the expression for seting the sample size for smaller populations, is included. A tabular array is provided that can be used to choose the sample size for a research job based on three alpha degrees and a set mistake rate.Stairss for utilizing sample size tabular arraies1. Contend the consequence size of involvement, ? , and ? . 2. Check sample size tabular array 1. Choose the tabular array matching to the selected ? 2. Locate the row matching to the coveted power 3. Locate the column matching to the estimated consequence size 4. The intersection of the column and row is the minimal sample size required.Sampling and informations aggregationGood informations aggregation involves:* Following the defined sampling procedure * Keeping the information in clip order * Noting remarks and other contextual events * Recording non-responses Most sampling books and documents written by non-statisticians focused merely in the informations aggregation facet, which is merely a little though of import portion of the sampling procedure.Mistakes in researchThere are ever mistakes in a research. By trying, the entire mistakes can be classified into trying mistakes and non-sampling mistakes.Sampling mistakeSampling mistakes are caused by trying design. It includes: ( 1 ) Choice mistake: Incorrect choice chances are used.( 2 ) Appraisal mistake: Biased parametric quantity estimation because of the elements in these samples.Non-sampling mistakeNon-sampling mistakes are caused by the errors in informations processing. It includes: ( 1 ) Overcoverage: Inclusion of informations from exterior of the population.( 2 ) Undercoverage: Sampling frame does non include elements in the population.( 3 ) Measurement mistake: The respondents misunderstand the inquiry.( 4 ) Processing mistake: Mistakes in informations cryptography. In many state of affairss the sample fraction may be varied by stratum and informations will hold to be weighted to right stand for the population. Thus for illustration, a simple random sample of persons in the United Kingdom might include some in distant Scots islands who would be extraordinarily expensive to try. A cheaper method would be to utilize a graded sample with urban and rural strata. The rural sample could be under-represented in the sample, but weighted up suitably in the analysis to counterbalance. More by and large, informations should normally be weighted if the sample design does non give each person an equal opportunity of being selected. For case, when families have equal choice chances but one individual is interviewed from within each family, this gives people from big families a smaller opportunity of being interviewed. This can be accounted for utilizing study weights. Similarly, families with more than one telephone line have a greater opportunity of being selected in a random figure dialing sample, and weights can set for this.

How does R. L. Stevenson create suspense in The Last...

Robert Louis Stevensons Dr. Jekyll and Mr. Hyde is a gothic novel in many of its aspects, but one of the most important reasons is that there is constant building of suspense. There are many ways that this is done: through his characters, through his vocabulary, the setting and even through the origins of the character of Hyde. Stevenson created the character of Utterson as a neutral base for the whole story; much like the table on which the dinner is served. But in the chapter of The Last Night, the table creates suspense too. Because the story is seen through the eyes of Utterson, the reader feel what he feels, so when he gets scared, the reader feels the same. When he is told not to go into the room that Jekyll is supposedly locked in,†¦show more content†¦He does this throughout the chapter; when they decide to break down the door (Poole, if you say that, it will become my duty to make [murder] certain. I shall consider it my duty to break in that door.), they take another 3 pages of planning and talking before they actually do it. Again, this makes the reader more and more impatient of the climax, which would be when they find Hyde behind the door. When information is finally given as they break down the door, there are more gaps for the reader to fill in. As they are breaking in, Hyde says: Utterson, for Gods sake, have mercy! There lay the body of a man sorely contorted and still twitching. Will make the reader think that something really terrible must have happened to make Hyde beg for mercy and kill himself. You then assume that Hyde is dead, therefore they will find Jekyll for him to then explain what happened. But Stevenson twists in the other direction. Poole and Utterson then go to find Jekylls body. [Utterson] said sternly, Hyde is gone to his account; and it only remains for us to find the body of your master. Nowhere was there and trace of Henry Jekyll, dead or alive. Stevenson has been deceptive in this state deliberately because the reader will think that they are near the happy, explained ending, but the author changes direction and adds yet another mystery for the reader to attempt to explain, building further suspense before the final chapters in which all is explained. Another way that